Investigating the Microbial Community Structure of Biofilters ......Alexander Hayes Doctor of...

Effect of Methanol on the Microbial Community

Structure of Biofilters Treating Dimethyl Sulphide

Alexander Hayes

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Graduate Department of Chemical Engineering and Applied Chemistry

University of Toronto

©Copyright by Alexander Hayes (2010)

ii

Effect of Methanol on the Microbial Community Structure of Biofilters Treating

Dimethyl Sulphide

Alexander Hayes

Doctor of Philosophy

Graduate Department of Chemical Engineering and Applied Chemistry

University of Toronto

2010

Abstract:

Odour emissions resulting from reduced sulphur compounds in the kraft pulping industry are

frequently found in dilute, high flowrate air streams that are costly to treat using incineration and

thermal oxidation. Biofiltration, an air treatment method involving passing air through a packed

bed of microorganisms, has emerged as a promising treatment strategy for these dilute waste gas

streams. However, biodegradation of dimethyl sulphide (DMS) in biofilters is rather poor and is

limiting the application of biofiltration to odour streams rich in DMS. Recently, our group has

shown that co-treatment of DMS with methanol can increase DMS removal significantly. In this

thesis, the effect of methanol on the microbiology of two biofilters treating DMS was explored.

Microbial community analysis revealed that the addition of methanol led to a significant increase

of up to an order of magnitude in the abundance of Hyphomicrobium spp. in a biofilter co-

treating DMS and methanol compared to a biofilter treating DMS alone with no significant

difference in the abundance of Thiobacillus spp. between the two biofilters. Further to the

biofiltration experiments, the growth kinetics of Hyphomicrobium spp. and Thiobacillus spp. on

DMS and methanol in an enrichment culture created from a biofilter co-treating DMS and

methanol were studied. A specific growth rate of 0.099 h-1

and 0.11 h-1

was determined for

iii

Hyphomicrobium spp. and Thiobacillus spp., respectively, growing on DMS at pH 7, double the

highest maximum specific growth rate for bacterial growth on DMS reported to date in the

literature. As the pH decreased from pH 7 to pH 5, the specific growth rate of Hyphomicrobium

spp. decreased significantly by 85% in the mixed culture while the specific growth rate of

Thiobacillus spp. remained similar through the same pH shift. When methanol was used as a

substrate, the specific growth rate of Hyphomicrobium spp. declined much less over the same pH

range (up to 30%). These results suggest that addition of methanol to biofilters co-treating DMS

and methanol can increase DMS removal rates by increasing the abundance of DMS-degrading

Hyphomicrobium spp. at pH levels not conducive to high growth rates on DMS alone.

iv

ACKNOWLEDGEMENTS

I would like to express my thanks and appreciation to the following people and organisations

who have helped me throughout my graduate studies. In particular, I would like to acknowledge:

Professors Grant Allen and Steven Liss for their supervision, guidance, and patience

throughout my graduate studies

Professors Elizabeth Edwards and Emma Master for their role on my reading committee.

Their critiques of this work and helpful suggestions contributed greatly to the completion

of this work

The Natural Science and Engineering Research Council of Canada (NSERC), Ontario

Graduate Scholarship, Ontario Graduate Scholarship in Science and Technology, and the

research consortium “Minimizing the Impact of Pulp and Paper Mill Discharges” for

funding this research

My colleagues in both the environmental bioprocess laboratory at the University of

Toronto and the environmental biotechnology lab at Ryerson University. In particular, I

would like to thank Yuefeng Zhang for operating the biofilters that are discussed in this

thesis and for the valuable discussions we have had on the role of methanol in improving

DMS removal in biofilters treating DMS

The staff, faculty, and students at both the University of Toronto and Ryerson University

who have aided me throughout my graduate studies

Last, but not least, my family who supported me throughout this endeavour but especially

Melissa, whose love and support throughout my graduate studies was greatly appreciated.

v

PREFACE

This thesis is based on the following manuscripts that have either been published or submitting

for publication in peer-review journals and conference proceedings:

1. Hayes AC, Zhang Y, Liss SN, and DG Allen. 2010. Linking performance to

microbiology in biofilters treating dimethyl sulphide in the presence and absence of

methanol. Applied Microbiology and Biotechnology 85:1151-1166.

2. Hayes AC, Liss SN, and DG Allen. 2010. Growth kinetics of Hyphomicrobium and

Thiobacillus spp. in mixed cultures degrading dimethyl sulfide and methanol. Applied

and Environmental Microbiology (in press).

3. Hayes AC, Liss SN, and DG Allen. 2008. Investigating the microbial community

structure of biofilters treating dimethyl sulfide (DMS). Proceedings of the 2008 USC-

UAM Conference on Biofiltration for Air Pollution Control, Long Beach, CA, P. 194-201.

vi

TABLE OF CONTENTS

ABSTRACT ii

ACKNOWLEGEMENTS iv

PREFACE v

TABLE OF CONTENTS vi

LIST OF TABLES vii

LIST OF FIGURES xi

1.0 INTRODUCTION 1

1.1 Biofiltration of Odour Emissions from the Pulp and Paper Industry 1

1.2 Hypotheses 6

1.3 Research Objectives 6

1.4 Significance 8

1.5 Thesis Outline 8

2.0 LITERATURE REVIEW 10

2.1 Introduction to Reduced Sulphur Compounds 10

2.2 Emission of Reduced Sulphur Compounds 10

2.3 Treatment of RSC Emissions 14

2.4 Fundamentals of Biofiltration 15

2.5 Biofiltration of RSC Emissions 18

2.6 Microbial Degradation of DMS 22

2.7 Methanol Emissions in the Kraft Pulping Process 26

2.8 Aerobic Metabolism of Methanol 27

2.9 Identifying and Quantifying Microorganisms in Mixed Communities 32

2.10 Analysis of Microbial Communities in Biofiltration Studies 40

2.11 Summary of Literature and Knowledge Gaps 41

3.0 EXPERIMENTAL 43

vii

3.1 Approach 43

3.2 Biofilter Setup 45

3.3 Sample Collection 47

3.4 Creation of Enrichment Culture from Biofilter co-treating DMS and MeOH 48

3.5 Batch Kinetic Assays 48

3.6 Molecular Analyses 49

3.6.1 Denaturing Gradient Gel Electrophoresis (DGGE) 50

3.6.2 16S rDNA Clone Library Construction 50

3.6.3 Quantitative PCR (qPCR) 51

3.7 Analytical Methods 52

4.0 BIOFILTER PERFORMANCE AND COMMUNITY STRUCTURE 53

4.1 Biofilter Operation and Performance Around Sampling Points 55

4.2 Microbial Community Structures of Biofilters Treating DMS 60

4.2.1 Construction of 16S rDNA Clone Libraries 61

4.2.2 DGGE Analysis of Microbial Community Structure 72

4.2.3 qPCR 84

4.3 Linking Biofilter Performance to Microbial Community Structure 88

5.0 KINETICS OF ENRICHMENT CULTURE ON DMS AND METHANOL 91

5.1 Characterization and Growth of Enrichment Culture on DMS 91

5.2 Growth Kinetics of Hyphomicrobium, Thiobacillus, and Chitinophaga 94

spp. in Enrichment Culture on DMS

5.3 Growth Kinetics of Hyphomicrobium spp. in Enrichment Culture on MeOH 101

6.0 OVERALL DISCUSSION 105

7.0 CONCLUSIONS 110

8.0 ENGINEERING SIGNIFICANCE 112

9.0 RESEARCH RECOMMENDATIONS 114

REFERENCES 115

viii

APPENDIX A: CALIBRATION CURVES 129

A.1 Gas chromatography calibration curves for DMS and methanol 129

A.2 Calibration curves for qPCR of 16S rDNA 130

A.3 Ion chromatography calibration curve for sulphate quantification 134

APPENDIX B: CLONE LIBRARY SEQUENCE DATA 136

B.1 16S rDNA sequences obtained from enrichment culture 136

B.2 16S rDNA sequences obtained from biofilter treating DMS alone 146

APPENDIX C: BIOENERGETICS CALCULATIONS 159

C.1 Theoretical biomass yield from dimethyl sulphide (DMS) 159

C.2 Theoretical biomass yield from methanol 161

C.3 Theoretical biomass yield from formaldehyde 163

C.4 Theoretical biomass yield from hydrogen sulphide 165

APPENDIX D: BIOMASS YIELD ESTIMATE CALCULATIONS 167

D.1 Biomass yield of Hyphomicrobium spp. from dimethyl sulphide (DMS) 167

D.2 Biomass yield of Hyphomicrobium spp. from methanol 168

D.3 Biomass yield of Thiobacillus spp. from dimethyl sulphide (DMS) 169

ix

LIST OF TABLES

Table 1.1 – Metabolic products of DMS biodegradation in the presence and absence 5

of methanol over 48 h (Zhang et al., 2006).

Table 2.1 – Physical Properties of Reduced Sulphur Compounds 10

Table 2.2 – Estimated Annual Global Emissions of Reduced Sulphur Compounds 11

(Kelly and Smith, 1991)

Table 2.3 – Typical Emissions of Reduced Sulphur Compounds from Kraft Pulp Mills 13

Table 2.4 – Interaction Effects Among RSC Emissions in Biofiltration 19

Table 2.5 – DMS Removal Rates in Biofiltration Studies 21

Table 2.6 – Reported kinetic parameters of known aerobic DMS-degrading 25

microorganisms

Table 2.7 - Methanol emissions in different streams at a kraft pulp and paper mill 26

(Vekatesh et al., 1997).

Table 2.8 - Culturability determined as a percentage of culturable bacteria in 33

comparison with total cell counts (Amman et al., 1995)

Table 3.1 – Composition of Nova Inert®

packing media (Minuth, 1999) 46

Table 3.2 - Real-time PCR primers and probes used in this study to quantify the 16S 52

rRNA gene of Bacteria, Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp..

Table 4.1 – Operation of biofilter co-treating DMS and methanol over its operating life. 53

Table 4.2 – Distribution of different bacterial groups identified in clone libraries 62

constructed from the enrichment culture created from the biofilter co-treating DMS and

methanol and from the biofilter treating DMS alone. Each library contained 3

duplicate sequences.

Table 4.3 - GenBank accession numbers and closest related sequences found in 66

the 16S ribosomal database project of clones from 16S rDNA clone libraries

Table 4.4 – Richness and Evenness of bacterial community for both biofilters 75

for samples collected on Day 457. Range represents 95% confidence intervals.

Table 4.5 Standardization of 16S rRNA gene copies of Hyphomicrobium spp., 87

Thiobacillus spp., and Chitinophaga spp. on a percent total bacterial 16S rRNA gene

basis for samples taken from both biofilters on Day 457.

x

Table 5.1 Effect of pH and nitrogen source on the specific growth rate and yield of 95

Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. in the enrichment

culture grown on DMS. Numbers in parentheses indicate standard deviation. N = 6.

Table 5.2 - Comparisons of yields of Hyphomicrobium and Thiobacillus grown on 97

DMS at pH 7 in NH4+-N media.

Table 5.3 – Combined yield of 16S rDNA copies of Hyphomicrobium and 99

Thiobacillus spp. in the enrichment culture at different pH values. Interval constructed

using ± 1 standard deviation.

Table 5.4 Effect of pH and nitrogen source on the specific growth rate and 101

yield of Hyphomicrobium spp. in the enrichment culture grown on methanol.

Numbers in parentheses indicate standard deviation. N = 3.

Table 5.5 Comparison of yields of Hyphomicrobium grown on methanol at pH 7 in 102

NH4+-N media

xi

LIST OF FIGURES

Figure 1.1 – Schematic representation of the biofiltration of dimethyl sulphide 2

Figure 1.2 - DMS removal rate of three parallel biofilters treating DMS. Two 3

biofilters went through periods of methanol addition and suspension while the third

was fed DMS alone as a control (Zhang et al. 2006).

Figure 1.3 – Biomass concentration and viability of biofilters (BF) at loadings of 4

32 g MeOH m-3

h-1

and 5 g DMS m-3

h-1

. Error bars indicate ±1 standard deviation

(Zhang et al., 2006).

Figure 2.1 – Visual representation of pollutant transport in biofiltration systems 16

Figure 2.2 – Proposed metabolism of DMS and methanol by Hyphomicrobium 23

spp. 1 – DMS monooxygenase; 2 – methanol dehydrogenase; 3 – methanethiol

oxidase; 4 – formaldehyde dehydrogenase; 5 – sulphide-oxidizing enzymes;

6 – formate dehydrogenase (Suylen et al., 1986).

Figure 2.3 - Metabolism of C1 compounds in aerobic methylotrophic bacteria. 28

1 – methane monooxygenase; 2 – methanol dehydrogenase; 3 – formaldehyde

oxidation pathway; 4 – formate dehydrogenase; 5 – halomethane oxidation

system; 6 – methylated amine oxidases; 7 – methylated sulphur oxidation system.

RuMP is ribulose monophosphate and RuBP is ribulose bisphosophate. Adapted

from Anthony (Anthony, 1982; Anthony 1996) and DeBont et al. (1981).

Figure 2.4 – Schematic representation of the electron transport system associated 30

with methanol oxidation in gram negative bacteria. Adapted from Goodwin and

Anthony (1995).

Figure 3.1 – Overall roadmap of experimental methodology and their connection to 43

fundamental questions in this thesis.

Figure 3.2 - Schematic diagram of biofilter setup. H – humidifier; B – biofilter; 45

A – adsorption tank; MFC – mass flow controller (Zhang et al., 2006)

Figure 3.3 - Biomass sampling locations from biofilters on Day 457. Samples taken 47

from the bed top refer to two samples taken on Day 457 from the biofilter co-treating

DMS with methanol. Samples obtained on Day 283 and Day 400 were obtained from

side media access ports at the center of the inlet and middle sections. Abbreviations T

(top) and C (centre) refer to plane where samples were taken within a section.

Figure 3.4 – Experimental apparatus used for maintenance of batch culture and time 48

course batch experiment.

Figure 4.1 - Transient-state behaviour of the biofilter co-treating DMS and methanol 56

compared to the DMS removal rate of the biofilter treating DMS alone. The DMS loading

xii

of the two biofilters was kept at 5 g m-3

h-1

and the EBRT of the biofilters was 40 sec. The

methanol loading of the biofilter co-treating DMS and methanol was 32 g m-3

h-1

and

methanol loading was suspended from Day 257 to Day 275. Adapted from Zhang, 2007.

Figure 4.2 The performance of the biofilter co-treating DMS and methanol on the 57

removal of DMS and methanol at various methanol addition rates. Experimental results

were obtained at 40 sec EBRT. Methanol addition was suspended on days 394 and 404

for 24 hours. During this experimental period, the biofilter treating DMS alone was kept

at a DMS loading of 3.4 g m-3

h-1

with an EBRT of 40 sec which resulted in a DMS

removal rate of 0.5 ± 0.1 g m-3

h-1

. MeOH denotes methanol, and REMeOH and REDMS

represent methanol and DMS removal efficiency, respectively (Zhang, 2007).

Figure 4.3 - The effects of EBRT, methanol loading rate and DMS loading rate on the 59

removal of DMS in the biofilter co-treating DMS and methanol. The dotted vertical line

indicates the change of EBRT. MeOH represents methanol. During this experimental

period the biofilter treating DMS alone was kept at a DMS mass loading of 5.0 g DMS

m-3

h-1

with an EBRT of 40 sec which resulted in a DMS removal rate of 0.65 ± 0.05 g

DMS m-3

h-1

(Zhang, 2007).

Figure 4.4 - Bayesian phylogeny of the 16S rDNA clone library created from the 64

enrichment culture of the biofilter co-treating DMS in the presence of methanol. The

bar indicates 0.1 substitutions per nucleotide and bootstrap values are labelled on the

branches. Clones are denoted ENR.

Figure 4.5 - Bayesian phylogeny of the 16S rDNA clone library created from the 65

biofilter treating DMS alone. The bar indicates 0.1 substitutions per nucleotide and

bootstrap values are labelled on the branches. Clones are denoted DMS.

Figure 4.6 – Digital photographs of DGGE fingerprints of biomass samples obtained 74

from both biofilters on Day 457 with samples obtained from the biofilter co-treating

DMS and methanol (MeOH) on top and the samples obtained from the biofilter treating

DMS alone below. The two right most lanes in the 2nd

gel in the samples from the biofilter

co-treating DMS and MeOH are the first two samples of the biofilter treating DMS alone

while the two rightmost samples in the 2nd

gel for the biofilter treating DMS alone are two

samples taken from above the restriction plate located above the packing material in the

biofilter co-treating DMS and MeOH. Bands that were identified through excision and

sequencing are labelled on the right. T – Top; C – Centre; BT – Bed Top

Figure 4.7 - Jaccard coefficient-based UPGMA dendrograms of DGGE fingerprints of 82

samples taken from both biofilters on Day 457 (left) and on days 283 and 457 (above).

Cophenetic correlations are labelled on branches. Fingerprints are labelled as follows:

The first two letters of the fingerprint label indicate whether biofilter was fed DMS alone

(DA) or DMS with methanol (DM). The third letter indicates whether the sample was taken

from the inlet (I), middle (M), outlet (O) section or bed top (B). The fourth letter indicates

whether the sample was taken from the top (T) or centre (C) of the biofilter section. The

first number indicates the location within the radial plane the sample was obtained (as

xiii

labelled in Figure 3.1), while the last three digits represent the day the sample was obtained

(283 or 457).

Figure 4.8 - Quantification of 16S rDNA for Bacteria, Hyphomicrobium spp., 86

Thiobacillus spp., and Chitinophaga spp. standardized per gram dry media on Day 457

for the biofilter co-treating DMS with methanol (top) and DMS alone (bottom).

Error bars represent 95% confidence intervals.

Figure 5.1 - Genus-wide A) Hyphomicrobium B) Thiobacillus and 92

family-wide C) Chitinophagaceae minimum evolution phylogenies for the

enrichment culture created from a biofilter co-treating DMS and methanol. Clones

are denoted ENR and Genbank accession numbers of all sequences used are listed.

The scale is in substitutions per nucleotide.

Figure 5.2 - DMS consumption and growth of Hyphomicrobium spp., Thiobacillus 93

spp., and Chitinophaga spp. in the enrichment culture in an NH4Cl-based media at

pH 7 versus time.

Figure 5.3 - A plot of the natural logarithm of the quantity of 16S rDNA 93

copies of Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. in the

enrichment culture versus time for the same experiment as shown in Figure 5.2.

Figure A-1 - Calibration curve for DMS quantification using gas chromatography. 129

Figure A-2 - Calibration curve for methanol quantification using gas chromatography. 130

Figure A.3 – Calibration curve of a bacteria specific qPCR assay using a plasmid 131

containing a 16S rRNA gene from a Hyphomicrobium spp..

Figure A.4 – Calibration curve of a Hyphomicrobium specific qPCR assay using a 132

plasmid containing a 16S rRNA gene from a Hyphomicrobium spp..

Figure A.5 – Calibration curve of a Thiobacillus specific qPCR assay using a plasmid 133

containing a 16S rRNA gene from a Thiobacillus spp..

Figure A.6 – Calibration curve of a Chitinophaga specific qPCR assay using a 134

plasmid containing a 16S rRNA gene from a Chitinophaga spp..

Figure A.7 – Calibration curve for sulphate quantification using ion chromatography 135

with Na2SO4 as a standard.

Introduction 1

1

1.0 INTRODUCTION

1.1 Biofiltration of Odour Emissions from the Pulp and Paper Industry

Odour issues are a long-standing challenge facing the Kraft pulping industry (Hedenhag and

Banks, 1998; O’Connor et al., 1999). Kraft pulp mills have the potential to produce significant

emissions of reduced sulphur compounds (RSC), such as hydrogen sulphide (H2S), methyl

mercaptan (MM), dimethyl sulphide (DMS), and dimethyl disulphide (DMDS) which are

believed to be the major constituents believed to responsible for odour issues at Kraft pulp mills

(Gravel and Gosselin, 1999; O’Connor et al., 1999). RSC emissions are particularly problematic

odour emissions due to their foul smell and an odour threshold in the low ppbv range.

Traditional end-of-pipe treatment methods for odour emissions consist of technologies such as

absorption, adsorption, and thermal oxidation. While these technologies are effective at reducing

odour emissions, they are not well-suited for many waste air streams in the Kraft pulping

industry which are characterized by dilute concentrations and high flowrates. The dilute nature of

these streams drives up costs for thermal oxidation technologies which typically need extra fuel

to reach the necessary incineration temperatures while technologies such as absorption and

adsorption result in the formation of secondary waste products (i.e. spent chemicals, wastewater)

which must still be treated.

An emerging technology that is particularly suited for the nature of many odour emissions

streams in the Kraft pulping industry is biofiltration. Biofiltration is an air pollution control

technology that involves passing humidified, polluted air through a packed bed of

microorganisms (see Figure 1.1). The packing material is a porous solid media (i.e. compost,

wood chips, peat, or inorganic media). Initially, the biofilter may be inoculated with a microbial

suspension (i.e. activated sludge). Microorganisms will attach to the packing media and form a

thin film known as the biofilm. As polluted air passes through the biofilter bed, pollutants come

into contact and diffuse into the biofilm where they are metabolized by microorganisms,

resulting in the formation of more biomass or innocuous end products such as carbon dioxide,

water and sulphate (in the case of RSC emissions).

Introduction 2

2

Figure 1.1 – Schematic representation of the biofiltration of dimethyl sulphide

As a treatment technology, biofiltration is attractive due to its low capital and operating costs,

low energy requirements, and the formation of innocuous end-products that usually require no

further downstream processing. Biofiltration is already a commercially-viable technology with

over 15,000 systems installed worldwide (Van Groenetijn, 2005). Although, biofiltration is used

to treat a wide variety of waste air streams, approximately half of the biofiltration systems

installed worldwide are used for the treatment of odour emissions at wastewater treatment plants

and aerobic composting facilities (Van Groenetijn, 2005) where RSC are emitted in mixtures of

H2S, MM, DMS, and DMDS.

While biofilters are used to treat many different substrates in mixtures, not all of these substrates

are removed with equal effectiveness. The biodegradation of DMS has long represented the

principal challenge in treating RSC emissions (Hirai et al., 1990; Shoda, 1991; Zhang et al.,

1991; Zhang et al., 1992) and currently limits the application of biofilters to waste gas streams

that are relatively rich in DMS. The poor biodegradation of DMS in biofiltration systems is a

major factor in the lack of the successful implementation of biofiltration technology to treat

odorous emissions from the Kraft pulping industry where DMS is typically found in

concentrations in the ppmv range as opposed to the ppbv range in wastewater treatment plants

and aerobic composting facilties where biofilters are successfully employed.

Introduction 3

3

Kraft pulp mills also have to potential to produce significant emissions of VOCs, particularly

methanol. VOCs are another group of compounds that are particularly well-suited to biofiltration

(at the appropriate concentrations and flowrates) because, for the most part, they are easily

biodegradable. Due to known interaction effects between different compounds in mixed waste

gas streams in biofiltration, there is interest in knowing how VOCs and RSCs will interact with

one another when treated together. This led to the development of a project in our group where

the effect of methanol on DMS removal in biofilters would be explored since it would be directly

of interest to treating waste gas streams in the Kraft pulping industry (Zhang et al., 2006; 2007a;

2007b; 2008).

0

0.5

1

1.5

2

2.5

110 125 140 155 170 185

Days

DM

S r

em

ov

al ra

te, g

/m3.h

32gMeOH/m3.h MeOH addition

suspended

MeOH addition

resumedMeOH addition

suspended

Period V Period VI Period VII Period VIII

Without MeOH

With MeOH

With MeOH

0

0.5

1

1.5

2

2.5

110 125 140 155 170 185

Days

DM

S r

em

ov

al ra

te, g

/m3.h

32gMeOH/m3.h MeOH addition

suspended

MeOH addition

resumedMeOH addition

suspended

Period V Period VI Period VII Period VIII

Without MeOH

With MeOH

With MeOH

Figure 1.2 - DMS removal rate of three parallel biofilters treating DMS. Two biofilters went

through periods of methanol addition and suspension while the third was fed DMS alone as a

control (Zhang et al. 2006).

The initial results from this investigation revealed that methanol addition appeared to have a

beneficial effect on DMS removal. In an experiment involving three parallel biofilters with the

same DMS loading (5 g m-3

h-1

), two biofilters were co-fed methanol (32 g m-3

h-1

loading) while

the third biofilter was fed DMS alone. As shown in Fig. 1.2, addition of methanol led to an

immediate increase in the DMS removal rate of both biofilters co-treating DMS and methanol.

Introduction 4

4

Furthermore, suspension of methanol addition in these two biofilters led to a further increase in

the DMS degradation. However, this increase was not sustainable and the DMS degradation rate

in both biofilters co-treating DMS and methanol gradually decreased back to the level of that in

the control biofilter treating DMS alone.

Figure 1.3 – Biomass concentration and viability of biofilters (BF) at loadings of 32 g MeOH

m-3

h-1

and 5 g DMS m-3

h-1

. Error bars indicate ±1 standard deviation (Zhang et al., 2006).

Biomass concentration and viability measurements of the biofilters revealed that addition of

methanol resulted in a three-fold increase in biomass concentration and more than a doubling of

the biomass viability for the given loadings. Furthermore, it was shown that during the methanol

suspension period, biomass concentration and viability in the biofilters co-treating DMS and

methanol decreased with the DMS degradation rate down to the level of the control biofilter

treating DMS alone (Zhang et al., 2006).

Finally, a sulphur balance on the biofilters revealed that the major metabolic end-product of

DMS biodegradation in these biofilters was sulphate (Table 1.1). In the case of the biofilter

treating DMS alone, 95% of the total sulphur in the leachate was in the form of sulphate. As for

the biofilters co-treating DMS and methanol, 75% of the total sulphur in the leachate was in the

form of sulphate while 22% of the total sulphur was in the form of inorganic sulphur. The

Introduction 5

5

formation of sulphate as the primary metabolic end-product of DMS biofiltration is consistent

with other biofiltration studies (Hirai et al., 1990; Smet et al., 1996).

Table 1.1 – Metabolic products of DMS biodegradation in the presence and absence of methanol

over 48 h (Zhang et al., 2006).

Metabolic Product in

Leachate

Increased Concentration (mg-S/L Leachate) and Percentage (%)

Biofilter Co-Treating DMS and

MeOH

Biofilter Treating DMS

Alone

Total Sulphur 180 (100) 135 (100)

SO42-

134 (74.5) 130 (95.7)

SO32-

2.3 (1.3) 3.0 (2.2)

S2-

2.1 (1.2) < 1.0 (0.7)

S2O32-

< 0.5 (0.3) < 0.5 (0.4)

S0 40 (22) 0.8 (0.6)

Organic Sulphur 1.5 (0.8) 0.5 (0.4)

Clearly, the addition of methanol leads to higher DMS removal rates for the given loadings in

these biofilters. Addition of methanol also resulted in higher biomass concentration and viability

and slightly reduced the conversion rate of DMS to sulphate for the given loading. What is not

clear is the mechanism behind improved DMS removal in these biofilters with methanol addition.

This thesis aims to resolve through the use of molecular tools in order to study the microbiology

of these systems. The focus of this thesis is largely on the microbiology within the biofilters

operated by Zhang (2007) and how the operating conditions of these biofilters led to the results

that he reported in his thesis for the biofiltration of DMS and methanol mixtures.

An initial survey of the literature revealed two groups of microorganisms that are known to grow

aerobically on DMS in freshwater. The first group, Hyphomicrobium spp., are methylotrophic

microorganisms that grow on C1 compounds such as methanol while the second group,

Thiobacillus spp., are sulphur chemolithotrophs which grow on inorganic sulphur compounds

such as hydrogen sulphide and use the energy derived from its oxidation to fix carbon dioxide.

While the microbial community structure of these biofilters is unknown, the ability of

Hyphomicrobium spp. to consume both methanol and DMS suggest that these microorganisms

Introduction 6

6

may play an important role in the degradation in these systems. However, it is also likely that

Thiobacillus are still present and a play some role in DMS degradation in these systems.

Given the lack of knowledge of the microbial community structure of these systems, it is not

possible to isolate the mechanism of improved DMS with methanol addition because there is no

knowledge of how the microbial community changes with methanol addition, nor is there any

knowledge of the microbial community structure in biofilters treating DMS alone. The goal of

this thesis is to fill this gap in the literature by describing the microbial community structure of

these biofilters and to observe how the microbial community structure changes with methanol

addition. The thesis also addresses the mechanism of improved DMS removal with methanol

addition by performing batch kinetic assays on an enrichment culture created from the biofilter

co-treating DMS and methanol under conditions relevant to biofilter operation. Using data

collected from both the biofilter and the batch studies, a mechanism of improved DMS removal

in biofilters with methanol co-treatment is proposed.

1.2 Hypotheses

The two hypotheses tested in this thesis are:

1. Removal of DMS in a biofilter at neutral pH and mesophillic temperatures is performed

by a community of microorganisms of which bacteria belonging to the Hyphomicrobium

and Thiobacillus genera are the prominent DMS degrading bacteria in the microbial

community.

2. Addition of methanol stimulates the growth of Hyphomicrobium spp. leading to increased

DMS degradation rates by increasing the quantity of Hyphomicrobium spp. in the

biofilter.

1.3 Research Objectives

The overall objective of this project was to study the microbial community structure of biofilters

treating DMS in the presence and absence of methanol in order to determine the mechanism by

which methanol addition leads to increased DMS removal in these systems. A review of

literature led to the development of the hypotheses listed in the previous section. In order to test

the hypotheses, the following objectives were developed:

Introduction 7

7

1. Identify bacteria and likely DMS degrading bacteria present in biofilters treating DMS at

neutral pH and mesophilic temperatures.

2. Compare the similarity of the microbial community of biofilters treating DMS in the

presence and absence of methanol at neutral pH and mesophilic temperatures.

3. Quantify microorganisms of interest in biofilters treating DMS in the presence and

absence of methanol at neutral pH and mesophilic temperatures.

4. Perform batch kinetic assays on enrichment culture created from a biofilter co-treating

DMS and methanol for conditions relevant to biofilter operation.

5. Link biofilter performance and community data to results from batch kinetic assays.

The first three objectives involve studying the microbial community structure of the Zhang (2007)

biofilters. The first objective specifically deals with the identification of bacteria in these

biofilters. In order to achieve this objective, 16S rDNA clone libraries were constructed from

biomass ultimately originating from a biofilter co-treating DMS and methanol and a biofilter

treating DMS alone. The second objective involves comparisons of the microbial community

structure of biomass samples taken from both biofilters which was carried out using denatured

gradient gel electrophoresis (DGGE). Finally, the third objective involves quantification of

microorganisms of interest in the biofilter microbial community. Microorganisms of interest

were determined from analysis of the 16S rDNA clone libraries and quantification was carried

out by using quantitative PCR (qPCR). qPCR assays were constructed from genetic information

obtained from the 16S rDNA clone libraries.

The fourth objective involves performing batch kinetic assays on an enrichment culture created

from the biofilter co-treating DMS and methanol under conditions relevant for biofilter operation.

The conditions explored were substrate (DMS and methanol), pH (5, 6, and 7), and nitrogen

source (NH4Cl and KNO3). During these batch assays, the growth of the microorganisms of

interest was tracked using qPCR in order to determine kinetic parameters.

Finally, the fifth objective involves linking performance and microbiological data obtained from

the biofilters to the results obtained from the batch kinetic assays. The goal of this is to determine

why DMS removal was poor in the control biofilter treating DMS alone and propose a

mechanism as to why methanol addition led to improved DMS removal in the biofilters co-

treating DMS and methanol.

Introduction 8

8

1.4 Significance

Biofiltration is an emerging technology that is particularly well-suited to treat RSC emissions.

Currently, the poor biodegradation rate of DMS limits the application of biofiltration technology

to treat waste gas streams that are relatively rich in DMS such as those found in the Kraft pulping

industry. Kraft pulp mills also have the potential to produce significant emissions of VOCs,

particularly methanol. Recently, it has been shown that methanol addition can increase DMS

removal in these systems. Understanding the mechanism behind the increase in DMS removal

will provide useful information that will lead to better biofilter design and operation that may

make treatment of relatively DMS-rich waste gas streams feasible.

Further to advances made in the knowledge of biofiltration of DMS, this thesis also contributes

significantly to the analysis of microbial communities by developing molecular-based methods,

such as qPCR, and applying them for the analysis of biological systems. This thesis demonstrates

how these novel techniques can be employed to link the microbiology of mixed microbial

systems, such as biofilters, to their performance. Furthermore, this thesis also demonstrates the

usefulness of these tools in measuring the potential of the mixed microbial community and

isolating factors that may lead to the poor performance of these systems.

1.5 Thesis Outline

This thesis is comprised of eight sections. The current section (Section 1) contains a brief

introduction to the thesis. Section 2 is a comprehensive literature containing information on RSC

emissions, including sources and treatment technologies, followed by a review of the

fundamentals of biofiltration technology and the biofiltration of RSC emissions. Following the

biofiltration section, the literature review focuses on what is known on the microbiology of DMS

degradation and the analysis of microorganisms in environmental samples before summarizing

microbial community studies performed in biofilters.

Section 3 is an amalgamation of the experimental approach and methodology and includes the

details of biofiltration operation, batch experimental set-up, molecular techniques, and analytical

techniques. Section 4 contains the results and discussion for experiments carried out on the

biofilters. Section 5 contains the results and discussion for the batch kinetic assays. Section 6

Introduction 9

9

discusses the engineering significance of the research while Section 7 contains the final

conclusions and Section 8 contains recommendations for future research.

Literature Review 10

Effect of methanol on the microbial community structure of biofilters treating dimethyl sulphide

2.0 LITERATURE REVIEW

2.1 Introduction to Reduced Sulphur Compounds (RSC)

Hydrogen sulphide (H2S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl

disulphide (DMDS) form a group of compounds known as the reduced sulphur compounds. As

shown in Table 2.1, RSC can be either gases (H2S and MM) or volatile liquids (DMS and DMDS)

at room temperature. RSC are also characterized by their distinctive smell and low odour

threshold. RSC also pose a potential health hazard to humans with the maximum concentration

value in workplace conditions, defined as the maximum permissible concentration of a chemical

compound in air within a working area that generally does not impair the health of the employee

and does not cause undue annoyance (ACGIH, 1991), ranging from 0.5 ppmv for MM to 20

ppmv for DMS. For H2S, severe toxic effects appear after exposure to a concentration of 200

ppmv for one minute and exposure to a concentration of 800 ppmv potentially causing

immediate fatality (Verschueren, 1983). With KOW values less than 60, RSC are neither highly

hydrophobic nor highly hydrophilic which is also reflected in their water solubility values.

Table 2.1 – Physical Properties of Reduced Sulphur Compounds

RSC Compound BP1 OT1

OQ1 MAK1 KOW

2 KAW3 Solubility in Water2

(°C) (ppbv) (ppmv) (25°C) (25°C) (g/L @ 25°C)

Hydrogen Sulphide -60.7 8.5-1000 Rotten Eggs 10 NA 0.41 3.37

Methyl Mercaptan 6.2 0.9-8.5 Decayed Cabbage 0.5 6.03 0.10 24.00*

Dimethyl Sulphide 37.3 0.6-40 Decayed Vegetables 20 8.32 0.07 19.61

Dimethyl Disulphide 109.7 0.1-3.6 Putrid, Foul < 20 58.88 0.04 3.40

1 – Smet et al., 1998a; 2 – Yaws, 2001; 3 – Smet et al., 1998b; ** data obtained at 15ºC; KAW

and solubility measurements were made at pH 7. BP – Boiling Point; OT – Odour Threshold;

OQ – Odour Quality; MAK – Maximum Concentration Value in Workplace Conditions; KOW –

Octanol/Water Partition Coefficient; KAW – Air/Water Partition Coefficient.

2.2 Emission of Reduced Sulphur Compounds

As shown in Table 2.2, RSC are emitted from both natural and anthropogenic sources with the

bulk of RSC emissions into the environment resulting from natural emission sources. The bulk of

sulphur emissions into the atmosphere are H2S and DMS. H2S in the atmosphere is believed to



be largely the result of dissimilatory sulphate reduction where sulphate-reducing bacteria use

sulphate as an electron acceptor to oxidize organic material. Other H2S emissions result from

heterotrophic organic sulphur compound catabolism (i.e. the breakdown of proteins),

assimilatory sulphur metabolism, and the chemical reduction of sulphate in seawater by ferrous

ion (a process which typically occurs near hydrothermal vents) (Kelly and Smith, 1990). The

largest source of DMS emissions is the ocean where DMS is produced through the cleavage of

dimethylsulphonioproprionate (DMSP) which is used as an osmolyte in marine algae (DeZwart

and Kuenen, 1992). In terrestrial environments, DMS is produced largely from the degradation

of sulphur-containing biological molecules such as the amino acids methionine and cysteine

(Bak et al., 1992; De Zwart and Kuenen, 1992; Drotar et al., 1987). Other possible sources of

DMS include the reduction of dimethyl sulphoxide (DMSO) and lignin degradation (Bak et al.,

1992; De Zwart and Kuenen, 1992; Drotar et al., 1987).

Table 2.2 – Estimated Annual Global Emissions of Reduced Sulphur Compounds (Kelly and

Smith, 1991)

Source Reduced Sulphur Compound Release (Tg year

-1)

Total SO2 H2S DMS DMDS + MM CS2 COS

Oceanic - 0-15 38-40 0-1 0.3 0.4 38.7-56.7

Salt Marsh - 0.8-0.9 0.58 0.13 0.07 0.12 1.7-1.8

Swamps - 11.7 0.84 0.2 2.8 1.85 17.4

Soil and Plants - 3.0-41 0.2-4.0 1 0.6-1.5 0.2-1.0 5.0-48.5

Burning of Biomass 7 0-1 - 0-1 0.11 7.1-9.1

Volcanoes and Fumeroles 8 1 - 0-0.02 0.01 0.01 9

Total 15 16.5-70.6 39.6-45.4 1.3-3.4 3.8-4.7 2.7-3.5 78.9-142.6

While the bulk of total RSC emissions arise from natural sources, the concentration of RSC in

the environment tends to be low. For example, the DMS concentration in ocean waters is

approximately 10-7

g/L (Lovelock et al., 1972; Andreae and Barnard, 1983). This translates to a

local air concentration of approximately 3 ppbv which is near the odour threshold for DMS.

Atmospheric concentrations of all other RSC are all less than 1 ppbv (Lovelock et al., 1972; De

Zwart and Kuenen, 1992) and well below the odour threshold of these compounds.



Anthropogenic RSC emissions, while insignificant in terms of total flux compared to emissions

from natural sources, tend to be emitted in concentrations that may result in localized odour

issues and, potentially, health concerns. Anthropogenic RSC emissions typically result from at

least one of two conditions: anaerobic processes and high temperature processes. Industries that

are well-documented sources of anthropogenic RSC emissions include wasterwater treatment,

aerobic composting, animal rendering, and kraft pulping (Gostelow et al., 2001; Van Durme et

al., 1992; Luo and Agnew, 2001; Springer, 1993).

While these four industries all produce mixtures of RSC emissions, they do not produce them in

the same ratio or abundance. RSC emissions from wastewater treatment plants tend to be

composed primarily of H2S (Mansfield et al., 1992), although other researchers have identified

volatile organic sulphur compounds at wastewater treatment facilities (Van Langenhove et al.,

1985; Bonnin et al., 1990; Hwang et al., 1994; Laplanche et al., 1994). Devai and DeLaune

(1999) found that H2S, on average, was responsible for more than 75% of total reduced sulphur

emissions at a wastewater treatment plant with a maximum concentration of 340 ppmv. However,

typical upper-end values were around 10 ppmv, concentrated around the bar screens and grit

chambers. For organic RSC emissions, the maximum measured concentration of MM and DMS

were 8.5 ppmv and 36 ppmv, respectively, with both maximum measurements taking place at the

central treatment plant digester dome. However, typical measurements for both compounds were

in the low ppbv range at other sites at the plant. The location of high RSC concentrations in the

Devai and DeLaune (1999) study was consistent with those determined by Frenchen (1988) in

his survey of 100 wastewater treatment plants in Germany where the bulk of odour at wastewater

treatment plants were found to originate from the headworks, the sludge thickener and

dewatering unit operations.

In aerobic composting, RSC emissions are typically emitted in complex mixtures. In the

production of compost used for mushroom cultivation, RSC emissions included H2S, carbonyl

sulphide (COS), MM, carbon disulphide (CS2), DMS, DMDS, and dimethyl trisulfide (DMTS)

ranging in concentration from 24 to 840 ppbv (Derikx et al., 1990). In contrast to the headworks

of wastewater treatment plants, the combination of anaerobic microsites and elevated

temperatures in the composting process typically leads to RSC emission mixtures that are rich in

volatile organic sulphur compounds compared to H2S. Smet et al. (1999) reported a maximum



DMS concentration of 3.2 ppmv during the thermophilic composting phase while Pöhle and

Klische (1996) reported DMDS concentrations of up to 10 ppmv during the aerobic composting

of biowaste.

Animal rendering involves converting slaughterhouse waste to value-added products such as

glue, bonemeal, fishmeal, grease and tallow. During the cooking process temperatures exceed

105°C, leading to the formation of a complex mixture of odorous compounds that include RSC,

acids, amines, alcohols, and aldehydes (Van Langenhove et al., 1982; Chélu and Nominé, 1984;

Prokop and Bohn, 1985). The concentrations of RSC emissions in the cooking process tend to be

elevated relative to those in wastewater treatment and aerobic composting. Chélu and Nominé

(1984) measured average concentrations of 30 ppmv for both H2S and MM (with peak

concentrations of 800 ppmv and 200 ppmv, respectively). Van Langenhove et al. (1992) reported

concentrations of 2-6 ppmv DMDS and 0.2-2 ppmv DMTS in the raw cooking gases of a

rendering plant.

Table 2.3 – Typical Emissions of Reduced Sulphur Compounds from Kraft Pulp Mills

Mill Source RSC Emissions (ppmv)

Temperature (°C) H2S MM DMS DMDS

Washer Hood Vent1 0-5 0-10 0-15 0-3 20-45

Washer Seal Tank1 0-2 10-50 10-700 1-150 39-75

Smelt Dissolving Tank1 0-75 0-18 0-4 0-3 70-110

Lime Kiln Exhaust1 0-250 0-100 0-50 0-20 65-95

Black Liquor Oxidation System2 0-5 0-10 0-3 0-1 33-48

Lime Slaker Vent1 0-20 0-1 0-1 0-1 65-75

1 – Springer, 1993; 2 – Wani et al., 2001

The Kraft pulping process is another well-documented source of RSC emissions. During the

Kraft pulping process, NaOH and Na2S are used to delignify wood chips to produce cellulose

fibres. After the delignification reaction, NaOH and Na2S are regenerated through the recovery

cycle which includes boiling the spent liquor (black liquor) in the recovery boiler and heating in

the lime kiln. As shown in Table 2.3, RSC emissions are ubiquitous throughout the Kraft pulping

process and can vary significantly from unit operation to unit operation. Sivela (1980) reported



RSC emissions from the digester for MM, DMS, and DMDS of 94.0 ppmv, 16.6 ppmv, and 21.7

ppmv, respectively.

2.3 Treatment of RSC Emissions

As discussed in the previous section, industrial RSC emissions are typically above the odour

threshold of these compounds which result in localized odour nuisances that have led

governments to regulate these industries to reduce their RSC emissions. Abatement technologies

for RSC emissions include both physicochemical methods such as scrubbing, adsorption and

incineration, and biotechnological methods such as biofiltration and bioscrubbing. The general

principle behind scrubbing and adsorption technologies is the transfer of RSC from the gaseous

phase to either a liquid phase (scrubbing) or solid phase (adsorption). Incineration (and catalytic

oxidation) involves the chemical conversion of RSC to carbon dioxide, sulphur dioxide, and

water. The mechanism of these technologies is beyond the scope of this thesis.

As opposed to physicochemical methods of treating RSC emission streams, biological methods

such as biofiltration, biotrickling filters, and bioscrubbers tend to be lower cost for dilute streams

but are currently less reliable than established physicochemical technologies. Biofiltration

involves passing humidified air through a packed bed of microorganisms where pollutants are

consumed by microorganisms to produce either new biomass or innocuous end products of

metabolism such as carbon dioxide and water. Biofiltration is generally considered to be the least

expensive odour treatment technology (Diks, 1992) and biofilters have become a well-

established technology with over 15,000 biofilters in operation worldwide (half of them at

wastewater treatment and aerobic composting plants) (Van Groenetijn, 2005). Biotrickling filters

differ from biofilters in that there is a constant recirculation of a mineral media through the

packed bed. This allows for tighter control of pH in the packed bed and ensures a constant supply

of nutrients throughout the biofilter. Bioscrubbers differ from both biofilters and biotrickling

filters in that they have to separate unit operations for gas-to-liquid transfer, which occurs in a

scrubbing tower, and microbial degradation, which occurs in a separate bioreactor. The

fundamentals of biofiltration and the use of biological technologies to treat RSC emissions will

be discussed in detail in the following sections.



2.4 Fundamentals of Biofiltration

While biofiltration can be considered a relatively young technology that is under development

for many industrial applications, the basic concept of using biofiltration to treat odour emissions

is almost a century old, being first proposed by Bach in 1923 (Leson and Winer, 1991). In North

America, the first research conducted on biofiltration to treat H2S emissions from sewage plants

was conducted by Carlson and Leiser (1966).

There are three fundamental processes that occur in biofiltration: the mass transfer of the gaseous

pollutant into the liquid phase (absorption), the mass transfer of the aqueous pollutant into the

biofilm, and the ultimate biodegradation of the pollutant (McNevin and Barford, 1998). The

extent to which compounds partition into the aqueous phase can generally be described by

Henry’s Law:

where CG = concentration in the gas phase, H = Henry’s constant, and CL = concentration in the

pollutant in the liquid phase.

While Henry’s Law describes the extent to which a compound will partition, it provides no

information about the rate of transfer of pollutant from the gas phase to the biofilm. In a biofilter,

the bulk air flow is usually turbulent which means that pollutants move through the air phase by

convection. Near the air-biofilm interface, the air-flow becomes laminar and pollutant transport

is strictly through molecular diffusion. This is typically the rate limiting step in the mass transfer

of the pollutant since molecular diffusion is much slower than convection. In biofilters, the

velocity of the bulk air is usually high enough to keep the laminar region thin enough so that

mass transfer in this layer is not limiting due to the slow nature of other processes. However,

mass transfer limitations may still occur for highly soluble compounds (Devinney et al., 1999).

Mass transfer from air to the biofilm (usually approximated as water) in biofilters is believed to

follow Fick’s Law where the rate of mass transfer is proportional to the difference between the

equilibrium pollutant concentration in the bulk water and the actual pollutant in the bulk water.

Fick’s Law is defined as follows:



where CL = concentration of the pollutant in the bulk water, CL* = equilibrium concentration of

the pollutant in the bulk water, CG = concentration of the pollutant in bulk air, H = Henry’s

constant, KL = overall mass transfer coefficient, and a = interfacial area

Biofilm (Water Phase) Air Phase

Po

lluta

nt

Co

nce

ntr

atio

n

Turbulent Flow

Laminar Flow

Phase Transfer

Figure 2.1 – Visual representation of pollutant transport in biofiltration systems

The driving force for transfer of pollutants from the air to the aqueous phase is the difference in

the equilibrium concentration of the pollutant in the water and the actual pollutant concentration

of the water. The actual rate of transfer also depends on both the overall mass transfer coefficient

which varies among contaminants and is affected by numerous factors and the interfacial area. In

biofilters, the packing media tends to be highly porous which gives it a high specific surface area

and makes the interfacial area relatively large. The end result of the large surface area in

biofilters is that resistance to gas phase diffusion is only rarely a rate-limiting factor which

allows for the assumption that the concentration of the contaminant at the surface of the aqueous

layer (biofilm) is equal to the equilibrium concentration and that the limiting step in pollutant

transport through the biofilm is liquid phase diffusion (Devinney et al., 1999).



The net flux of the contaminant into the biofilm is ultimately determined by the concentration

gradient of the contaminant in the gas phase and the biofilm. In order to maintain this gradient,

the contaminant must be removed from the biofilm. This is done by microorganisms which

metabolise contaminants to produce either more biomass or provide energy. The rate of

microbial growth is generally considered proportional to the amount of biomass and can be

described as follows:

where X = biomass concentration, and μ = specific growth rate

The specific growth rate is usually a function of the contaminant concentration and follows the

Monod relationship:

where μmax = maximum specific growth rate, KS = half velocity constant, and S = substrate

concentration

The two constants, μmax and KS, have the effect of making a smooth transition from zero-order

kinetics when substrate concentrations are high to first-order kinetics when substrate

concentrations are low. The value of μmax and KS is intrinsic to each combination of

microorganism and substrate while assuming that no other nutrients or oxygen is limiting. The

value of μmax and KS also varies with conditions such as pH and temperature, meaning that these

values could change if conditions within the biofilter change appreciably with time.

Another important parameter for biodegradation of pollutants in biofilters is the yield coefficient,

YX/S, which is defined as follows:

The yield coefficient varies from bacteria to bacteria and substrate to substrate since not all

bacteria are equally efficient at growing on the same substrate and not all substrates contain the



same level of energy per unit mass. The yield coefficient provides the connection from bacterial

growth rate to the rate of substrate degradation as follows:

Ultimately, the biological removal of a pollutant is proportional to the amount of biomass and the

intrinsic ability of that biomass to metabolize the pollutant in question. Another important factor

that affects biodegradation of the pollutant is the death of cells, which has the effect of reducing

the amount of biomass. The rate of cell death is usually expressed as the endogenous decay

coefficient, kd, modifying the substrate degradation rate equation as follows:

In the end, the removal of substrate in a biofilter is a balance between the ability of the substrate

to diffuse through the biofilm and the ability of the microorganisms in the biofilm to degrade the

substrate which creates two situations that will limit the overall performance of the biofilter. In

situations where pollutants and oxygen can fully diffuse through the biofilm (thin-film model),

the performance of the biofilter is limited by the ability of the microorganisms to degrade the

pollutant and is said to be reaction limited. In situations where pollutants and oxygen cannot

fully penetrate the biofilm (thick-film model), the performance of the biofilter is limited by the

ability of the pollutant to reach the bacteria and is said to be diffusion limited.

2.5 Biofiltration of RSC Emissions

As stated in the previous section, the idea of using biofiltration to control odour emissions is a

longstanding concept. The last 20 years have seen significant research on using biofiltration to

treat RSC emissions.

H2S is generally considered to be the RSC emission most readily removed by biofiltration.

Typically acclimation times are very short with high removal efficiencies and elimination

capacities. Fucich et al. (1997) were able to obtain 95% removal efficiencies for H2S

concentrations up to 4000 ppmv with retention times as short as 10 seconds. Several studies have



reported H2S removal rates of up to 150 g m-3

h-1

(Smet et al., 1998a; Wani et al., 1999; Sologar

et al., 2003).

The organic reduced sulphur compounds tend to have lower removal rates than H2S. This is

especially true when they are present with H2S in waste air streams as mixtures as is often the

case in industrial emissions. Table 2.4 lists several biofiltration studies that have been carried out

over the last 20 years that have investigated the interaction effects of different RSC emissions on

their removal in biofilters.

Table 2.4 – Interaction Effects Among RSC Emissions in Biofiltration

Trickling filter (polypropylene) 7-65 ppmv H2S 5% decrease in DMS removal rate in presence of H2S Tanji et al ., 1989

3-40 ppmv MM

2-21 ppmv DMS

Biofilter (peat) 50 ppmv H2S Removal of H2S and MM were independent of other RSC Cho et al ., 1991b

20 ppmv MM Removal of DMS was enhanced by H2S but inhibited by MM

25 ppmv DMS

Biofilter (peat) 300 ppmv H2S Removal rates of both H2S and MM in mixtures were 50% of single species removal rate Cho et al ., 1991c

15 ppmv MM

Biofilter (peat) 50-120 ppmv H2S DMS removal rate inhibited by both H2S and MM Zhang et al ., 1991a

40 ppmv MM

12-20 ppmv DMS

Biofilter (peat) 0.04-71 ppmv H2S DMS removal inhibited by H2S Park et al ., 1993a,b

0.025-2.73 ppmv MM

0.0045- 1.86 ppmv DMS

0.01-0.24 ppmv DMDS

Biofilter (compost) 10-450 ppmv H2S DMS removal inhibited by MM and DMDS Wani et al ., 2001

37-141 ppmv MM

3-25 ppmv DMS DMDS removal inhibited by H2S and DMS

5-54 ppmv DMDS

Biofilter (peat) 93 ppmv MM DMS removal inhibited by MM Hartikainen et al ., 2002

90 ppmv DMS

SourceReactor Type Concentration Interaction effects

The deteriorating performance of biofilters on organic reduced sulphur compounds in the

presence of other RSC emissions is particularly problematic for streams relatively rich in DMS

since DMS is generally considered to be the most poorly removed RSC emission. Wani (1999)

compared removal rates of H2S, MM, DMS, and DMDS in biofilters and found that DMS had

the lowest removal rate among the four compounds at 4 g m-3

h-1

. The removal rate of DMDS,

MM, and H2S were 2.5, 5, and 35 times greater, respectively. While loadings varied among the

species during this study, which partly accounts for the difference in removal rates, DMS

removal was also plagued with lower removal efficiencies. This result was consistent with those



of Cho et al. (1991b) who performed similar experiments on peat biofilters inoculated with pure

cultures. The combination of poor DMS removal and even worse performance in mixed streams

currently makes the application of biofiltration to waste streams relatively rich in DMS such as

those found in Kraft pulping unfeasible.

Table 2.5 lists DMS removal rates obtained in biofiltration studies over the last 20 years. These

studies used a variety of different packing materials, innocula, loadings, concentrations, and

substrate mixtures. Typically, the trickling filter studies tended to produce the highest removal

rates although the higher loadings of the biofilter trickling studies are as much of a contributor to

this as the improved removal efficiencies seen in these systems over traditional biofilters. Recent

studies are particularly encouraging in terms of improving biological DMS removal in these

systems with the Sercu et al. (2006) biofilter trickling study achieving 100% removal efficiencies

of DMS at both high and low concentrations and loadings and the Ho et al. (2008) biofilter study

achieving 100% removal efficiencies at low DMS concentration and loading. However, the Ho et

al. (2008) study is likely limited economically due to the need to add glucose to the system as a

growth substrate for Pseudomonas sp.. Zhang et al. (2006) was able to show an eleven-fold

improvement of the DMS removal rate in a biofilter co-treating DMS with methanol. This is

could be more cost-effective due to the presence of methanol in waste gas streams in industries

where DMS emissions are present such as Kraft pulping but the removal efficiency of these

biofilters only reached a maximum in the 55-70% range.

Several explanations have been put forth to explain why the removal rate of DMS is low

compared to those of other RSC emissions. Budwill and Coleman (2002) stated hydrophobic

compounds like DMS have high Henry’s constants and do not diffuse as readily into the biofilm.

However, as shown in Table 2.1, DMS actually has a lower Henry’s law constant and is more

soluble in water than H2S, a compound that is easily treated by biofiltration. Other researchers

have postulated that the low DMS removal rate is a result of microbial limitation and have

sought to bioaugment biofilters with known DMS degrading microorganisms (De Zwart and

Kuenen, 1992; Brennan et al., 1996; Smet et al., 1996b) with mixed results. Smet et al. (1996b)

also reported that DMS biodegradation can be inhibited by decreasing pH that results from the

build-up of sulphuric acid in biofilters.



Table 2.5 – DMS Removal Rates in Biofiltration Studies

Reactor Type Innoculant

Concentration Range Removal Rate

Source

(ppmv) (g DMS m-3

h-1

)

Trickling filter (polypropylene) T. thioparus Tk-m 0-21 9.58 Tanji et al., 1989

Biofilter (peat) Night soil sludge 5-57 3.33 Hirai et al., 1990

Biofilter (peat) T. thioparus DW44 25-27 4.17 Cho et al., 1991a

Biofilter (peat) Hyphomicrobium I55 0-40 4.58 Zhang et al., 1991a

Trickling filter (polyurethane) Hyphomicrobium VS 20-45 23 Pol et al., 1994

Biofilter (compost) Activated sludge 3-25 2.5 Wani et al., 1999

Trickling filter (polypropylene) Enriched sludge 236-664 71 Ruojarvski et al., 2001

Biofilter (peat) Activated sludge 90 14 Hartikainen et al., 2002

Trickling filter (polyethylene) Hyphomicrobium VS 100-1,058 58 Sercu et al., 2004

Trickling filter (polyehtylene) T. Thioparus Tk-m + Sludge 0.5-555 35 Sercu et al., 2006

Biofilter (silica) Activated sludge 25 2.2 Zhang et al., 2006

Biofilter (granular activated carbon) Pseudomonas sp. 0.2-2.2 0.84 Ho et al., 2008

Trickling filter (polyurethane) T. thioparus ATCC23654 0-240 37.3 Arellano-Garcia et al., 2008

Given the high DMS elimination capacities observed in recent biofilter trickling studies, in

which tight pH control is fairly easily obtained, the adverse effects of sulphuric acid

accumulation would seem to be the most likely explanation for low DMS removal in biofilters.

Biofilter studies with high DMS removal efficiencies reported tight pH control (Ho et al., 2008).

This was facilitated by washing the biofilter once a day and likely also the result of low DMS

loading and, thus, low sulphuric acid accumulation. Zhang et al. (2006) reported poor removal in

their control biofilter which treated DMS alone. The pH of the biofilter bed in the control

biofilter was allowed to drop to 5 from 7 before being neutralized. However, the DMS removal

rate of the biofilter co-treating DMS with methanol remained high despite using the same pH

control method.

At this point, it is difficult to conclude what the reason is behind poor biofiltration of DMS in

biofilters due to limited knowledge of what microorganisms are present in these systems and

how they react to different operational conditions (temperature, pH, nutrient supply, etc.). In



order to explore this further knowledge on which microorganisms degrade DMS and how they

behave in biofilters would be beneficial.

2.6 Microbial Degradation of DMS

Microorganisms are present in nature that are capable of metabolizing DMS both aerobically and

anaerobically. Given that the biofilter is an aerobic environment, it is expected that the dominant

DMS degraders will be aerobic microorganisms. This theory is supported by the fact that the

kinetics of aerobic pathways are roughly ten times faster than that of anaerobic pathways

(Lomans et al, 2002) and by the production of sulphate in peat biofilters (Hirai et al, 1990). The

majority of isolated aerobic DMS degrading microorganisms belong to the Thiobacillus,

Hyphomicrobium, and Methylophaga genera (Cho et al., 1991b; De Bont et al., 1981; De Zwart

et al., 1996; Pol et al., 1994; Smith and Kelly, 1988; Visscher and Taylor, 1993; Visscher et al.,

1991; Zhang et al., 1991a). As such, it would be expected that these microorganisms would be

present in biofilters treating DMS.

Members of the Hyphomicrobium genus are small, gram-negative, rod-shaped cells (0.3-1.2 x 1-

3 μm) (Hirsch, 1989) that cluster phylogenetically within the α-proteobacteria. They produce

either monopolar or bipolar filaments (hyphae) which are roughly 0.2-0.3 μm in diameter. One

defining characteristic of Hyphomicrobium is that daughter cell formation occurs through a

budding process at one hyphal tip. Mature buds become motile, break off, and may attach to

other cells or surfaces to form rosettes. Originally isolated through sub-culture on methanol,

member species are aerobic chemoorganotrophs capable of metabolizing C1 compounds, with

one known species capable of facultative denitrification (Urakami et al., 1995). Some species

have been shown to be capable of growing on organic nitrogen and organic sulphur compounds.

The genus is generally mesophilic, but growth has been shown to occur anywhere from as low as

4-6˚C and as high as 45˚C. The optimum pH seems to be around 7.0 (Hirsch, 1989). DMS

degradation by Hyphomicrobium spp. begins with the oxidation of DMS to methyl mercaptan

(MM) and formaldehyde via an NADH-dependent monooxygenase (Figure 2.2). MM is then

further oxidized to H2S and formaldehyde. H2S is completely oxidized to H2SO4 while

formaldehyde is either oxidized to CO2 for energy or incorporated into biomass via the serine



pathway. When methanol is used as a substrate, it is oxidized to formaldehyde by methanol

dehydrogenase.

CH3OH

NADH + H+ NAD+NAD+

O2 H2O

CH3SH

HCHO

O2 H2O2

NADH + H+NAD+

H2O

H2O HCHO

H2S

HCOOH

H2SO4

NADH + H+NAD+

CO2

1

2 H+ + 2 e-

CH3SCH3

2

3

4

5

6

Assimilation via

Serine Pathway

Figure 2.2 – Proposed metabolism of DMS and methanol by Hyphomicrobium spp. 1 – DMS

monooxygenase; 2 – methanol dehydrogenase; 3 – methanethiol oxidase; 4 – formaldehyde

dehydrogenase; 5 – sulphide-oxidizing enzymes; 6 – formate dehydrogenase (Suylen et al.,

1986).

Members of the Thiobacillus genus have been described as small, gram-negative, rod-shaped

cells (~0.5 x 1.0-4.0 μm) with some members being capable of motility by means of polar

flagella (Kelly and Harrison, 1989) that cluster phylogenetically within the β-proteobacteria.

They derive energy from the oxidation of reduced sulphur compounds and all species are capable

of fixing carbon dioxide through the Calvin-Benson cycle. While some species are obligate

chemolithotrophs, meaning that they fix carbon dioxide using energy derived from the oxidation

of inorganic compounds, other members of the genus are capable of chemoorganotrophic growth.

Furthermore, while the majority of the species in the genus are obligate aerobes, some members

are facultative denitrifiers. Finally, the Thiobacilli seem to be ubiquitously distributed in aquatic

and soil environments and exhibit a pH optima of 2-8 with a temperature optima of 20-43˚C

(Kelly and Harrison, 1989). DMS degradation by Thiobacillus spp. are believed to be carried out

using a similar pathway to Hyphomicrobium spp. due to the presence of methanethiol oxidase in

both Hyphomicrobium EG (Suylen et al., 1987) and Thiobacillus thioparus TK-m (Gould and

Kanagawa, 1992). However, Thiobacillus sp. ASN-1 has been shown to catabolise DMS through



a different pathway than Thiobacillus thioparus T5, and is capable of denitrification, suggesting

that there may be multiple DMS degradation pathways among the Thiobacillus spp. (Visscher

and Taylor, 1993).

The Methylophaga genus clusters phylogenetically within the γ-proteobacteria and, like the

Hyphomicrobium and Thiobacillus genera, is also comprised of members that are small, gram-

negative, rod-shaped cells (~0.2 x 1 μm) that are motile by means of a single polar flagellum.

Originally isolated through sub-culture on methanol (Janvier et al., 1985), member species are

obligately aerobic chemoorganotrophs capable of metabolizing C1 compounds. Some members

have also been shown to be capable of growth on fructose, organic nitrogen compounds, and

organic sulphur compounds. Colonies grown on agar appear pale pink and vitamin B12 is

necessary for growth. The optimum growth temperature is 30-37˚C but growth has been show to

occur at as low as 10˚C and as high as 40˚C. The optimum pH is around 7.0 (Janvier et al., 1985).

Although a clear pathway of DMS degradation could not be found in the literature, the metabolic

pathway appears to be different than Hyphomicrobium spp. or Thiobacillus spp. since the end-

product of DMS degradation was thiosulphate and carbon was assimilated via the ribulose

monophosphate pathway (de Zwart et al., 1996).

In terms of the reported kinetic parameters of these three microbial groups on DMS (Table 2.6),

the half-velocity constant for the Hyphomicrobium and Methylophaga genera are lower than

those of the Thiobacillus spp.. Consequently, Hyphomicrobium and Methylophaga will reach

their maximum growth rate at lower concentrations than Thiobacillus, and in theory, out-

compete Thiobacillus for DMS at low concentrations. A study of mixed cultures of

Methylophaga sulfidovorans and Thiobacillus thioparus T5 in the presence of DMS and

hydrogen sulphide supports this theory. The authors found that DMS was preferentially degraded

by Methylophaga sulfidovorans, while inorganic sulphides, such as hydrogen sulphide and

thiosulfate, were preferentially degraded by Thiobacillus thioparus T5 (de Zwart et al., 1997).

As for the growth kinetics on methanol, Hyphomicrobium and Methylophaga both have superior

kinetics on methanol compared to those on DMS with the specific growth rate on methanol being

at least triple of that on DMS, the cell yield being more than double, and the half velocity



constant being approximately the same. The end result is that Hyphomicrobium and

Methylophaga grow faster and produce more biomass per unit mass of methanol than DMS.

Table 2.6 – Reported kinetic parameters of known aerobic DMS-degrading microorganisms

Species μmax (h

-1) KS (μM) Yield (g cells / g substrate)

Methanol DMS Methanol DMS Methanol DMS

Methylophaga sulfidovorans1 0.28 - 0.30 0.05 2.1 - 3.1 1.2 - 1.8 0.30 - 0.33 0.14 - 0.15

Hyphomicrobium VS2 0.14 0.04 3 3 0.38 - 0.45 0.26

Thiobacillus thioparus Tk-m3 N/A 0.05 N/A 45 N/A 0.49

1 – de Zwart et al., 1996; 2 – Pol et al., 1994; 3 - Kanagawa and Kelly, 1986

While the general pathway of aerobic metabolism in Hyphomicrobium spp. and Thiobacillus spp.

is thought to proceed as in Figure 2.2, there is little information in the literature on the enzymes

involved in the pathway. De Bont et al. (1981) performed the first studies on cell free extracts of

Hyphomicrobium S and assayed both DMS monooxygenase and methyl mercaptan oxidase at pH

7.2 but reported low activities for DMS monooxygenase that may have been the result of

suboptimal assay conditions. Suylen et al. (1987) reported that the pH optimum for methyl

mercaptan oxidase in Hyphomicrobium EG was pH 8.2. For Thiobacillus spp., a methyl

mercaptan oxidase has been isolated from Thiobacillus thioparus TK-m where maximum

enzymatic activity occurred between pH 8.0 and pH 9.6 (Gould and Kanagawa, 1992). No other

DMS monooxygenase of methyl mercaptan oxidase purifications or characterizations have been

reported in the literature.

Aerobic degradation of DMS to sulphate is not the only pathway by which DMS is transformed

in the environment. Oxidation of DMS to dimethylsulphoxide (DMSO) is also carried out by a

wide variety of plants, fungi, and bacteria in the presence of an additional carbon source

(Andreae, 1990; Holland 1988). Such a phenomenon has been observed in peat biofilters with

Pseudomonas acidovorans DMR-11 (Zhang et al., 1991b). Anaerobically, DMS can be

metabolized through several pathways. Some anoxygenic phototrophic sulphur bacteria, such as

Thiocapsa roseopersicina, can oxidize DMS to DMSO (Visscher and van Gemerden, 1991;

Zeyer et al., 1987). Methanogenic bacteria have also been shown to reduce DMS to methane and

methyl mercaptan (which is usually further reduced to methane). Most methanogenic isolates



have been isolated from marine, estuarine and salt lake sediments (Rajagopal and Daniels, 1986;

Finster et al., 1992). However, methanogenic bacteria capable of DMS reduction, such as

Methanomethylovorans hollandica, have been isolated from freshwater systems (Lomans et al.,

1999). Evidence of sulphate-reduction as a mechanism for DMS removal is largely based on

inhibitor studies with molybdate and tungstate (Kiene and Visscher, 1987), although one

sulphate-reducer, a Desulfotomaculum sp., has been isolated from a thermophilic digester

capable of using DMS as a substrate (Tanimoto and Bak, 1994). Studies of mixed consortia have

shown that sulphate-reducing bacteria are only capable of competing with methanogenic cultures

at DMS concentrations below 10 μM (Kiene et al., 1986). Denitrifying bacteria have also been

implicated in DMS removal with a lone isolate, Thiobacillus sp. ASN-1, being isolated from a

marine sediment (Visscher and Taylor, 1993) and denitrification of DMS being detected in

freshwater habitats (Lomans et al., 1999).

2.7 Methanol Emissions in the Kraft Pulping Process

In conjunction with RSC emissions, kraft pulp mills also produce significant quantities of waste

methanol which is typically generated from the demethylation of methoxy groups in

hemicellulose and lignin during digester cooking (Hrutfiord et al., 1973). Methanol

concentrations can vary by orders of magnitude in different streams of the mill (Table 2.7) and

can change significantly depending how the mill is operated (Venkatesh et al., 1997).

Table 2.7 - Methanol emissions in different streams at a kraft pulp and paper mill (Vekatesh et

al., 1997).

Mill Stream Methanol Concentration, ppm

Secondary Condenser Condensate 10,240

Evaporator Clean Condensate 578

Evaporator Contaminated Condensate 1,312

Ejector Condensate 90

Concentrator Hotwell 31

Screen Room Sewer 200



2.8 Aerobic Metabolism of Methanol

Microbial degradation of methanol has been studied in much greater detail at this point than

microbial degradation of DMS largely due to the long known ubiquitous presence of methanol in

the environment that results principally from demethylation reactions (Anthony, 1982). Methanol

is consumed as a substrate through both aerobic and anaerobic pathways but, as is the case with

DMS, aerobic methylotrophs are expected to be dominant in the biofilter due to the fact that the

biofilter is an aerobic system.

Aerobic methylotrophic bacteria are phylogenetically quite diverse with members being found in

different subdivisions of the Proteobacteria as well as the Firmicutes. Aerobic methylotrophs

break down into several subdivisions such as methanotrophs, which are capable of growth of

methane, and non-methane utilizing methylotrophs which grow on other C1 compounds such as

methanol, formaldehyde, methylamines and methylated sulphur compounds. Several

methylotrophic microorganisms are incapable of growth on substrates containing carbon-carbon

bonds and are defined as obligate methylotrophs while those methylotrophic microorganisms

capable of growth on both C1 compounds and compounds containing carbon-carbon bonds are

defined as facultative methylotrophs.

Generally, the obligate methylotrophs tend to be the methanotrophs of which there are two types.

The Type I methanotrophs cluster among the γ-proteobacteria and include genera such as

Methylomonas, Methylobacter, Methylococcus, Methylomicrobium, etc. (Anthony, 1982;

Bowman et al., 1995). The Type II methanophs cluster among the α-proteobacteria and include

genera such as Methylosinus, Methylocystis, and Methylocella (Anthony, 1982; Dedysh et al.,

2000). Restricted facultative methylotrophs, capable of growth on a limited number of carbon-

carbon substrates tend to consist of methanol utilizers such as the α-proteobacterium

Hyphomicrobium (Harder and Attwood, 1978), the β-proteobacteria Methylophilus and

Methylobacillus (Jenkins and Jones, 1987; Bratina et al., 1992), and the γ-proteobacterium

Methylophaga (Janvier and Grimont, 1995). Finally, the facultative methylotrophs consist of a

large variety of α-proteobacteria, γ-proteobacteria, and gram positive bacteria (Lidstrom, 2006).

As previously mentioned, methylotrophic bacteria are phylogenetically quite diverse. This has

resulted in the development of several different metabolic pathways to assimilate C1 compounds



by microorganisms. Although metabolism of C1 compounds is carried out using different

metabolic pathways, the strategy for metabolising C1 compounds is similar for all aerobic

methylotrophic bacteria and centres around formaldehyde as an intermediate. Figure 2.3 is an

amalgam of the metabolism of C1 compounds in aerobic methylotrophic bacteria. While there is

no single bacterium known to carry out all these functions, the diagram encompasses most of

what is known about aerobic methylotrophic metabolism and serves as a useful frame of

reference in discussing C1 metabolism.

Figure 2.3 - Metabolism of C1 compounds in aerobic methylotrophic bacteria. 1 – methane

monooxygenase; 2 – methanol dehydrogenase; 3 – formaldehyde oxidation pathway; 4 – formate

dehydrogenase; 5 – halomethane oxidation system; 6 – methylated amine oxidases; 7 –

methylated sulphur oxidation system. RuMP is ribulose monophosphate and RuBP is ribulose

bisphosophate. Adapted from Anthony (Anthony, 1982; Anthony 1996) and DeBont et al. (1981).

The principal difference in the metabolism of C1 compounds among widely phylogenetically

diverse methylotrophs lies in assimilatory pathways of formaldehyde into cell biomass. This is

achieved through one of three metabolic pathways. The α-proteobacteria typically assimilate



formaldehyde into cell biomass via the serine cycle while the β-proteobacteria, γ-proteobacteria,

and gram positive bacteria assimilate formaldehyde via the ribulose monophosphate cycle

(Lidstrom, 2006). These two metabolic pathways are heterotrophic pathways for assimilating

formaldehyde into cell biomass. A third pathway involving the complete oxidation of

formaldehyde to carbon dioxide and then assimilation of carbon dioxide into cell biomass

through the ribulose bisphosphate cycle is used by autotrophic methylotrophs (Anthony, 1982).

As for dissimilatory metabolism, aerobic methylotrophs typically oxidize the methylated group

in the substrate to formaldehyde via oxidases or dehydrogenases. Generally, reactions carried out

by dehydrogenases tend to be energy conserving while those carried out by oxidases are non-

energy conserving reactions (Lidstrom, 2006). Formaldehyde is then further oxidized to acetate

and then carbon dioxide via two energy conserving dehydrogenase reactions. While there are a

variety of C1 compounds that are oxidized to carbon dioxide via the formaldehyde intermediate,

the focus in this section will be on how methanol is metabolized.

Aerobic methylotrophic bacteria oxidize methanol to carbon dioxide using formaldehyde as an

intermediate. The initial reaction consists of the oxidation of methanol to formaldehyde which is

carried out by methanol dehydrogenase. The oxidation of methanol to formaldehyde is typically

carried out by an NAD(P)-independent alcohol dehydrogenase that use either pyrroloquinoline

(PQQ), heme or F420 as a cofactor with the periplasmic PQQ-containing methanol dehydrogenase

being by far the most common form of methanol dehydrogenase for gram negative bacteria

(Duine and Frank, 1980). In gram negative bacteria, methanol dehydrogenase is part of an

electron transport chain located in the periplasm and catalyzes the oxidation of methanol to

formaldehyde, the reduction of cytochrome cL, and pumps two protons outside the cell. The end

fate of the electrons is to reduce oxygen to water inside the cell (Figure 2.4). The overall reaction

is an oxidation of methanol to formaldehyde and the creation of a proton motive force across the

cell membrane that yields less than 1 mole of ATP per mole of methanol oxidized (Anthony,

2000).

While cytochrome cL is the physiological electron acceptor, methanol dehydrogenase activity is

typically assayed in vitro in a dye-linked system with phenazine methosulphate or ethosulphate

linked to oxygen in an oxygen electrode (Day and Anthony, 1990). The pH optimum for this



assay is typically pH 9 or higher because at this pH phenazine derivatives form free radicals and

possible act as the true electron acceptor (Duine et al., 1978). However, it should be noted that

this is an artificial assay and the in vivo optimum is likely much lower since the optimum for the

oxidation of cytochrome cL is pH 7.0 (Anthony, 2000). The in vitro assay also requires NH4+

ions as an activator (Schär et al., 1985), however this does not appear to be the case in vivo

where a different, unknown activator operates with cytochrome cL (Dijkstra et al., 1989).

Methanol dehydrogenase has been shown to remain stable and retain its activity after incubation

for 45 hours in buffer from pH 5 to pH 10, with the optimum activity occurring for samples

incubated at pH 6 (Ohta et al., 1981).

Figure 2.4 – Schematic representation of the electron transport system associated with methanol

oxidation in gram negative bacteria. Adapted from Goodwin and Anthony (1995).

After the oxidation of methanol to formaldehyde, aerobic methylotrophic bacteria oxidize

formaldehyde further to formate. Formaldehyde is a problematic compound for microorganisms

to metabolize due to non-specific interactions between formaldehyde and proteins and nucleic

acids. Cellular formaldehyde concentrations may be tightly controlled since formaldehyde is

formed in the perisplasmic space but a formaldehyde specific transporter has yet to be identified



(Vorholt, 2002). Upon entry to the cytoplasm, formaldehyde can be metabolized via several

different pathways. Due to the toxicity of formaldehyde, most bacteria have metabolic pathways

for formaldehyde for detoxification purposes. Many methylotrophic bacteria have more than one

metabolic pathway for oxidizing formaldehyde and it is believed that different pathways may be

used for dissimilatory metabolism and detoxification. The five known formaldehyde oxidation

pathways are as follows:

1. Tetrahydrofolate-dependent formaldehyde oxidation

2. Tetrahydromethanopterin-dependent formaldehyde oxidation

3. Thiol-dependent formaldehyde oxidation

4. Cyclic oxidative ribulose monophosphate pathway

5. Formaldehyde oxidation by dye-linked formaldehyde dehydrogenase

The first three pathways are all co-factor dependent and proceed by oxidizing formaldehyde to

formate by coupling the oxidation of formaldehyde to the reduction of NAD(P)+ to NAD(P)H

(Vorholt, 2002). The tetrahydrofolate-dependent pathway becomes highly induced by methanol

and methylamine in bacteria that use the serine cycle for biosynthesis (Marison and Attwood,

1982), suggesting it is the primary formaldehyde dissimilatory pathway in these bacteria. The

tetrahydromethanopterin-dependent pathway has been identified in all bacteria tested among the

proteobacteria which assimilate formaldehyde via the serine or ribulose monophosphate cycle

(Vorholt et al., 1999). The thiol-dependent formaldehyde pathways are the most abundant

formaldehyde oxidation pathways in nature and are found in a wide variety of bacteria, yeasts,

plants, and mammals where it appears to serve largely as a detoxification pathway (Gutheil et al.,

1997).

As for the non-cofactor dependent pathways, the cyclic oxidative ribulose monophosphate

pathway is found in some methylotrophic bacteria that assimilate formaldehyde via the ribulose

monophosphate pathway (Anthony, 1982). The oxidation of formaldehyde proceeds in a similar

fashion to the assimilatory ribulose monophosphate pathway with the participation of 6-

phosphogluconate dehydrogenase (Anthony, 1982). The pathway has been shown to play an

dissimilatory role in Methylobacillus flagellatum which also assimilates formaldehyde via the

ribulose monophosphate pathway (Chrisoserdova et al., 2000). Finally, dye-linked formaldehyde



dehydrogenases are not generally believed to play a dissimilatory role in formaldehyde

metabolism as they are non-specific for formaldehyde, are not induced by methanol, and are

expressed at levels that are too low for observed growth rates (Attwood, 1990). They likely play

a role in detoxification.

The final step in dissimilatory C1 metabolism usually involves the oxidation of formate to carbon

dioxide via formate dehydrogenase. The oxidation of formate by formate dehydrogenase is

coupled with the reduction of NAD+ to NADH in an energy-conserving reaction (Vorholt, 2002).

Formate dehydrogenase activity has been detected in a wide variety of methylotrophs (Vorholt

and Thauer, 2001) and methylotrophs can contain multiple formate dehydrogenases

(Chistoserdova et al., 2004). However, it appears that formate dehydrogenase is not important

for growth on methanol as triple null mutants were shown to accumulate formate during

methanol consumption (Chistoserdova et al., 2004).

2.9 Identifying and Quantifying Microorganisms in Mixed Communities

A long-standing challenge in working the mixed microbial communities is both the ability to

identify and accurately quantify specific types of these microorganisms. Traditional methods

include both microscopy-based methods and culture-dependent methods. Difficulty in resolving

microorganisms, both from species to species and from each other in cases where

microorganisms flocculate, tend to make direct microscopy unreliable as a quantitative tool. As

for culture-dependent techniques such as plate-counting, they also have many restrictions and

biases (Brock, 1987) but the main problem is that they select for viable culturable

microorganisms which distorts the results. Staley and Konopka (1985) described the generalized

situation where direct microscopy counts exceeded those of viable plate counts by as much as

several orders of magnitude as “The Great Plate Count Anomaly”. It would later be shown that

this was due to the low culturability of microorganisms from environmental samples (Table 2.8).

Given the limitations of both direct microscopy and culture-dependent techniques, there was a

need to develop new ways of identifying and quantifying microorganisms in environment

samples. Culture-independent techniques were developed that were based on phylogenetics,

which is the systematic ordering of species into larger groupings based on inheritable traits. The

concept of using macromolecules, specifically polypeptides, as documents of genetic history was



first proposed in 1965 (Zuckerkandl and Pauling, 1965) but the major breakthrough came in

1977 when the archaebacteria were discovered as a distinct group of microorganisms through

comparisons of partial rRNA sequences (Woese and Fox, 1977). A year later the successful

sequence determination of the complete 16S ribosomal RNA gene from Escherichia coli was

completed (Brosius et al., 1978) and, due to its length (1541 base pairs in E. coli), its presence in

every cell, and that it is under less selection pressure than protein coding genes, the 16S rRNA

gene became an important gene in the study of microbial phylogenetics. A compilation of 16S

rDNA sequences from 275 different species revealed that the 16S rDNA contained regions

where the sequence is highly conserved, as well as regions where there is high sequence

variability amongst even closely related microorganisms (Neefs et al., 1990). This property of

the 16S rRNA gene has led to the development of several molecular-based techniques that allow

for both the identification and quantification of microorganisms in environmental samples.

Table 2.8 - Culturability determined as a percentage of culturable bacteria in comparison with

total cell counts (Amman et al., 1995)

Habitat Culturability* (%)

Seawater 0.001 - 0.1

Freshwater 0.25

Mesotrophic Lake 0.1 - 1.0

Unpolluted Estuarine Waters 0.1 - 3.0

Activated Sludge 1.0 – 15

Sediments 0.25

Soil 0.3

*Culturable bacteria measured as CFU

In Situ Hybridization

In 1986, it was proposed that in situ hybridization could be used to identify and quantify

microorganisms (Olsen et al., 1986). This was quickly followed by the first microscopic

identification of single microbial cells with radioactively labelled rRNA-targeting

oligonucleotide probes (Giovannoni et al., 1988). Today, the use of fluorescently labelled



oligonucletide probes is quite common, with probes being designed for broad targets (such as the

bacterial domain), down to much narrower targets (such as the genus or species level).

Oligonucleotide probes targeting the 16S rRNA have been published that were designed to

identify members of the Hyphomicrobium genus (Layton et al., 1999), the Methylophaga genus

(Janvier et al., 2003), and several Thiobacillus spp. (Peccia et al., 2000).

Limitations of in situ hybridization tend to be based on the target molecule, the 16S rRNA. The

quantity of 16S rRNA in a given cell is known not to remain constant over time and has been

shown to be directly correlated with growth rate (Schaechter et al., 1958). This is problematic in

that slowly growing or inactive cells may be difficult to detect due to their low 16S rRNA

content. However, it also means that measuring the fluorescent intensity of a given cell can be

used as an estimate of the growth rate of that cell. Also, in situ hybridization is often used in

combination with microscopy and, as such, is subject to the same lack of automation and low

throughput that is characteristic of microscopy-based methods. Finally, in situ hybridization is

dependent on the ability of the oligonucleotide probe to reach and bind to the 16S rRNA. While

transport of the oligonucleotide probe into the cell is feasible, cell wall-limited accessibility has

been encountered with aldehyde-fixed gram positive bacteria (Hahn et al., 1992). Other biases

may be introduced by the cell-fixing procedure.

16S rDNA Clone Libraries

Direct cloning of PCR fragments was first described by Scharf et al. (1986) and is frequently

used to study diversity in microbial ecology studies and have been employed as far back as 1992

when Liesack and Stackebrandt used the technique to study Australian soil samples (Liesack and

Stackebrandt, 1992). The technique is particularly useful in the study of microbial communities

since it is an effective way to separate homologous genes. To construct 16S rDNA clone library,

the 16S rRNA gene is amplified from genomic DNA. PCR fragments are then ligated into a

plasmid and transformed into a bacterium (typically E. coli). Transformants are then separated

on agar plates, re-grown, and the gene-containing plasmids can be extracted for sequencing and

species identification.

Construction of a 16S rDNA clone library requires numerous steps, each of which has the

potential to introduce biases to the results. Variations in sample collection and handling have



been known to effect analyses (Rochelle et al., 1994). Thus, it is important to freeze samples as

quickly as feasible and to minimize disturbances while sampling. The extraction procedure is

also important. Incomplete or preferential cell lysis can lead to a distorted view of the

community while different genomic DNA extraction methods will results in different yields and

purity of DNA. Harsher extraction methods may also lead to high DNA fragmentation which

may lead to the formation of chimeric products during PCR (Liesack et al., 1991).

Several biases can also be introduced into the analysis during PCR amplification. PCR

amplification may be inhibited by co-extracted contaminants such as humic acids (Tebbe and

Vahjen, 1993). Also, biases are introduced due to differential PCR amplification of different 16S

rRNA gene sequences. The choice of PCR primers and the number of cycles is well known to

bias amplification (Suzuki and Giovanni, 1996). Simulations of bacteria-specific primers have

shown that the hybridization efficiency and specificity of these primers are not equal across the

bacterial domain (Brunk et al., 1996).

A third bias that may occur during PCR amplification is the formation of PCR artefacts such as

chimeric molecules, deletion mutants, and point mutants. Chimeric molecules may form as two

different DNA sequences with high similarity compete for primers during the annealing step.

Chimeric sequences are problematic as they are not genuine 16S rRNA gene sequences and give

the impression of increased diversity and novel bacteria. Chimeric sequences can form a

significant fraction of the end clone library. Choi et al. (1994) reported a clone library where

almost 10% of the clones were chimeric sequences. Deletion mutants, where large gaps in the

sequence are omitted, are known to occur during PCR amplification of templates with stable

secondary structures (Cariello et al., 1991). Point mutations, which result from the mis-

incorporation of nucleic acids by Taq polymerase may also lead to errors in clone libraries

(Gelfand, 1992).

It is also worth noting that not only do different genomes contain different numbers of 16S rRNA

gene copies (Farrely et al., 1995), but in some cases there are multiple 16S rRNA gene

sequences in one species (Rainey et al., 1996). In the case of most uncultured bacteria, it is not

known how many 16S rRNA operons may be present in each cell or if there is 16S rRNA gene



heterogeneity. However, advances in DNA sequencing and increased focus on metagenomics

may make this data more available in the future.

Biases in clone libraries may also be introduced by the molecular cloning procedure although

these biases are not as well understood as PCR biases. Rainey et al. (1994) demonstrated that,

using the same batch of DNA, three different cloning systems produced three different

distributions of clones. The difference in distributions was explained by different ligation

efficiencies of the DNA fragments into the vector and non-uniform transformation efficiencies of

plasmids.

Genetic Fingerprinting Techniques

The requirement of cloning and sequencing in the construction of clone libraries makes it

impractical as an everyday tool to study the structure of microbial communities. The

development of genetic fingerprinting techniques such as denaturing gradient gel electrophoresis

(DGGE), temperature gradient gel electrophoresis (TGGE), terminal restriction fragment length

polymorphism (T-RFLP), and others allow researchers to study microbial community structure

without going through the process of cloning and sequencing. Fingerprinting techniques

eliminate the lengthy cloning and sequencing of 16S rDNA clone libraries by separating DNA

fragments in a different manner. In the case of DGGE and TGGE, PCR products are separated in

a polyacrylamide gel with a linear gradient. In the case of DGGE, the linear gradient is made of

DNA denaturants (formamide and urea), while TGGE uses a temperature gradient. In both cases,

DNA products are separated according to their melting temperature which allows DNA products

of similar length but different sequences to be resolved. In the case of T-RFLP, PCR products are

separated on an agarose gel after digestion with restriction enzymes.

The ability to resolve PCR fragments of similar lengths but different sequences makes it

particularly useful for the study of microbial communities. The development of bacteria-specific

primers for DGGE has created a relatively quick procedure for quantifying diversity in bacterial

communities (Muyzer et al., 1995). Commonly used parameters to compare banding patterns

include: species richness, evenness, and similarity indices such as the Jaccard coefficient.

Species richness is simply defined as the number of different species present in the community

while the evenness describes how species are distributed and is defined as follows:



where S = species richness and pi = the relative abundance of species i

While easy to calculate, the species richness and evenness do not provide any information on the

similarity of one community sample to another. In order to do this, similarity indices such as the

Jaccard coefficient can be used. The Jaccard coefficient is defined as follows:

where J(A,B) is the Jaccard coefficient between community A and B

Further to comparing the community similarity between samples, the Jaccard coefficient can be

calculated among many samples, allowing a comparison of the similarity many samples. This

can be represented visually through the construction of a dendrogram using the unwieghted pair

group method using arithmetic averages (UPGMA).

Fingerprinting techniques do have their limitations. Since they are typically used in conjunction

with PCR methods they pick up all the biases that are associated with PCR-based methods.

Typically, fingerprinting techniques only detect the predominant microbial species which make

up more than 1% of the population (Muyzer et al., 1993; Murray et al., 1996; Lee et al., 1996).

Techniques such as DGGE and TGGE are based on melting temperatures which creates

situations where two species with different sequences but similar melting temperatures may

migrate to the same spot on the gel. Co-migration of bands can also be an issue for species

identification using sequencing since it is possible that one band may contain more than

sequence.

Quantitative PCR (qPCR)

Quantitative PCR (qPCR) allows for the quantification of DNA using the principles of the

polymerase chain reaction. In qPCR, the amount of DNA is measured via fluorescence at the end



of every amplification cycle. In theory, the amplification of DNA is governed by the following

equation:

where Nn is the quantity of DNA after the nth cycle, N0 is the quantity of DNA before

amplification and η is the amplification efficiency.

During the PCR reaction, reagents typically become limiting and the exponential growth

typically does not hold throughout the entire course of the experiment. By measuring the amount

of DNA after each cycle, qPCR allows for the determination of the point where exponential

growth ceases, making it possible to develop a standard curve of known initial DNA quantities

and determining unknown values by determining after how many cycles a certain quantity of

DNA (fluorescence level) is reached.

Several different types of qPCR assays can be used to measure fluorescence of DNA and vary on

their level of complexity and specificity. SYBR Green I assays use a double-stranded DNA

(dsDNA)-binding dye to measure the progress of the PCR reaction. In the case of the SYBR

Green method, the generation of PCR amplicons is detected through the measurement of the

SYBR Green I fluorescence signal. SYBR Green I is a dye that intercalates into the DNA double

helix (Zipper et al., 2004). The fluorescence of the bound dye increases by a factor of 200

compared to unbound dye (Zhang and Fang, 2006). The dye detaches during the denaturation

step so it does not interfere with the PCR reaction (Zipper et al., 2004) and re-anneals to dsDNA

during the annealing and extension step when the temperature is lowered. Because SYBR Green

I is a non-specific dye that binds to all dsDNA, it is necessary to analyze the PCR product after

the completion of the PCR reaction to ensure the amplified DNA is the desired product and not

the result of non-specific amplification or primer dimer formation. This is typically done through

melting curve analysis.

A second type of qPCR assay is the Taq nuclease assay (TNA). The TNA exploits the 5’→3’

exonuclease activity of Taq DNA polymerase (Holland et al., 1991) to cleave a labelled

oligonucleotide (hydrolysis probe) that hybridizes to the DNA but is phosphorylated at the 3’



terminus to prevent polymerization by the polymerase (Lee et al., 1993). The hydrolysis probe is

labelled at the 5’ terminus with a fluorescein derivative and labelled at the 3’ terminus with a

rhodamine derivative. While the probe is intact, the fluorescence of fluorescein derivative is

quenched by the rhodamine derivative through Förster Resonance Energy Transfer (FRET).

During the elongation step of the PCR reaction, the Taq polymerase adding bases to the 3’

terminus of the upstream primer comes into contact with the hydrolysis probe and hydrolyses it,

releasing its individual nucleotides into free solution and separating the fluorescein derivative

from the rhodamine derivative. This results in an increase in fluorescence. As with the SYBR

Green method, the increase in fluorescence is proportional to the amount of PCR amplicons

generated.

A third type of qPCR assay involves the use of a molecular beacon. Similar to the TNA, the

molecular beacon is an oligonucleotide that has a fluorescein derivative at one end of the probe

and a rhodamine derivative at the other. The probe is specifically designed with a hairpin

structure that causes the probe to bind to itself and keep the reporter and the quencher close in

space to each other (Tyagi and Kramer, 1996). During the PCR reaction, the probe binds during

the annealing step where fluorescence can be measured. The probe detaches and reforms its

hairpin structure when the temperature is raised for the extension step.

All three assays have their advantages and disadvantages. The principal advantage of SYBR

Green is its simplicity. No probe is required and it can be used to monitor the amplification of

any dsDNA sequence. The main disadvantage of the SYBR Green method is the possibility of

generating false positives. It is possible to check for non-specific products through a combination

of gel electrophoresis and melting curve analysis but in certain situations this may be difficult

(i.e. quantification of multiple 16S rDNA sequences with same primer set). As for the TNA, the

main advantage over SYBR Green I is that the specific hybridization between the probe and the

desired PCR product is required to generate fluorescence, thus eliminating false positives. Also,

due to the elimination of false positives, post-PCR processing is not required. The main

disadvantage of the TNA is the extra costs associated with synthesizing the probe. It may also

not be possible to design an adequate probe for a TNA in all cases. As for molecular beacons,

they are the most specific assays but also the most difficult to design.



2.10 Analysis of Microbial Communities in Biofiltration Studies

Due to the limitations of culture-dependent methods in describing the microbial community

within biofilters and an interest in determining the biofilm architecture within these systems

there has been a growing interest in the use of molecular techniques in biofiltration studies. It has

also been proposed that tracking microbial populations could aid in biofilter optimization

(Williams and Millers, 1993). One of the earliest examples was a 1996 study which combined

fluorescent in situ hybridization (FISH) with confocal laser scanning microscopy to demonstrate

the homogenous distribution of Pseudomonas putida within the biofilm of a biotrickling filter

treating toluene (Møller et al., 1996). A related study demonstrated that after startup, the relative

abundance of P. putida decreased from 40% to 10% (Pedersen et al., 1997).

The construction of 16S rDNA clone libraries has also been employed in the biofiltration

literature. Sakano and Kefkhof (1998) constructed a 16S rDNA clone library from biofilters

treating ammonia emissions and demonstrated that throughout the 102 day experiment, the

ammonia oxidizers went from a mixture of betaproteobacteria and gammaproteobacteria to

predominantly gammaproteobacteria. Friedrich et al. (2002) created a large 16S rDNA clone

library from an industrial biofilter which showed the wide diversity of microorganisms in these

systems. The genetic information collected from the clone library construction was used to create

FISH probes where the biofilm architecture of this biofilter was explored (Friedrich et al., 2003).

There are also studies in the literature that have used molecular methods to study the microbial

community structure of biofilters treating DMS. Sercu et al. (2005) used DGGE to track changes

in the microbial community in a two-stage biotrickling filter system treating H2S and DMS and

found that the initial innoculum. Hyphomicrobium VS, was not the dominant bacterium in the

biotrickling filter after 44 days of operation. Furthermore, DGGE revealed that the microbial

population became more stable with time. In another study of biotrickling filters treating DMS,

Sercu et al. (2006) used DGGE and qPCR to show that Thiobacillus spp. were more dominant

than Hyphomicrobium spp., suggesting that these bacteria were responsible for DMS degradation.

Finally, Ho et al., (2008) used FISH to demonstrate that in a biofilter treating a mixture of

volatile reduced sulphur compounds, including DMS, that Pseudomonas sp. remained by far the

dominant bacterium (56-70%) after 415 days of operation.



2.11 Summary of Literature and Knowledge Gaps

It has been established that biofiltration is a promising technology for the treatment of RSC

emissions. While some RSC emissions, such as hydrogen sulphide, are treated very effectively,

the biofiltration of volatile organic reduced sulphur compounds, such as DMS, tends to be more

difficult with DMS showing the poorest removal rates in biofiltration of waste gas streams

containing mixtures of RSC emissions. The poor removal rates of DMS in biofiltration systems

is currently a key factor limiting the application of biofiltration technologies to treat waste

streams that are relatively rich in DMS such as those in the kraft pulping industry.

At this point, it is not entirely clear as to what are the factors that are limiting DMS removal in

biofiltration systems. There are some who propose that the physical properties of DMS create a

limit on the removal to DMS (Budwill and Coleman, 2002). However, the physical properties of

DMS reveal that DMS is actually more hydrophilic than hydrogen sulphide which is very

effectively treated by biofiltration. Another theory is that the limitations are a result of poor

microbial kinetics (Cho et al., 1991; Park et al., 1993; Smet et al., 1996). This may very well be

the case given the low maximum specific growth rates of bacteria on DMS reported in the

literature. However, to date, microbial community studies on biofilters treating DMS have

largely been confined to isolation of bacteria from these systems and do not provide a very

accurate description of the microbial community within the biofilter.

As well as having limited knowledge of the bacteria degrading DMS in these systems, there is

also limited knowledge on the kinetics of these microorganisms on DMS under conditions that

they can reasonably be expected to be growing under in the biofilter. Most kinetic studies in the

literature on isolated aerobic DMS degraders have been limited to studying their growth on pH 7

whereas conditions in biofilters typically fluctuate and can easily reach pH levels at 5 and below

if pH is not controlled due to acidification as a result of DMS oxidation.

The limited knowledge of the microbial community structure of biofilters treating DMS also

makes it difficult to explain the reason behind interaction effects (both positive and negative) in

multiple substrate biofilters. As an example, it is known that methanol addition can increase

DMS removal rates in biofilters (Zhang, 2007). While it may be possible to infer possible

mechanisms in binary systems, community analysis allows for verification of these hypotheses



and the identification and quantification of microbial species in situ and will likely be necessary

in more complex systems with multiple substrates.

This thesis contributes to several areas of the literature. First, as of the beginning of this research

project, the microbiology of biofilters treating DMS is currently not well understood and is

limited to a handful of studies where bacteria were isolated from biofilters. In this thesis,

molecular methods were developed and utilized to study the microbial community in situ,

providing the most comprehensive knowledge of the microbial community structure of a biofilter

treating DMS to date. Further to this, an enrichment culture was created from the microbial

community in a biofilter co-treating DMS and methanol that allowed for controlled kinetic

assays to be performed on microorganisms growing on DMS in the biofilter under conditions

that were relevant to biofilter operation. This provided insights that is currently lacking from the

literature as to how these bacteria respond to different conditions in the biofilter. Furthermore,

the experiments performed in this thesis allowed us to propose a mechanism behind improved

DMS removal in biofilters co-treating DMS with methanol which was previously discovered by

our research group, illustrating the usefulness of the approach taken in this thesis to link

microbiology to the performance of biological systems.

Experimental 43


3.0 EXPERIMENTAL

3.1 Approach

The overall goal of this thesis was to understand how methanol co-treatment can lead to

improved DMS removal in inorganic biofiltration systems treating DMS. It was hypothesized

that the addition of methanol has an effect on the microorganisms present in the biofilter that

ultimately leads to a change in the community structure of these biofilters and results in

improved DMS removal. With this in mind, an experimental approach was developed that would

test this hypothesis. A roadmap of the experimental plan in this thesis is found in Figure 3.1 that

describes how the various parts of this thesis link together to accomplish the objectives of this

thesis and ultimately test the over-arching hypotheses.

Figure 3.1 – Overall roadmap of experimental methodology and their connection to fundamental

questions in this thesis.

This thesis is composed of experiments that were carried out in lab-scale biofiltration systems as

well as small-scale batch experiments carried out on an enrichment culture created from a

Experimental 44


biofilter co-treating DMS and methanol in the biofiltration study. The biofiltration experiments

were carried out by a previous student in our research group, Yuefeng Zhang, with his focus

being primarily on the implications of methanol co-treatment on biofilter operations. His thesis

led to the discovery of both the beneficial effect methanol addition can have on the biofiltration

of DMS as well as the competitive effect between DMS and methanol. This thesis focuses on the

implications of methanol addition on the microbiology of these systems and explains why

methanol addition leads to improved DMS removal in these biofilters.

The results of this thesis are split into two chapters. Chapter 4 focuses on the effect of methanol

addition on the performance and microbiology of biofilters treating DMS in the presence and

absence of methanol. In order to explore the microbiology, three different molecular-based

methods were used. To identify bacteria present in the biofilters, two 16S rDNA clone libraries

were constructed. One library was constructed directly from biomass harvested from a biofilter

treating DMS alone while a second was constructed from an enrichment culture created from a

biofilter co-treating DMS and methanol. The genomic data acquired from the 16S rDNA clone

libraries was then used to develop qPCR assays that allow for the quantification of these

microorganisms in situ. Finally, denatured gradient gel electrophoresis (DGGE) was used to

study the similarity of the microbial community structure in different regions of these biofilters.

Chapter 5 focuses on the growth kinetics of bacteria identified as potentially important to the

degradation of DMS in these systems in an enrichment culture created from one of the biofilters.

The batch studies included an investigation into the effect of pH and nitrogen source on the

kinetics of microorganisms identified as potential DMS degraders in the biofilters on both DMS

and methanol. In conjunction with the biofilter performance and microbiology data, a mechanism

behind improved DMS addition with methanol addition was proposed.

The details of the experimental procedure are presented in the following sections. Section 3.2

contains the detail behind the set-up and operation of the biofilters used in these experiments.

Section 3.3 contains the details on biomass harvesting from the biofilters as well as the biofilter

operating conditions at the time of sampling. Further information on biofilter operation near the

sampling times is provided in Chapter 4. Section 3.4 provides the details in the creation of the

enrichment culture from the biofilter co-treating DMS in the presence of methanol while the

Experimental 45


methodology describing how the batch kinetic assays were carried out is provided in Section 3.5.

Finally, the details of how the different molecular analyses were performed are provided in

Section 3.6 while Section 3.7 contains information on how other analytical methods, such as gas

chromatography and ion chromatography, were performed.

3.2 Biofilter Setup

Two identical lab-scale biofilters constructed from rigid polymethyl methacrylate tubing were

used in this study. The biofilters had an inner diameter of 101.6 mm and a height of 480 mm.

The biofilter consisted of three sections separated by a plenum of 20 mm. Four gas sampling

ports were located along the length of the biofilter before each section and one gas sampling port

at the outlet. Gas concentrations were measured using gas chromatography. Three biofilter media

sampling ports were designed for biomass sampling from each section of the biofilters.

The experimental apparatus included humidification, methanol addition, DMS addition, and a

micronutrient/dilute NaOH delivery system (Figure 3.2). The air streams were humidified by

counter-current exchange with water in humidifiers to ensure that a relative humidity of greater

than 97% was maintained for the biofilters. The temperature of the biofilters was maintained at

30 oC.

A

H

Water

Bath

MeOH

DMS

B

MFC

MFC

Treated Air

Nutrient Addition

Water

Air

Gas Sampling Port

Media Sampling Port

Figure 3.2 - Schematic diagram of biofilter setup. H – humidifier; B – biofilter; A – adsorption

tank; MFC – mass flow controller (Zhang et al., 2006)

Experimental 46


The biofilters were packed with Nova Inert®, a highly porous packing material made from silica

pellets (Nova Biotech, Dr. Fechter GmbH, Berlin), and was used to eliminate the impact an

organic based packing material may have on community structure. The packing material was

approximately spherical with a particle diameter of 5 – 7 mm, had a particle density of 0.25 –

0.74 g cm-3

, a void fraction of 0.35. The effective surface area was estimated to be 585 m2 m

-3

(Perry and Green, 2000).

Controlled-release fertilizer pellets (Nutricote 20-7-10, Plant Products Co., Ltd., Brampton, ON)

containing nitrogen and phosphorous were added to the packing material at 18 kg m-3

of packing

material. Dolomitic lime was added at 25 kg m-3

of packing material as a pH buffer.

Table 3.1 – Composition of Nova Inert®

packing media (Minuth, 1999)

Compound Mass Fraction (%)

CaO 9

MgO 2

SiO2 69

K2O 1

Al2O3 1

Na2O 16

The two biofilters were inoculated with 400 mL of concentrated activated sludge (~3 g dry

biomass per litre) obtained from the North Toronto Wastewater Treatment Plant. Gaseous DMS

(4000 ppmv, balance nitrogen, BOC gases, Mississauga, ON) was fed to each biofilter subjected

to the same downward flow rates of 0.12 to 0.24 m3 h

-1. Methanol was added to the biofilter co-

treating DMS and methanol via syringe pump (74900 series, Cole-Parmer Instrument Co.,

Barrington, IL), while the second biofilter was fed only DMS throughout the experimental

periods. A micronutrient solution (100 mL) containing (in g L-1

) MgSO4.7H2O (0.8),

FeSO4.7H2O (0.01), and CaCl2 (0.25) was added weekly to each biofilter. The solution

accumulated at the bottom of the biofilter and was re-circulated with a peristaltic pump for 30

minutes once a day. NaOH solution (0.1 M) was used to re-adjust the pH of the biofilter bed to 7

Experimental 47


once it dropped to 5 (pH re-adjustment was usually required every four days in control biofilter).

Both the micronutrient and dilute NaOH solutions were added to the top of the biofilter.

3.3 Sample Collection

Samples were collected from biofilters treating DMS alone, and co-treating DMS with methanol

on days 283, 400, and 457 of operation. For the biofilter co-treating DMS with methanol, the

DMS and methanol loadings on days 283, 400, and 457 were 5.2 g m-3

h-1

and 32 g m-3

h-1

, 3.4 g

m-3

h-1

and 90 g m-3

h-1

, and 1.0 g m-3

h-1

and 6.0 g m-3

h-1

, respectively. The bulk pH on these

days was 5.8, 6.2, and 5.6, respectively. For the biofilter treating DMS alone, the DMS loading

on days 283, 400, and 457 was 5.2 g m-3

h-1

, 3.4 g m-3

h-1

, and 5.0 g m-3

h-1

, respectively. The

bulk pH on these days was 5.1, 5.4, and 6.0, respectively. Biomass samples collected on days

283 and 400 were obtained from the media access ports. On Day 457, the biofilters were shut

down and opened from the top (near the inlet section). The biofilter packing material was

removed and biomass samples were collected at the points indicated in Figure 3.3. Packing

media was dried and weighed to standardize the quantity of biomass on a gram dry media basis.

There was no noticeable settling of fertilizer pellets or over time.

Figure 3.3 - Biomass sampling locations from biofilters on Day 457. Samples taken from the

bed top refer to two samples taken on Day 457 from the biofilter co-treating DMS with methanol.

Samples obtained on Day 283 and Day 400 were obtained from side media access ports at the

center of the inlet and middle sections. Abbreviations T (top) and C (centre) refer to plane where

samples were taken within a section.

Experimental 48


3.4 Creation of Enrichment Culture from Biofilter co-treating DMS and

Methanol

An enrichment culture was established by inoculating 40 mL of mineral media (Pol et al., 1994)

in 250 mL glass bottles sealed with mininert® valves (Supelco, Bellefonte, PA) (Figure 3.4) with

biomass obtained from a biofilter co-treating DMS and methanol on Day 400 and fed 1 μL DMS

using a gas-tight syringe (approximately initial DMS concentration was 0.3 mM). The

enrichment culture was maintained through serial 1:2500 dilutions of grown culture with fresh

media and re-feeding liquid DMS (1 μL). The enrichment culture was originally created in

March 2006 and maintained for more than 3 years. The enrichment culture was grown in media

at ambient temperature (22-24°C), pH 7, and shaken at 150 rpm. The pH of the media was set

using a dilute 0.2 M H2SO4 solution.

Rubber Septum

Threaded Cap

Bottle

Headspace

Media

Mini Inert® Valve

Figure 3.4 – Experimental apparatus used for maintenance of batch culture and time course

batch experiment.

3.5 Batch Kinetic Assays

Batch kinetic assays on the enrichment culture were performed by inoculating 40 mL of mineral

media (Pol et al., 1994) in 250 mL glass bottles sealed with mininert® valves (Supelco) with a

1:2500 dilution of a fully grown culture that had been growing under the same conditions for at

least 2 full growth cycles (2 serial dilutions). Cultures were grown at ambient temperature (22-

24°C) and shaken at 150 rpm in a LAB-LINE Model No. 3528-5 Shaker (Pegasus Scientific).

Conditions tested in the batch kinetic assays included substrate, pH of the media, and the

nitrogen source in the media. For experiments where DMS was the substrate, an initial quantity

Experimental 49


of 1 μL liquid DMS (~ 0.3 mM DMS initial concentration) was added to the media while for the

experiments where methanol was the substrate, an initial quantity of 100 μL liquid methanol (~

60 mM MeOH initial concentration) was added to the mineral media. The pH of the media was

adjusted using 0.2 M H2SO4 so as to minimize the volume that must be added to reduce the pH

(the volume of H2SO4 solution added was less than 0.5 % v/v). Finally, to test the effect of the

nitrogen source on the growth of these microorganisms an NH4+-N source (NH4Cl) and an NO3

--

N source (KNO3) were used. In the case of NH4+-N experiments, a concentration of 0.4 g L

-1

NH4Cl was used while, in the case of NO3--N experiments, a concentration of 0.9 g L

-1 KNO3

was used.

Gas-phase concentration of DMS and methanol were measured using gas chromatography,

sulphate formation was measured by ion chromatography, and the quantity of 16S rDNA from

Bacteria, Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. was measured using

qPCR. Batch kinetic assays for the DMS experiments were performed in triplicate and repeated

(n=6 for each condition) while batch kinetic assays for methanol were carried out in triplicate

once (n=3 for each condition). Biomass samples were taken at approximately 24 hours intervals

over the course of the approximately 90 hours it took for the substrate (DMS or methanol) to be

degraded. Biomass samples were harvested by centrifuging 1.5 mL of liquid culture at 10,000g

for 5 minutes and removing the supernatant which could then be analyzed using ion

chromatography. The processing of the biomass is discussed in Section 3.6.

In order to determine the specific growth rate of microorganisms, Monod kinetics were assumed,

where a plot of the natural logarithm of the biomass concentration (as estimated by 16S rDNA

copies per mL media) versus time yields a straight line with a slope equal to the specific growth

rate. The results and validity of this assumption is discussed further in Chapter 5.

3.6 – Molecular Analyses

All three molecular assays performed were carried out on genomic DNA. Genomic DNA was

isolated from harvested biomass samples using the regular extraction protocol of the Ultraclean

Soil DNA Kit (Mo Bio Laboratories, Carlsbad, CA).

Experimental 50


3.6.1 - Denaturing Gradient Gel Electrophoresis (DGGE)

Polymerase chain reaction (PCR) amplifications were carried out in 25 μL volumes with 2.0 μL

undiluted genomic DNA template, 0.4 μM of primers F357GC and R518 (Muyzer et al., 1993),

0.4 mM dNTP, 10 μg BSA, 2.5 μL Taq buffer, and 0.5 units of Taq DNA polymerase using a

MyCycler Personal Thermal Cycler (Bio-Rad Laboratories, Hercules, CA). The thermal cycling

program consisted of 4 minutes at 94˚C, followed by 35 cycles of 94˚C for 30 seconds, 54˚C for

1 minute and 72˚C for 1 minute, followed by 10 minutes at 72˚C. DGGE was performed with the

DCode system (Bio-Rad Laboratories, Hercules, CA). PCR products were loaded onto 8%

(wt/vol) polyacrylamide gels containing a linear denaturing gradient of 30-55% (100% was

defined as 7 M urea and 40% (vol/vol) formamide). The gels were electrophoresed in TAE

buffer at 60˚C and 200 V for 3 hours before staining in TAE buffer containing 1 μg/mL ethidium

bromide for 1 hour and digitally photographed under UV light. Bands of interest were excised

from the gel using a razor blade and eluted in 100 μL of sterilized ddH2O through 3 freeze-thaw

cycles accompanied with vigorous vortexing. Eluted DNA was re-amplified, cloned and

sequenced using the same protocol as the 16S rDNA clone libraries. GelCompar II (Version 5.10,

Applied Maths, Kortrijk, Belgium) was used to compare DGGE fingerprints by constructing

dendrograms using unweighted pair group method with arithmetic mean (UPGMA) based on

Jaccard similarity coefficients using optimization and band position tolerance of 1.0%.

3.6.2 16S rDNA Clone Library Construction

PCR amplification of genomic DNA (50 ng) was carried out in 50 μL reactions containing 200

μM dNTP, 1 mM MgCl2, 0.5 μM of EUB27f and EUB1492r primer (Weisburg et al., 1991), and

0.5 units of Taq DNA polymerase (Fermentas Inc., Glen Burnie, MD) using a MyCycler

Personal Thermal Cycler (Bio-Rad Laboratories, Hercules, CA). The thermal cycler program

consisted of an initial denaturation step of 94˚C for 10 minutes followed by 27 cycles of 94˚C for

1 minute, 55˚C for 2 minutes, and 72˚C for 2 minutes. Purified PCR products were ligated into a

pCR2.1 vector and transformed into One-Shot TOPO10 Chemically Competent E. coli cells

(Invitrogen Corp., Carlsbad, CA). Transformants were selected by growing overnight on agar

plates containing Luri-Bertani Broth and 50 μg/mL ampicillin at 37˚C. Plasmid DNA was

isolated from positive transformants clones and sequenced using the ABI3730XL DNA Analyzer

Experimental 51


(Applied Biosystems Inc., Foster City, CA). Clones were identified using the 16S Ribosomal

DNA Project (http://rdp.cme.msu.edu) (Cole et al., 2007). Sequences were then aligned using

ClustalX (Thompson et al. 1997) and chimeras were removed using Mallard (Ashelford et al.,

2006). Chimeras accounted for less than 10% of total clones in both libraries. The final

enrichment culture and DMS biofilter libraries contained 36 and 41 clones respectively.

MrBayes 3.1 (Ronquist and Huelsenbeck, 2003) was used to construct Bayesian phylogenies.

Phylogenetic trees were constructed with TREEVIEW (Page, 1996). Sequences were submitted

to GenBank and assigned accession numbers FJ53681-FJ536941. 16S rDNA sequences obtained

from the 16S rDNA clone library construction can be found in Appendix B.

3.6.3 Quantitative PCR (qPCR)

Primers and probes used for qPCR are listed in Table 3.2. Primers and probes developed in this

study were constructed using information obtained from the 16S rDNA clone library through the

creation of an alignment of 16S rDNA copies of members of the same genus and designing the

oligonucleotides to bind within the variable regions of the 16S rDNA. After designing, the

oligonucleotides sequences were compared against other sequences to ensure specificity and

ultimately tested against non-specific sequences. For Hyphomicrobium spp., two fluorescent in

situ hybridization (FISH) probes were modified to work within a Taq nuclease assay while in the

case of Thiobacillus spp. and Chitinophaga spp. used SYBR Green I assays. Methodology for

standard curves for the qPCR assays can be found in Appendix A.

All reactions were carried out using a Roche Lightcycler 2.0 (Roche Diagnostics Corp.,

Indianapolis, IN). For quantification of Bacteria and Hyphomicrobium spp., Taq nuclease assays

were carried out in a total volume of 20 μL using the Lightcycler TaqMan Master kit (Roche

Diagnostics Corp., Indianapolis, IN) with primer concentrations of 0.5 μM and a hydrolysis

probe concentration of 0.1 μM.. The thermal cycling program consisted of an initial 10 min

denaturation at 95˚C, followed by 45 cycles of 95˚C for 10 sec, 55˚C for 20 sec, and 72˚C for 20

sec. For quantification of Thiobacillus spp. and Chitinophaga spp., SYBR Green I assays were

carried out in a total volume of 20 μL using the Lightcycler FastStart DNA MasterPLUS

SYBR

Green I kit (Roche Diagnostics Corp., Indianapolis, IN) with primer concentrations of 0.5 μM..

For the Thiobacillus assay, the cycling program included a pre-incubation of 10 min at 95˚C and

http://rdp.cme.msu.edu/

Experimental 52


45 cycles of 95˚C for 10 sec, 52˚C for 20 sec, and 72˚C for 20 sec. For the Chitinophaga assay,

the cycling program included a pre-incubation of 10 min at 95˚C and 45 cycles of 95˚C for 10

sec, 57˚C for 20 sec, and 72˚C for 20 sec. Product purity was verified by melting curve analysis.

Table 3.2 - Real-time PCR primers and probes used in this study to quantify the 16S rRNA gene

of Bacteria, Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp..

Bacteria BAC Forward 5' - TCCTACGGGAGGCAGCAGT - 3' Nadkarni et al., 2002

BAC Probe 5' - FAM - CGTATTACCGCGGCTGCTGGCAC - TAMRA - 3' Nadkarni et al., 2002

BAC Reverse 5' - GGACTACCAGGGTATCTAATCCTGTT - 3' Nadkarni et al., 2002

Hyphomicrobium Hyp Forward 5' - GGCTCAACCTCGGAACT - 3' This study

Hyp Reverse 5' - CGAATTTCACCTCTACACTAGGAT - 3' This study

Hyp Probe 1 5' - FAM - TGAGTCCGATAGAGGTGGGTGG - TAMRA - 3' This study

Hyp Probe 2 5' - FAM - AGTCTTGAGTCCGGAAGAGG - TAMRA - 3' This study

Thiobacillus Thio Forward 5' - CCTCACGTTATTCGAGCGG - 3' This study

Thio Reverse 5' -ACGCACTCTAGACTGCCA -3' This study

Chitinophaga Chi Forward 5' - TTRAAGATGGSYGTGCRYC - 3' This study

Chi Reverse 5' - CGCTACATGACATATTCCGCT - 3' This study

SourceMicrobial Group Oligonucleotide Name Oligonucleotide Sequence

3.7 Analytical Methods

Gas-phase concentrations of DMS and methanol in the headspace of the batch culture bottles

were measured with a gas chromatograph (Varian 3400 Cx) (Palo Alto, CA, USA) equipped with

a pulse flame photometric detector (PFPD) and flame ionization detector (FID). The column

used was a 5.0 μm ID by 30 m long with 0.32 mm thickness capillary column (DB-1 column). A

constant column temperature of 120ºC was used with the injection port at 120ºC and the

detection temperature at 250ºC. A 50 μL or 250 μL sample was injected into the GC using a 250

μL gas-tight syringe. Aqueous sulphate ion concentrations were measured by ion

chromatography using a Dionex (Sunnyvale, CA, USA) 300 series ion chromatograph equipped

with an Ionpac®

AS11 4 mm column. The eluent flowrate was 1.0 mL/min with the following

concentration gradient: 5 mM NaOH for 5 min, increased linearly to 25 mM NaOH over next 10

min, maintained at 25 mM NaOH for the next 3 min, reduced linearly to 5 mM NaOH over next

2 min and maintained at 5 mM NaOH over next 5 min. For details of instrument calibration, see

Appendix A.

Biofilter Performance and Community Structure 53


4.0 BIOFILTER PERFORMANCE AND COMMUNITY STRUCTURE

The work in this chapter relates the performance of biofilters treating dimethyl sulphide (DMS)

in the presence and absence of methanol to the microbiology of these systems. The biofilters

were constructed and operated by another PhD student and more details of the operation of these

biofilters and the effect of methanol adddition on DMS removal rates can be found in his thesis

(Zhang, 2007). This thesis focuses on the effect of methanol addition on the microbiology of the

biofilter and how the microbiology affects DMS removal rates. Most of the results and

discussion have been published in Applied Microbiology and Biotechnology (Hayes et al., 2010).

The species richness and evenness data and discussion is unpublished.

A summary of the operating conditions of the biofilter co-treating DMS and methanol is

presented in Table 4.1. While loadings were varied over the course of biofilter operation, the

biofilter co-treating DMS and methanol was typically fed 32 g MeOH m-3

h-1

and 3.4 g DMS m-3

h-1

. The removal efficiency of the biofilter co-treating DMS and methanol was typically over

90% with the exception of the period when methanol loadings were raised to upwards of 137.5 g

m-3

h-1

. The DMS loading of the biofilter was also 3.4 g m-3

h-1

over the course of biofilter

operation. The DMS removal rate in the biofilter treating DMS alone gradually increased from

0.2 – 0.5 g DMS m-3

h-1

over the 457 days of operation.

Table 4.1 – Operation of biofilter co-treating DMS and methanol over its operating life.

Period Substrate Loading Approximate DMS

Removal Rates Experimental Purpose

Day 1 - 18 0 g MeOH m

-3 h

-1

0.4 g DMS m-3

h-1

Start-up 3.4 g DMS m

-3 h

-1

Day 19 - 31 12 g MeOH m

-3 h

-1

0.8 g DMS m-3

h-1

Start-up (methanol) 3.4 g DMS m

-3 h

-1

Day 32 - 43 0 g MeOH m

-3 h

-1

1.3 g DMS m-3

h-1

Test DMS alone 3.4 g DMS m

-3 h

-1



Day 44 - 70 32 g MeOH m

-3 h

-1

1.8 g DMS m-3

h-1

Test higher MeOH

loading 3.4 g DMS m-3

h-1

Day 71 - 110 32 g MeOH m

-3 h

-1

0.4 g DMS m-3

h-1

Maintenance 3.4 g DMS m

-3 h

-1

Day 110 - 128 32 g MeOH m

-3 h

-1

1.3 g DMS m-3

h-1

Reproducibility of

MeOH effect

3.4 g DMS m-3

h-1

Day 129 - 155 0 g MeOH m

-3 h

-1 2.0 g DMS m

-3 h

-1 to 0.5 g

DMS m-3

h-1

3.4 g DMS m-3

h-1

Day 156 - 170 32 g MeOH m

-3 h

-1 0.4 g DMS m

-3 h

-1 to 1.3 g

DMS m-3

h-1

3.4 g DMS m-3

h-1

Day 171 - 185 0 g MeOH m

-3 h

-1 2.3 g DMS m

-3 h

-1 to 1.0 g

DMS m-3

h-1

3.4 g DMS m-3

h-1

Day 186 - 229 32 g MeOH m

-3 h

-1

1.0 g DMS m-3

h-1

Maintenance 3.4 g DMS m

-3 h

-1

Day 230 - 257 32 g MeOH m

-3 h

-1

1.4 g DMS m-3

h-1

Effect of MeOH on pH

reduction

3.4 g DMS m-3

h-1

Day 258 - 275 0 g MeOH m

-3 h

-1 2.5 g DMS m

-3 h

-1 to 0.8 g

DMS m-3

h-1

3.4 g DMS m-3

h-1

Day 280 - 315

32 g MeOH m-3

h-1

1.0 g DMS m

-3 h

-1 to 2.5 g

DMS m-3

h-1

Effect of DMS

concentration on DMS

removal rate 3.4 - 5.0 g DMS m

-3

h-1

Day 320 - 369 32 g MeOH m

-3 h

-1 1.3 g DMS m

-3 h

-1 to 2.4 g

DMS m-3

h-1

Effect of MeOH pulse

loading on DMS

removal rate 3.4 g DMS m-3

h-1

Day 370 - 412

32 - 137.5 g MeOH

m-3

h-1

2.0 g DMS m-3

h-1

to 0.3 g

DMS m-3

h-1

Effect of MeOH loading

on DMS removal rate

3.4 g DMS m-3

h-1

Day 420 - 457

10 - 6 g MeOH m-3

h-1

1.0 g DMS m-3

h-1

to 1.6 g

DMS m-3

h-1

3.4 g DMS m

-3 h

-1



4.1 Biofilter Operation and Performance around Sampling Points

Transient-state behaviour of the biofilter co-treating DMS and methanol was investigated under

dynamic methanol addition (Figure 4.1). The biofilter co-treating DMS and methanol had a

steady-state DMS removal rate of approximately 1.4 g DMS m-3

h-1

at a methanol loading of 32

g m-3

h-1

. For the same DMS loading, the biofilter treating DMS alone had a stable DMS removal

rate of approximately 0.45 g DMS m-3

h-1

.

Suspension of methanol addition to the biofilter co-treating DMS and methanol (on Day 257)

resulted in a significant and immediate increase in DMS removal in that biofilter. However, the

increase in the DMS removal upon suspension of methanol addition was short-lived (a couple of

days) and was followed by a gradual decline in the DMS removal rate over the rest of the

methanol suspension period. The resumption of methanol addition to the biofilter co-treating

DMS and methanol (on Day 275) resulted in an increase in the DMS removal rate in this biofilter.

The DMS removal rate of the biofilter co-treating DMS and methanol recovered to a similar

level as before the suspension of methanol addition (1.6 g DMS m-3

h-1

after methanol

resumption compared to 1.4 g DMS m-3

h-1

between days 250 – 257) on the order of a couple of

days. The steady state DMS removal rate of the biofilter co-treating DMS and methanol was

approximately 4 times higher than that of the biofilter treating DMS alone (0.4 -0.5 g DMS m-3

h-1

) for this methanol loading (32 g m-3

h-1

).



Figure 4.1 - Transient-state behaviour of the biofilter co-treating DMS and methanol compared

to the DMS removal rate of the biofilter treating DMS alone. The DMS loading of the two

biofilters was kept at 5 g m-3

h-1

and the EBRT of the biofilters was 40 sec. The methanol loading

of the biofilter co-treating DMS and methanol was 32 g m-3

h-1

and methanol loading was

suspended from Day 257 to Day 275. Adapted from Zhang, 2007.

To further understand the effect of methanol loading on DMS removal in the biofilter co-treating

DMS and methanol by increasing the methanol loading from 15 to 150 g m-3

h-1

while

maintaining the DMS loading at 3.4 g m-3

h-1

, an empty bed residence time (EBRT) of 40 sec,

and allowing the biofilter co-treating DMS with methanol to reach a steady state performance

after each adjustment. Since the EBRT was kept at a constant 40 sec, increasing the methanol

loading was accomplished by increasing the inlet methanol concentration up to a maximum of

1100 ppmv.



0

200

400

600

800

1000

1200

Inle

t C

DM

S (

pp

m)

Inle

t C

Me

OH (

pp

m)

370 375 380 385 390 395 400 405 4100

10

20

30

40

50

60

70

80

90

DM

S r

em

ov

al

eff

icie

nc

y (

%)

Time (day)

Me

OH

re

mo

va

l e

ffic

ien

cy

(%

) 0

5

10

15

20

0

10

20

30

40

50

60

MeOH

3.4 ± 0.2 g DMS m-3 h-1

REMeOH

REDMS

Figure 4.2 The performance of the biofilter co-treating DMS and methanol on the removal of

DMS and methanol at various methanol addition rates. Experimental results were obtained at 40

sec EBRT. Methanol addition was suspended on days 394 and 404 for 24 hours. During this

experimental period, the biofilter treating DMS alone was kept at a DMS loading of 3.4 g m-3

h-1

with an EBRT of 40 sec which resulted in a DMS removal rate of 0.5 ± 0.1 g m-3

h-1

. MeOH

denotes methanol, and REMeOH and REDMS represent methanol and DMS removal efficiency,

respectively (Zhang, 2007).

As shown in Figure 4.2, for the biofilter co-treating DMS and methanol, removal efficiencies

greater than 90% were achieved for methanol for inlet methanol concentrations lower than 300

ppmv. However, as the inlet methanol concentration was increased above 300 ppmv, the removal

efficiency of methanol decreased. At an inlet methanol concentration of 1100 ppmv, the removal

efficiency of methanol was only 57% in the biofilter co-treating DMS with methanol. For the

given DMS loading (3.4 g m-3

h-1

), increasing the methanol concentration over a range of 150 –

1100 ppmv led to a decrease in the steady-state removal rate of DMS in the biofilter co-treating

DMS with methanol. In other words, as the methanol loading was increased, the DMS removal



rate decreased in this biofilter. When the inlet methanol concentration was increased above 800

ppmv (a methanol loading of 90 g m-3

h-1

), the DMS removal rate in the biofilter co-treating

DMS with methanol (0.3 – 0. 6 g DMS m-3

h-1

) was as low as that in the biofilter treating DMS

alone (0.5 – 0. 6 g DMS m-3

h-1

), meaning that for a methanol loading rate higher than 90 g m-3

h-

1, the overall effect of methanol on the biofiltration of DMS becomes negative for the given

DMS loading in this biofilter. A suspension of methanol addition during higher methanol loading

periods (Days 294 and 404) resulted in a rapid and significant increase in the DMS removal rate.

The effect of methanol loading on DMS removal in the biofilter co-treating DMS and methanol

was further studied for low methanol loadings (Figure 4.3). Between days 420 – 440, using an

empty bed residence time (EBRT) of 40 sec and a DMS loading of 3.4 g m-3

h-1

, the effect of

methanol loadings between 6 and 10 g m-3

h-1

on the DMS removal rate in the biofilter co-

treating DMS and methanol was investigated. For a methanol loading of 10 g m-3

h-1

, the steady

state DMS removal rate was approximately 1.3 g m-3

h-1

. When the methanol loading was

decreased to 6 g m-3

h-1

on Day 431, there was an immediate spike in the DMS removal rate to

approximately 1.6 g m-3

h-1

, followed by a gradual decline to a steady state DMS removal rate of


h-1

. During this time period, the DMS removal efficiency of the biofilter

treating DMS alone was 0.65 ± 0.05 g DMS m-3

h-1

. Therefore, at a DMS loading of 3.4 g m-3

h-1

,

raising the methanol loading from 0 – 10 g m-3

h-1

results in increased DMS removal rates in the

biofilter co-treating DMS and methanol.

On Day 441, the empty bed residence time (EBRT) of the biofilter co-treating DMS and

methanol was doubled to 80 sec while maintaining the same methanol and DMS loadings of 6 g

m-3

h-1

and 3.4 g m-3

h-1

, respectively. Once the EBRT was doubled to 80 sec, there was an

immediate spike in the DMS removal rate in the biofilter co-treating DMS and methanol from


h-1

to approximately 1.8 g m-3

h-1

before reaching a steady-state DMS

removal rate of approximately 1.7 g m-3

h-1

. Increasing the EBRT results in a decrease in gas

velocity through the biofilter which would be expected to increase mass transfer resistance in the

system. The increase in the DMS removal rate with increasing EBRT suggests that mass transfer

of DMS is not a limiting factor for DMS removal in the biofilter co-treating DMS and methanol



under these conditions. This was further tested by reducing the DMS loading in the biofilter co-

treating DMS and methanol from 3.4 g m-3

h-1

to 1.0 g m-3

h-1

. This resulted in a decrease in the

DMS removal rate from approximately 1.7 g m-3

h-1

to approximately 0.8 g m-3

h-1

. However, the

removal efficiency of DMS in the biofilter co-treating DMS and methanol increased from 45% to

75%.

3.4 g DMS m-3 h-1

10 g MeOH m-3 h-1

6 g MeOH m-3 h-1

1 g DMS m-3 h-1

EBRT 40 sec EBRT 80 sec

0

1

2

3

4

MeO

H l

oa

din

g (

g m

-3 h

-1)

DM

S l

oa

din

g (

g m

-3 h

-1)

420 425 430 435 440 445 450 4550.6

0.8

1.0

1.2

1.4

1.6

1.8

Removal rate

DM

S r

em

ov

al

eff

icie

ncy

(%

)

DMS

Time (day)

DM

S r

em

ov

al

ra

te (

g m

-3 h

-1) 0

3

6

9

12

15

0

20

40

60

80

Removal efficiency

MeOH

Figure 4.3 - The effects of EBRT, methanol loading rate and DMS loading rate on the removal

of DMS in the biofilter co-treating DMS and methanol. The dotted vertical line indicates the

change of EBRT. MeOH represents methanol. During this experimental period the biofilter

treating DMS alone was kept at a DMS mass loading of 5.0 g DMS m-3

h-1

with an EBRT of 40

sec which resulted in a DMS removal rate of 0.65 ± 0.05 g DMS m-3

h-1

(Zhang, 2007).

In summary, the performance of the biofilter co-treating DMS and methanol consistently

demonstrated the importance of methanol addition on the DMS removal rate. For a DMS loading



of 3.4 g m-3

h-1

and an empty bed residence time (EBRT) of 40 sec, increasing methanol loading

resulted in an increase in the DMS removal rate up to a methanol loading of 10 g m-3

h-1

. At this

methanol loading, the DMS removal rate was approximately 1.4 g m

-3 h

-1 for a DMS removal

efficiency of roughly 40%. Increasing the methanol loading beyond 15 g m-3

h-1

resulted in a

gradual decrease in the DMS removal rate although methanol addition still led to an increase in

the DMS removal rate compared to the biofilter treating DMS alone up to a methanol loading of

90 g m-3

h-1

at which point, the DMS removal rate in the biofilter co-treating DMS and methanol

fell below that of the biofilter treating DMS alone.

While addition of methanol clearly led to an increase in the DMS removal rate for methanol

loadings up to 90 g m-3

h-1

, the results suggest that methanol actively competed with DMS as a

substrate for microorganisms in the biofilter. This was evident when methanol addition was

suspended in the biofilter co-treating DMS and methanol which resulted in an immediate spike in

the DMS degradation rate in the biofilter. This was observed at all methanol loadings tested in

the biofilter co-treating DMS and methanol with the difference between the DMS degradation

rate upon methanol suspension and the steady state DMS degradation rate at a given methanol

loading increasing with increasing methanol loading (i.e. the competitive effect of methanol

increases with methanol loading).

Although methanol suspension led to an increase in the DMS removal rate in the biofilter co-

treating DMS and methanol, the increase in the DMS removal rate was not sustainable and

gradually decreased back down to the level of the biofilter treating DMS alone. Previous work

demonstrated that the gradual decrease in the DMS removal rate in the biofilter co-treating DMS

and methanol after methanol suspension is correlated to a decrease in the active biomass

concentration in the biofilter (Zhang et al., 2006).

4.2 Microbial Community Structure of Biofilters Treating DMS

The microbial community of both biofilters were described from data derived from a variety of

molecular techniques. 16S rDNA clone libraries were constructed from both biofilters to identify

bacteria present in both biofilters, identify bacteria potentially responsible for DMS degradation



in these systems and provide genetic information for the construction of PCR primers for qPCR

studies. DGGE analysis was carried out on samples harvested from both biofilters to compare the

bacterial community similarity and qPCR was used to quantify bacteria identified as potentially

important to DMS degradation in these biofilters.

4.2.1 Construction of 16S rDNA Clone Libraries

Two 16S rDNA clone libraries were constructed from biomass originating from both biofilters

on Day 400. For the biofilter treating DMS alone, the biomass was harvested directly from the

biofilter. For the biofilter co-treating DMS and methanol, biomass was collected from the

biofilter enriched on DMS in a liquid mineral media. This was done because a primary goal of

the clone library construction was to identify bacteria that may be responsible for DMS

degradation in these systems and obtain sequence data for their 16S rRNA gene to allow for

construction of specific PCR primers for quantification by qPCR. Given the methanol loading

and elimination was much higher than that of DMS in the biofilter co-treating DMS and

methanol, there was a concern that the clone library would be dominated by purely

methylotrophic microorganisms. Enriching on DMS allowed for the removal of purely

methylotrophic microorganisms and increased the prominence of DMS degrading

microorganisms present in the biofilter co-treating DMS and methanol, facilitating identification.

Construction of 16S rDNA clone libraries from the enrichment culture created from the biofilter

co-treating DMS and methanol and directly from the biofilter treating DMS alone revealed that

DMS degradation was carried out by a community of microorganisms in these systems. The two

libraries contained three microbial groups in common: Hyphomicrobium spp., Thiobacillus spp.

and a group of microorganisms that clustered near the Chitinophaga spp. and will be described

as such in this thesis. In total, the enrichment library contained 12 different clusters while the

DMS biofilter clone library contained 14 different clusters. Here, clusters are defined

qualitatively as clones which group among the same genus or family and not quantitatively as

having minimum 97% identity in pairwise alignment.



Table 4.2 – Distribution of different bacterial groups identified in clone libraries constructed

from the enrichment culture created from the biofilter co-treating DMS and methanol and from

the biofilter treating DMS alone. Each library contained 3 duplicate sequences.

Chitinophaga spp. 9 (25.0) Chitinophaga spp. 7 (17.1)

Hyphomicrobium spp. 8 (22.2) Hyphomicrobium spp. 6 (14.6)

Thiobacillus spp. 7 (19.4) Thiobacillus spp. 1 (2.4)

Cupriavidus spp. 4 (11.1) OP10 spp. 3 (7.3)

Sphingomonas spp. 2 (5.6) Thiomonas spp. 2 (4.9)

Pseudomonas sp. 1 (2.8) Gemmatimonas sp. 2 (4.9)

Hydrogenophaga sp. 1 (2.8) Acidobacterium sp. 1 (2.4)

Novosphingobium sp. 1 (2.8) Parvibaculum sp. 1 (2.4)

Aminobacter sp. 1 (2.8) Blastopirellula sp. 1 (2.4)

Microbacterium sp. 1 (2.8) Acidisphaera sp. 1 (2.4)

Achromobacter sp. 1 (2.8) Unclassified Xanthomonadaceae 9 (22.0)

Unclassified Bacillales 1 (2.4)

Unclassified Planctomycetaceae 1 (2.4)

Unclassified Bacteria 5 (12.2)

Closest Related Species Number of Clones (% of Total) Closest Related Species Number of Clones (% of Total)

DMS + MeOH Clone Library (DMS enriched) (n = 36) DMS Biofilter Clone Library (n = 41)

*Clones identified as Ralstonia sp. and Wautersia sp. are classified as Cupriavidus spp.

Bacteria-wide Bayesian phylogenetic trees were constructed for both clone libraries to observe

where in the bacteria domain identified microorganisms clustered and to aid in predicting the

role the particular bacterium plays in the microbial community within the biofilter. The Bayesian

method was chosen to construct the phylogenies because of the quick search algorithm used in

constructing the tree and because it is a discrete method that provides more realistic estimates of

evolutionary distance over long evolutionary times than distance-based methods such as

neighbour-joining (Holder and Lewis, 2003).

There were three groups of microorganisms common to both clone libraries which suggest that

these bacteria may be strongly connected to DMS degradation in these systems due to their

presence in the biofilter treating DMS alone and their ability to persist through serial dilutions in

the enrichment culture created from the biofilter co-treating DMS and methanol. These three

bacterial groups were Hyphomicrobium spp., Thiobacillus spp., and a group of bacteria that

clustered near the Chitinophaga spp.. Other than these three common bacterial groups, the

composition of the two clone libraries were quite different as would be expected due to the

different conditions present in each system at the time of biomass harvesting.



The clone library constructed from the enrichment culture created during the incremental

increasing of the inlet methanol concentration of the biofilter co-treating DMS with methanol

(Day 400) was composed of a wide variety of bacteria. The largest microbial cluster in this

library, accounting for 25% of all clones, clustered within the Bacteroidetes, particularly near the

Chitinophaga genus. Hyphomicrobium spp. (22.2% of clones) were the next largest followed by

Thiobacillus spp. (19.4%), Wautersia spp. (8.3%), Sphingomonas spp. (5.6%) and seven other

groups (2.8%). The clone library constructed from the biofilter treating DMS alone during the

same time period (Day 400) was also comprised of a wide variety of bacteria and more diverse

than the clone library derived from the enrichment culture. The largest group clustered among

the Xanthomonadales (22.2%) followed by Chitinophaga spp. (17.1%), Hyphomicrobium spp.

(14.6%), unclassified Bacteria (12.2%), OP10 spp. (7.3%), Thiomonas spp. and Gemmatimonas

spp. (4.9%), and Thiobacillus spp. and six other groups (2.4%).



Figure 4.4 - Bayesian phylogeny of the 16S rDNA clone library created from the enrichment

culture of the biofilter co-treating DMS in the presence of methanol. The bar indicates 0.1

substitutions per nucleotide and bootstrap values are labelled on the branches. Clones are

denoted ENR.



Figure 4.5 - Bayesian phylogeny of the 16S rDNA clone library created from the biofilter

treating DMS alone. The bar indicates 0.1 substitutions per nucleotide and bootstrap values are

labelled on the branches. Clones are denoted DMS.



Table 4.3 - GenBank accession numbers and closest related sequences found in the 16S

ribosomal database project of clones from 16S rDNA clone libraries

ENR01 FJ536909 α Caulobacter leidyia AF331660 0.996

ENR02 FJ536910 α Aminobacter sp. MI-p2a DQ196478 0.987

ENR03 FJ536911 B uncultured Sphingobacteriales bacterium; 3-H EU305589 0.886

ENR04 FJ536912 α Sphingomonas sp. MD-1 AB110635 0.970

ENR05 FJ536913 α Hyphomicrobium zavarzinii (T); ZV-622 Y14305 0.994

ENR06 FJ536941 β uncultured Hydrogenophilaceae bacterium; D10_45 EU266809 0.962


ENR08 FJ536915 β uncultured bacterium; CD145 DQ441396 0.961



ENR11 FJ536918 α Sphingomonas melonis (T); PG-224 AB055863 0.986


ENR13 FJ536920 F Microbacterium sp. DB-1 EU439401 0.940




ENR17 FJ536924 B uncultured bacterium; 28RHF48 AJ863367 0.902





ENR22 FJ536929 B uncultured Bacteroidetes bacterium; AS56 EU283377 0.800

ENR23 FJ536930 α Hyphomicrobium denitrificans ; DSM 1869 AJ854111 0.984


ENR25 FJ536932 α Hyphomicrobium denitrificans ; DSM 1869 AJ854111 0.990

ENR26 FJ536933 β uncultured Hydrogenophaga sp.; 2-E EU305579 0.983


ENR28 FJ536935 β Cupriavidus metallidurans ; NE12 EU429939 1.000


ENR30 FJ536937 γ Pseudomonas citronellolis; L3 EU301767 0.975


ENR32 FJ536939 β Ralstonia sp. 161 EF422180 0.997


DMS01 FJ536871 α Hyphomicrobium sp. TW5 AY934491 0.995

DMS02 FJ536872 O uncultured bacterium; FCPS481 EF516982 0.661

DMS03 FJ536873 γ uncultured bacterium; KD8-80 AY218694 0.831

DMS04 FJ536874 γ uncultured bacterium; 8 DQ011842 0.883

DMS05 FJ536875 B uncultured bacterium; KD8-73 AY218691 0.906

DMS06 FJ536876 G uncultured bacterium; KD4-106 AY218623 0.933


DMS08 FJ536878 NA uncultured bacterium; HOClCi9 AY328558 0.775

DMS09 FJ536879 P uncultured planctomycete; Amb_16S_1239 EF018777 0.833




DMS13 FJ536883 β beta-proteobacterium 5Z-C1 AJ224618 0.852


DMS15 FJ536885 B uncultured sphingobacteriales cum 'Crenotrichaeceae ' bacterium; Elev_16S_493 EF019302 0.808


DMS17 FJ536887 α Parvibaculum lamentivorans DS-1 CP000774 0.854

DMS18 FJ536888 B uncultured bacterium; IFD_11 DQ984541 0.837

DMS19 FJ536889 γ uncultured bacterium; 8 DQ011842 0.901



DMS22 FJ536892 B uncultured bacterium; FCPP558 EF515956 0.775

DMS23 FJ536893 α uncultured bacterium; SK967 DQ834219 0.686

DMS24 FJ536894 β Thiomonas intermedia (T); ATCC 15466 AY455809 0.976


DMS26 FJ536896 β Thiomonas intermedia (T); ATCC 15466 AY455809 0.975


DMS28 FJ536898 γ Frauteria sp. WJ69 AY495959 0.926

DMS29 FJ536899 P uncultured planctomycete; YNPRH54A AF465657 0.859

DMS30 FJ536900 O uncultured bacterium; FCPT516 EF515998 0.677

DMS31 FJ536901 O uncultured bacterium; FCPT516 EF515998 0.672

DMS32 FJ536902 γ gamma proteobacterium CA4; pBC16S3-27 AY138998 0.932

DMS33 FJ536903 F Tumebacillus permanentifrigoris ; Eur1 9.5 DQ444975 0.847

DMS34 FJ536904 γ Frauteria sp. DH-HM DQ419968 0.989

DMS35 FJ536905 A uncultured forest soil bacterium; DUNssu362 (+7C) (OTU#049) AY913535 0.930

DMS36 FJ536906 α Hyphomicrobium sp. MC8b AJ854112 0.980

DMS37 FJ536907 α Hyphomicrobium facile AB222020 0.935


α - Alphaproteobacteria; β - Betaproteobacteria; γ - Gammaproteobacteria; A - Acidobacteria; B - Bacteroidetes; F - Firmicutes

G - Gemmatimonadetes; O - OP10; P - Planctomycetes; NA - Not Available

SAB ScoreCloneGenBank

accesion no.Class/phylum* Closest related species, strain or clone

GenBank

accesion no.



As mentioned in the literature review, Hyphomicrobium spp. are well-documented DMS

degrading microorganisms (De Bont et al., 1981; Suylen et al., 1986; Zhang et al., 1991; Pol et

al., 1994) that are better known as methylotrophs (Atwood and Harder, 1972). Even though

Hyphomicrobium spp. are known to degrade DMS, little information exists on how broad DMS

degradation is spread across the genus. Only one known DMS-degrading Hyphomicrobium spp.

has had its 16S rRNA gene sequenced when Hyphomicrobium VS (Pol et al., 1994) was shown

to have 100% sequence similarity to Hyphomicrobium methylovorum MBIC 3196 for base

positions 8-464 and 722-1260 (Sercu et al., 2006). While the DMS biofilter clone library was

weighted towards clones clustering near Hyphomicrobium denitrificans, the enrichment clone

library contained clones from across the Hyphomicrobium genus with clones clustering near H.

vulgare, H. zavarzinii, H. denitrificans, and H. facile, suggesting that DMS degradation may be a

trait found across the Hyphomicrobium genus. Also, the strong bias for clones from the DMS

biofilter clone library to cluster near Hyphomicrobium denitrificans suggests that these

Hyphomicrobium clones are better adapted to growing in the biofilter than the Hyphomicrobium

clones that cluster elsewhere in the genus in the enrichment culture. One possibility is that the

bacteria that cluster near Hyphomicrobium denitrificans are better adapted to growing in biofilms.

Thiobacillus spp. have also been documented to be capable of aerobic DMS degradation

(Kanagawa and Kelly, 1986; Smith and Kelly, 1988; Cho et al., 1991b; Visscher and Taylor,

1993). There is no phylogenetic information on DMS-degrading Thiobacillus spp. and neither

Thiobacillus thioparus Tk-m nor Thiobacillus sp. ASN-1 have had their 16S rRNA gene

sequenced. In both clone libraries, contrary to Hyphomicrobium spp., all clones that clustered

among the Thiobacillus spp. clustered near Thiobacillus thioparus. The absence of clones

clustering near other Thiobacillus spp., particularly in the enrichment library where multiple

Thiobacillus clones were sequenced, is consistent with what we know about DMS degradation

within the Thiobacillus genus where Thiobacillus thioparus is the only identified DMS degrader

within the genus whose 16S rDNA has been sequenced.

The existence of Chitinophaga spp. in the DMS biofilter and its retention in the enrichment

culture suggest that these bacteria may be strongly linked to DMS degradation in these biofilters.



Chitinophaga spp. have no reported role in DMS degradation and belong to a group of bacteria

known as the Cytophagales, Flavobacteriales and Bacteroides (CFB). CFB have been shown to

be enriched in low-carbon environments at the end of industrial biofilters (Friedrich et al., 2003)

and have been shown to be present in significant quantities in nitrifying biofilms (Okabe et al.,

1999; Okabe et al., 2002; Paungfoo et al., 2006) where they were believed to play a role in

carbon cycling by feeding on soluble microbial products and breaking down complex organics.

While no nitrifying bacteria were identified in either clone library, ammonia-oxidizing bacteria

were identified in the biofilter treating DMS alone using fluorescent in situ hybridization (Zhang

et al., 2007a). Furthermore, only one CFB bacterium has been reported to have role in sulphur

oxidation (Vitolins and Swaby, 1969), suggesting that Chitinophaga spp. may be involved in the

more traditional CFB role of carbon cycling, perhaps even predating on other bacteria.

Chitinophaga pinensis UQM 2034 has been shown to be capable of lysing Staphylococcus

aureus (Sangkhobol and Skerman, 1981) and it has been shown that CFB bacteria can uptake

radio-labelled N-acetylglucosamine, a structural compound in bacterial peptidoglycan (Kindaichi

et al., 2004). Given that an inorganic media was used, this would suggest Chitinophaga spp. may

be breaking down biomass resulting directly from DMS degradation (and methanol degradation

in the biofilter co-treating DMS and methanol). This is further supported by the retention of

Chitinophaga spp. at high levels in the enrichment culture where other bacteria found in high

levels in the DMS biofilter clone library, such as Xanthomonadales, are no longer present after

20 cycles.

The largest group identified in the DMS biofilter clone library, accounting for 25% of clones,

were the Xanthomonadales. The Xanthomonadales is a large order in the γ-proteobacteria but all

of the clones clustered in one of three locations within the order. Two clones clustered in a clade

with Rhodanobacter lindaniclasticus, one clone clustered in a clade with Frauteria aurantia, and

six clones did not cluster within any known genus or family within the Xanthomonadales order.

Xanthomonas-like bacteria have been identified in waste gas biofilters (Friedrich et al., 2002)

and a Xanthomonas sp. has been shown to degrade H2S to polysulphide but requires organic

compounds as a growth substrate (Cho et al., 1992). However, polysulphide levels were not



measured from these biofilters and there was no additional carbon source added in the biofilter

treating DMS alone. This, along with the absence of Xanthomonadales clones in the enrichment

culture clone library, suggest that these bacteria do not play a direct role in DMS degradation in

these biofilters. The Xanthomonadales contain bacteria that carry out a wide range of functions

such as the degradation of complex organics such as lindane by Rhodanobacter lindaniclasticus

(Nalin et al., 1999) or growth on simple sugars in acidic environments such as Frauteria

aurantia (Swings et al., 1980). Given that Xanthomonadales are found generally in biofiltration

systems and that these Xanthomonadales do not appear to be DMS degraders, they likely play a

role in the cycling of carbon in the biofilter.

Several bacteria identified in the clone library made from the enrichment culture constructed

from the biofilter co-treating DMS and methanol belong to a group of microorganisms known as

the β-hydrogenotrophs. These bacteria include Hydrogenophaga sp., Wautersia sp., and

Ralstonia sp., and Achromobacter sp.. The β-hydrogenotrophs are members of the β-

proteobacteria and contain many bacteria that have been shown to be involved in sulphur

metabolism. Hydrogenophaga palleronii DSM 63 and Hydrogenophaga intermedia S1 are both

capable of oxidizing thiosulphate to sulphate (Kampfer et al., 2005) while a closely related

bacterium has been isolated that was capable of direct oxidation of sulphur (Stubner et al., 1998).

Ralstonia spp. and Wautersia spp. are also known hydrogenotrophic microorganisms which are

frequently found in industrial and polluted biotypes, especially Ralstonia metallidurans CH34

due to their tolerance of toxic metals, but are suspected to originate from sulphur-rich volcanic

zones due to their hydrogenotrophic nature (Mergeay et al., 2003). As for Achromobacter sp.,

sulphur oxidation has been shown to occur in one isolate (Vitolins and Swaby, 1969).

The presence of members of the β-hydrogenotrophs in the clone library constructed from the

enrichment culture created from biofilter co-treating DMS and methanol may be an indicator of

incomplete DMS oxidation by DMS-degrading microorganisms in the enrichment culture. This

would provide an explanation of the presence of microorganisms in the enrichment that are

suspected of being solely inorganic sulphur oxidizers. It is also likely that these bacteria exist in

the biofilters as well given that a sulphur balance on the biofilter revealed the presence of



multiple inorganic sulphur species in the biofilter (Zhang et al., 2006), indicating that the

conversion of DMS to sulphate was not entirely complete, providing these bacteria with suitable

substrates for growth. The percentage of inorganic sulphur species was higher in the biofilter co-

treating DMS and methanol than DMS alone but the clone library created directly from biomass

harvested from the biofilter treating DMS alone also contained bacteria that are known to be

grow on inorganic sulphur compound such as two clones identified as being closely related to

Thiomonas intermedia. Thiomonas spp. have been shown to grow aerobically on many inorganic

sulphur compounds such as inorganic sulphur, thiosulphate, and hydrogen sulphide (Chen et al.,

2004).

Other bacteria identified in the clone libraries in which related members have been reported to be

involved in sulphur cycling include Pseudomonas sp., Microbacterium sp., and Blastopirellula

sp.. Pseudomonas acidovorans DMR-11 has been shown to be capable of oxidizing DMS to

DMSO and has been isolated from a peat biofilter (Zhang et al., 1991b). A Pseudomonas sp. has

also been used to treat a mixture of volatile sulphur compounds, including DMS, in a

biofiltration system but required that a nutrient medium containing glucose be supplied to the

system (Ho et al., 2007). However, no extra carbon source was added to the enrichment culture

or the biofilter co-treating DMS alone, nor was DMSO detected in either the biofilter, making it

unlikely that this reaction was being carried out to any large extent if, in fact, Pseudomonas sp.

were carrying out this reaction. Recently, Microbacterium sp. NTUT26 was also shown to be

capable of DMS degradation in a biofiltration type system with DMS removal rates of up to 1.71

g-S/day/kg-dry packing material (Shu and Chen, 2009). However, these bacteria were not

identified in the biofilter clone library in our study suggests that these are not the primary

bacteria responsible for DMS degradation in our biofilters. Finally, Blastopirellula spp. have

been linked to the production of reduced sulphur compounds, being capable of producing

hydrogen sulphide from thiosulphate. The ability to produce hydrogen sulphide from

thiosulphate is quite common among the Planctomycetes phylum, being documented in bacteria

from all three genera and many unclassified Planctomycetales (Schlesner et al., 2004). Although

there is no net production of reduced sulphur compounds in these biofilters and no reduced



sulphur compounds other than the DMS feed were detected, the presence of many bacteria

involved in sulphur metabolism and the variety of sulphur species identified and quantified in the

biofilter by Zhang et al. (2006) suggest that there is a cycling of sulphur compounds in the

biofilter.

The clone libraries also identified bacteria that, while they have no known role in DMS

degradation or sulphur oxidation, have been found in sulphide-degrading biofilms and

communities. These include both Sphingomonas spp. and Caulobacter spp. (Vincke et al., 2001;

Ferrera et al., 2004). Acidobacteria are also widely distributed in hot springs and soil sediments.

The lone isolate from the Acidobacteria phylum, Acidobacterium capsulatum, is an acidophilic

chemoorganotrophic microorganism capable of growth in a pH range of 3.0 - 6.0 (Kishimoto et

al., 1991) which overlaps with the pH range at which these biofilters were operated.

Other bacteria identified in the clone libraries have no reported role in sulphur metabolism and

have not been identified in biofilters. Several have little to no information in the literature about

how they function or what role they play. As an example, 7.3% of clones from the library

constructed from the biofilter treating DMS alone were classified as being OP10 spp. To date,

there are no cultivated members of the OP10 genus so no data exists on its metabolism. A clone

library with a predominance of OP10 clones (approximately 50%) has been published (Stein et

al., 2002). The source of the biomass for this library was a freshwater reservoir that was rich in

iron, manganese, and metal particles. A plausible explanation for their role would be that they

are chemolithotrophic microorganisms that sustain themselves by oxidizing metals. OP10 spp.

were not the only group of bacteria where function is difficult to interpret. Another group of

clones in the clone library constructed from the biofilter treating DMS alone did not cluster

among any of the known bacterial phyla. However, it is unlikely that these bacteria have any

direct role in DMS degradation due to their absence in the clone library constructed from the

enrichment culture where DMS consumption was much higher and dominant DMS degraders

would be more easily identified.



In summary, the clone libraries revealed a community of microorganisms were present in these

systems. The aerobic nature of the systems led to the identification of the known aerobic DMS

degraders Hyphomicrobium spp. and Thiobacillus spp. suggesting that aerobic metabolism is the

dominant pathway by which DMS is eliminated in these systems. This is supported by the lack

of identification of sulphate-reducing bacteria in the bacterial clone library and the failure to

generate amplicons from archaebacterial primers (Tajima et al., 2001) suggests that

methanogenic DMS-degrading microorganisms were not present in the biofilter. The absence of

anaerobic bacteria from these systems is also supported by the inability to detect H2S or methane

(in the case of methanogens). In terms of oxidation of DMS to DMSO, bacteria were identified

that are reported to be capable of this transformation, but were much lower in number than

Hyphomicrobium spp. and Thiobacillus spp.. Furthermore, DMSO was not detected when a

sulphur species analysis was performed for the biofilters (Zhang et al., 2006). Finally,

observations of the enrichment culture using light microscopy did not reveal the presence of

fungi in the enrichment culture, suggesting that microorganisms from this kingdom are not

responsible for DMS degradation in these systems. Finally, there were no bacteria identified in

either clone library capable of photo-oxidation of DMS. Given the microbial community data

retrieved through clone library construction, the results of previously performed sulphur species

analysis, and the current knowledge of DMS degradation pathways in the literature, it is very

likely that the pre-dominant method of DMS elimination in this system is through an aerobic

metabolic pathway where DMS is ultimately converted to carbon dioxide, water and sulphate.

4.2.2 DGGE Analysis of Microbial Community Structure

Further investigation of the microbial community structure of the biofilters was performed using

Denaturing Gradient Gel Electrophoresis (DGGE). DGGE fingerprinting was performed to

compare the community structure between samples taken from different locations within each

biofilter as well as to make comparisons on the community structure between both biofilters.

Biomass samples were obtained during the recovery period of the biofilter co-treating DMS and

methanol after a methanol suspension period (Day 283) and after the biofilter co-treating DMS

and methanol reached steady state at a DMS loading of 1 g DMS m-3

h-1

at an EBRT of 80 sec



(Day 457). DGGE fingerprints were analyzed using several methods. Digital photographs of the

DGGE fingerprints were analyzed using GelCompar software that allowed for the determination

of species richness and evenness while also allowing the similarity of the fingerprints to be

compared by constructing a UPGMA-tree based on the Jaccard coefficients between each pair of

samples. Further to quantitative measures, bands were excised from the DGGE gels, cloned, and

then sequenced to allow for the identification of the bacteria that each band represents which

allowed for a rudimentary comparison between community composition as determined by clone

library construction and DGGE analysis.

Identification of bands in DGGE fingerprints revealed much of what was already discovered in

the clone library construction. Prominent bands were excised from the DGGE gels for samples

obtained on Day 457 and identified through sequencing included Hyphomicrobium spp., and

Thiobacillus spp., as well as some members of Xanthomonadales, Bacteroidales, and

Acidobacteria, to name a few. The most dominant bacteria (> 7% of clones) in the clone library

constructed from the biofilter treating DMS alone were identified through band excision and

sequencing with the sole exception of the unclassified Bacteria clones. It is also worth noting

that the Bacteroidales sequence obtained from the gel does not match any of those obtained from

the clone library and, when blasted against the 16S rDNA database, the sequence did not cluster

as precisely within the phylum as those from the clone library. However, this could be due to the

smaller amount of genetic information obtained from the DGGE sequence (~150 bp) compared

to that from the clone library (~1500 bp).

As for Hyphomicrobium spp., the corresponding band appears as a very bright band in the

DGGE fingerprints from the biofilter co-treating DMS and methanol which is a potential

indicator of its relative abundance in this biofilter. Also worth noting is that no other

methylotrophic bacteria were identified among the bands that were successfully identified from

the samples obtained from this biofilter. This suggests that Hyphomicrobium spp. may be the

dominant methanol degrader in the biofilter co-treating DMS and methanol. In the DGGE

fingerprints from the biofilter treating DMS alone, the band corresponding for Hyphomicrobium

spp. appears less bright in comparison to other bands in the fingerprints. Again, this may suggest



a lower relative abundance of Hyphomicrobium spp. in this biofilter compared to the biofilter co-

treating DMS methanol.

Figure 4.6 – Digital photographs of DGGE fingerprints of biomass samples obtained from both

biofilters on Day 457 with samples obtained from the biofilter co-treating DMS and methanol

(MeOH) on top and the samples obtained from the biofilter treating DMS alone below. The two

right most lanes in the 2nd

gel in the samples from the biofilter co-treating DMS and MeOH are

the first two samples of the biofilter treating DMS alone while the two rightmost samples in the

2nd

gel for the biofilter treating DMS alone are two samples taken from above the restriction

plate located above the packing material in the biofilter co-treating DMS and MeOH. Bands that

were identified through excision and sequencing are labelled on the right. T – Top; C – Centre;

BT – Bed Top



For Thiobacillus spp., the other known aerobic DMS degrader, the band was only identified in

the DGGE fingerprints of the biofilter treating DMS alone suggesting that its relative abundance

is likely higher in the biofilter treating DMS alone than the biofilter co-treating DMS and

methanol. This suggests that the distribution of bacteria in the enrichment culture is not

representative of the biofilter co-treating DMS and methanol given the high relative abundance

of Thiobacillus clones in the enrichment culture. However, the enrichment culture still remains a

useful tool in identifying possible DMS-degrading microorganisms in these biofilters.

As an initial quantitative comparison of the microbial community structure of these biofilters the

richness and evenness of each biomass sample was determined. For samples collected on Day

457, the values for biomass samples taken from the same radial plane were averaged in order to

create a confidence interval. This was done on the basis that in a plug flow system such as a

biofilter, samples in the same radial plane should be exposed to similar conditions as long as

distortionary effects such as channelling are negligible. In theory, biomass samples exposed to

similar conditions should evolve similarly and result in similar DGGE fingerprints. A qualitative

assessment of the DGGE fingerprints reveals that, in general, banding patterns of samples taken

from the same radial plane are very similar to each other while those of samples taken from

different radial planes are not quite as similar. This qualitative observation will be tested

quantitatively through the construction of UPGMA phylogenetic trees constructed based on

Jaccard coefficients.

Table 4.4 – Richness and Evenness of bacterial community for both biofilters for samples

collected on Day 457. Range represents 95% confidence intervals.

Location DMS + MeOH Biofilter DMS Biofilter

Richness Evenness Richness Evenness

Bed Top 17.5 ± 6.3 0.87 ± 0.01 - -

Inlet Top 8.7 ± 1.3 0.85 ± 0.03 10.8 ± 2.3 0.82 ± 0.05

Inlet Centre 9.5 ± 4.9 0.80 ± 0.05 14.0 ± 6.5 0.88 ± 0.08

Middle Top 11.3 ± 3.3 0.85 ± 0.08 13.0 ± 6.5 0.86 ± 0.08

Middle Centre 12.3 ± 1.6 0.82 ± 0.03 16.5 ± 2.1 0.91 ± 0.02

Outlet Top 12.0 ± 2.2 0.85 ± 0.02 13.5 ± 3.3 0.85 ± 0.08

Outlet Centre 14.0 ± 2.2 0.88 ± 0.02 18.8 ± 2.4 0.91 ± 0.03



For samples collected from the biofilter co-treating DMS and methanol after it reached steady

state at a DMS loading of 1 g DMS m-3

h-1

at an EBRT of 80 sec (Day 457), both the species

richness and evenness were fairly consistent throughout the biofilter bed. The species richness

was lowest at the top of the inlet section where it averaged 8.7 and reached a maximum within

the biofilter bed at the centre of the outlet section where the richness reached 14.0. The species

richness was highest for the biomass that collected from on top of the restriction plate before the

biofilter bed with a value of 17.5 (with considerable error). In terms of evenness, the minimum

value was found at the centre of the inlet section where it was measured at 0.80 and reached a

maximum value of 0.88 at the centre of the outlet section. These values compare with the sample

taken from the same biofilter during the recovery period after a methanol suspension period (Day

283) where species richness and evenness were measured at 11 and 0.91, respectively, at the

centre of the inlet section.

For samples collected from the biofilter treating DMS alone on Day 457, the species richness and

evenness were also fairly consistent along the biofilter bed. The species richness was lowest at

the top of the inlet section where it averaged 10.8 and reached its maximum value at the centre of

the outlet section where it averaged 18.8. In terms of evenness, the lowest value was again

measured at the top of the inlet section where evenness averaged 0.82 and reaching a maximum

value of 0.91 at both the centre of the middle and outlet sections. These values compare with two

samples collected from the centre of the inlet and middle sections of the biofilter treating DMS

alone on Day 283 where species richness was measured at 7 and 11, respectively, and evenness

was measured at 0.86 and 0.80, respectively.

Although the species richness and evenness along the length of each biofilter on Day 457 were

somewhat similar in value, there were some significant differences between radial planes for

these values. Generally, the significant differences were found in comparisons between radial

planes located in the inlet section of the biofilter and the centre of the outlet section. In the case

of the biofilter co-treating DMS and methanol the species richness of the top of the inlet section

was significantly less than that of the centre of the outlet section (p = 0.00011) while the

evenness of the centre of the inlet section was significantly less than that of the centre of the



outlet section (p = 0.0034). For the biofilter treating DMS alone, both the species richness (p =

0.00034) and evenness (p = 0.0031) of the top of the inlet section were significantly less than

that of the centre of the outlet section while the evenness of the top of the inlet section was also

significantly less than that of the centre of the middle section (p = 0.0021).

There are several factors that should be considered when explaining why the species richness and

evenness of sections towards the front of the biofilter may contain less diversity (less species

richness) and a more uneven distribution of microorganisms (lower evenness). Firstly, due to the

plug flow nature of the system, microorganisms at the top of the reactor are exposed to higher

concentrations of substrate. Given that there are only one or two substrates being fed to these

reactors and, in the case of the biofilter co-treating DMS and methanol significantly more of one

substrate than the other, it would be expected that there would be a selection of dominant

microorganisms that are capable of growth on these substrates. This would lead to certain

dominant microorganisms increasing in relative abundance which could affect evenness directly

by creating a more uneven distribution of microorganisms in the community (lower evenness)

and affect species richness indirectly by lowering the relative abundance of other

microorganisms in the community below the detection threshold, giving the impression of lower

species richness. Secondly, due to the down-flow set-up of these reactors, there is a natural

tendency of microorganisms to settle to the bottom of the reactor through gravity. This leads to

an accumulation of both active and inactive biomass towards the bottom of the biofilter which

provides a diverse source of substrates for other microorganisms to grow. Also, the consumption

of the inlet substrate at this point in the biofilter is typically pretty low. Most of the substrate has

already been consumed towards the top of the reactor. The end result is that, at the bottom of the

biofilter, there is a wide variety of substrates on which microorganisms may grow and substrate

consumption is not dominated by one or two particular substrates. This makes the distribution of

the microbial community more even which directly leads to higher evenness while also reducing

the large relative abundance of a particular group of microorganisms which may indirectly allow

more microorganisms to reach detectable levels (> 1%), leading to higher species richness values.



As for comparisons of species richness and evenness between the two sampling time points (Day

283 and Day 457), there appears to be some correlation between species richness but not the

evenness. For the biofilter co-treating DMS and methanol, the species richness at the centre of

the inlet section was measured as 11 on Day 283 and averaged 9.5 on Day 457. For the biofilter

co-treating DMS alone, the species richness at the centre of the inlet section was measured as 7

on Day 283 and averaged 14.0 on Day 457. The centre of the middle section had measurements

of 11 and 16.5, respectively. While it is important to consider that the samples obtained on Day

283 represent a single sample versus the four samples obtained on Day 457, it is worth noting

that the species richness value of the sample taken on Day 283 from the biofilter co-treating

DMS and methanol falls within the 95% confidence interval of the samples obtained on Day 457

while neither of the species richness values for samples taken from the biofilter treating DMS

alone fell within the 95% confidence intervals of those taken on Day 457. Also worth noting is

that the species richness increased from the inlet section to the middle section for the biofilter

treating DMS alone. Given that these values are determined from a lone sample, it is impossible

to say whether it is a significant increase but it does support the more comprehensive observation

for samples obtained on Day 457.

As for evenness, there appears to be no correlation whatsoever between the two time points. For

the biofilter co-treating DMS and methanol, the evenness of the microbial community in the

centre of the inlet section on Day 283 was 0.91 as opposed to the average of 0.80 on Day 457.

For the biofilter treating DMS alone, the evenness at the centre of the inlet section was 0.86 on

Day 283 compared to 0.88 for the samples obtained on Day 457. For the centre of the middle

section the evenness values were 0.80 and 0.91, respectively. Only the evenness for the centre of

the inlet section of the biofilter treating DMS alone fell within the 95% confidence interval of the

samples obtained on Day 457. As with the species richness values obtained on Day 283, it is

worth remembering that the evenness values obtained on Day 283 are only from a single sample

but it is still worth considering the considerable difference observed in the evenness of the centre

of the inlet section for the biofilter co-treating DMS and methanol on Day 283 and Day 457.

Given the larger methanol loading (32 g m-3

h-1

) on Day 283 compared to Day 457 (6 g m-3

h-1

),



it might be expected that there would be a more uneven distribution within the microbial

community on Day 283 compared to Day 457. However, the sampling on Day 283 occurred just

after the biofilter co-treating DMS and methanol had recovered from a period of methanol

suspension while on Day 457 the biofilter co-treating DMS and methanol had experienced near-

constant methanol addition for at least a month. Perhaps the period of low substrate loading

before sampling on Day 283 explains the lower evenness of the microbial community in this

sample compared to that taken on Day 457. For the biofilter treating DMS alone, the evenness of

the microbial community structure at the centre of the inlet is approximately the same for the

samples taken on Day 283 and Day 457. This would be expected given that the control biofilter

did not go through any of the large fluctuations in substrate loading that the biofilter co-treating

DMS and methanol did during this period with the DMS loading being 3.4 g m-3

h-1

on Day 283

compared to 5.0 g m-3

h-1

on Day 457. While the evenness of the microbial community was

approximately the same on Day 283 and Day 457 for the centre of the inlet section of the

biofilter treating DMS alone, the evenness of the microbial community at the centre of the

middle section of the biofilter appeared to increase between these two time points from 0.80 to

0.91. While it may be expected for the evenness of the microbial community to become more

even with time after inoculation, it is likely that the effect of the initial inoculum on the evenness

would be negligible after 283 days of operation.

In terms of comparisons of species richness as determined from the clone library constructed

from the biofilter treating DMS alone to that determined using DGGE fingerprinting, the values

are quite similar. The clone library constructed from biomass harvested directly from the centre

of the inlet section of the biofilter treating DMS alone revealed at least 14 different groups of

bacterial species while the species richness as determined on Day 283 and Day 457 was 7 and 14,

respectively. This provides additional confidence that the clone library covers the diversity of

bacteria in the biofilter treating DMS alone, especially since one or two large groupings in the

clone library, such as the Xanthomonadales, resulted in multiple bands in the DGGE fingerprints.

The evenness of the microbial community structure in a biofilter appears to be driven by several

factors which makes obtaining meaningful information from comparisons of evenness between



biofilters difficult. A survey of the biofilter literature led to the discovery of evenness value in

biofilters that were as low as 0.1 and as high as 0.976. Ho et al. (2008) found a particularly

uneven distribution of 0.1 in a lab-scale biofilter treating trimethylamine (31 – 308 mg-N m-3

h-1

)

and ammonia emissions (22 – 604 mg-N m-3

h-1

). The biofilter was operated at near neutral pH

and was initially inoculated with an immobilized Arthrobacter sp. which remained a dominant

ribotype in the DGGE fingerprints throughout the entire biofilter operation period. At the other

extreme, Friedrich et al. (2002) determined the evenness of the microbial community in an

industrial waste gas biofilter at an animal rendering facility to be 0.976. Inlet loadings of

pollutants were not reported but the biofilter was reportedly fed a wide variety of substrates. As

for lab-scale biofilters treating reduced sulphur compounds, Sercu et al. (2006b) reported

Shannon Diversity Index values of approximately 2.1 for DGGE fingerprints constructed from

16S rRNA (corresponding to an evenness of approximately 0.2) for a compost biofilter operated

at neutral pH treating H2S (5.6 – 30 g m-3

h-1

). For a two-stage biotrickling filter, Sercu et al

(2005) reported Shannon Diversity Index values of 0.32 – 1.36 (evenness of roughly 0.1) for an

acidic biotrickling filter (pH 2 -4) treating up to 83 g H2S m-3

h-1

and approximately 2.8 for a

neutrophilic biotrickling filter treating up to 58 g DMS m-3

h-1

(evenness of roughly 0.25).

The evenness value of the microbial communities found in the biofilters treating DMS in the

presence and absence of methanol is at the upper end compared to that found in other studies.

However, it appears several factors may have an effect on evenness. First, in the case of the lab-

scale biofilters, all of these biofilters were fed a lone substrate at a fairly high concentration and

loading. This could lead to the proliferation of a narrow group of microorganisms capable of

growing on this substrate to high relative abundance in the biofilter, leading to a more uneven

distribution and lower evenness values. It also appears that environmental conditions such as pH

and temperature may lead to different evenness. Under acidophilic conditions, a biotrickling

filter treating H2S had an evenness of approximately 0.1 while, under neutrophilic conditions, a

biofilter treating H2S had an evenness of approximately 0.2. The different conditions in the

biofilter lead to different bacteria proliferating which will ultimately affect the structural



parameters like evenness, making this parameter as a comparator, at least among different

biofiltration studies, rather limited.

While species richness and evenness provide a rudimentary comparison of the microbial

community within the biofilter, a more sophisticated method of comparing DGGE fingerprints

from biomass samples is to compare the similarity of the banding patterns in the fingerprint to

the banding pattern of other fingerprints. This method is superior to a simple species richness

measurement since the identity of the particular microorganism is factored into the comparison.

Figure 4.7 shows the UPGMA dendrograms constructed from the DGGE profiles based on their

Jaccard coefficients. One dendrogram compares all samples collected after the biofilter co-

treating DMS and methanol reached steady state at a DMS loading of 1 g DMS m-3

h-1

at an

EBRT of 80 s (Day 457) while the other compares samples taken from the same radial plane

during the two different periods (Days 283 and 457). For the comprehensive sampling of the two

biofilters on Day 457, samples obtained from the same radial plane generally cluster closer to

each other than to samples from different planes in the biofilter. Samples from the same radial

plane could have similarities as high as 100% in some cases but were for the most part at least

70% and often more than 80% similar to each other in terms of band similarity. As a comparison,

DGGE fingerprints from samples located in the inlet section of the biofilter co-treating DMS and

methanol were roughly 45% similar to those not located in the inlet section of that biofilter.

Comparing samples taken from the same radial plane obtained from both biofilters during the

two different time periods and conditions reveals that samples obtained during the

comprehensive sampling on Day 457 were more similar to other samples taken from the same

radial plane on Day 457 than the sample obtained from the same plane on Day 283. Also, all

samples obtained from the biofilter co-treating DMS and methanol were more similar to each

other than any of the samples obtained from the biofilter treating DMS alone.



Figure 4.7 - Jaccard coefficient-based UPGMA dendrograms of DGGE fingerprints of samples

taken from both biofilters on Day 457 (left) and on days 283 and 457 (above). Cophenetic

correlations are labelled on branches. Fingerprints are labelled as follows: The first two letters of

the fingerprint label indicate whether biofilter was fed DMS alone (DA) or DMS with methanol

(DM). The third letter indicates whether the sample was taken from the inlet (I), middle (M),

outlet (O) section or bed top (B). The fourth letter indicates whether the sample was taken from

the top (T) or centre (C) of the biofilter section. The first number indicates the location within the

radial plane the sample was obtained (as labelled in Figure 3.1), while the last three digits

represent the day the sample was obtained (283 or 457).



The clustering of bands from the same radial plane of each biofilter into distinct groups suggests

a fairly ordered structure in these biofilm systems. Biofilters are plug flow systems and, as such,

microorganisms in the same radial plane should be exposed to similar conditions over time in

terms of substrate concentration, pH, and nutrient supply as long as distorting effects such as

channelling are negligible. Given this, it may be expected that microbial communities in the

same radial plane would evolve similarly over time. As can be seen from the comparison of

samples obtained on Day 283 and Day 457, samples taken from the same radial plane do change

over time and the difference in the microbial community similarity between two days is greater

than any of the differences within samples taken from the same radial plane, suggesting that the

changes that occur over time as a result of different loadings and environmental conditions such

as pH have a greater effect on microbial community structure than any variations that occur

because of the location of the biomass sample in the radial plane.

The high similarity of the microbial community structure of biomass samples taken from the

same radial plane has several important implications on biofiltration studies. On a practical level,

it validates the use of less destructive biomass sampling procedures such as sampling the biofilter

from the media access ports (as opposed to the highly destructive sampling on Day 457 where

the entire packing bed was removed) as providing biomass that is representative of the biomass

throughout the radial plane of that biofilter and provides increased confidence in results from

biofiltration studies that have used such sampling procedures. Also, if microbial communities

exposed to similar conditions evolve similarly with time, this suggests that the microbial

structure within a biofilter should be predictable and can likely be estimated through modelling

should enough information be available.

In summary, DGGE analysis of microbial fingerprints obtained from both biofilters were

consistent with the results from the 16S rDNA clone libraries. The identification of bands from

the fingerprints resulted in many of the same microbial groups being present such as

Hyphomicrobium spp., Thiobacillus spp., Xanthomonadales, Bacteroidales, and Acidobacteria.

In terms of species richness, the 16S rDNA clone library resulted in the identification of at least

14 different microbial grouping at the centre of the inlet section. This is similar to the species



richness values determined for this location of the biofilter on Day 283 and Day 457 where the

values were 7 and 14, respectively. In terms of the microbial community structure, it was found

that the microbial community from samples taken within a radial plane were more similar to

each other than they were to biomass samples taken along the length of the column, suggesting a

plug flow nature to these biofilters.

4.2.3 qPCR

Based on the identification of three bacterial genera common to both the enrichment and the

DMS biofilter clone libraries, primers were designed to quantify the 16S rRNA gene of

Hyphomicrobium spp., Thiobacillus spp. and Chitinophaga spp.. Biomass samples obtained after

recovery from a methanol suspension period (Day 283) and after the biofilter co-treating DMS

and methanol reached steady state at a DMS loading of 1 g DMS m-3

h-1

at an EBRT of 80 sec

(Day 457). Due to the high similarity of the microbial community structure of samples taken

from the same radial plane, multiple samples from the same plane taken on Day 457 were

averaged to construct a confidence interval.

On Day 457, the biofilter co-treating DMS with methanol had total bacterial 16S rDNA copies

per gram dry media that varied between a low of 1.2 x 107 at the top of the inlet section to a high

of 3.7 x 107 at the centre of the middle section of the biofilter (Figure 4.8). For Hyphomicrobium

spp., the 16S rDNA copies varied from a low of 1.7 x 106 (8.0% of total bacterial copies) at the

top of the inlet section to a high of 1.1 x 107 (31.9%) at the centre of the middle section. For

Thiobacillus spp., the 16S rDNA copies varied from a low of 1.0 x 105 (0.6%) at the top of the

inlet section to a high of 4.0 x 105 (1.3%) at the centre of the inlet section. For Chitinophaga spp.,

the 16S rDNA copies varied from a low of 3.7 x 105 (1.9%) at the top of the inlet section to a

high of 7.9 x 106 (22.5%) at the centre of the middle section.

For the biofilter treating DMS alone, total bacterial 16S rDNA copies per gram dry media on

Day 457 varied between a low of 1.6 x 106 at the top of the inlet section to a high of 7.2 x 10

6 at

the top of the middle section of the biofilter. For Hyphomicrobium spp., the 16S rDNA varied

from a low of 8.3 x 104 (5.2% of total bacterial copies) at the top of the inlet section to a high of



9.6 x 105 (13.0%) at the centre of the middle section. For Thiobacillus spp., the 16S rDNA copies

varied from a low of 1.3 x 104 (0.7%) at the top of the inlet section to a high of 2.5 x 10

5 (2.1%)

at the top of the middle section. For Chitinophaga spp., the 16S rDNA copies varied from a low

of 5.4 x 105 (24.9%) at the top of the inlet section to a high of 3.4 x 10

6 (49.4%) at the centre of

the middle section.

Samples obtained after the methanol suspension period (Day 283) were taken from the media

access ports on the side of the biofilters. For the centre of the inlet section of the biofilter co-

treating DMS with methanol, total 16S rDNA from Bacteria was 2.2 x 107 copies per gram dry

media of which Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. accounted for

8.5 x 106 copies (42.1% of total bacterial copies), 1.5 x 10

5 copies (0.80%), and 2.5 x 10

5 copies

(1.2%), respectively. For the biofilter treating DMS alone, the centre of the inlet section had total

bacterial 16S rDNA copies of 3.4 x 106 copies per gram dry media of which Hyphomicrobium

spp., Thiobacillus spp., and Chitinophaga spp. accounted for 2.0 x 106 copies (59.5%), 2.4 x 10

5

copies (7.0%), and 5.4 x 105 copies (16.0%), respectively. For the centre of the middle section of

the biofilter treating DMS alone, total bacterial 16S rDNA corresponded to 6.3 x 105 copies per

gram dry media of which Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp.

accounted for 1.1 x 105 copies (17.6%), 9.8 x 10

3 copies (1.6%), and 9.3 x 10

4 copies (14.9%),

respectively.



Figure 4.8 - Quantification of 16S rDNA for Bacteria, Hyphomicrobium spp., Thiobacillus spp.,

and Chitinophaga spp. standardized per gram dry media on Day 457 for the biofilter co-treating

DMS with methanol (top) and DMS alone (bottom). Error bars represent 95% confidence

intervals.



Table 4.5 Standardization of 16S rRNA gene copies of Hyphomicrobium spp., Thiobacillus spp.,

and Chitinophaga spp. on a percent total bacterial 16S rRNA gene basis for samples taken from

both biofilters on Day 457.

Hyphomicrobium(% BAC 16S rDNA) Thiobacillus(% BAC 16S rDNA) Chitinophaga(% BAC 16S rDNA)

DMS/MeOH DMS DMS/MeOH DMS DMS/MeOH DMS

Biofilter Biofilter Biofilter Biofilter Biofilter Biofilter

Bedtop 2.80 ± 0.98 N/A 0.27 ± 0.01 N/A 18.9 ± 22.2 N/A

Inletsection Top 8.04 ± 8.26 5.16 ± 0.95 0.58 ± 0.43 0.74 ± 0.33 1.87 ± 1.75 24.9 ± 18.4

Centre 21.1 ± 13.2 10.9 ± 3.1 1.25 ± 1.11 4.24 ± 2.94 11.0 ± 7.3 45.5 ± 24.1

Middlesection Top 18.8 ± 18.4 10.9 ± 5.1 1.20 ± 0.68 2.06 ± 2.17 15.7 ± 6.9 28.2 ± 25.0

Centre 31.9 ± 10.3 13.0 ± 4.5 1.15 ± 0.29 2.89 ± 0.94 22.5 ± 6.6 49.4 ± 8.1

Outletsection Top 10.7 ± 15.5 10.0 ± 14.5 1.03 ± 0.77 2.03 ± 1.91 9.52 ± 13.57 41.4 ± 47.4

Centre 15.4 ± 1.6 15.2 ± 8.5 1.12 ± 0.33 3.91 ± 1.44 16.9 ± 3.3 41.5 ± 14.8

* Ranges represent 95% confidence intervals

Comparisons between the 16S rDNA copy numbers of total bacteria and those of the three

microbial groups taken from equivalent levels of the two biofilters after the biofilter co-treating

DMS and methanol reached steady state at a DMS loading of 1 g DMS m-3

h-1

at an EBRT of 80

sec (Day 457) revealed that there were significantly more 16S rDNA copies from total bacteria

(p = 0.00006) and Hyphomicrobium spp. (p = 0.00009) at the centre of the inlet section of the

biofilter co-treating DMS with methanol compared to the biofilter treating DMS alone and

significantly more Hyphomicrobium spp. than Thiobacillus spp. at the centre of the biofilter co-

treating DMS and methanol (p = 0.00158). Furthermore, samples obtained from both biofilters

after the biofilter co-treating DMS and methanol recovered from a methanol suspension period

(Day 283) and after the biofilter co-treating DMS and methanol reached steady state on Day 457

had more 16S rDNA copies of Hyphomicrobium spp. in the centre of the inlet section of the

biofilter co-treating DMS and methanol compared to the biofilter treating DMS alone on both

days (four times higher on Day 283 and an order of magnitude higher on Day 457). 16S rDNA

copies of Hyphomicrobium spp. also exceeded those of Thiobacillus spp. at the centre of the inlet

section where the bulk of biodegradation was occurring in both biofilters. As for Chitinophaga

spp., they became more abundant in the biofilter between Day 283 and Day 457 despite the

overall loading into the biofilter being lower leading up to and on Day 457 than leading up to and



on Day 283. Although periods of high loading occurred between these two periods, neither

Hyphomicrobium spp. nor Thiobacillus spp. increased to the same magnitude as Chitinophaga

spp., suggesting that the growth of Chitinophaga spp. is not directly linked to methanol and

DMS loadings like Hyphomicrobium spp. and Thiobacillus spp..

In summary, the addition of methanol to the biofilter co-treating DMS and methanol led to a

statistically significant increase in the copies of 16S rDNA per gram dry media from Bacteria

and Hyphomicrobium spp. at the centre of the inlet section where the bulk of substrate

degradation was occurring in these biofilters. The addition of methanol did not result in a

significant increase in either Thiobacillus spp. or Chitinophaga spp.. Also, the quantity of 16S

rDNA per gram dry media from Chitinophaga spp. increased for by roughly an order of

magnitude between Day 283 and Day 457, while the quantity of 16S rDNA per gram dry media

remained similar for the two time points. Finally, in terms of the relative abundances of these

three microbial groups, they represented a significant fraction of the total 16S rDNA copies in

the biofilter at the centre of the inlet section (> 30%) in both biofilters.

4.3 Linking Biofilter Performance to Microbial Community Structure

As shown in this chapter, the performance of the biofilters treating DMS can be significantly

improved through the addition of methanol. The addition of methanol was shown to have two

effects of DMS removal in these biofilters. At lower methanol loadings, the addition of methanol

led to an increase in the baseline DMS removal rate of the biofilter co-treating DMS and

methanol compared to the biofilter treating DMS alone. However, as the methanol loading was

increased, the DMS removal rate reached a maximum before it began to decrease with increasing

methanol loadings. Eventually, the methanol loading reached a point where it had a net negative

effect on DMS removal and the DMS removal rate in the biofilter co-treating DMS and methanol

had a lower removal rate than the biofilter treating DMS alone.

The competitive effect of methanol on DMS removal appears to be the result of methanol being a

favoured substrate by microorganisms present in the biofilter microbial community which are

capable of degrading both substrates. This is supported by the immediate spike in the DMS



degradation rate that was routinely observed when methanol addition was suspended. This spike

in the DMS degradation occurred no matter what the methanol loading in the biofilter was before

methanol addition was suspended and became larger as the methanol loading rate before

methanol suspension increased.

Microbial community analysis revealed three groups of bacteria that appeared to be tightly

linked to DMS degradation in these biofilters. While Hyphomicrobium spp. and Thiobacillus spp.

are known DMS degrading microorganisms, Chitinophaga spp. had no reported role in DMS

degradation and its increase in 16S rDNA copies between Day 283 and Day 457 cannot be

explained through DMS and methanol loadings during those periods. The identification of

significant quantities of Hyphomicrobium spp. and Thiobacillus spp. in these biofilters coupled

with sulphur balance data showing that the fate of sulphur in these biofilters was largely to be

oxidized to sulphate also provide strong evidence that this is the mechanism behind which DMS

is eliminated in these systems.

In the literature, there is significant reporting of the oxidation of DMS to DMSO in aerobic

environments. However, most of these reactions are carried out by microorganisms that are

growing primarily on other substrates. The inability to identify these bacteria in significant

quantities in the enrichment culture and the lack of large quantities of other organic substrates on

which heterotrophic may grow in the biofilter treating DMS alone further suggest DMS to

sulphate through an aerobic pathway as a dominant mechanism for DMS removal in these

systems. As for the biofilter co-treating DMS and methanol, methanol appears to be largely

consumed by Hyphomicrobium spp. given that no other methylotrophic microorganisms were

identified through band excision and sequencing in DGGE analysis and that Hyphomicrobium

spp. accounted for approximately 20% of total bacterial 16S rDNA copies in the biofilter.

Furthermore, the increase in DMS removal in the biofilter co-treating DMS and methanol

immediately upon methanol suspension was linearly proportional to the increase in

Hyphomicrobium spp. 16S rDNA copies between the two biofilters, as would be predicted by

most biokinetic models, suggesting that Hyphomicrobium spp. are growing on both DMS and

methanol and that the presence of methanol slightly inhibits the consumption of DMS by



Hyphomicrobium spp., although the net effect of methanol addition may be positive on DMS

removal. This behaviour has been modelled in two different papers from first principles (Zhang

et al., 2007b, 2008).

Kinetics of Enrichment Culture on DMS and Methanol


91

5.0 KINETICS OF ENRICHMENT CULTURE ON DMS AND METHANOL

The work in this chapter describes the growth kinetics of Hyphomicrobium spp., Thiobacillus

spp., and Chitinophaga spp. in an enrichment culture created from a biofilter co-treating

dimethyl sulphide (DMS) and methanol. Several conditions were tested including the substrate,

DMS and methanol, the nitrogen source, NH4Cl and KNO3, and pH. All the results and

discussion in this chapter have been published in Applied and Environmental Microbiology

(Hayes et al., 2010a) and are currently in press.

5.1 Characterization and Growth of Enrichment Culture on DMS

An enrichment culture was created by inoculating mineral media with biomass collected from a

biofilter co-treating DMS and methanol and feeding the culture DMS as the sole organic carbon

source. A Bacteria-wide Bayesian phylogeny presented in the previous chapter revealed that the

enrichment culture was composed of a variety of bacteria and a comparison of this clone library

with one constructed from a biofilter treating DMS alone revealed three groups of bacteria

(Hyphomicrobium, Thiobacillus, and Chitinophaga) that appear to be linked to DMS degradation

in these systems. Minimum evolution phylogenies of the Hyphomicrobium and Thiobacillus

genera and the Chitinophagaceae family reveal that while there is genetic diversity in the 16S

rDNA sequences within each of these genera, the identified clones from Thiobacillus spp. tend to

be much less genetically diverse in terms of evolutionary distance compared to clones from

Hyphomicrobium spp. and Chitinophaga spp. (Figure 5.1).

The growth of Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. in the

enrichment culture with DMS as the sole carbon source was studied. As shown in Figure 5.2, the

DMS degradation rate was slow at the beginning of the time course while the quantity of

microorganisms is low but increases greatly throughout the experiment as microorganisms

proliferate exponentially with a concomitant decrease in DMS. This was the case for all time-

course batch experiments performed in this study. In order to determine the specific growth rate

(μ), an exponential relationship for biomass growth with time was assumed. Therefore, a plot of

the natural logarithm of 16S rDNA copy number versus time should yield a straight line where

the slope is equal to the specific growth rate. As shown in Figure 5.3, this was indeed the case for



92

the example shown where regression plots of the natural logarithm of 16S rDNA copies versus

time of Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. all had R2 values above

0.93. This is representative of all the time-course batch experiments carried out in this study. It is

also important to note that, for Chitinophaga spp., there was no significant difference in the yield

of 16S rDNA copies for Chitinophaga spp. in negative control cultures (no DMS) that were run

under the same conditions compared to the DMS-grown mixed cultures while neither

Hyphomicrobium spp., nor Thiobacillus spp., grew in the negative control cultures.

Figure 5.1 - Genus-wide A) Hyphomicrobium B) Thiobacillus and family-wide C)

Chitinophagaceae minimum evolution phylogenies for the enrichment culture created from a

biofilter co-treating DMS and methanol. Clones are denoted ENR and Genbank accession

numbers of all sequences used are listed. The scale is in substitutions per nucleotide.



93

1.0E+00

1.0E+01

1.0E+02

1.0E+03

1.0E+04

1.0E+05

1.0E+06

1.0E+07

0

2

4

6

8

10

12

14

16

0 10 20 30 40 50 60 70 80 90 100

Bio

ma

ss Co

nc. (1

6S

rDN

A C

op

ies per m

L M

edia

)D

MS

Co

ncen

tra

tio

n (μ

mo

l p

er B

ott

le)

Time (hours)

DMS

Hyphomicrobium

Thiobacillus

Chitinophaga

Figure 5.2 - DMS consumption and growth of Hyphomicrobium spp., Thiobacillus spp., and

Chitinophaga spp. in the enrichment culture in an NH4Cl-based media at pH 7 versus time.

Hyphomicrobium

Thiobacillus

Chitinophaga

y = 0.085x + 8.0

R² = 0.94

y = 0.086x + 5.5

R² = 0.93

y = 0.073x + 4.1

R² = 0.99

0

2

4

6

8

10

12

14

16

18

0 10 20 30 40 50 60 70 80 90 100

Ln

(1

6S

rD

NA

Co

py

Nu

mb

er p

er m

L M

edia

)

Time (Hours)

Figure 5.3 - A plot of the natural logarithm of the quantity of 16S rDNA copies of

Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. in the enrichment culture versus

time for the same experiment as shown in Figure 5.2.



94

5.2 Growth Kinetics of Hyphomicrobium, Thiobacillus, and Chitinophaga spp.

in Enrichment Culture on DMS

The effect of pH and nitrogen source on the growth kinetics of Hyphomicrobium spp.,

Thiobacillus spp., and Chitinophaga spp. on DMS in the enrichment culture was investigated

(Table 5.1). Specific growth rate values of Hyphomicrobium spp. on DMS were strongly affected

by the pH of the mineral medium while the nitrogen source had no significant effect on the

specific growth rate of Hyphomicrobium spp.. Maximum specific growth rate values for

Hyphomicrobium spp. were achieved at pH 7 in both the NH4+-N and NO3

--N media (0.099 h

-1

and 0.089 h-1

, respectively). At pH 6, both media saw a significant decrease in the specific

growth rate to 0.033 h-1

(p = 0.00152 and p = 0.0063, respectively) and, at pH 5, the specific

growth rates were 0.015 h-1

and 0.020 h-1

, respectively. The same trend was observed for the

yield of Hyphomicrobium spp. where large declines were observed in the yield with decreasing

pH. At pH 7, Hyphomicrobium spp. had respective yields in the NH4+-N and NO3

--N media of

1.8 x 107 and 4.0 x 10

6 16S rDNA copies per μmol DMS consumed with the NO3

--N being

significantly lower than the NH4+-N yield (p = 0.00018). It is important to note that in this

context yield is determined with respect to DMS consumed by the entire community and not just

Hyphomicrobium spp. At pH 6, the yield of Hyphomicrobium spp. in the NH4+-N and NO3

--N

media was 1.0 x 106 and 1.8 x 10

5 16S rDNA copies per μmol DMS consumed, respectively, and

at pH 5 the respective yields were 1.5 x 105 and 1.6 x 10

5 16S rDNA copies per μmol DMS

consumed. There was no significant difference at the 95% confidence level between any of the

Hyphomicrobium yields at pH 6 or pH 5 and all four yield values were significantly lower than

the yields obtained at pH 7 in the respective media.

For Thiobacillus spp., at pH 7, the specific growth rate in the NH4+-N and NO3

--N media were

0.076 h-1

and 0.110 h-1

, respectively. At pH 6, the specific growth rate in the NH4+-N media was

0.080 h-1

which was not significantly different from the specific growth rate at pH 7 (p = 0.769).

However, in the NO3--N media the specific growth rate was 0.065 h

-1 which was significantly

lower than the specific growth rate in the NO3--N media at pH 7 (p = 0.0223). At pH 5, the

specific growth rate in the NH4+-N and NO3

--N media were 0.084 h

-1 and 0.110 h

-1, respectively.

Neither of the specific growth rates was significantly different at the 95% confidence level from

the values in the respective media at either pH 6 or pH 7. As for the effect of the nitrogen source



95

on the specific growth rate of Thiobacillus spp., at pH 7, the specific growth rate of Thiobacillus

spp. was significantly higher in the NO3--N media (p = 0.0195) but at pH 5 and pH 6 there was

no significant difference at the 95% confidence level. As for the yield of Thiobacillus spp., the

minimum yield of 1.4 x 106 16S rDNA copies per μmol DMS consumed occurred at pH 6 in the

NH4+-N media while the maximum yield of 3.5 x 10


consumed occurred at pH 7 in the NH4+-N media. None of the yields of Thiobacillus spp. under

any of the conditions were significantly different from any of the other yields at the 95%

confidence level.

Table 5.1 Effect of pH and nitrogen source on the specific growth rate and yield of

Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. in the enrichment culture grown

on DMS. Numbers in parentheses indicate standard deviation. N = 6.

Microbial Group Nitrogen

Source

Specific Growth Rate (h-1

) Yield (16S rDNA copies/μmol DMS consumed)

pH pH

5 6 7 5 6 7

Hyphomicrobium

NH4Cl 0.015 (0.015) 0.033 (0.029) 0.099 (0.016) 1.5 (1.4) x 105 1.0 (0.9) x 10

6 1.8 (0.8) x 10

7

KNO3 0.020 (0.019) 0.033 (0.018) 0.089 (0.022) 1.6 (1.1) x 105 1.8 (1.1) x 10

5 4.0 (1.7) x 10

6

Thiobacillus

NH4Cl 0.084 (0.052) 0.080 (0.033) 0.076 (0.019) 3.0 (1.8) x 106 1.4 (0.9) x 10

6 3.5 (2.4) x 10

6

KNO3 0.110 (0.04) 0.065 (0.030) 0.110 (0.026) 3.0 (2.5) x 106 2.2 (1.9) x 10

6 2.5 (1.5) x 10

6

Chitinophaga NH4Cl 0.052 (0.038) 0.044 (0.036) 0.061 (0.011) 3.3 (2.5) x 10

5 3.9 (1.2) x 10

4 1.4 (0.9) x 10

6

KNO3 0.039 (0.030) 0.063 (0.021) 0.075 (0.020) 3.2 (2.1) x 105 2.1 (1.6) x 10

5 1.5 (1.1) x 10

6

As for Chitinophaga spp., at pH 7, the specific growth rate in the NH4+-N and NO3

--N media

were 0.061 h-1

and 0.076 h-1

, respectively. At pH 6, the specific growth rate in the NH4+-N and

NO3--N media were 0.044 h

-1 and 0.063 h

-1, respectively, neither of which were significantly

different from the specific growth rate in the respective media at pH 7. At pH 5, the specific

growth rate in the NH4+-N and NO3


-1 and 0.039 h

-1, respectively, neither

of which were significantly different from the specific growth rate in the respective media at pH

6. There was no significant difference at the 95% confidence level between specific growth

values at the same pH with different nitrogen sources. At pH 7, the yield of Chitinophaga spp. in

the NH4+-N and NO3

--N media was 1.4 x 10

6 and 1.5 x 10


consumed, respectively. At pH 6, the yield declined significantly to 3.9 x 104 (p = 0.0096) and



96

2.1 x 105 (p = 0.0046) 16S rDNA copies per μmol DMS consumed, respectively and, at pH 5, the

yield was 3.3 x 105 and 3.2 x 10

5 16S rDNA copies per μmol DMS consumed, respectively. The

NH4+-N media showed a statistically significant increase at pH 5 compared to pH 6 (p = 0.0304)

while there was no significant difference in the yield of Chitinophaga spp. in the NO3--N media

for these two pH values. Also, both pH 5 yields were significantly lower at the 95% confidence

level than the yield in the respective media at pH 7 (p = 0.0177 and p = 0.00785, respectively).

The only significant difference at the 95% confidence level that nitrogen source had on yields of

Chitinophaga spp. was a higher yield of Chitinophaga spp. in the NO3--N media compared to

the NH4+-N media at pH 6 (p = 0.0370).

The yield of Hyphomicrobium spp. and Thiobacillus spp. on DMS in this enrichment culture was

compared to the reported yield of Hyphomicrobium spp. and Thiobacillus thioparus Tk-m on

DMS from the literature as well as the theoretical biomass yield at pH 7 with an NH4+-N

nitrogen source calculated from bioenergetics (Table 5.2). Theoretical yields can be estimated

from the free energy released from the oxidation of DMS. This calculation resulted in an

estimate of the maximum yield equal to 0.565 eeq cells/eeq DMS which corresponds to 1.03 g

cells/g DMS, assuming 113 g cells/20 eeq cells and 20 eeq/mol of DMS (Rittman and McCarty,

2001). Quantitative PCR yields were converted using estimates of the average biovolume of a

cell, average carbon content of cellular dry mass, and the number of 16S rRNA genes per cell.

For Hyphomicrobium and Thiobacillus, the average biovolume was estimated to be 8.8 x 10-13

cm3/cell (Hirsch, 1989) and 8.6 x 10

-14 cm

3/cell (Kelly and Harrison, 1989), respectively. To

convert biovolume to biomass (carbon content) a conversion factor of 0.22 g C/cm-3

was used

(Bratbak and Dundas, 1984). No information was found on the amount of 16S rRNA genes per

Hyphomicrobium cell were found so an assumption of 1 16S rDNA copy per cell was assumed.

For Thiobacillus, no information was found on the quantity of 16S rDNA copies per cells for

Thiobacillus thioparus. However, Thiobacillus denitrificans ATCC 25259 was reported to have

2 16S rDNA copies per cell (Beller et al., 2006) so this value was assumed as an estimate in our

calculations for Thiobacillus.

Further to tracking the degradation of DMS by gas chromatography and the growth of

Hyphomicrobium spp., Thiobacillus spp., and Chitinophaga spp. by qPCR, total bacterial 16S

rDNA was quantified at the end of the time-course batch experiments to verify that



97

Hyphomicrobium spp. and Thiobacillus spp. did account for the bulk of total bacteria and that

other bacteria were not responsible for the bulk of the DMS degradation. Furthermore, sulphate

analysis was carried out on the media to determine the extent of the conversion of DMS to

sulphate. At pH 7, the ratio of the sum of 16S rDNA copies from Hyphomicrobium spp. and

Thiobacillus spp. to total bacterial 16S rDNA averaged 1.76 ± 0.73. At pH 6 and pH 5, the

average ratio was 1.08 ± 0.94 and 0.97 ± 0.49, respectively. For sulphate analysis, at pH 7 it was

determined the increase in sulphate in the batch reactors meant that the conversion of DMS to

sulphate was 107 ± 1%. Determination of the percent conversion of DMS to sulphate was not

feasible for the pH 6 and pH 5 experiments due to the use of H2SO4 to lower the pH of the

mineral media which resulted in high background sulphate levels.

Table 5.2 - Comparisons of yields of Hyphomicrobium and Thiobacillus grown on DMS at pH 7

in NH4+-N media.

Yield on DMS

Source 16S rDNA copies/μmol g (dry weight)/ g substrate

Hyphomicrobium 1.80E+07 0.1b This study

Thiobacillus 3.50E+06 0.01b This study

Hyphomicrobium VS - 0.26 Pol et al., 1994

Hyphomicrobium EG - 0.31 Suylen et al.,1986

T. thioparus TK-m - 0.49 Kanagawa and Kelly, 1986

Theoreticala - 1.03 This study

a See text for bioenergetics assumptions and evaluations

b Calculated from qPCR data using assumptions (see text)

The time-course batch experiments of the enrichment culture on DMS revealed that, of the three

microbial groups previous identified to be tightly linked to DMS degradation in biofilters

treating DMS, only Hyphomicrobium spp. and Thiobacillus spp. were actually growing on DMS

while the third group, Chitinophaga spp., were growing on some other substrate, likely utilizing

complex organic compounds originating from other bacteria, given that other Chitinophaga spp.

have been reported to grow on chitin (Sangkhobol and Skerman, 1981) and carboxymethyl

cellulose (Yasir et al., 2010), suggesting these bacteria may be involved in carbon cycling rather

than DMS degradation. Further evidence that Chitinophaga spp. are not responsible for DMS

degradation in these biofilters is provided in the previous chapter where the concentration of



98

Chitinophaga biomass in the biofilters did not appear to be correlated with either DMS loading

or removal in the biofilters. However, its continuous long-term presence in the enrichment

culture is intriguing given other non-DMS degrading bacteria that were present at equally high

concentrations in the biofilters (i.e. Xanthomonadales) did not appear to be retained to any

significant extent in the enrichment culture.

The kinetic data extracted from the DMS-only time course experiments also suggest that

microbial degradation of DMS may not be as limiting of a factor in the biofiltration of DMS as

other researchers have proposed. The maximum specific growth rate for Hyphomicrobium spp.

(0.099 h-1

) and Thiobacillus spp. (0.11 h-1

) on DMS were double the highest reported maximum

specific growth rates of microorganisms growing on DMS in the literature of 0.05 h-1

for

Methylophaga sulfidovorans (de Zwart et al., 1996), 0.05 h-1

for Thiobacillus thioparus Tk-m

(Kanagawa and Kelly, 1986) and 0.04 h-1

for Hyphomicrobium VS (Pol et al., 1994). The

increased specific growth rates of Hyphomicrobium spp. and Thiobacillus spp. on DMS in this

enrichment culture may be the result of the mixed culture improving growth on DMS relative to

pure cultures. Mixed cultures provide several situations where microbial growth of a particular

organism may be enhanced such as the removal of inhibitory compounds or the production of

vitamins or other growth factors by other microorganisms (Meers and Jannasch, 1973) which

may improve growth compared to a pure culture growing in a defined media. However, it may

also be the result of relatively few pure culture kinetic studies being carried out with DMS as the

substrate. Whatever the reason behind the high specific growth rates of Hyphomicrobium spp.

and Thiobacillus spp. in this enrichment culture, the results suggest that these microorganisms

are quite capable of achieving higher DMS degradation rates on DMS alone provided they are

growing in optimal conditions.

Biomass yields of both Hyphomicrobium spp. and Thiobacillus spp. were significantly lower in

the enrichment culture at pH 7 then both the theoretical yield calculated using bioenergetics and

biomass yields of pure cultures grown on DMS reported in the literature. This is a product of the

enrichment culture being a mixed culture, the experimental method employed, and how the yield

was calculated. Since the enrichment culture is a mixed culture, both Hyphomicrobium spp. and

Thiobacillus spp. are growing on DMS and there is no way to determine how much of the DMS

substrate each group is consuming, this leads to an underestimate of their respective yields. The



99

experimental method also leads to an underestimate of the yield due losses that may accrue in the

extra processing steps involved compared to VSS measurements, particularly the genomic DNA

extraction. Finally, the biomass yield is calculated using estimates of biovolume, cell carbon, and

16S rDNA copies per genome. A possible concern raised by the low biomass yields is that there

is another microorganism degrading DMS in the enrichment culture. This concern inreases as the

pH decreases as highlighted in Table 5.3 whichs shows the decline in the combined yield of

Hyphomicrobium and Thiobacillus 16S rDNA copies as the pH is decreased from pH 7 to pH 5.

Since there were no significant differences in the yield of Thiobacillus spp. at different pH values,

the decline in the combined yield suggests that another microorganism may be growing on DMS

at the lower pH values.

Table 5.3 – Combined yield of 16S rDNA copies of Hyphomicrobium and Thiobacillus spp. in

the enrichment culture at different pH values. Interval constructed using ± 1 standard deviation.

Hyphomicrobium + Thiobacillus

(16S rDNA copies per μmol DMS consumed)

NH4+-N media NO3

--N media

pH 5 1.2 - 5.1 x 106 0.5 - 5.8 x 10

6

pH 6 0.6 - 4.2 x 106 0.6 - 4.2 x 10

6

pH 7 1.1 - 3.1 x 107 0.4 - 1.2 x 10

7

In light of the possibility of another microorganism consuming DMS it is worth noting that DMS

was completely converted to sulphate in the pH 7 experiments and that 16S rDNA copies from

Hyphomicrobium spp. and Thiobacillus spp. covered the bulk of bacterial 16S rDNA in the batch

experiments. Given that only bacteria are known to carry out the aerobic pathway of converting

DMS to sulphate (as opposed to DMSO), this makes the likelihood of another microorganism

being responsible for significant DMS removal in the enrichment culture unlikely. However,

there is a decline of Hyphomicrobium 16S rDNA copies as the pH decreases from pH 7 to pH 5

without a significant increase in Thiobacillus 16S rDNA copies to compensate. However,

Thiobacillus 16S rDNA copies do still cover the bulk of total bacterial 16S rDNA copies at the

lower pH values. This could be explained by changes in morphology of these bacteria with pH

(i.e. the biovolume and cellular mass are not constant with pH). Other possibilities is that the rate



100

of cell death increases with pH or another bacterium is consuming DMS, particularly at the lower

pH values.

The effect of the pH of the media had different effects on the kinetics of Hyphomicrobium spp.

and Thiobacillus spp.. While there was a clear deterioration in the ability of Hyphomicrobium

spp. to grow on DMS as the pH decreases from pH 7 to pH 5, Thiobacillus spp. maintained their

ability to grow on DMS as the pH is decreased from pH 7 to pH 5. There is very little

information on the effect of pH on the growth kinetics of Hyphomicrobium spp. and Thiobacillus

spp. in the literature. In the case of Hyphomicrobium spp., experiments on pure cultures were all

carried out at pH 7 only. However, a study on an enrichment culture believed to be dominated by

Hyphomicrobium spp. (observed by microscopy) had a maximum DMS degradation rate

somewhere between pH 6.0 and 7.0. When the pH of the enrichment culture was decreased to 5.0,

the DMS degradation rate decreased to 50% of the maximum DMS degradation rate (Smet et al.,

1996). This is consistent with observations in the enrichment in this study that showed that the

specific growth rate and, by extension, the DMS degradation rate of Hyphomicrobium spp.

decreased by up to 85% when the pH was decreased from pH 7 to pH 5. Furthermore, no

sulphate inhibition was observed in the Hyphomicrobium enrichment for sulphate concentrations

below 24 g SO42-

/L (Smet et al., 1996a), a sulphate concentration that exceeds any sulphate

concentration measured in this study by at least an order of magnitude, suggesting the decline in

the specific growth rate is due to pH rather than sulphate inhibition. As for Thiobacillus spp., the

specific growth rate of Thiobacillus thioparus Tk-m was reported to decrease by 50% as the pH

decreased from pH 7.7 to pH 6.1 with no growth occurring at pH 5.6 (Kanagawa and Kelly,

1986). However, in a biotrickling filter study where nearly 100% removal efficiencies were

achieved and the microbial community was typically composed of more than 10% Thiobacillus

spp. and less than 0.1% Hyphomicrobium spp. on a total bacterial 16S rDNA basis, the DMS

degradation rate remained robust (~90% removal efficiency) as the pH of the recirculation media

was adjusted to pH 5 over the course of one day (Sercu et al., 2006). Given that these

experiments were only carried out over the course of a day, it is unknown if they would have

been sustainable in the long term but the batch data in this study suggests that there are

Thiobacillus spp. that can grow equally well at pH 5 and pH 7 over the long term.



101

5.3 Growth Kinetics of Hyphomicrobium spp. in Enrichment Culture on

Methanol The growth of Hyphomicrobium spp. in the enrichment culture was further tested with methanol

as a substrate under the same conditions used for growth with DMS (Table 5.4). The pH of the

mineral media had only a slight effect on the specific growth rate of Hyphomicrobium spp. for

the range tested. At pH 7, the specific growth rate of Hyphomicrobium spp. in the NH4+-N and

NO3--N media was 0.12 h

-1 and 0.14 h

-1, respectively. At pH 6, the specific growth rate of

Hyphomicrobium spp. in the NH4+-N and NO3


-1 and 0.15 h

-1, respectively,

but neither was significantly at the 95% confidence level from the specific growth rate in the

respective media at pH 7. At pH 5, the specific growth rates for the NH4+-N and NO3

--N media

to 0.095 h-1

and 0.11 h-1

, respectively. Both were a significant decrease at the 95% confidence

level from the respective value at pH 6 (p = 0.0184 and p = 0.0027, respectively). There were no

significant differences at the 95% confidence level in the specific growth rate as a result of

nitrogen source. As for yield, at pH 7, the yield of Hyphomicrobium spp. in the NH4+-N and

NO3--N media was 1.5 x 10

7 and 1.2 x 10

7 16S rDNA copies per μmol methanol consumed,

respectively. At pH 6, the yield of Hyphomicrobium spp. in the NH4+-N and NO3

--N media was

6.5 x 106 and 6.3 x 10

6 16S rDNA copies per μmol methanol consumed, respectively. This

represented a significant decrease at the 95% confidence level for the NO3--N media (p =

0.00339) while the difference was not significant for the NH4+-N media. At pH 5, the yield of

Hyphomicrobium spp. in the NH4+-N and NO3

--N media was 4.2 x 10

6 and 6.2 x 10

6 16S rDNA

copies per μmol methanol consumed, respectively. Neither of these was significantly different at

the 95% confidence from the respective yields obtained at pH 6 but both were significantly lower

than the respective yields at pH 7 (p = 0.0337 and p = 0.00298, respectively). As for the effect of

nitrogen source on yield, there were no significant differences at the 95% at any pH value.

Table 5.4 Effect of pH and nitrogen source on the specific growth rate and yield of

Hyphomicrobium spp. in the enrichment culture grown on methanol. Numbers in parentheses

indicate standard deviation. N = 3.

pH Specific Growth Rate (h

-1) Yield (16S rDNA copies/μmol MeOH consumed)

NH4Cl KNO3 NH4Cl KNO3

5 0.095 (0.005) 0.111 (0.009) 4.2 (0.6) x 106 6.2 (1.5) x 10

6

6 0.134 (0.010) 0.148 (0.002) 6.5 (3.8) x 106 6.3 (1.6) x 10

6

7 0.120 (0.011) 0.136 (0.015) 1.5 (0.5) x 107 1.2 (0.1) x 10

7



102

The yield of Hyphomicrobium spp. on methanol in this enrichment culture was compared to the

reported yield of Hyphomicrobium VS from the literature as well as the theoretical biomass yield

at pH 7 with an NH4+-N nitrogen source calculated from bioenergetics (Table 5.4). Theoretical

yields were estimated from the free energy released from the oxidation of methanol. This

calculation resulted in an estimate of the maximum yield equal to 0.699 eeq cells/eeq methanol

which corresponds to 0.741 g cells/g methanol, assuming 113 g cells/20 eeq cells and 6 eeq/mol

of methanol (Rittman and McCarty, 2001). Quantitative PCR yields were converted using

estimates of the average biovolume of a cell, average carbon content of cellular dry mass, and the

number of 16S rRNA genes per cell. For Hyphomicrobium, the average biovolume was

estimated to be 8.8 x 10-13

cm3/cell (Hirsch, 1989). To convert biovolume to biomass (carbon

content) a conversion factor of 0.22 g C/cm-3

was used (Bratbak and Dundas, 1984). No

information was found on the amount of 16S rRNA genes per Hyphomicrobium cell were found

so an assumption of 1 16S rDNA copy per cell was assumed.

As with the enrichment culture grown on DMS, total bacterial 16S rDNA was quantified at the

end of the experiment to confirm that Hyphomicrobium spp. accounted for a significant fraction

of the total bacterial 16S rDNA copies. At pH 7, the ratio of 16S rDNA copies from

Hyphomicrobium spp. to total bacterial 16S rDNA averaged 1.09 ± 0.68. At pH 6 and pH 5, the

average ratio was 1.06 ± 0.19 and 0.82 ± 0.27, respectively.

Table 5.5 Comparison of yields of Hyphomicrobium grown on methanol at pH 7 in NH4+-N

media

Yield on Methanol

Source 16S rDNA

copies/μmol

g (dry weight)/ g

substrate

Hyphomicrobium 1.2E+07 0.13b This study

Hyphomicrobium

VS - 0.38 -0.45 Pol et al., 1994

Theoreticala - 0.74 This study

a See text for bioenergetics assumptions and evaluations

b Calculated from qPCR data using assumptions (see text)



103

While the maximum specific growth rate of Hyphomicrobium spp. was double the highest

reported maximum specific growth rates of microorganisms growing on DMS reported in the

literature, the maximum specific growth of Hyphomicrobium spp. on methanol (0.15 h-1

) was

similar to maximum specific growth rates on methanol for Hyphomicrobium VS (0.14 h-1

) (Pol et

al., 1994) and Hyphomicrobium X (0.10 h-1

) (Harder et al., 1973). The similar specific growth

rate values measured compared to values reported in the literature sets aside the possible concern

that the high specific growth rates of Hyphomicrobium spp. and Thiobacillus spp. may be the

result of using qPCR as a technique to estimate biomass (versus volatile suspended solids (VSS),

for example).

Biomass yields of Hyphomicrobium spp. were significantly lower in the enrichment culture at pH

7 then both the theoretical yield calculated using bioenergetics and biomass yields of pure

cultures grown on DMS reported in the literature. Again, this is a product of the experimental

method employed and how the yield was calculated. Also, a similar approximately 65%

underestimate of the biomass yield was observed at pH 7 for Hyphomicrobium spp. growing on

methanol and DMS compared to the biomass yield of Hyphomicrobium VS in pure culture. This

suggests that the biomass yield underestimate is mainly an artifact of experimental methodology.

While decreasing the pH of the media from pH 7 to pH 5 had a deleterious effect on the growth

kinetics of Hyphomicrobium spp. on DMS, it had only a minimal effect on the growth kinetics of

Hyphomicrobium spp. on methanol. It has long been known that Hyphomicrobium spp. could be

enriched from environmental samples equally quickly in media between pH 5.5 and pH 8.0

(Attwood and Harder, 1972), suggesting approximately equal specific growth rates for

Hyphomicrobium spp. within this pH range which is consistent with results in this study for

Hyphomicrobium spp. growing on methanol.

The difference in growth rates on methanol and DMS with pH for Hyphomicrobium spp. could

be the result of different pH optima of enzymes involved in the metabolic pathway of these

compounds (see Figure 5.4). In Hyphomicrobium spp., methanol is first oxidized to

formaldehyde by a methanol dehydrogenase (Duine and De Ruiter, 1979) while DMS is oxidized

to methyl mercaptan and formaldehyde by a DMS monooxygenase (De Bont et al., 1981; Suylen

et al., 1986) and methyl mercaptan is further oxidized to hydrogen sulphide and formaldehyde by



104

a methyl mercaptan oxidase (Suylen et al., 1987). Methanol dehydrogenase is a well-studied

enzyme in the literature where methanol oxidation occurs in the periplasmic space via an

electron transport chain with the initial step of methanol dehydrogenase oxidizing cytochrome cL

at a pH optimum of pH 7.0 (Anthony, 2000). However, methanol dehydrogenases have been

shown to remain stable and retain their activity after incubation for 45 hours in buffer from pH 5

to pH 10, with the optimum activity occurring for samples incubated at pH 6 (Ohta et al., 1981)

and the fact that Hyphomicrobium spp. can be isolated almost equally as quickly from

environmental samples from pH 5.5 to pH 8.0 suggests a wide pH range in which methanol

dehydrogenase is active in vivo (Attwood and Harder, 1972). Enzymatic studies on the

metabolism of DMS by Hyphomicrobium spp. are limited. De Bont et al. (1981) performed the

first studies on cell free extracts and assayed both DMS monooxygenase and methyl mercaptan

oxidase at pH 7.2 but reported low activities for DMS monooxygenase that may have been the

result of suboptimal assay conditions. Suylen et al. (1987) reported that the pH optimum for

methyl mercaptan oxidase in Hyphomicrobium EG was pH 8.2. Perhaps the higher pH optimum

of methyl mercaptan oxidase and, perhaps DMS monooxygenase, results in the more rapidly

decline of the specific growth rate of Hyphomicrobium spp. on DMS than on methanol as the pH

decreases from pH 7 to pH 5. The ability of Hyphomicrobium spp. in the enrichment culture to

grow approximately equally well on methanol at pH 5 through pH 7 further suggests that the

likely source of the reduced specific growth rate at lower pH values in the presence of DMS lies

somewhere in the enzymatic pathway responsible for dissimilatory DMS metabolism. Given that

no methyl mercaptan formation was detected in the batch experiments or during the biofiltration

experiments, it is most likely the initial DMS monooxygenase step that results in the reduced

growth rates either directly, through suboptimal enzyme functionality, or indirectly, through

regulation of the production and activity of this enzyme by the cell.

Overall Discussion 105


6.0 OVERALL DISCUSSION

The primary goal of this thesis was to determine the mechanism behind the increase in DMS

removal rates observed in biofilters co-treating DMS with methanol. An underlying assumption

at the beginning of this thesis was that methanol addition had some sort of effect on the

microorganisms within the microbial community. At that point in time there were conflicting

explanations on the cause of poor DMS removal rates in biofilters in the literature. Budwill and

Coleman (2002) believed that DMS removal rates were limited by physical properties of DMS,

such as hydrophobicity, as opposed to others who believed it was due to poor microbial kinetics

(Cho et al., 1991; Park et al., 1993; Smet et al., 1996; Hartikainen et al., 2002). However, the

performance of the biofilters in this thesis as well as other studies published during this literature

suggest that it is in fact a limitation of microbial kinetics that has historically led to poor DMS

elimination in biofilters.

In this study, it was observed that when DMS was co-treated with methanol there was an

increase in the DMS removal rate in the biofilter. This alone suggests that diffusion of DMS into

the biofilm is not the limiting step in DMS elimination in this system but rather the ability of

microorganisms in the biofilm to degrade DMS. Additionally, once methanol addition was

suspended, there was an immediate increase in the DMS removal rate suggesting, again, that

diffusion of DMS into the biofilm is not a limiting step to DMS elimination in this system.

However, while suspension of methanol addition led to an increase in the DMS removal rate of

the biofilter, the increase was not sustainable and the biofilm gradually decayed and the DMS

removal rate decreased back down to the levels observed in the biofilter treating DMS alone

suggesting that, for some reason, the microorganisms in the biofilm were not able to sustain

growth.

Other studies published while the experiments in this thesis were being performed also provide

evidence that the main factor limiting DMS removal in biofiltration system is microbiological

and not physical. In two separate studies using biotrickling filters, Sercu et al. (2004; 2006) were

able to achieve DMS removal rates of an order of magnitude higher than observed in these

systems and, in the case of the 2006 study, they were able to achieve near 100% removal

efficiencies at a similar loading to the biofilters in this thesis. Similarly, Ho et al. (2007) and Shu



and Chen (2009) were able to achieve near 100% removal efficiencies of DMS in biofiltration

systems. In the case of Ho et al. (2007), the DMS loading was quite low and treated among a

mixture of odorous compounds while, in the case of Shu and Chen (2009), the DMS loading was

quite high. All this suggests that diffusion of DMS through the biofilm should not be the limiting

factor in the elimination of DMS in biofilters and, under appropriate conditions, DMS can be

removed quite effectively microbiologically.

Given that the evidence suggests that DMS elimination in biofilters faces a limitation of

microbial kinetics and not a limitation of diffusion, it was an initial goal of this thesis to be able

to describe the microbial community in biofilters treating DMS. An initial survey of the literature

suggested that, under the conditions the biofilters were being operated, that the likely bacteria

expected to be dominant DMS degraders would likely be Hyphomicrobium spp. and Thiobacillus

spp.. In using three different molecular ecology techniques to describe the microbial community

in these biofilters it was found that this was indeed the case. In using qPCR to estimate biomass

it was found that in the case of the biofilter treating DMS alone, Hyphomicrobium spp. and

Thiobacillus spp. accounted from approximately 10% and 4% of the bacterial community,

respectively. While in the case of the biofilter co-treating DMS and methanol, there was an

increase of approximately an order of magnitude in the 16S rDNA copies of Hyphomicrobium

spp. which accounted for approximately 21% of total bacterial copies while there was no

significant difference in the 16S rDNA copy numbers of Thiobacillus spp. between the two

reactors.

The microbial community structure of these biofilters is distinct from those in the other

biofiltration studies cited earlier. In the case of the Sercu et al. (2006) biotrickling filters, the

dominant bacterial species in the microbial community was Thiobacillus spp. which typically

accounted for 10% or more of bacterial 16S rDNA copies while those of Hyphomicrobium spp.

were less than 0.01%. In the case of the Ho et al. (2007) and Shu and Chen (2009), the dominant

species were a Pseudomonas sp. and a Microbacterium sp., respectively.

Clearly, different microbial community structures are capable of eliminating DMS in biofilters

and raises questions as to how these different microbial community structures form and what

conditions are required to maximize DMS removal. Important considerations in the development



of the microbial community in biofiltration systems include both the inoculum and the operating

conditions. In the case of both the Ho et al. (2007) and Shu and Chen (2009) studies, the

biofilters were inoculated with pure cultures of the desired microorganisms. In the Ho et al.,

(2007) study, an immobilized Pseudomonas sp. was used to treat a mixture of volatile sulphur

compounds. This required re-circulating a glucose solution to maintain the Pseudomonas sp.

which converted DMS to DMSO. In the case of the Shu and Chen (2009) study, the biofilter was

inoculated with Microbacterium sp. NTUT26. However, rather than converting DMS to DMSO

or sulphate, DMS was converted to a mixture of methyl mercaptan and hydrogen sulphide. Both

of these systems have practical limitations. In the case of the Ho et al. (2007) system, the use of

glucose as an additional carbon source reduces the economics of the system while the Shu and

Chen (2009) would require further downstream processing to treat the methyl mercaptan and

hydrogen sulphide. Unlike the other two studies, the Sercu et al. (2006) biotrickling filters were

inoculated with a sludge sample taken from a membrane bioreactor treating landfill leachate. In

terms of the inoculum, the Sercu et al. (2006) study is the most similar to this study which was

inoculated with activated sludge from a municipal wastewater treatment plant. The biotrickling

filters used in the Sercu et al. (2006) study treated DMS alone and fully converted DMS to

sulphate.

In terms of performance, the biofilter treating DMS alone in this thesis had a poor DMS removal

efficiency compared to the other three studies mentioned in this section. A significant difference

between the biofilter studied in this thesis and the other biofiltration studies mentioned was how

the pH of the biofilter bed was controlled. In this thesis, the pH was allowed to decrease from pH

7 to pH 5 before being neutralized using a dilute sodium hydroxide solution. In the case of the

other biofiltration studies the pH was controlled more tightly. In both the Ho et al. (2007) and the

Shu and Chen (2009) studies, the high DMS removal efficiencies were only achieved when the

pH was above pH 6 and decreased significantly when the pH was allowed to decrease below pH

6. In the Sercu et al. (2006) study, high DMS removal efficiencies were achieved at pH 7.

However, when the pH was decreased to pH 5 over the course of one day, three of the four

biotrickling filters maintained high DMS removal efficiencies. The biotrickling filter that saw its

DMS removal rate decrease by 50% was inoculated with a pure culture of Thiobacillus thioparus

Tk-m which is known to have low activity on DMS at pH 5 (Kanagawa and Kelly, 1986).



The fluctuations in the pH of the biofilter bed in this study led to the testing of this parameter in

the batch kinetic assays on the enrichment culture created from the biofilter co-treating DMS and

methanol. As discussed in Chapter 5, it was found that the pH of the medium has a strong effect

on the kinetics of Hyphomicrobium spp.. As the pH decreased from pH 7 to pH 5, the specific

growth rate of Hyphomicrobium spp. on DMS decreased by 85%, which has significant

implications on the performance of biofilters treating DMS given that Hyphomicrobium spp.

were a predominant DMS-degrading microorganism in these biofilters. As for the other

prominent DMS-degrading bacteria in biofilters treating DMS, it was shown that Thiobacillus

spp. were able to maintain their specific growth rate on DMS as the pH is reduced from pH 7 to

pH 5. This is consistent with what was observed in the Sercu et al. (2006) biotrickling filter

study but does not explain why Thiobacillus spp. grew so poorly on DMS in the biofilters in this

study. It has been shown that certain Thiobacillus spp., such as Thiobacillus thioparus Tk-m, do

not grow well on DMS at pH 5 (Kanagawa and Kelly, 1986). It is possible that the enrichment

culture is made up of different Thiobacillus spp. that have different pH optima for growth on

DMS and the fluctuating pH results in none of them growing particularly well. Another

possibility is that the Thiobacillus spp. in this reactor have high KS values that results in them

grower much slower at low DMS concentrations. However, Sercu et al. (2006) were able to get

Thiobacillus growth at similarly low concentrations.

While the pH management of the biofilters studied in this thesis led to conditions where

Hyphomicrobium spp. grew at low specific growth rates on DMS, Hyphomicrobium spp. were

still able to grow at high specific growth rates when methanol was present. As shown in Chapter

5, throughout the entire pH range to which bacteria in the biofilter bed were exposed

Hyphomicrobium spp. were able to grow at high growth rates. From the qPCR data presented in

Chapter 4, it can be seen that the addition of methanol led to the proliferation of

Hyphomicrobium spp. in biofilters co-treating DMS and methanol compared to the biofilter

treating DMS alone. Upon suspension of methanol addition, the DMS removal rate increased by

approximately an order of magnitude relative to the biofilter treating DMS alone, proportional to

the difference in Hyphomicrobium 16S rDNA copies between the two reactors, suggesting that

methanol addition does not inherently improve the kinetics of Hyphomicrobium spp. on DMS but

simply increases the abundance of Hyphomicrobium spp. in the reactor. In fact, the spike in the



DMS removal rate once methanol is removed suggests that methanol actively competes with

DMS as a substrate for Hyphomicrobium spp.. The net benefit of adding methanol is that, even

though it reduces the kinetics of Hyphomicrobium spp. on DMS, methanol addition results in the

proliferation of Hyphomicrobium spp., increasing the amount of Hyphomicrobium biomass in the

biofilter and creating a net increase in the DMS removal rate under the appropriate loadings.

The results of this thesis point to two significant conclusions about the operation of biofilters

treating DMS. First, they highlight the importance of pH control in order to maintain high DMS

removal rates in these systems. This is supported by other published studies which used tight pH

control to maintain high DMS removal rates in biofiltration systems (Sercu et al., 2006; Ho et al.,

2007; Shu and Chen, 2009). The results from Chapter 5 suggest this is especially true for

biofilters where Hyphomicrobium spp. form a significant fraction of the microbial community as

their specific growth rates on DMS decrease dramatically with the acidification of the media.

Evidence from the batch kinetic studies for Thiobacillus spp. suggest that these bacteria should

be able to proliferate in the conditions present in these biofilters. This is also supported by the

Sercu et al. (2006) biotrickling filter study where Thiobacillus spp. grew on DMS down to pH 5.

However, for some reason, Thiobacillus spp. did not proliferate in these biofilters and remains an

area worth investigating going forward.

The second significant conclusion worth highlighting is the role of methanol in increasing DMS

removal rates in biofilters. While methanol and DMS compete as substrates for Hyphomicrobium

spp., there can be a net increase in the DMS removal rates in biofilters as long as a critical

methanol loading is not exceeded for the given DMS loading. The net increase is a result of the

concentration of Hyphomicrobium biomass increasing by a larger factor than the specific growth

rate on DMS is reduced due to the competitive effect of methanol. While this phenomenon

should be applicable under any circumstances, it may be particularly advantageous in situations

where control of the pH of the biofilter bed is expensive. Also, while this thesis has demonstrated

how methanol addition can improve DMS removal rates in biofilters, methanol addition may also

improve removal rates of other substrates that Hyphomicrobium spp. may metabolize, such as

methyl mercaptan, and are worth investigating further.

Conclusions 110


7.0 CONCLUSIONS

1. In biofilters treating dimethyl sulphide (DMS) under mesophillic temperatures and

operating with a bed pH between pH 5 and pH 7, Hyphomicrobium spp. and Thiobacillus

spp. are the predominant DMS-degrading bacteria present in the microbial community.

2. While Chitinophaga spp. comprise a significant fraction of the microbial community in

these biofilters, they appear to have no direct role in DMS degradation.

3. Addition of methanol leads to a significant proliferation of Hyphomicrobium spp. in

biofilters co-treating DMS and methanol.

4. The maximum specific growth rates on DMS of the Hyphomicrobium spp. and

Thiobacillus spp. in these biofilters were 0.099 h-1

and 0.084 h-1

, respectively, in the

enrichment culture. These growth rates are significantly higher than the highest specific

growth rate reported on DMS to date of 0.05 h-1

.

5. The specific growth rate of Hyphomicrobium spp. on DMS is sensitive to pH changes in

the pH range of the biofilter bed. As the pH decreases from pH 7 to pH 5, the specific

growth rate of Hyphomicrobium spp. on DMS decreases from 0.099 h-1

to 0.015 h-1

.

6. The specific growth rate of Thiobacillus spp. on DMS is not sensitive to pH changes in

the pH range of the biofilter bed, showing no significant difference at the 95% confidence

level as the pH goes from pH 7 to pH 5.

7. The specific growth rate of Hyphomicrobium spp. on methanol is not sensitive to pH

changes in the pH range of the biofilter bed, showing no significant difference at the 95%

confidence level as the pH goes from pH 7 to pH 5.

8. Changing the nitrogen source from NH4+-N to NO3

--N has no significant impact on the

trends observed of the kinetic parameters of Hyphomicrobium spp. and Thiobacillus spp.

on DMS and methanol with respect to pH.

Conclusions 111


9. Addition of methanol leads to a proliferation of DMS-degrading Hyphomicrobium spp.

under pH conditions not conducive to growth on DMS which can lead to increased DMS

removal rates in biofiltration systems over a broad pH range.

Engineering Significance 112


8.0 ENGINEERING SIGNIFICANCE

Biofiltration of odorous waste gas streams often involves the treatment of gas mixtures. Low

removal rates of dimethyl sulphide (DMS) in biofiltration systems have limited the application of

this techology to treat waste gas streams that are relatively rich in DMS such as those in the kraft

pulping industries. Another common pollutant in waste gas streams at kraft pulp mills is

methanol and work by a previous student demonstrated that biofiltration of DMS with methanol

can lead to higher DMS removal rates in these systems. It was found that methanol helped

stabilize the performance of biofilters treating DMS and led to less volatility in pH decreases due

to the incomplete oxidation of DMS to sulphate and the inhibition of nitrification. It was further

shown that while methanol addition could result in a net increase in DMS removal rates,

methanol also competed with DMS as a substrate for microorganisms in the biofilter.

This thesis aimed to further explore this effect to further explain the mechanism behind increased

DMS removal rates in the presence of methanol and to understand why DMS removal rates are

so low in these systems in the case of DMS alone. It was found that methanol addition led to the

proliferation of Hyphomicrobium spp. in the biofilter which are capable of using both methanol

and DMS as a substrate. It was the higher abundance of Hyphomicrobium spp. in the biofilter co-

treating DMS and methanol that led to increased DMS removal rates in that system compared to

the biofilter treating DMS alone. Furthermore, it was shown that the growth kinetics of

Hyphomicrobium spp. decreased with pH for the pH range in which the biofilter was operated

while their growth kinetics on methanol were only minimally sensitive to decreases in pH in this

range. As a result, methanol addition to the biofilter co-treating DMS and methanol resulted in

the proliferation of Hyphomicrobium spp. on methanol which were also consuming DMS, albeit

at low rates. Even though the specific DMS consumption rate by Hyphomicrobium spp. in the

biofilter co-treating DMS and methanol is lower than the biofilter treating DMS alone (due to the

competitive effect of methanol) the DMS removal rate of the biofilter co-treating DMS and

methanol is higher due to a higher concentration of Hyphomicrobium biomass. Growth of

Hyphomicrobium spp. in the biofilter treating DMS alone is limited by the low specfic growth

rate on DMS which is effectively balanced out by the endogenous decay rate after only minimal

removal of DMS.



The engineering significance of these results suggest that DMS removal rates can be improved in

two different ways. First, pH can be controlled more tightly around pH 7 in the biofilter treating

DMS alone which should result in higher specific growth rates of Hyphomicrobium spp. on DMS

and increase the DMS removal rates of the system. A second option would be to co-treat DMS

with an appropriate amount of methanol. This would result in the proliferation of

Hyphomicrobium spp. in the system and increase DMS removal rates. This option would be

preferable in systems where pH control would be considered prohibitively expensive and where

methanol is readily available and inexpensive such as at kraft pulp mills.



9.0 RESEARCH RECOMMENDATIONS

At this point, future work in this project can go in several directions. In terms of improved

biofiltration performance, an investigation of the effect of methanol on other RSC emissions

similar to this study may be informative. While this has been done for methanol addition in

biofilters treating hydrogen sulphide (Sologar et al., 2002), there are no studies investigating the

effect of methanol addition on the removal of organic RSC emissions such as methyl mercaptan

and dimethyl disulphide. These organic RSC emssions can be degraded by Hyphomicrobium spp.

and may show similar increases in removal rates with methanol co-treatment as DMS. It would

also be of interest to test how an improved pH control strategy of the biofilter improves DMS

removal in these systems.

Another interesting research question is the mechanism behind the decline of the DMS removal

rate with pH. It was hypothesized in this thesis that the mechanism behind poor DMS removal

with pH was due to the kinetics of DMS monooxygenase or methyl mercaptan oxidase. This

could be explored through repeating the batch studies on a Hyphomicrobium spp. isolated from

the enrichment culture using methyl mercaptan and hydrogen sulphide. Furthermore, due to the

limited knowlege of DMS monooxygenase, it would be of interest to better characterize this

enzyme. Finally, due to the resilience of the enrichment culture to degrade DMS over a wide pH

range at high specific growth rates, it would be interesting to study this culture further to see if

the high growth rates are due to interactions among different bacteria in the culture.



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Appendix A 129


APPENDIX A: CALIBRATION CURVES

A.1 Gas chromatography calibration curves for DMS and methanol

The calibration of the GC for the measurement of DMS was achieved through the serial dilution

of 500 ppmv DMS standard (balance nitrogen) with air in 1.0 L Tedlar bags. The calibration

range of DMS was 0-100 ppmv at ambient temperature (~20ºC). Air was metered into the Tedlar

bags using a MKS type 247 mass flow controller and DMS was serial diluted using a gas-tight

syringe to extract DMS-containing air from one Tedlar bag and insert it into another Tedlar bag

with the appropriate quantity of air for the desired DMS concentration. A gas-tight syringe was

also used to inject 250 μL of DMS-containing air into the GC. For each data point, the average of

three samples was used to construct the standard curve which was fitted using a power regression

due to the second-order reaction in the pulse-flame photometric detector (Figure A-1).

y = 2.60x1.97

R² = 0.98

0

5000

10000

15000

20000

25000

30000

0 20 40 60 80 100 120

Pea

k A

rea

Gaseous DMS Concentration (ppmv)

Figure A-1 - Calibration curve for DMS quantification using gas chromatography.

Appendix A 130


The calibration of the GC for methanol measurement was achieved by creating methanol

standards in 250 mL bottles. The appropriate volume of liquid methanol was transferred to the

bottle via pipette and mixed with 40 mL of mineral media. After reaching equilibrium, 250 μL of

gas was extracted from the headspace using a gas-tight syringe and injected into the GC. The

average of three injections for each point was used to construct a standard curve and fitted with a

linear regression (Figure A-2).

y = 41400x + 519

R² = 0.993

0

10000

20000

30000

40000

50000

60000

70000

80000

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18

Pea

k A

rea

Aqueous Methanol Concentration (M)

Figure A-2 - Calibration curve for methanol quantification using gas chromatography.

A.2 Calibration curves for qPCR of 16S rDNA

Absolute quantification of 16S rDNA was calibrated via the construction of a standard curve

where a dilution series of a known quantity of 16S rRNA gene copies was created. To

accomplish this, plasmids were extracted from transformed E. coli gronw in LB broth and

quantified spectrophotometrically at 260 nm with gene copy number being calculated based on

plasmid and insert size. Due to the exponential growth nature of the PCR reaction the threshold

Appendix A 131


cycle of each standard was plotted against the logarithm of the 16S rDNA copy number of the

standard yielding a straight line with a negative slope that is related to the efficiency of the PCR

reaction. Ideally, the slope of the standard curve should be between -3.3 and -3.9, which

corresponds to a PCR efficiency of 90-100%.

For standardizing the qPCR assay to measure total bacterial 16S rDNA copies, a plasmid

containing a copy of the 16S rDNA from Hyphomicrobium spp. was used. The choice of

sequence can have an effect on the quantification but choosing a species that forms a significant

part of the community as a standard can minimize these variations (Nadkarni et al., 2002).

y = -3.73x + 41.8

R² = 0.999

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10

Th

resh

old

Cy

cle

Log copy number

Figure A.3 – Calibration curve of a bacteria specific qPCR assay using a plasmid containing a

16S rRNA gene from a Hyphomicrobium spp..

For the standardization of the Hyphomicrobium qPCR assay, the standard used was a plasmid

containing a 16S rRNA gene copy from a Hyphomicrobium spp. that clustered phylogenetically

among the Hyphomicrobium denitrificans clade of the genus as these 16S rDNA copies were

more abundant than those that clustered phylogenetically near Hyphomicrobium zavarzinii clade.

Appendix A 132


y = -3.41x + 40.4

R² = 0.997

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10

Th

resh

old

Cy

cle

Log Copy Number

Figure A.4 – Calibration curve of a Hyphomicrobium specific qPCR assay using a plasmid

containing a 16S rRNA gene from a Hyphomicrobium spp..

For calibrating the Thiobacillus qPCR assay, a plasmid containing a 16S rRNA gene copy of a

Thiobacillus spp. was used. The Thiobacillus 16S rDNA copy used for the calibration clustered

phylogenetically near to Thiobacillus thioparus, as did all Thiobacillus clones in both clone

libraries. The calibration curve can be seen in Figure A.5.

Unlike the calibrations for the bacteria and Hyphomicrobium assays, it is possible to determine

the melting temperature of the PCR product of the Thiobacillus assay which provides a bit of

extra confidence on specificity for SYBR Green I assays (as opposed to the binding and

hydrolysis of the Förster Resonance Energy Transfer (FRET) probe in a Taq nuclease assay). In

the case of the Thiobacillus assay, the melting temperature of the PCR product was

approximately 89ºC and easily distinguished from the primer dimer that formed in the no

template control which formed a product that had a melting temperature of approximately 70ºC.

Appendix A 133


y = -3.66x + 37.3

R² = 0.999

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9

Th

resh

old

Cy

cle

Log Copy Number

Figure A.5 – Calibration curve of a Thiobacillus specific qPCR assay using a plasmid containing

a 16S rRNA gene from a Thiobacillus spp..

For calibrating the Chitinophaga qPCR assay, a plasmid containing a 16S rRNA gene copy of a

Chitinophaga spp. was used. As mentioned in Chapter 4, none of the 16S rDNA copies actually

clustered phylogenetically within the Chitinophaga genus but this genus was the closest related

genus. As the group of clones that were identified as Chitinophaga in the thesis were more

phylogenetically diverse than either the Hyphomicrobium or Thiobacillus groupings., a

Chitinophaga 16S rDNA copy used for the calibration clustered phylogenetically among the

largest group of identified Chitinophaga clones. The calibration curve can be seen in Figure A.6.

Similar to the Thiobacillus assay, because the Chitinophaga assay is based on the SYBR Green I

method this allows for the determination of the melting temperature of the PCR product. As was

the case with the Thiobacillus assay, the melting temperature of the PCR product was

approximately 89ºC and easily distinguished from the primer dimer that formed in the no

template control which formed a product that had a melting temperature of approximately 70ºC.

Appendix A 134


y = -3.90x + 42.8

R² = 0.990

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9

Th

resh

old

Cy

cle

Log Copy Number

Figure A.6 – Calibration curve of a Chitinophaga specific qPCR assay using a plasmid

containing a 16S rRNA gene from a Chitinophaga spp..

A.3 Ion chromatography calibration curve for sulphate quantification

The calibration of the IC for the measurement of methanol was achieved through the serial

dilution of a filter sterilized 50 mM Na2SO4 solution with ddH2O air. The calibration range of

sulphate was 0-0.5 g L-1

SO42-

at ambient temperature (~20ºC). For each data point, the average

of two samples was used to construct the standard curve which was fitted using a linear

correlation (Figure A-7).

Appendix A 135


y = 4E+09x + 2E+08

R² = 0.99

0.E+00

5.E+08

1.E+09

2.E+09

2.E+09

3.E+09

0 0.1 0.2 0.3 0.4 0.5 0.6

Pea

k A

rea

Aqueous Sulphate Concentration (g/L)

Figure A.7 – Calibration curve for sulphate quantification using ion chromatography with

Na2SO4 as a standard.

Appendix B 136


APPENDIX B: CLONE LIBRARY SEQUENCE DATA

B.1 16S rDNA sequences obtained from enrichment culture

>ENR01 FJ536909 [organism=uncultured Caulobacter sp. ENR01]

AACGAACGCTGGCGGCATGCCTAACACATGCAAGTCGAACGAGACCTTCGGGTCTAGTGGCGCACGGGTGCGTAACGCGTGGGA

ATCTGCCCTTGGGTTCGGAATAACTCGCCGAAAGGCGTGCTAATACCGGATGATGTCGTAAGACCAAAGATTTATCGCCCAGGGA

TGAGCCCGCGTAAGATTAGCTAGTTGGTGAGGTAAAGGCTCACCAAGGCGACGATCTTTAGCTGGTCTGAGAGGATGATCAGCCA

CACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCAA

TGCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTACCCGGGATGATAATGACAGTACCGGGAGAATAAGCTCCGGC

TAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGAGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGCTTT

GTAAGTCAGAGGTGAAAGCCTGGAGCTCAACTCCAGAACTGCCTTTGAGACTGCATCGCTTGAATCCAGGAGAGGTGAGTGGAAT

TCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAGGCGGCTCACTGGACTGGTATTGACGCTGAGGT

GCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGATAACTAGCTGTCCGGGCACTTGGT

GCTTGGGTGGCGCAGCTAACGCATTAAGTTATCCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGGCC

TGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGCAGAACCTTACCAGCGTTTGACATGTCCGGACGATTTCCAGAG

ATGGATCTCTTCCCTTCGGGGACTGGAACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGC

AACGAGCGCAACCCTCGCCTTTAGTTACCATCATTTAGTTGGGGACTCTAAAGGAACCGCCGGTGATAAGCCGGAGGAAGGTGGG

GATGACGTCAAGTCCTCATGGCCCTTACGCGCTGGGCTACACACGTGCTACAATGGCGGTGACAGTGGGCAGCAAACTCGCGAGA

GTGCGCTAATCTCCAAAAGCCGTCTCAGTTCGGATTGTTCTCTGCAACTCGAGAGCATGAAGGCGGAATCGCTAGTAATCGCGGA

TCAGCATGCCGCGGTGAATACGTTCCCAGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGGTTCACCCGAAGGCGTTGC

GCTAACTTGCAAGAGAGGCAGGCGACCACGGTGGGCTTAGCGACTGGGGTG

>ENR02 FJ536910 [organism=uncultured Aminobacter sp. ENR02]

AACGAACGCTGGCGGCAGGCTTAACACATGCAAGTCGAGCGCCCCGCAAGGGGAGCGGCAGACGGGTGAGTAACGCGTGGGAA

TCTACCCATCTCTACGGAATAACTCAGGGAAACTTGTGCTAATACCGTATACGCCCTTCGGGGGAAAGATTTATCGGAGATGGAT

GAGCCCGCGTTGGATTAGCTAGTTGGTGGGGTAATGGCCTACCAAGGCGACGATCCATAGCTGGTCTGAGAGGATGATCAGCCAC

ACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCCAT

GCCGCGTGAGTGATGAAGGCCCTAGGGTTGTAAAGCTCTTTCACCGGTGAAGATAATGACGGTAACCGGAGAAGAAGCCCCGGC

TAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGATTG

TTAAGTTAGGGGTGAAATCCCAGGGCTCAACCCTGGAACTGCCTCTAATACTGGCAATCTCGAGTCCGAGAGAGGTGAGTGGAAT

TCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTCACTGGCTCGGTACTGACGCTGAGGT

GCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACTATGAGAGCTAGCCGTCGGCAAGTTTACTT

GTCGGTGGCGCAGCTAACGCATTAAGCTCTCCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGGCCC

GCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGCAGAACCTTACCAGCCCTTGACATCCCGGTCGCGGTTTCCAGAG

ATGGATCCCTTCAGTTCGGCTGGACCGGTGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCG

CAACGAGCGCAACCTTCGCCCTTAGTTGCCATCATTAAGTTGGGCACTCTAAGGGGACTGCCGGTGATAAGCCGCGAGGAAGGTG

AAGATGACGTCAAGTCCTCATGGCCCTTACGGGCTGGGCTACACACGTGCTACAATGGTGGTGACAGTGGGCAGCGAGACCGCG

AGGTCGAGCTAATCTCCAAAAGCCATCTCAGTTCGGATTGCACTCTGCAACTCGAGTGCATGAAGTTGGAATCGCTAGTAATCGC

AGATCAGCATGCTGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTTTTACCCGAAGGCGC

TGTGCTAACCGCAAGGAGGCAGGCGACCACGGTAGGGTCAGCGACTGGGGTG

>ENR03 FJ536911 [organism=uncultured Sphingobacteriales bacterium ENR03]

GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGAGGGGCAGCGCAGTGTAGCAATACATGGGCGGCGACCGGCAAACGGG

TGCGGAACACGTACAGAACCTTCCTTCGAGCGGGGAATAGCCCAGAGAAATTTGGATTAATACCCCATAGTATATTGAAGTGGCA

TCATTTTAATATTAAAGATTTATCACTTGAAGATGGCTGTGCGTCTGATTAGGTAGTTGGCGGGGTAACGGCCCACCAAGCCTACG

ATCAGTAACTGGTGTGAGAGCACGACCAGTCACACGGGCACTGAGACACGGGCCCGACTCCTACGGGAGGCAGCAGTAAGGAAT

ATTGGTCAATGGACGCAAGTCTGAACCAGCCATGCCGCGTGGAGGATGAAGGTCCTCTGGATTGTAAACTTCTTTTATCTGGGAC

GAAAAAAGGGTTTTCTAACTCGTCTGACGGTACCAGATGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAG

GGTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGCGGGCATGTAAGTCCGTGGTGAAATCTCCGAGCTTAACTC

GGAAACTGCCATGGGTACTGTGTGTCTTGAATGTTGTGGAGGTGAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGAC

ATAGAACACCAATTGCGAAGGCAGCTCACTACACAAATATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGATA

CCCTGGTAGTCCACGCCCTAAACGATGGATACTCGACATACGCGATACACAGTGTGTGTCTGAGCGAAAGCATTAAGTATCCCAC

CTGGGAAGTACGACCGCAAGGTTGAAACTCAAAGGAATTGACGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATG

ATACGCGAGGAACCTTACCTGGGCTAGAATGCTGGGGGACCGTGGGTGAAAGCTCACTTTGTAGCAATACACCGCCAGTAAGGTG

CTGCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCTTTAGTTGCCAACAGGT

CAAGCTGGGAACTCTAAAGAAACTGCCGTCGTAAGACGCGAGGAAGGAGGGGATGATGTCAAGTCATCATGGCCTTTATGCCCA

GGGCTACACACGTGCTACAATGGGGCGTACAAAGGGCTGCCACTTAGTGATAAGGAGCGAATCCCAAAAAACGCCTCTCAGTTC

GAATCGGAGTCTGCAACTCGACTCCGTGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGA

Appendix B 137


CCTTGTACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGTCGATAACCGCAAGGAGTCGCCTAGGGTAAAACTAGT

AACTGGGGCT

>ENR04 FJ536912 [organism=uncultured Novosphingobium sp. ENR04]

AACGAACGCTGGCGGCATGCCTAACACATGCAAGTCGAACGAACCCTTCGGGGTTAGTGGCGCACGGGTGCGTAACGCGTGGGA

ATCTGCCCTTTGCTTCGGAATAACTCAGGGAAACTTGTGCTAATACCGGATGATGTCTTCGGACCAAAGATTTATCGGCAAGGGAT

GAGCCCGCGTAGGATTAGGTAGTTGGTGGGGTAAAGGCCTACCAAGCCGACGATCCTTAGCTGGTCTGAGAGGATGATCAGCCAC

ACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCAAT

GCCGCGTGAGTGATGAAGGCCCTCGGGTCGTAAAGCTCTTTTACCAGGGATGATAATGACAGTACCTGGAGAATAAGCTCCGGCT

AACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGAGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGCTACT

CAAGTCAGAGGTGAAAGCCCGGGGCTCAACCCCGGAACTGCCTTTGAAACTAGGTGGCTAGAATCTTGGAGAGGCGAGTGGAAT

TCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAGGCGACTCGCTGGACAAGTATTGACGCTGAGG

TGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGATAACTAGCTGTCCGGGTACTTGGT

ACTTGGGTGGCGCAGCTAACGCATTAAGTTATCCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGGCC

TGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGCAGAACCTTACCAGCCTTTGACATCCCGCGCTACTTCCAGAGA

TGGAAGGTTCCCTTCGGGGACGCGGTGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCA

ACGAGCGCAACCCTCGTCCTTAGTTGCCATCATTTAGTTGGGCACTCTAAGGAAACCGCCGGTGATAAGCCGGAGGAAGGTGGGG

ATGACGTCAAGTCCTCATGGCCCTTACAGGCTGGGCTACACACGTGCTACAATGGCGGTGACAGTGGGCAGCAAGCACGCGAGTG

TGAGCTAATCTCCAAAAGCCGTCTCAGTTCGGATTGTTCTCTGCAACTCGAGAGCATGAAGGCGGAATCGCTAGTAATCGCGGAT

CAGCATGCCGCGGTGAATACGTTCCCAGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTTTCACCCGAAGGCAGTGCG

CTAACCGCAAGGAGGCAGCTGACCACGGTGGGATCAGCGACTGGGGTG

>ENR05 FJ536913 [organism=uncultured Hyphomicrobium sp. ENR05]

AACGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAACGCTGTAGCAATACAGAGTGGCAGACGGGTGAGTAACACGTGGGA

ATCTTCCTATCGGTACGGAATAGCTCAGGGAAACTTGGGGTAATACCGCATACGCCCTTCGGGGGAAAGATTTATCGCCGATAGA

TGAGCCCGCGTCTGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCAGTAGCTGGTCTGAGAGGATGACCAGCCA

CACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCCA

TGCCGCGTGAGTGACGAAGGTCTTCGGATTGTAAAGCTCTTTTGGCGGGGACGATAATGACGGTACCCGCAGAATAAGCCCCGGC

TAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGGCTAGCGTTGCTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGATTG

CTAAGTCAGGGGTGAAATCCCGGGGCTCAACCTCGGAACTGCCTTTGATACTGACAGTCTTGAGTCCGGAAGAGGTGAGTGGAAT

TCCTAGTGTAGAGGTGAAATTCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTCACTGGTCCGGTACTGACGCTGAGGT

GCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACTATGGATGCTAGCCGTCGGCAAGCTTGCTT

GTCGGTGGCGCAGCTAACGCTTTAAGCATCCCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAAGGAATTGACGGGGGCCC

GCACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCAGCTCTTGACATTTACGGACCGTTTCCAGAGA

TGGATTCATCCTAGCAATAGGCCGTAGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCA

ACGAGCGCAACCCTCGCCATTAGTTGCCATCATTTAGTTGGGCACTCTAGTGGGACTGCCGGTGATAAGCCGGAGGAAGGTGGGG

ATGACGTCAAGTCATCATGGCCCTTACGGGCTGGGCTACACACGTGCTACAATGGCGGTGACAATGGGCAGCAACACAGCAATGT

GAAGCTAATCTCAAAAAGCCGTCTCAGTTCGGATTGGGCTCTGCAACTCGAGCCCATGAAGTTGGAATCGCTAGTAATCGCGCAT

CAGCATGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTTTTACCCGAAGACGGTGCG

CTAACCGCAAGGAGGCAGCCGGCCACGGTAAGGTCAGCGACTGGGGTG

>ENR06 FJ536941 [organism=uncultured Thiobacillus sp. ENR06]

ATTGAACGCTGGCGGAATGCTTTACACATGCAAGTCGAACGGCAGCACGGGAGCTTGCTCCTGGTGGCGAGTGGCGAACGGGTG

AGTAATGCGTCGGAACGTACCGAGTAATGGGGGATAACGCAGCGAAAGCTGTGCTAATACCGCATACGCCCTGAGGGGGAAAGT

GGGGGATCGCAAGACCTCACGTTATTCGAGCGGCCGACGTCTGATTAGCTAGTTGGTGGGGTAATGGCCTACCAAGGCGACGATC

AGTAGCGGGTCTGAGAGGATGATCCGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATTTT

GGACAATGGGGGCAACCCTGATCCAGCCATTCCGCGTGAGTGAAGAAGGCCTTCGGGTTGTAAAGCTCTTTCAGCTGGAACGAAA

CGGTACAGGTTAATACTCTGTGCTAATGACGGTACCGGCAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGT

AGGGTGGGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGATTGTTAAGCAAGATGTGAAATCCCCGGGCTTAA

CCTGGGAATGGCATTTTGAACTGGCAGTCTAGAGTGCGTCAGAGGGGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGAT

GTGGAGGAACACCAATGGCGAAGGCAGCTCCCTGGGATGACACTGACGCTCATGTACGAAAGCGTGGGTAGCAAACAGGATTAG

ATACCCTGGTAGTCCACGCCCTAAACGATGTCAACTGGTTGTTGGGGGAGTGAAATCCCTTAGTAACGAAGCTAACGCGTGAAGT

TGACCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGATTAA

TTCGATGCAACGCGAAAAACCTTACCTACCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCCGAAAGGGAATCGGA

ACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCATTAGTTG

CTACGCAAGGGCACTCTAATGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATAACGTCAAGTCCTCATGGCCCTTATGGGT

AGGGCTTCACACGTCATACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCCAATCCCAGAAAGCCGATCGTAGTC

CGGATTGTTCTCTGCAACTCGAGAGCATGAAGTCGGAATCGCTAGTAATCGCGGATCAGCATGTCGCGGTGAATACGTTCCCGGG

TCTTGTACACACCGCCCGTCACACCATGGGAGTGGAATCTGGCAGAAGTAGGTAGCCTAACCGCAAGGGGGGCGCTTACCACGCT

GGGTTTCATGACTGGGGTG


Appendix B 138








AGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGATTGTTAAGCAAGATGTGAAATCCCCGGGCTTAAC

CTGGGAATGGCATTTTGAACTGGCAGTCTAGAGTGCGTCAGAGGGGGGGTGGAATACCACGTGTAGCAGTGAAATGCGTAGAGA

TGTGGAGGAACACCAATGGCGAAGGCAGCCCCCTGGGATGACACTGACGCTCATGTACGAAAGCGTGGGTAGCAAACAGGATTA

GATACCCTGGTAGTCCACGCCCTAAACGATGTCAACTGGTTGTTGGGGGAGTGAAATCCCTTAGTAACGAAGCTAACGCGTGAAG

TTGACCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGATTA

ATTCGATGCAACGCGAAAAACCTTACCTACCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCCGAAAGGGAATCGG

AACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCATTAGTT

GCTACGCAAGGGCACTCTAATGAGACTGCCGGTGACAAACCGGAGGAACGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGG

GTAGGGCTTCACACGTCATACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCCAATCCCAGAAAGCCGATCGTAG

TCCGGATTGTTCTCTGCAACTCGAGAGCATGAAGTCGGAATCGCTAGTAATCGCGGATCAGCATGTCGCGGTGAATACGTTCCCG

GGTCTTGTACACACCGCCCGTCACACCATGGGAGTGGAATCTGGCAGAAGTAGGTAGCCTAACCGCAAGGGGGGCGCTTACCAC

GCTGGGTTTCATGACTGGGGTG

>ENR08 FJ536915 [organism=uncultured Achromobacter sp. ENR08]

ATTGAACGCTAGCGGGATGCCTTACACATGCAAGTCGAACGGCAGCACGGACTTCGGTCTGGTGGCGAGTGGCGAACGGGTGAG

TAATGTATCGGAACGTGCCCAGTAGCGGGGGATAACTACGCGAAAGCGTAGCTAATACCGCATACGCCCTACGGGGGAAAGCAG

GGGATCGCAAGACCTTGCACTATTGGAGCGGCCGATATCGGATTAGCTAGTTGGTGGGGTAACGGCTCACCAAGGCGACGATCCG

TAGCTGGTTTGAGAGGACGACCGGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATTTTGG

ACAATGGGGGAAACCCTGATCCAGCCATCCCGCGTGTGCGATGAAGGCCTTCGGGTTGTAAAGCACTTTTGGCAGGAAAGAAACG

TCATGGGTTAATACCCCGTGAAACTGACGGTACCTGCAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAG

GGTGCAAGCGCTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCGGAAAGAAAGATGTGAAATCCCAGAGCTTAACT

TTGGAACTGCATTTTTAACTACCGGGCTAGAGTGTGTCAGAGGGAGGTGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGC

GGAGGAACACCGATGGCGAAGGCAGCCTCCTGGGATAACACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGAT

ACCCTGGTAGTCCACGCCCTAAACGATGTCAACTAGCTGTTGGGGTCTTCGGACCTTGGTAGCGCAGCTAACGCGTGAAGTTGAC

CGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGATTAATTCG

ATGCAACGCGAAAAACCTTACCTACCCTTGACATGTCTGGAATTCCGAAGAGATTTGGAAGTGCTCGCAAGAGAACCGGAACACA

GGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCAGTAGTTGCTACG

AAAGGGCACTCTAATGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGTAGGG

CTTCACACGTCATACAATGGTCGGGACAGAGGGTCGCCAACCCGCGAGGGGGAGCCAATCCCAGAAACCCGATCGTAGTCCGGA

TCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGCGGATCAGAATGTCGCGGTGAATACGTTCCCGGGTCTT

GTACACACCGCCCGTCACACCATGGGAGCGGGTCTCGCCAGAAGTGGGTAGCCTAACCGCAAGGAGGGCGCTTACCACGGCGGG

GTTCGTGACTGGGGTG













ATACGCGAGGAACCTTACCTGGGCTAGAATGCTGGGGAACCGTGGGTGAAAGCTCACTTTGTAGCAATACACCGCCAGTAAGGTG






AACTGGGGCT





Appendix B 139






CTGGGAATGGCATTTTGAACTGGCAGTCTAGAGTGCGTCAGAGGGGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATG

TGGAGGAACACCAATGGCGAAGGCAGCCCCCTGGGATGACACTGACGCTCATGTACGAAAGCGTGGGTAGCAAACAGGATTAGA

TACCCTGGTAGTCCACGCCCTAAACGATGTCAACTGGTTGTTGGGGGAGTGAAATCCCTTAGTAACGAAGCTAACGCGTGAAGTT

GACCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGATTAAT

TCGATGCAACGCGAAAAACCTTACCTACCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCCGAAAGGGAATCGGAA

CACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCATTAGTTGC

TACGCAAGGGCACTCTAATGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGT




GGGTTTCATGACTGGGGTG

>ENR11 FJ536918 [organism=uncultured Sphingomonas sp. ENR11]

AACGAACGCTGGCGGCATGCCTAACACATGCAAGTCGAACGAGATCTTCGGGTCTAGTGGCGCACGGGTGCGTAACGCGTGGGA

ATCTGCCCTTTGGTTCGGAATAACAGTTGGAAACGACTGCTAATACCGGATGATGACGAAAGTCCAAAGATTTATCGCCAGAGGA

TGAGCCCGCGTAGGATTAGCTAGTTGGTGTGGTAAAGGCGCACCAAGGCGACGATCCTTAGCTGGTCTGAGAGGATGATCAGCCA

CACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCAA

TGCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTACCCGGGATGATAATGACAGTACCGGGAGAATAAGCTCCGGC

TAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGAGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGCTTT

GTAAGTTAGAGGTGAAAGCCTGGAGCTCAACTCCAGAACTGCCTTTAAGACTGCATCGCTTGAATCCAGGAGAGGTGAGTGGAAT

TCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAGGCGGCTCACTGGACTGGTATTGACGCTGAGGT

GCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTGCACGCCGTAAACGATGATAACTAGCTGTCCGGGGACTTGGT

CTTTGGGTGGCGCAGCTAACGCATTAAGTTATCCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCC

TGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGCAGAACCTTACCAGCGTTTGACATGTCCGGACGATTTCCAGAG

ATGGATCTTTTCCCTTCGGGGACTGGAACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGC

AACGAGCGCAACCCTCGCCTTTAGTTACCATCATTCAGTCGGGGACTCTAAAGGAACCGCCGGTGATAAGCCGGAGGAAGGTGG

GGATGACGTCAAGTCCTCATGGCCCTTACGCGCTGGGCTACACACGTGCTACAATGGCGGTGACAGTGGGCAGCAAACTCGCGAG

AGTGCGCTAATCTCCAAAAGCCGTCTCAGTTCGGATTGTTCTCTGCAACTCGAGAGCATGAAGGCGGAATCGCTAGTAATCGCGG

ATCAGCATGCCGCGGTGAATACGTTCCCAGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGGTTCACCCGAAGGCGTTG

CGCTAACTCGCAAGAGAGGCAGGCGACCACGGTGGGCTTAGCGACTGGGGTG





AGTAGCGGGTCTGAGAGGATGATCCGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGGATTTT




CTGGGAATGGCATTTTGAACTGGCAGTCTAGAGTGCGTCAGAGGGGGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGAT

GTGGAGGAACACCAATGGCGAAGGCAGCCCCCTGGGATGACACTGACGCTCATGTACGAAAGCGTGGGTAGCAAACAGGATTAG



TTCGATGCAACGCGAAAAACCTTACCTACCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCCGAAAGGGAATCGGA

ACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCATTAGTTG

CTGCGCAAGGGCACTCTAATGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGT




GGGCTTCATGACTGGGTG

>ENR13 FJ536920 [organism=uncultured Microbacterium sp. ENR13]

GATGAACGCTGGCGGCGTGCTTAACACATGCAAGTCGAACGATGAACCTGGGTGCTTGCATCTGGGGGATTAGTGGCGAACGGGT

GAGTAACACGTGAGCAACCTGCCCCTGACTCTGGGATAAGCGCTGGAAACGGCGTCTAATACTGGATATGTCCTATCACCGCATG

GTGTGTGGGTGGAAAGATTTTTCGGTTGGGGATGGGCTCGCGGCCTATCAGCTTGTTGGTGAGGTAATGGCTCACCAAGGCGTCG

ACGGGTAGCCGGCCTGAGAGGGTGACCGGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAAT

ATTGCACAATGGGCGGAAGCCTGATGCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTTTTAGCAGGGAAG

AAGCGTGAGTGACGGTACCTGCAGAAAAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTAT

CCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTTTGTCGCGTCTGCTGTGAAATCCCGAGGCTCAACTTCGGGCTTGCAGTGG

Appendix B 140


GTACGGGCAGACTAGAGTGCGGTAGGGGAGATTGGAATTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAGGAACACCGAT

GGCGAAAGCAGATCTCTGGGCCGTAACTGACGCTGAGGAGCGAAAGGGTGGGGAGCAAACAGGCTTAGATACCCTGGTAGTCCA

CCCCGTAAACGTTGGGAACTAGTTGTGGGGTCCTTTCCACGGATTCCGTGACGCAGCTAACGCATTAAGTTCCCCGCCTGGGGAGT

ACGGCCGCAAGGCTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGCGGAGCATGCGGATTAATTCGATGCAACGCGAA

GAACCTTACCAAGGCTTGACATACACCAGAACACCCTGGAAACAGGGGACTCTTTGGACACTGGTGAACAGGTGGTGCATGGTTG

TCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTCGTTCTATGTTGCCAGCACGTAATGGTGGGAA

CTCATGGGATACTGCCGGGGTCAACTCGGAGGAAGGTGAGGATGACGTCAAATCATCATGCCCCTTATGTCTTGGGCTTCACGCA

TGCTACAATGGCCGGTACAATGGGCTGCGATACCGTAAGGTGGAGCGAATCCCAAAAAGCCGGTCCCAGTTCGGATTGAGGTCTG

CAACTCGACCTCATGAAGTCGGAGTCGCTAGTAATCGCAGATCAGCAACGCCGCGGTGAATACGTTCCCGGGTCTTGTACACACC

GCCCGTCAAGTCATGAAAGTCGGTAACACCTGAAGCCGGTGGCCTAACCCTTGTGGAGGGAGCCGTCGAAGGTGGGATTGGTAAT

TAGGACT




TGAGCCCGCGTCTGATTAGCTAGTTGGTGAGGTAATGGCACACCAAGGCGACGATCAGTAGCTGGTCTGAGAGGATGACCAGCCA


































AACGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAACGCCCCGCAAGGGGAGTGGCAGACGGGTGAGTAACACGTGGGAA

CCTTCCCTATAGTACGGAATAGCCCAGGGAAACTTGGAGTAATACCGTATACGCCCGAAAGGGGAAAGATTTATCGCCGATAGAT

GAGCCCGCGTCTGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCAGTAGCTGGTCTGAGAGGATGACCAGCCAC


GCCGCGTGAGTGACGAAGGTCTTCGGATTGTAAAGCTCTTTTGGCGGGGACGATAATGACGGTACCCGCAGAATAAGCCCCGGCT

AACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGATTGC

TAAGTCAGGGGTGAAATCCCGGGGCTCAACCTCGGAACTGCCTTTGATACTGACAGTCTTGAGTCCGGAAGAGGTGAGTGGAATT

CCTAGTGTAGAGGTGAAATTCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTCACTGGTCCGGTACTGACGCTGGGGTG

CGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACTATGGATGCTAGCCGTCGGCAAGCTTGCTTG

TCGGTGGCGCAGCTAACGCTTTAAGCATCCCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAAGGAATTGACGGGGGCCCG

CACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCAGCTCTTGACATTTACGGACCGTTTCCAGAGAT

GGATTCATCCTAGCAATAGGCCGTAGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCA

Appendix B 141





CAGCATGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTTTTACCCAAAGACGGTGCG




TGCGGAACACGTACACAACCTTCCTTTAAGCGGGGAATAGCCCGGGGAAACCCGGATTAATACCCCATAGTACGTCGGAGAGGC

ATCTCTCTGATTTTAAAGATTTATCACTTAAAGATGGGTGTGCGGCTGATTAGGTAGTTGGCGGGGTAACGGCCCACCAAGCCTAC

GATCAGTAACTGGTGTGAGAGCACGACCAGTCACACGGGCACTGAGACACGGGCCCGACTCCTACGGGAGGCAGCAGTAAGGAA

TATTGGTCAATGGACGAAAGTCTGAACCAGCCATGCCGCGTGGAGGATGAAGGTCCTCTGGATTGTAAACTTCTTTTATATGGGA

CGAAAAAGGGACTTTCTAGTTCAACTGACGGTACCATATGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGA

GGGTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGTGGCTTGGTAAGTCAGTGGTGAAATCTCCGAGCTTAACT

TGGAAACTGCCATTGATACTATCAGTCTTGAATACCGTGGAGGTCAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGA

CATAGAACACCAATTGCGAAGGCAGCTGGCTACACGAATATTGACACTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGAT

ACCCTGGTAGTCCACGCCCTAAACTATGGATACTCGACATACGCGATACACTGTGTGTGTCTGAGCGAAAGCATTAAGTATCCCA

CCTGGGAAGTACGATCGCAAGATTGAAACTCAAAGGAATTGGCGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAT

GATACGCGAGGAACCTTACCTGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGCAATACACTGCCAGTAAGGT

GCTGCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCCATCACTAGTTGCCATCAG

GTAACGCTGGGAACTCTAGTGAAACTGCCGTCGTAAGACGTGAGGAAGGAGGGGATGATGTCAAGTCATCATGGCCTTTATGCCC

AGGGCTACACACGTGCTACAATGGGGAGGACAAAGAGCTGCCACTTAGTGATAAGGAGCTAATCTCAAAAACCTCCTCTCAGTTC

AGATTGGAGTCTGCAACTCGACTCCATGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGA

CCTTGCACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGTCGGTAACCGCAAGGAGCCGCCTAGGGTAAAACTAGT

GACTGGGGCT


GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGAGGGGCAGCGCAGTGTAGCAATACATGGGCGGCGACCAGCAAACGGG




ATTGGTCAATGGACGCAAGTTTGAACCAGCCATGCCGCGTGGAGGATGAAGGTCCTCTGGATTGTAAACTTCTTTTATCTGGGACG

AAAAAAGGGTTTTCTAACTCGTCTGACGGTACCAGATGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGG

GTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGCGGGCATGTAAGTCCGTGGTGAAATCTCCGAGCTTAACTCG

GAAACTGCCATGGGTACTGTGTGTCTTGAATGTTGTGGAGGTGAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGACA

TAGAACACCAATTGCGAAGGCAGCTCACTACACAAATATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGATAC

CCTGGTAGTCCACGCCCTAAACGATGGATACTCGACATACGCGATACACAGTGTGTGTCTGAGCGAAAGCATTAAGTATCCCACC

TGGGAAGTACGACCGCAAGGTTGAAACTCAAAGGAATTGACGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGA

TACGCGAGGAACCTTACCTGGGCTAGAATGCTGGGGGACCGTGGGTGAAAGCTCACTTTGTAGCAATACACCGCCAGTAAGGTGC

TGCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCTTTAGTTGCCAACAGGTC

AAGCTGGGAACTCTAAAGAAACTGCCGTCGTAAGACGCGAGGAAGGAGGGGATGATGTCAAGTCATCATGGCCTTTATGCCCAG

GGCTACACACGTGCTACAATGGGGCGTACAAAGGGCTGCCACTTAGTGATAAGGAGCGAATCCCAAAAAACGCCTCTCAGTTCG

AATCGGAGTCTGCAACTCGACTCCGTGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGAC

CTTGTACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGTCGATAACCGCAAGGAGTCGCCTAGGGTAAAACTAGTA

ACTGGGGCT











CCCTGGTAGTCCACGCCCTAAACGATGGATACTCGACATACGCGATACACAGTGCGTGTCTGAGCGAAAGCATTAAGTATCCCAC







Appendix B 142



AACTGGGGCT










GCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACTATGTATGCTAGCCGTCGGCAAGCTTGCTT










GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGAGGGGCAGCATGGTGTAGCAATACACTGATGGCGACCGGCAAACGGG

TGCGGAACACGTACAGAACCTTCCTTCAAGCGGGGAATAGCCCAGAGAAATTTGGATTAATACCCCATAGCATAACAGAGTGGCA

TCACTTTGTTATTAAAGATTTATCACTTGAAGATGGCTGTGCGTCTGATTAGGTAGTTGGCGGGGTAACGGCCCACCAAGCCTACG






ATAGATCACCAATTGCGAAGGCAGCTCACTACACAAATATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGATA

CCCTGGTAGTGCACGCCCTAAACGATGGATACTCGACATACGCGATACACAGTGTGTGTCTGAGCGAAAGCATTAAGTATCCCAC








AACTGGGGCT


GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGAGGGGCAGCATGGTGTAGCAATACACTGATGGCGACCGGCAAACGGG

TGCGGAACACGTACAGAACCTTCCTTCAAGCGGGGAATAGCCCAGAGAAATTTGGATTAATACCCCATAGCATAACAGAGTGGCA

TCACTTTGTTATTAAAGATTTATCACTTGAAGATGGCTGTGCGGCTGATTAGGTAGTTGGCGGGGTAACGGCCCACCAAGCCTGCG


ATTGGTCAATGGACGGAAGTCTGAACCAGCCATGCCGCGTGGAGGATGAAGGTCCTCTGGATTGTAAACTTCTTTTATCTGGGAC

GAAAAAAGGTCTTTCTAGATCGTCTGACGGTACCAGATGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAG

GGTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGTGGGTTGGTAAGTCAGTGGTGAAATCTTCGAGCTTAACTC

GGAAACTGCCATTGATACTATCAGTCTTGAATATTGTGGAGGTTAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGAC

ATAGAACACCAATTGCGAAGGCAGCTGGCTACACATATATTGACACTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGATA

CCCTGGTAGTCCACGCCCTAAACTATGGATACCCGACATACGCGATACACTGTGTGTGTCTGAGCGAAAGCATTAAGTATCCCAC

CTGGGAAGTACGATCGCAAGATTGAAACTCAAAGGAATTGGCGGGGGTCCGCACAAGCGGTGGAGCATGTGGCTTAATTCGATG


CTGCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAATGAGCGCAACCCTTATCTTTAGTTGCCAACAGGT

CAAGCTGGGAACTCTAAAGAAACTGCCGTCGTAAGACGCGGGGAAGGAGGGGATGATGTCAAGTCACCATGGCCTTTATGCCCA


GAATCGGAGTCTGCAACTCGACTCCGTGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGTATACGTTCCCGGA

CCTTGTATACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGTCGATAACCGCAAGGAGTCGCCTAGGGTAAAACTAGT

AACTGGGGCT


Appendix B 143



CCTTCCCTATAGTACGGAATAGCCCAGGGAAACTTGGAGTAATACCGTATACGCCCGAAAGGGGAAAGAATTTCGCTATAGGATG

GGCCCGCGCAGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCTTAGCTGGTTTGAGAGAACGACCAGCCACA

CTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCCATG

CCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTGGCGGGGACGATAATGACGGTACCCGCAGAATAAGTCCCGGCTA

ACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGACTAGCGTTGTTCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGATATGC

CAGTCAGGGGTGAAATCCCGGGGCTCAACCTCGGAACTGCCCTTGATACAGCATGTCTTGAGTCCGATAGAGGTGGGTGGAATTC

CTAGTGTAGAGGTGAAATTCGTAGATATTAGGAAGAACACCGGTGGCGAAGGCGGCCCACTGGATCGGTACTGACGCTGAGGTG

CGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGGATGCTAGCCGTCGGATAGCTTGCTAT

TCGGTGGCGCAGCTAACGCATTAAGCATCCCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAAGGAATTGACGGGGGCCCG

CACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCAGCTCTTGACATTCACTGATCGCCGGTAGAGAT

GCCGGAATTCCAGCAATGGACAGTGGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCA

ACGAGCGCAACCCTCGCCATTAGTTGCCATCATTCAGTTGGGCACTCTAGTGGGACTGCCGGTGATAAGCCGGAGGAAGGTGGGG

ATGACGTCAAGTCATCATGGCCCTTACGGGCTGGGCTACACACGTGCTACAATGGCGGTGACAATGCGCAGCCACCTAGTAATAG

GGAGCTAATCGCAAAAAGCCGTCTCAGTTCAGATTGAGGTCTGCAACTCGACCTCATGAAGTCGGAATCGCTAGTAATCGCGCAT

CAGCAAGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTCTTACCCTAAAACGGTGCG







GGACAATGGGGGCAACCCTGATCCAGCCATTCCTCGTGAGTGAAGAAGGCCTTCGGGTTGTAAAGCTCTTTCAGCTGGAACGAAA



CTGGGAATGGCATTTTGAACTGGCAGTCTAGAGTGCGTCAGAAGGGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATG


TACCCTGGTAGTCCACGCCCTAAACGATGTCAACTGGTTGTTGGGGGAGTGAAATCCCTTAGTAACGAAGCTAACGCGTGAAGTT

GACCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGATTAAT

TCGATGCAACGCGAAAAACCTTACCTACCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCCGAAAGGGAATCGGAA

CACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCATTAGTTGC

TACGCAAGGGCACTCTAATGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGT


CGGATTGTTCTCTGCAACTCGAGAGCATGAAGTCGGAATCGCTAGTAATCGCGGATCAGCACGTCGCGGTGAATACGTTCCCGGG


GGGTTTCATGACTGGGGTG



CCTTCCCTATAGTACGGAATAGCCCAGGGAAACTTGGAGTAATACCGTATACGCCCGAAAGGGGAAAGAATTTCGCTATAGGATG

GGCCCGCGTAGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCTTAGCTGGTTTGAGAGAACGACCAGCCACA

CTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCCATG

CCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTGGCGGGGACGATAATGACGGTACCCGCAGAATAAGTCCCGGCTA

ACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGACTAGCGTTGTTCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGATATGC

CAGTCAGGGGTGAAATCCCGGGGCTCAACCTCGGAACTGCCCTTGATACAGCATGTCTTGAGTCCGATAGAGGTGGGTGGAATTC

CTAGTGTAGAGGTGAAATTCGTAGATATTAGGAAGAACACCGGTGGCGAAGGCGGCCCACTGGATCGGTACTGACGCTGAGGTG

CGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGGATGCTAGCCGTCGGATAGCTTGCTAT


CACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCAGCTCTTGACATTCACTGATCGCCGGTAGAGAT

GCCGGAATTCCAGCAATGGACAGTGGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCA

ACGAGCGCAACCCTCGCCATTAGTTGCCATCATTCAGTTGGGCACTCTAGTGGGACTGCCGGTGATAAGCCGGAGGAAGGTGGGG

ATGACGTCAAGTCATCATGGCCCTTACGGGCTGGGCTACACACGTGCTACAATGGCGGTGACAATGCGCAGCCACCTAGTAATAG

GGAGCTAATCGCAAAAAGCCGTCTCAGTTCAGATTGAGGTCTGCAACTCGACCTCATGAAGTCGGAATCGCTAGTAATCGCGCAT

CAGCATGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTCTTACCCTAAAACGGTGCG


>ENR26 FJ536933 [organism=uncultured Hydrogenophaga sp. ENR26]

ATTGAACGCTGGCGGCATGCTTTACACATGCAAGTCGAACGGTAACAGGCCTTCGGGTGCTGACGAGTGGCGAACGGGTGAGTAA

TGCATCGGAACGTGCCCAGTCGTGGGGGATAACGCAGCGAAAGCTGCGCTAATACCGCATACGATCTGCGGATGAAAGCGGGGG

ACCTTCGGGCCTCGCGCGATTGGAGCGGCCGATGTCAGATTAGGTAGTTGGTGGGGTAAAGGCTCACCAAGCCGACGATCTGTAG

CTGGTCTGAGAGGACGACCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATTTTGGACA

ATGGGGGCAACCCTGATCCAGCAATGCCGCGTGCAGGAAGAAGGCCTTCGGGTTGTAAACTGCTTTTGTACGGAACGAAAAGGCT

Appendix B 144


CTGGTTAATACCTGGGCACATGACGGTACCGTAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTG

CAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTGCTGTAAGACAGGCGTGAAATCCCCGGGCTTAACCTAGG

AATGGCGCTTGTGACTGCAGTGCTGGAGTGTGGCAGAGGGGGATGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGGA

GGAACACCGATGGCGAAGGCAATCCCCTGGGCCTGCACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACC

CTGGTAGTCCACGCCCTAAACGATGTCAACTGGTTGTTGGGTCTTTTCTGACTCAGTAACGAAGCTAACGCGTGAAGTTGACCGCC

TGGGGAGTACGGCCGCAAGGTTGAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGTTTAATTCGATGC

AACGCGAAAAACCTTACCCACCTTTGACATGTACGGAAGTTGCCAGAGATGGCTTCGTGCTCGAAAGAGAGCCGTAACACAGGTG

CTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCATTAGTTGCTACGAAAG

GGCACTCTAATGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATAGGTGGGGCTAC

ACACGTCATACAATGGCCGGTACAAAGGGCAGCCAACCCGCGAGGGGGAGCCAATCCCATAAAGCCGGTCGTAGTCCGGATCGC

AGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGTGGATCAGCATGTCACGGTGAATACGTTCCCGGGTCTTGTAC

ACACCGCCCGTCACACCATGGGAGCGGGTCTCGCCAGAAGTAGGTAGCCTAACCGTAAGGAGGGCGCTTACCACGGCGGGGTTC

GTGACTGGGGTG





AGTAGCGGGTCTGAGAGGATGATCCGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGGATTTT




CTGGGAATGGCATTTTGAACTGGCAGTCTAGAGTGCGTCAGAGGGGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATG


TACCTGGTAGTCACGCCTAAACGATGTCAACTGGTTGTTGGGGGAGKGAAATCCTTTAGTAACGAAGCTAACGCGTGAAGTTGAC

CGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGATTAATTCG

ATGCAACGCGAAAAACCTTACCTACCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCCGAAAGGGAATCGGAACAC

AGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCATTAGTTGCTGC

GCAAGGGCACTCTAATGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGTAGG

GCTTCACACGTCATACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCCAATCCCAGAAAGCCGATCGTAGTCCGG

ATTGTTCTCTGCAACTCGAGAGCATGAAGTCGGAATCGCTAGTAATCGCGGATCAGCATGTCGCGGTGAATACGTTCCCGGGTCTT

GTACACACCGCCCGTCACACCATGGGAGTGGAATCTGGCAGAAGTAGGTAGCCTAACCGCAAGGGGGGCGCTTACCACGCTGGG

CTTCATGACTGGGTG

>ENR28 FJ536935 [organism=uncultured Cupriavidus sp. ENR28]

ATTGAACGCTGGCGGCATGCCTTACACATGCAAGTCGAACGGCAGCGCGGACTTCGGTCTGGCGGCGAGTGGCGAACGGGTGAG

TAATACATCGGAACGTACCCTGTTGTGGGGGATAACTAGTCGAAAGATTAGCTAATACCGCATACGACCTGAGGGTGAAAGTGGG

GGACCGCAAGGCCTCACGCAGCAGGAGCGGCCGATGTCTGATTAGCTAGTTGGTGGGGTAAAGGCCCACCAAGGCGACGATCAG

TAGCTGGTCTGAGAGGACGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATTTTGG

ACAATGGGGGCAACCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTGTCCGGAAAGAAATC

GCGCTGGTTAATACCTGGCGTGGATGACGGTACCGGAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTA

GGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTTGTAAGACAGGCGTGAAATCCCCGGGCTTAACC

TGGGAATTGCGCTTGTGACTGCAAGGCTAGAGTGCGTCAGAGGGGGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGT

GGAGGAATACCGATGGCGAAGGCAGCCCCCTGGGACGTGACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGAT

ACCCTGGTAGTCCACGCCCTAAACGATGTCAACTAGTTGTTGGGGATTCATTTTCTCAGTAACGTAGCTAACGCGTGAAGTTGACC

GCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGATTAATTCGA

TGCAACGCGAAAAACCTTACCTACCCTTGACATGCCACTAACGAAGCAGAGATGCATTAGGTGCCCGAAAGGGAAAGTGGACAC

AGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCTCTAGTTGCTAC

GCAAGAGCACTCTAGAGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGTAGG

GCTTCACACGTCATACAATGGTGCGTACAGAGGGTTGCCAACCCGCGAGGGGGAGCTAATCCCAGAAAACGCATCGTAGTCCGG

ATCGTAGTCTGCAACTCGACTACGTGAAGCTGGAATCGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGTCT

TGTACACACCGCCCGTCACACCATGGGAGTGGGTTTTGCCAGAAGTAGTTAGCCTAACCGCAAGGAGGGCGATTACCACGGCAGG

GTTCATGACTGGGGTG


GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGAGGGGCGGCGCAGTGTAGCAATACATGGGCGGCGACCGGCAAACGGG






GGTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGCGGGCATGCAAGTCCGTGGTGAAATCTCCGAGCTTAACTC


Appendix B 145





ATCCGCGAGGAACCTTACCTGGGCTAGAATGCTGGGGGACCGTGGGTGAAAGCTCACTTTGTAGCAATACACCGCCAGTAAGGTG


CAAGCTGGGAACTCTAATGAAACTGCCGTCGTAAGACGCGAGGAAGGAGGGGATGATGTCAAGTCATCATGGCCTTTATGCCCAG

GGCTACACACGTGCTACAATGGGGCGTACAAAGGGCTGCCACTTAGTGATAAGGAGCGAATCCCAAAAAACGCCTCTCAGTTCG

AATCGGAGTCTGCAACTCGACTCCGTGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGAC

CTTGTACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGTCGATAACCGCAAGGAGTCGCCTAGGGTAAAACTAGTA

ACTGGGGCT

>ENR30 FJ536937 [organism=uncultured Pseudomonas sp. ENR30]

ATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGATGAAGGGAGCTTGCTTCCTGATTCAGCGGCGGACGGGTGAG

TAATGCCTAGGAATCTGCCTGGTAGTGGGAGACAACGTTCCGAAAGGAGCGCTAATACCGCATACGTCCTACGGGAGAAAGTGG

GGGATCTTCGGACCTCACGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTAGGTGGGGTAATGGCTCACCTAGGCGACGATCCG

TAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGG

ACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGG

GCAGTAAGTTAATACCTTGCTGTTTTGACGTTACCAACAGAATAAGCACCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAA

GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGTGCGCATAGGTGGTTTGGTAAGATGGATGTGAAATCCCCGGGCTCAACC

TGGGAACTGCATCCATAACTGCCTGACTAGAGTACGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGATATGCGTAGATATAG

GAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGAT

ACCCTGGTAGTCCACGCCGTAAACGATGTCGACTAGCCGTTGGGATCCTTGAGATCTTAGTGGCGCAGCTAACGCGATAAGTCGA

CCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCGCACAAGCGGTGGAGCATGTGGTTTAATTCG

AAGCAACGCGAAGAACCTTACCTGGCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCTTCGGGAATCGGAACACAG

GTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCA

CGTTATGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGG

CCAGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCAGAAAACCGATCGTAG

TCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGTGAATCAGAATGTCACGGTGAATACGTTCCCG

GGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCTCCAGAAGTAGCTAGTCTAACCGCAAGGGGGACGGTTACCACG

GAGTGATTCATGACTGGGGTG



TAATACATCGGAACGTACCCTGTTGTGGGGGATAACTAGTCGAAAGATTAGCTAATACCACATACGACCTGAGGGTGAAAGTGGG







GGAGGAGTACCGATGGCGAAGGCAGCCCCCTGGGACGTGACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGAT



TGCAACGCGAAAAACCTTACCTACCCTTGACATGCCACTAACGAAGCAGAGATGCATTAGGTGCCCGAAAGGGAAAGTGGGCAC





TGTACACACCGCCCGTCACACCATGGGAGTGGGTTTTGCCAGAAGTAGTTAGCCTAACCGCAAGGAGGGCGATTACCACGGCAGG

GTTCATGACTGGGGTG

>ENR32 FJ536939 [organism=uncultured Ralstonia sp. ENR32]

ATTGAACGCTGGCGGCATGCCTTACACATGCAAGTCGAACGGCAGCATGATCTAGCTTGCTAGATTGATGGCGAGTGGCGAACGG

GTGAGTAATACATCGGAACGTGCCCTGTAGTGGGGGATAACTAGTCGAAAGATTAGCTAATACCGCATACGACCTGAGGGTGAA

AGTGGGGGACCGCAGGGCCTCATGCTATAGGAGCGGCCGATGTCTGATTAGCTAGTTGGTGGGGTAAAGGCCCACCAAGGCGAC

GATCAGTAGCTGGTCTGAGAGGACGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAA

TTTTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTGTCCGGAAA

GAAATGGCTCTGGTTAATACCTGGGGTCGATGACGGTACCGGAAGAATAAGGACCGGCTAACTACGTGCCAGCAGCCGCGGTAA

TACGTAGGGTCCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTGTGCAAGACCGATGTGAAATCCCCGAGC

TTAACTTGGGAATTGCATTGGTGACTGCACGGCTAGAGTGTGTCAGAGGGGGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAG

AGATGTGGAGGAATACCGATGGCGAAGGCAGCCCCCTGGGATAACACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGG

ATTAGATACCCTGGTAGTCCACGCCCTAAACGATGTCAACTAGTTGTTGGGGATTCATTTCCTTAGTAACGTAGCTAACGCGTGAA

GTTGACCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGATT

Appendix B 146


AATTCGATGCAACGCGAAAAACCTTACCTACCCTTGACATGCCACTAACGAAGCAGAGATGCATTAGGTGCTCGAAAGAGAAAGT

GGACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCTCTAGT

TGCTACGAAAGGGCACTCTAGAGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATG

GGTAGGGCTTCACACGTCATACAATGGTGCATACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCAGAAAATGCATCGTA

GTCCGGATCGTAGTCTGCAACTCGACTACGTGAAGCTGGGATCGCTAGTAATCGTATATCAGCAATGATGCGGTGAATACGTTCC

CGGATCTTGTACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGTCGATAACCGCAAGGAGTCGCCTAGGGTAAAAC

TAGTAACTGGGGTT



TAATACATCGGAACGTACCCTGTTGTGGGGGATAACTAGTCGAAAGATTAGCTAATACCGCATACGACCTGAGGGTGAAAGTGGG







GGAGGAATACCGATGGCGAAGGCAGCCCCCTGGGACGTGACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGAT



TGCAACGCGAAAAACCTTACCTACCCTTGACATGCCACTAACGAAGCAGAGATGCATTAGGTGCCCGAAAGGGAAAGTGGACAC





TGTACACACCGCCCGTCACACCATGGGAGTGGGTTTTGCTAGAAGTAGTTAGCCTAACCGCAAGGAGGGCGATTACCACGGCAGG

GTTCATGACTGGGGTG

B.2 16S rDNA sequences obtained from biofilter treating DMS alone

>DMS01 FJ536871 [organism=uncultured Hyphomicrobium sp. DMS01]


CCTTCCCTATAGTACGGAATAGCCCAGGGAAACTTGGAGTAATACCGTATACGCCCGAGAGGGGAAAGATTTATCGCTATAGGAT

GGGCCCGCGTAGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCTTAGCTGGTTTGAGAGAACGACCAGCCAC


GCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTGCCGGGGACGATAATGACGGTACCCGGAGAATAAGTCCCGGCT

AACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGACTAGCGTTGTTCGGAATCACTGGGCGTAAAGCGCACGTAGGTGGATTTG

TAAGTCAGGGGTGAAATCCCGGGGCTCAACCTCGGAACTGCCTTTGATACTGCAAGTCTTGAGTCCGATAGAGGTGGGTGGAATT

CCTAGTGTAGAGGTGAAATTCGTAGATATTAGGAAGAACACCGGTGGCGAAGGCGGCCCACTGGATCGGTACTGACACTGAGGT

GCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCACGCCGTAAACGATGGATGCTAGCCGTTGGATAGCTTGCTAT


CACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCAGCTCTTGACATTCACTGATCGCCTGGAGAGAT

CCGGGAGTCCCAGCAATGGGCAGTGGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCA

ACGAGCGCAACCCTCGCCATTAGTTGCCATCATTAAGTTGGGCACTCTAGTGGGACTGCCGGTGATAAGCCGGAGGAAGGTGGGG

ATGACGTCAAGTCATCATGGCCCTTACGGGCTGGGCTACACACGTGCTACAATGGCGGTGACAATGCGCAGCCACCTAGCAATAG

GGCGCTAATCGCAAAAAGCCGTCTCAGTTCAGATTGAGGTCTGCAACTCGACCTCATGAAGTCGGAATCGCTAGTAATCGCGCAT

CAGCATGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTCTTACCCTAAAACGGTGCG


>DMS02 FJ536872 [organism=uncultured OP10 bacterium DMS02]

GATGAACGTTGGCGGCGTGCCTTAAGCATGCAAGTCGAACGGTGCAGCAATGCACAGTGGCGAACGGCGAAGTAAGACATAAGC

AACGTGCCCCGAAGACTGGGATAGTCGTTGGAAACGACGGGTAATACCAGATGTGACCGCAGATTGGCATCAATTTGCGATTAAA

AGGTTTTTCGCTTCGGGAGCGGCTTATGGCCTATCAGGTAGTTGGTGGGGTAATGGCCTACCAAGCCGACGACGGGTAGCGGGTC

TGAGAGGATGATCCGCTCGAGTGGGACTGAGACACGGCCCACACACCTACGGGTGGCAGCAGCTTGGAATCTTGCACAATGGGG

GGAACCCTGATGCAGCGACGCCGCGTGGAGGACGAAGGGCTTAGGCTTGTAAACTCCTTTTGACAGGAAAGACTTAGGACGGTA

CCTGTCGAATAAGGTCCGGCTAACTACGTGCCAGCAGCCGCGGTAAGACGTAGGGACCAAACGTTGTCCGGATTTACTGGGCGTA

AAGAGCGCGTAGGCGGCTCGTTAAGTGTGGAGTGAAATCTCCGGGCTCAACCCGGAAACTGCTTTGCATACTGGCGGGCTAGAGG

AGTGAAGAGGTTTGTAGAATTCCCGGTGTAACGGTGAAATGTGTAGATATCGGGAGGAATACCAATGGCGAAGGCAGCAAACTG

GTCACGACCTGACGCTGAGGCGCGAAAGCGTGGGTAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGGATA

CTAGGCGTAAGAGGTATCGACCCCTCTTGTGCCGCAGCTAACGCATTAAGTATCCCGCCTGGGGAGTACGACCGCAAGGTTGAAA

CTTAAATGAATTGACGGGGACCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATACTAACCGAAGAACCTTACCCAGACTTGA

CATCGCGTGACAACCCACGAAAGTGGGCCTTCCCAAAAGGACACAAAGACACTTGTTGCATGGCTGTCGTCAGCTCGTGCCGTGA

Appendix B 147


GGTGTACGGTTAAGTCCGCCAACGAGCGCAACCCTCGTCCTGTGTTACCAGCATGTAATGGTGGGGACTCGCAGGAGACCGCCGG

TGTAAGCCGGAGGAAGGTGAGGATGATGTCAAGTCAGCATGGCAGTTACGTCTGGGGCTACACACATGCTACAATGGACGAAAC

AAAGGGCAGCAATACCGCGAGGTGGAGCCAATCCCAAAAATACGTCCTCAGTTCAGATTGCAGTCTGCAACTCGACTGCATGAA

GTCGGAATCGCTAGTAAACGCAGGTCAGCTATACTGCGTTGAATACGTTCCCGGGTCTTGTACACACTGCCCGTCAAGTCACCTGA

ATTGTCTTCACCCGAAGTCCGTGGCCTAACCGTAAGGAGGGAGCGGCCGAAGGTGAGGGGAGTAAGGGGGACT

>DMS03 FJ536873 [organism=uncultured Xanthomonadaceae bacterium DMS03]

ATTGAACGCTGGCGGCATGCCTAACACATGCAAGTCGAACGGCAGCATGAGGTGTAGCAATACACCTTGATGGCGAGTGGCGGA

CGGGTGAGGAATACATCGGAATCTGCCCAATCGTGGGGGACAACGCAGGGAAACTTGCGCTAATACCGCATACGACCTTCGGGT

GAAAGCAGGGGATCTTCGGACCTTGCGCGATTGGATGAGCCGATGTCGGATTAGCTTGTTGGCGGGGTAACGGCCCACCAAGGCG

ACGATCCGTAGCTGGTCTGAGAGGATGATCAGCCACATTGGGACTGAGACACGGCCCAAACTCCTACGGGAGGCAGCAGTGGGG

AATATTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGTGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCAGGA

ACGAAATCTGCACGGTTAATACCCGTGTAGTCCGACGGTACCTGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTA

ATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGTCTGTTGTGAGATCCCCGAG

CTCAACTTGGGAATTGCAATGGATACTGGCAAGCTGGAGTATGGTAGAGGAAGGTGGGATTCCCGGTGTAGCAGTGAAATGCGTA

GAGATCGGGAGGAACACCAGTGGCGAAAGCGGCCTTCTGGACCAATACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGG

ATTAGATATCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGGTACATTACGGTACTCAGTGTCGAAGCTAACGCGTT

AAGTTCGCCGCCTGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTATGTGGT

TTAATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATCTGTCGAATCCTGCAGAGATGCGGGAGTGCCGCAAGGAACGAC

AAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCCCTAGTT

GCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCC

CTTACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCGAAGCCGCGAGGCGAAGCCAATCCCAGAAAACCG

ATCCCAGTCCGGATCGGAGTCTGTAACTCGACTCCGTGAAGTCGGAATCGCTAGTAATCGCGAATCAGCTATGTCGCGGTGAATA

CGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGCTGCATCAGAAGCAGGTAGTCTAACCGCAAGGAGGACGC

CTACCACGGTGTGGTCAATGACTGGGGTG



CGGGTGAGGAATGCATCGGAATCTGCCCAGTCGTGGGGGACGACGCAGGGAAACTTGCGCTAATACCGCATACGACCTTCGGGT

GAAAGCAGGGGATCTTCGGACCTTGCGCGATTGGATGAGCCGATGCCGGATTAGCTAGTTGGCGGGGTAATGGCCCACCAAGGC

GACGATCCGTAGCTGGTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGG

GAATATTGGACAATGGGGGCAACCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCGGG

AACGAAACATTGTCGGCTAATACCCGGCAAGACTGACGGTACCCGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGG

TAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGTCTGTTGTGAAATCCCCG

AGCTCAACTTGGGAATTGCAATGGATACTGGCAAGCTGGAGTACGGTAGAGGAAGGTGGAATTCCCGGTGTAGCAGTGAAATGC

GTAGAGATCGGGAGGAACACCAGTGGCGAAAGGCGGCCTTCTGGACCAGTACTGGCGCTCATGCACGAAAGCGTGGGGAGCAAA

CAGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGGTACATTACGGTACTCAGTGTCGAAGCTAAC

GCGTTAAGTTCGCCGCCTGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTAT

GTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATCTGTCGAATCCTGCAGAGATGCGGGAGTGCCGCAAGGA

ACGACAAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCC

CTAGTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTC

ATGGCCCTTACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCGAAGCCGCGAGGTGGAGCCAATCCCAGA

AAACCGATCCCAGTCCGGATCGGAGTCTGCAACTCGACTCCGTGAAGTCGGAATCGCTAGTAATCGCGAATCAGCTATGTCGCGG

TGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGCTGCACCAGAAGCAGGTAGTCTAACCGCAAGGA

GGACGCCTACCACGGTGTGGTCGATGACTGGGGTG

>DMS05 FJ536875 [organism=uncultured Sphingobacteriales bacterium DMS05]

GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGTGGGGCAGCGCGGGTAGCAATATCTGGCGGCGACCGGCAAACGGGTG

AGGAACATGTACGCAACCTTCCTTTAACTGGAGAATAGCCCCTCGAAAGAGGGATTAATACTCCGTAACATAATGAAGTGGCATC

ACTTTATTATTATAGCTCCGGCGGTTAAAGATGGGCGTGCACCTGATTAGATAGTTGGCGGGGTAACGGCCCACCAAGTCTGCGA

TCAGTAACTGGTGTGAGAGCACGACCAGTCACACGGGCACTGAGACACGGGTCCGACTCCTACGGGAGGCAGCAGTAAGGAATA

TTGGTCAATGGACGCAAGTCTGAACCAGCCATGCCGCGTGGAGGATTAAGGCCCTCTGGGTTGTAAACTCCTTTTATCTGGGAAG

AAATGTACTTTTTCTTGAGTACTTGACGGTACCAGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGG

GTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGCGGGCAGGTAAGTCAGTGGTGAAATCTCCGGGCTTAACCCG

GAAACTGCCGTTGATACTATTTGTCTTGAATATTGTGGAGGTAAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGACAT

AGAACACCAATTGCGAAGGCAGCTTACTACGCAATGATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGATACC

CTGGTAGTCCACGCCCTAAACGATGATTACTCGACGTGTGCGATACACTGTACGCGTCTGAGCGAAAGCATTAAGTAATCCACCT

GGGAAGTACGTTCGCAAGAATGAAACTCAAAGGAATTGACGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAT

ACGCGAGGAACCTTACCTGGGCTAGAATGCAGTCTGATTGCCGGTGAAAGCTGGTTTTGTAGCAATACACAGATTGTAAGGTGCT

GCATGGCTGTCGTCAGCTCGTGCCGTGAGGCGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCACTAGTTGCCATCAGGTA

ATGCTGGGAACTCTAGTGAAACTGCCGTCGTAAGACGCGAGGAAGGAGGGGATGATGTCAAGTCATCATGGCCTTTATGCCCAGG

GCTACACACGTGCTACAATGGCGGGTACAAAGGGTTGCTACCTGGTAACAGGATGCTAATCTCAAAAAACCCGTCTCAGTTCGAA

TTGAGGTCTGCAACTCGACCTCATGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACCTT

Appendix B 148


GTACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGCCGGTAACCGCAAGGAGCCGCCTAGGGTAAAACTAGTAAC

TGGGGCT

>DMS06 FJ536876 [organism=uncultured Gemmatimonas sp. DMS06]

GACGAACGCTGGCGGCGTGCATAACACATGCAAGTCACGGGGGCCAGCAATGGCAACCGGCGAACGGGTGCGTAACACGTAAGC

GACCTACCTCGATGAGGGGCATAGCCGGCCTAACGGCCGGGTAATTCCGCATGTGATCCTTGGGGGGCATATCCCGGGGATGAAA

CCAGTAATGGGCATTGAGAGGGGCTTGCGGCCTATCAGCTTGTTGGCGAGGTAACGGCTCACCAAGGCTACGACGGGTAGCTGGT

CTGAGAGGATGGCCAGCCACATTGGGACTGAGAAACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATCTTGCGCAATGGC

CTAACGGCTGACGCAGCGACGCCGCGTGAGGGATGAAGCTCCTCGGAGTGTAAACCTCTGTTGCCCGGGACGAATAGCGGATTTT

TCCGACTTGACGGTACCGGGTGAGGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGGGGGTGCGAGCGTTGTCC

GGAATCACTGGGCGTAAAGGGCGCGTAGGTGGCTTTGTAAGTTTGCGGTGAAAGCCCGGGGCTCAACCCCGGGTCTGCCGTGGAT

ACTGCAAGGCTTGAGTACTGTAGAGGCAGGTAGAATATCGGGTGTAGCGGTGGAATGCGTAGAGATCCGATAGAAGACCGGTGG

CGAAGGCGGCCTGCTGGGCAGTAACTGACACTGAGGCGCGACAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACG

CCGTAAACGATGGGTACTAGGTGCTTCGGGGAGCGACCCCTGGAGTGCCGGCGCTAACGCAGGAAGTACCCCGCCTGGGGAGTA

CGGCCGCAAGGCTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAG

AACCTTACCCAGGCTTGACATGCGCAGGAAGTATGGCAGAAACGCCATGCGCTCTTCGGAGTCTGCGCACAGGTGCTGCATGGCT

GTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCCGTTCCTAGTTGCCAGCGAGTAAAGTCGGG

GACTCTAGGAAGACTGCCGGTGCGAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGTCCTTACGTCTGGGGCTACAC

ACGTGCTACAATGGATGCGACAGTAGGGACCGACCCCGCAAGGGTGAGGCAATCCTCAAACGCATCCTCAGTTCGGATTGTGGGC

TGCAACTCGCCCACATGAAGCTGGAATCGCTAGTAATCGTGGATCAGCTACGCCACGGTGAATACGTTCCCGGGCCTTGTACACA

CCGCCCGTCACGCCATGGAAGCTGTGAGCGCCCGAAGTCCGTGTCGGATCCCGCAAGGGGCCAAGCGGCCGAAGGCGAGCGCGG

TGACTGGGGCG


GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGCGGGGCAGCGCGTCTCTACGCAAGTAGAGATGGCGGCGACCGGCAAA

CGGGTGAGGAACATGTACGCAACCTTCCTTTAACTGGAGAATAGCCCCTCGAAAGAGGGATTAATACTCCGTAACATCCCGATGT

GGCATCACAACGGGATTATAGCTCCGGCGGTTAAAGATGGGCGTGCACCTGATTAGATAGTTGGCGGGGTAACGGCCCACCAAGT

CTGCGATCAGTAACTGGTGTGAGAGCACGACCAGTCACACGGGCACTGAGACACGGGTCCGACTCCTACGGGAGGCAGCAGTAA

GGAATATTGGTCAATGGACGCAAGTCTGAACCAGCCATGCCGCGTGGAGGATTAAGGCCCTCTGGGTTGTAAACTCCTTTTATCTG

GGAAGAAATGTACTTTTTCTTGAGTACTTGACGGTACCAGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATAC

GGAGGGTGCAAGCGTTATCCGGATTTACTGGGTTTAAAGGGTGCGTAGGCGGATAAGTAAGTCAGTGGTGAAATCTCCGGGCTTA

ACCCGGAAACTGCCGTTGATACTATTTGTCTTGAATATTGTGGAGGTAAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATAT

GACATAGAACACCAATTGCGAAGGCAGCTTACTACACAATCATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAG

ATACCCTGGTAGTCCACGCCCTAAACGATGATTACTCGACGTGTGCGATACACTGTACGCGTCTGAGCGAAAGCATTAAGTAATC

CACCTGGGAAGTACGACCGCAAGGTTGAAACTCAAAGGAATTGACGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCG

ATGATACGCGAGGAACCTTACCTGGGCTAGAATGCAGTCTGACCGCCGGTGAAAGCTGGTTTTGTAGCAATACACAGATTGTAAG

GTGCTGCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCACTAGTTGCCATCA

GGTAATGCTGGGAACTCTAGTGAAACTGCCGCCGTAAGGCGTGAGGAAGGAGGGGATGATGTCAAGTCATCATGGCCTTTATGCC

CAGGGCTACACACGTGCTACAATGGCGAGTACAAAGGGTTGCTACCTGGTAACAGGATGCTAATCTCAAAAAACTCGTCTCAGTT

CGAATTGAGGTCTGCAACTCGACCTCATGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGG

ACCTTGTACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGTCGGTAACCGCAAGGAGCCGCCTAGGGTAAAACTA

GTAACTGGGGCT

>DMS08 FJ536878 [organism=uncultured bacterium DMS08]

GACGAACGCTGGCGGTGTGCCTCACACATGCAAGTCGAACGGGGTAGCAATACCCAGTGGCGGACGGGTGAGTAACACGTAGGA

ATCTGCCCTCAGGTGGGGGATAGCAGACCGAAAGGTCTATTAATACCGCATATGTACATATCTCGACAGAGGTGTGTATGAAAGG

AGTAATCCGCCTATGGATGAGCCTGCGTCCGATTAGCTAGTTGGTAGGGTAAAGGCCTACCAAGGCGACGATCGGTAGCTGGTCT

GAGAGGATGATCAGCCACAATGGGACTGAGACACGGCCCATACTCCTACGGGAGGCAGCAGTGGGGAATTTTACGCAATGGGCG

AAAGCCTGACGTAGCGACACCGCGTGAGCGAAGAAGCCCTTTGGGGTGTAAAGCTCTGTCAGCTGGAACGAACACAATGACGGT

ACCAGCAGAGGAAGCATCGGCTAACTACGTGCCAGCAGCCGCGGTAAGACGTAGGATGCAAGCGTTGTCCGGATTTATTGGGCGT

AAAGAGTTCGTAGGCGGTCTGTTAAGTCTGGTGTTAAAGATCAGGGCTCAACCCTGGGAGTGCATTGGATACTGGCAGACTGGAG

TGCGGTAGAGGCGAGTGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGTAGGCGACTCGCT

GGGCCGTAACTGACGCTGAGGAACGAAAGCCAGGGGAGCGAATGGGATTAGATACCCCAGTAGTCCTGGCCGTAAACGATGGAT

ACTAGGCGTAGTGGGTATCGACCCCTACTGTGCCGCAGCAAACGCGATAAGTATCCCGCCTGAGTAGTACGGCCGCAAGGTTGAA

ACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAACATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGGCTT

GACATGTGAGGAACCTTTCGGAAACGAGAGGGTGCCCGCAAGGGAGCCTCAACACAGGTGGTGCATGGCTGTCGTCAGCTCGTGT

CGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCCGTTGTTAGTTGCCATCAGGTAAAGCTGGGCACTCTAGCGAGACT

GCCGGTGACAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGCCCCTTATGTCCTGGGCTACACACGTGTTACAATGGCT

AGGACAATGTGATGCAAACCCGCGAGGGGGAGCGAATCGCCAAACCTAGTCTCAGTTCGGATCGCAGGCTGCAACTCGCCTGCG

TGAAGTCGGAATCGCTAGTAACCGCCGATCAGCACGCGGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACGTCAT

GGGAGCTGGTCACGCCCGAAGTCGGTATGCCAACCGTAAGGAAGCAGCCGCCTAAGGCAGGGCCGGTGACTGGGACG

>DMS09 FJ536879 [organism=uncultured Planctomycetaceae bacterium DMS09]

Appendix B 149


AATGAACGTTGGCGGCGTGGATTAGGCATGCAAGTCGAGCGAGAACCCCGCAAGGGGGGAAAGCGGCGAAAGGGACAGTAATA

CGTAGATCATCTGCCCTCGGGTCCGGGATAGCTGCGGGAAACTGCAGGTAATACCGGATGACATCTCCGGATCAAAGGTGTGATT

CCGCCCGAGGATGAGTTTGCGTCCTATTAGCTTGTTGGTGGGGTAACGGCCTACCAAGGCAATGATGGGTAGCGGGTGTGAGAGC

ACGACCCGCGTCACTGGGACTGAGACACTGCCCAGACACCTACGGGTGGCTGCAGTCGAGAATCTTCGGCAATGGGCGCAAGCCT

GACCGAGCGACGCCGCGTGCGGGATGAAGGCTTTCGGGTTGTAAACCGCTGTCAGAGGGGATGAAATGTAGTTGGGTTCTCCCTT

CTACTTGACATATCCTCAGAGGAAGTACGGGCTAAGTTCGTGCCAGCAGCCGCGGTAAGACGAACCGTACGAACGTTATTCGGAA

TTACTGGGCTTAAAGGGTGCGTAGGCTGCGTAGTAAGTTGGGTGTGAAATCCCTCGGCTCAACCGAGGAACTGCGCCCAAAACTA

CTATGCTCGAGGGAGACAGAGGTGAGCGGAACTTAGGGTGGAGCGGTGAAATGCGTTGATATCCTAAGGAACACCCGTGGCGAA

AGCGGCTCACTGGGTCTCTTCTGACGCTGAGGCACGAAAGCTAGGGTAGCGAACGGGATTAGATACCCCGGTAGTCCTAGCTGTA

AACGATGAGCACTGGGTTGGAGGGCCCTCCATAGCCTTCCAGCCGCAGCGAAAGTGTTAAGTGCTCCGCCTGGGGAGTATGGTCG

CAAGGCTGAAACTCAAAGGAATTGACGGGGGCTCACACAAGCGGTGGAGGATGTGGCTTAATTCGAGGCTACGCGAAGAACCTT

ATCCTAGTCTTGACTTGCACGGATTAACTCCCTGAAAGGGGAGCCAGGCCTTCGGGTACAACGTGCACAGGTGCTGCATGGCTGT

CGTCAGCTCGTGTCGTGAGATGTCGGGTTAAGTCCCTTAACGAGCGAAACCCTTGTCACTAGTTGCCAGCGCGTCATGGCGGGGA

CTCTAGTGAGACTGCCGGTGTTAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCTTTATGACTAGGGCTGCACACG

TCCTACAATGGCATGTACAAAGGGATGCCAACTCGCGAGAGCAAGCAAATCCCAAAAAACATGCCCCAGTTCGGATTGCAGGCT

GCAACTCGCCTGCATGAAGCCGGAATCGCTAGTAATCGCGGGTCAGCATACCGCGGTGAATGTGTTCCTGAGCCTTGTACACACC

GCCCGTCAAGCCACGAAAGTGGGGGGCATCCGAAGTCGCCGCGCCAACCGCAAGGGGGTAAGCGCCGAAGATGAACTCCGCGAT

TGGGACT



ATCTGCCCTCAGGTGGGGGATAGCAGACCGAAAGGTCTATTAATACCGCATATGTACATATCTCGACAGAGGAGTGTATGAAAGG

AGTAATCCGCCTATGGATGAGCCTGCGTCCGATTAGCTAGTTGGTGGGGTAAAGGCCTACCAAGGCGACGATCGGTAGCTGGTCT






GGGCCGTAACTGACGCTGAGGAACGAAAGCCAGGGGAGCGAATGGGATTAGATACCCCAGTAGTCCTGGCCGTAACAATGGATA

CTAGGCGTAGTGGGTATCGACCCTACTGTGCCGCAGCAAACGCGATAAGTATCCCGCTGAGTAGTACGGCCGCAAGGTTGAAACT

CAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAACATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTATCAGGGCTTGAC

ATGTGAGGAACCTTTCGGAAACGAGAGGGTGCCCGCAAGGGAGCCTCAACACAGGTGGTGCATGGCTGTCGTCAGCTCGTGTCGT

GAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCCGTTGTTAGTTGCCATCAGGTAAAGCTGGGCACTCTAGCGAGACTGCC

GGTGACAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGCCCCTTATGTCCTGGGCTACACACGTGTTACAATGGCTAGG

ACAATGTGATGCAAACCCGCGAGGGGGAGCGAATCGCCAAACCTAGTCTCAGTTCGGATCGCAGGCTGCAACTCGCCTGCGTGA

AGTCGGAATCGCTAGTAACCGCCGATCAGCACGCGGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACGTCATGGG

AGCTGGTCACGCCCGAAGTCGGTATGCCAACCGTAAGGAAGCAGCCGCCTAAGGCAGGGCCGGTGACTGGGACG



AGGAACATGTACGCAACCTTCCTTTAACTGGAGAATAGCCCCTCGAAAGAGGGATTAATACTCCGTAACATAATGAAGTGGCATC

ACTTTATTATTATAGCTCCGGCGGTTAAAGATGGGCGTGCACCTGATTAGATAGTTGGCGGGGTAACGGCCCACCAAGTCTGCGA

TCAGTAACTGGTGTGAGAGCACGACCAGTCACACGGGCACTGAGACACGGGTCCGACTCCTACGGGAGGCAGCAGTAAGGAATA

TTGGTCAATGGACGCAAGTCTGAACCAGCCATGCCGCGTGGAGGATTAAGGCCCTCTGGGTTGTAAACTCCTTTTATCTGGGAAG

AAATGTACTTTTTCTTGAGTACTTGACGGTACCAGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGG

GTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGCGGGCAGGTAAGTCAGTGGTGAAATCTCCGGGCTTAACCCG

GAAACTGCCGTTGATACTATTTGTCTTGAATATTGTGGAGGTAAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGACAT

AGAACACCAATTGCGAAGGCAGCTTACTACACAATGATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGATACC

CTGGTAGTCCACGCCCTAAACGATGATTACTCGACGTGTGCGATACACTGTACGCGTCTGAGCGAAAGCATTAAGTAATCCACCT

GGGAAGTACGTTCGCAAGAATGAAACTCAAAGGAATTGACGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAT

ACGCGAGGAACCTTACCTGGGCTAGAATGCAGTCTGATTGCCGGTGAAAGCTGGTTTTGTAGCAATACACAGATTGTAAGGTGCT

GCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCACTAGTTGCCATCAGGTA


GCTACACACGTGCTACAATGGCGGGTACAAAGGGTTGCTACCTGGTAACAGGATGCTAATCTCAAAAAACCCGTCTCAGTTCGAA

TTGAGGTCTGCAACCCGACCTCATGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACCTT

GTACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGCCGGTAACCGCAAGGAGCCGCCTAGGGTAAAACTAGTAAC

TGGGGCT


ATTGAACGCTGGCGGCATGCCTAACACATGCAAGTCGAACGGCAGCATGAGGTGTAGCAATACACCTTGATGGCGAGCGGCGGA




Appendix B 150


AATATTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCAGGA

ACGAAATCTGCACGGTTAATACCCGTGTAGTCTGACGGTACCTGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTA

ATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGTCTGTTGTGAAATCCCCGAG

CTCAACTTGGGAATTGCAATGGTTACTGGCAAGCTGGAGTATGGTAGAGGAAGGTGGAATTCCCGGTGTAGCAGTGAAATGCGTA

GAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTTCTGGACCAATACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGG

ATTAGATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGGTACATTACGGTACTCAGTGTCGAAGCTAACGCGT

TAAGTTCGCCGCCTGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTATGTGG

TTTAATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATCTGTCGAATCCTGCAGAGATGCGGGAGTGCCGCAAGGAACGA

CAAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCCTTAGT

TGCCAGCACGTAATGGTGGGAACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGC

CCTTACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCGAAGCCGCGAGGTGGAGCCAATCCCAGAAAGCC

GATCCCAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTTGGAATCGCTAGTAATCGCGAATCAGCTATGTCGCGGTGAAT

ACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGCTGCACCAGAAGCAGGTAGTCTAACCGCAAGGAGGACG

CCTACCACGGTGTGGTCAATGACTGGGGTG

>DMS13 FJ536883 [organism=uncultured Thiobacillus sp. DMS13]




AGTAGCGGGTCTGAGAGGATGATCCGCCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATTTT


CGGTGCGCTCTAACATAGCGTGCTAATGACGGTACCAGCAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACG

TAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGATTGTTAAGCAAGATGTGAAATCCCCGGGCTTAA

CCTGGGAATGGCATTTTGAACTGGCAGTCTAGAGTGCGTCAGAGGGGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGAT

GTGGAGGAACACCAATGGCGAAGGCAGCCCCCTGGGATGACACTGACGCTCATGTACGAAAGCGTGGGTAGCAAACAGGATTAG



TTCGATGCAACGCGAAAAACCTTACCTACCCTTGACATGTCCAGAACCCTGCAGAGATGCGGGGGTGCCCGAAAGGGAATTGGA

ACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTCGCCCTTAGTTG

CCATCATTCAGTTGGGCACTCTAAGGGGACTGCCGGTGATAAGCCGCGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTT

ACAGGCTGGGCTACACACGTGCTACAATGGCGGTGACAATGGGAGGCAATGGGGCAACCCTGCGCAAATCTCAAAAAGCCGTCT

CAGTTCGGATTGCACTCTGCAACTCGAGTGCATGAAGTTGGAATCGCTAGTAATCGTGGATCAGCATGCCACGGTGAATACGTTC

CCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTTTTACCTTAAGACGGTTTGCTAACCGCAAGGAGGCAGCCGGCC

ACGGTAAGGTCAGCGACTGGGGTG



AGGAACATGTACGCAACCTTCCTTCGACTGGAGAATAGCCCCTCGAAAGAGGGATTAATACTCCGTAACATAAAGAAGTGGCATC

ACTTTTTTATCAAAGCTTCGGCGGTTGAAGATGGGCGTGCATCTGATTAGGCAGTTGGCGGGGTAACGGCCCACCAAACCGACGA

TCAGTAACTGGTGTGAGAGCACGACCAGTCACATGGGCACTGAGACACGGGCCCAACTCCTACGGGAGGCAGCAGTAAGGAATA

TTGGTCAATGGACGCAAGTCTGAACCAGCCATGCCGCGTGAAGGATGAAGGCCCTCTGGGTTGTAAACTTCTTTTATATGGGAAG

AAATGTGTCGGTTCTCCGACACTTGACGGTACTATAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGG

GTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGTGGGCAGATAAGTCAGTGGTGAAATCTTTCGGCTTAACTGG

AAAACTGCCGTTGATACTATTTGTCTTGAATATTGCGGAGGTAAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGACAT

AGAACACCAATTGCGAAGGCAGCTTACTACACAATTATTGACACTGAGGCACGAAAGCGTGGGTAGCGAACAGGATTAGATACC

CTGGTAGTCCACGCCCTAAACGATGATTACTCGACATATGCGATATACTGTATGTGTCTGAGCGAAAGCATTAAGTAATCCACCTG

GGAAGTACGACCGCAAGGTTGAAACTCAAAGGAATTGACGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGATA

CGCGAGGAACCTTACCTGGGCTAGAATGCTAAGTGACAGTAGCTTGAAGCCTATCTTGTAGCAATACACACTTAGTAAGGTGCTG

CATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCACTAGTTGCCATCAGGTAAT

GCTGGGAACTCTAGTGAAACTGCCGCTGTAAGGCGTGAGGAAGGAGGGGATGACGTCAAGTCATCATGGCCTTTATGCCCAGGGC

TACACACGTGCTACAATGGGCGCTACAATGGGTTGCAACACAGCAATGTGAAGCTAATCTCAAAAAAGCGCTCTCAGTTCAGATT

GGAGTCTGCAACTCGACTCCATGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACCTTG

TACACACCGCCCGTCAAGCCATGGAAGCTGGGTGTACCTAAAGTCGGTAACCGCAAGGAGCCGCCTAGGGTAAAACTAGTAACT

GGGGCT


GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGTGGGGCAGCGCGGGTAGCAATACCTGGCGGCGACCGGCAAACGGGTG

CGGAACACGTACGCAACCTTCCTTTAAGTGAGGAATAGCCCAGGGAAACTTGGATTAATACCTCGTAATACTATGAGATGGCATC

ATCTTATAATTATAGCTCAGGCGCTTAAAGATGGGCGTGCGGCTGATTAGGTAGTTGGCGGGGTAACGGCCCACCAAGCCTACGA

TCAGTAACTGGTGTGAGAGCACGACCAGTCACACGGGCACTGAGACACGGGCCCGACTCCTACGGGAGGCAGCAGTAAGGAATA

TTGGTCAATGGACGCAAGTCTGAACCAGCCATGCCGCGTGGAGGATGAAGGTCCTCTGGATTGTAAACTTCTTTTATCTGGGAAG

AAACCGGACTTTTCTAAGACCGTTGACGGTACCAGATGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGG

GTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGTGGGCAGGTAAGTCAGTGGTGAAATCTCTGGGCTTAACCCA

Appendix B 151


GAAACTGCCGTTGATACTATCTGTCTTGAATATAGTGGAGGTGAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGACA

TAGAACACCCATTGCGAAGGCAGCTCGCTACACTATTATTGACACTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGATACC

CTGGTAGTCCACGCCCTAAACGATGGATACTCGACATCAGCGATACACTGTTGGTGTCTGAGCGAAAGCATTAAGTATCCCACCT

GGGAAGTACGACCGCAAGGTTGAAACTCAAAGGAATTGACGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAT

ACGCGAGGAACCTTACCTGGGCTAGAATGCTGGTGGACCGAGGGTGAAAGCTCTCTTTGTAGCAATACACCGCCAGTAAGGTGCT

GCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCCATCACTAGTTGCCATCAGGTA


GCTACACACGTGCTACAATGGGGAGGACAAAGGGCAGCAACACGGCGACGTGAAGCTAATCCCAAAAACCTCTTCTCAGTTCAG

ATTGCAGTCTGCAACTCGACTGCATGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC

TTGTACACACCGCCCGTCACACCATGGGAGTTGGTCTTACCCTAAAACGGTGCGCTAACCGCAAGGAGGCAGCCGGCCACGGTAA

GGTCAGCGACTGGGGTG


ATTGAGCGCTGGCGGCATGCCTAACACATGCAAGTCGAACGGCAGCATGATCTGTAGCAATACAGATTGATGGCGAGTGGCGGA

CGGGTGAGGAACACATCGGAATCTGCCCTATCGTGGGGGATAACCCGGGGAAACCCGGACTAATACCGCATACGACCTTCGGGT

GAAAGCAGGGGCTCGTAAGACCTTGCGCGATTGGATGAGCCGATGTCGGATTAGCTTGTTGGCGGGGTAATGGCCCACCAAGGCA


AATATTGGACAATGGGGGCAACCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCGGGA

ACGAAACATTGTCGGTTAATACCCGGCAAGACTGACGGTACCCGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGT

AATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGTCTGTTGTGAAATCCCCGA

GCTCAACTTGGGAATTGCAATGGATACTGGCAAGCTAGAGTACGGTAGAGGAAGGTGGAATTCCCGGTGTAGCAGTGAAATGCG

TAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTTCTGGACCAGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA

GGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGGTACATTACGGTACTCAGTGTCGAAGCTAACGC

GTTAAGTTCGCCGCCTGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTATGT

GGTTTAATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATCTGTTGAATCCTGCAGAGATGCGGGAGTGCCGCAAGGAAC

AACAAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCCCT

AGTTGCCAACGAGTAAAGTCGGGAACTCTAGGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCAT

GGCCCTTACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCGAAGCCGCGAGGTGGAGCCAATCCCAGAAA

GCCGATCCCAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGCGAATCAGCTATGTCGCGGTG

AATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGCTGCACCAGAAGCAGGTAGTCTAACCGCAAGGAGG

ACGCCTACCACGGTGTGGTCAATAACTGGGGTG

>DMS17 FJ536887 [organism=uncultured Parvibaculum sp. DMS17]

AACGAACGCTGGCGGCAGGCTTAACACATGCAAGTCGAACGCCCCGCAAGGGGAGTGGCAGACGGGTGAGTAACACGTGGGAA

CCTACCTAGGGGTACGGAATAACTCGGGGAAACTCGTGCTAATACCGTATACGTCCTATGTGAGAAAGATTTATCGCCCCTAGAG

GGGCCCGCGTTGGATTAGCTAGTTGGTGGGGTAAAGGCCTACCAAGGCAACGATCCATAGCTGGTCTGAGAGGATGATCAGCCAC

ACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATCTTGGACAATGGGGGAAACCCTGATCCAGCCAT

GCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTCGCCAGGGAAGATAATGACGGTACCTGGATAAGAAGCCCCGGCA

AACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGCTTTC

CAAGTTGGCGGTGAAATCCCCGGGCTTAACCCGGGAACTGCCCCCAAGACTGGAAGGCTCGAGTCCGAGAGAGGTGAGTGGAAT

TTCCAGTGTAGAGGTGAAATTCGTAGATATTGGAAAGAACACCAGTGGCGAAGGCGGCTCACTGGCTCGGTACTGACGCTGAGGT

GCGACAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGGATGCTAGTTGTCAGGCAGCTTGCTG

CTTGGTGACGCAGCTAACGCATTAAGCATCCCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAAAGGAATTGACGGGGGCCC

GCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGCAGAACCTTACCAACCCTTGACATGTCTCGTATGGTTTCCAGAG

ATGGATTCCTTCAGTTCGGCTGGCGAGAACACAGGTGTTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCG

CAACGAGCGCAACCCTCGCCTTTAGTTGCCATCATTAAGTTGGGCACTCTAGAGGGACTGCCGGTGATAAGCCGGAGGAAGGTGG

GGATGACGTCAAGTCCTCATGGCCCTTACGGGTTGGGCTACACACGTGCTACAATGGCGGCGACAATGGGCAGCGAAGGGGCGA

CCCGGTGCTAATCCCAAAAAGCCGTCTCAGTTCGGATTGCACTCTGCAACTCGAGTGCATGAAGGTGGAATCGCTAGTAATCGCG

TAACAGCATGACGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTTTTACCCGAAGCTAGT

GTGCTAACCGCAAGGAGGCAGCTAACCACGGTAAGGTCAGCGACTGGGGTG


GATGAACGCTAGCGGCAGGCTTAATACATGCAAGTCGTGGGGCAGCATGGTGTAGCAATACACTGATGGCGACCGGCAAACGGG

TGCGGAACACGTACGCAACCTTCCTTTAAGTGGTGAATAGCTTTCGGAAACGAAAATTAATACACCGTAACATTATGAAGTGGCA

TCATTTTATAATTATAGCTCCGGCGCTTAAAGATGGGCGTGCGTCTGATTAGGTAGTTGGCGGGGTAACGGCCCACCAAGCCTGCG


ATTGGTCAATGGACGCAAGTCTGAACCAGCCATGCCGCGTGGAGGATGAAGGTCCTCTGGATTGTAAACTTCTTTTATTTGGGAA

GAAACCCACGATTTCTATTGTGGTTGACGGTACCAGATGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAG

GGTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGCGGGTAGGTAAGTCAGTGGTGAAATCTCCGGGCTTAACCC

GGAAACTGCCGTTGATACTATCTGTCTTGAATATAGTGGAGGTGAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGAC

ATAGAACACCTATTGCGAAGGCAGCTCGCTACACTATCATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACAGGATTAGATA

CCCTGGTAGTCCACGCCCTAAACGATGATTACTCGACATCAGCGATACACTGTTGGTGTCTGAGCGAAAGCATTAAGTAATCCAC


Appendix B 152


ATACGCGAGGAACCTTACCTGGGCTAGAATGCAGTCTGACTGCCGGTGAAAGCTGGTTTTGTAGCAATACACAGATTGTAAGGTG

CTGCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCACTAGTTGCCATCAGGT

AATGCTGGGAACTCTAGTGAAACTGCCGTCGTAAGACGCGAGGAAGGAGGGGATGATGTCAGGTCATCATGGCCTTTATGCCCAG

GGCTACACACGTGCTACAATGGGGAGGACAAAGGGCAGCAACACAGCGATGTGAAGCTAATCCCAAAAACCTCTTCTCAGTTCA

GATTGGAGTCTGCAACTCGACTCCATGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGAC

CTTGTACACACCGCCCGTCAAGCCATGGAAGCCGGGTGTACCTAAAGTCGGTAACCGCAAGGAGCCGCCTAGGGTAAAACTGGT

GACTGGGGCT



CGGGTGAGGAATACATCGGAATCTGCCCAGTCGTGGGGGACAACGCAGGGAAACTTGCGCTAATACCGCATACGACCTTCGGGT

GAAAGCAGGGGATCTTCGGACCTTGCGCGATTGGATGAGCCGATGCCGGATTAGCTAGTTGGCGGGGTAATGGCCCACCAAGGC

GACGATCCGTAGCTGGTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGG

GAATATTGGACAATGGGGGCAACCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCGGG

AACGAAACATTGTCGGCCAATACCCGGCAAGACTGACGGTACCCGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGG

TAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGTCTGCTGTGAAATCCCCG

AGCTCAACTTGGGAATTGCAGTGGATACTGGCAAGCTGGAGTACGGTAGAGGAAGGTGGAATTCCCGGTGTAGCAGTGAAATGC

GTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTTCTGGACCAGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAAC

AGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGGTACATTACGGTACTCAGTGTCGAAGCTAACG

CGTTAAGTTCGCCGCCTGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTATG

TGGTTTAATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATCTGTCGAATCCTGCAGAGATGCGGGAGTGCCGCAAGGAA

CGACAAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGCTAAGTCCCGCAACGAGCGCAACCCTTGTCCC

TAGTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCA

TGGCCCTTACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCGAAGCCGCGAGGTGGAGCCAATCCCAGAA

AACCGATCCCAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGCGAATCAGCTATGTCGCGGT

GAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGCTGCACCAGAAGCAGGTAGTCTAACCGCAAGGAG

GACGCCTACCACGGTGTGGTCAATGACTGGGGTG










GCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGGATGCTAGCCGTTGGATAGCTTGCTA

TTCGGTGGCGCAGCTAACGCATTAAGCATCCCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAAGGAATTGACGGGGGCCC

GCACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCAGCTCTTGACATTCACTGATCGCCTGGAGAGA

TCCGGGAGTCCCAGCAATGGGCAGTGGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGC

AACGAGCGCAACCCTCGCCATTAGTTGCCATCATTAAGTTGGGCACTCTAGTGGGACTGCCGGTGATAAGCCGGAGGAAGGTGGG

GATGACGTCAAGTCATCATGGCCCTTACGGGCTGGGCTACACACGTGCTACAATGGCGGTGGCAATGCGCAGCCACCTAGCAATA

GGGCGCTAATCGCAAAAAGCCGTCTCAGTTCAGATTGAGGTCTGCAACTCGACCTCATGAAGTCGGAATCGCTAGTAATCGCGCA

TCAGCATGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTCTTACCCTAAAACGGTGC

GCTAACCGCAAGGAGGCAGCCGGCCACGGTAAGGTCAGCGACTGGGGTG













TTCGGGAGTCCCAGCAATGGGCAGTGGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGC


GATGACGTCAAGTCATCATGGCCCTTACGGGCTGGGCTACACACGTGCTACAATGGCGGTGACAATGCGCAGCCACCTAGCAATA


Appendix B 153






CGGAACACGTACGCAACCTTCCTTTAAGCGGGGAATAGCCCAGGGAAACTTGGATTAATACCCCATAAGATTATGGTGTGGCATC

ACACAGTAATTAAAGCTCCGGCACTTAAAGATGGGCGTGCGGCTGATTAGGTAGTTGGCGGGGTAACGGCCCACCAAGCCTACGA

TCAGTAACTGGTGTGAGAGCACGACCAGTCACACGGGCACTGAGACACGGGCCCGACTCCTACGGGAGGCAGCAGTAAGGAATA

TCGGTCAATGGACGCAAGTCTGAACCAGCCATGCCGCGTGGAGGATGAAGGTCCTCTGGATTGTAAACTTCTTTTATCTGGGAAG

AAACCCATGTTTTCTAACGTGGTTGACGGTACCAGATGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGG

GTGCAAGCGTTATCCGGATTCACTGGGTTTAAAGGGTGCGTAGGTGGGCAGGTAAGTCAGTGGTGAAATCTCTGGGCTTAACCCA

GAAACTGCCGTTGATACTATCTGTCTTGAATGTAGTGGAGGTGAGCGGAATATGTCATGTAGCGGTGAAATGCTTAGATATGACA

TAGAACACCTATTGCGAAGGCAGCTTACTACGCAGATATTGACGCTCATGCACGAAAGCGTGGGGATCAAACAGGATTAGATACC

CTGGTAGTCCACGCCCTAAACGATGATTACTCGACATACGCGATACACAGTGTGTGTCTGAGCGAAAGCATTAAGTAATCCACCT

GGGAAGTACGACCGCAAGGTTGAAACTCAAAGGAATTGGCGGGGGTCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAT

ACGCGAGGAACCTTACCTGGGCTAGAATGCGGTTTGACCGCCGGTGAAAGCCGGTTTTGTAGCAATACACAGATCGTAAGGTGCT

GCATGGCTGTCGTCAGCTCGTGCCGTGAGGTGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCAATAGTTGCCATCAGGTA

ATGCTGGGAACTCTATTGAAACTGCCGTCGTAAGACGTGAGGAAGGAGGGGATGATGTCAAGTCATCATGGCCTTTATGCCCAGG

GCTACACACGTGCTACAATGGTAACTACAAAGGGCTGCCACTTGGCAACAAGGAGCTAATCCCAAAAAAGTTATCTCAGTTCAGA

TTGCAGGCTGCAACTCGCCTGCATGAAGCTGGAATCGCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACCTT

GCACACACCGCCCGTCAAGCCATGGGAGCCGGGTGTACCTAAAGTCGGTAACCGCAAGGAGCTGCCTAGGGTAAAATCGGTGAC

TGGGGCT

>DMS23 FJ536893 [organism=uncultured Acetobacteraceae bacterium DMS23]

AGCGAACGCTGGCGGCATGCTTAACACATGCAAGTCGCACGGTCTGGGGGCAACCTCAGGCAGTGGCGGACGGGTGAGTAACGC

GTAGGAATCTGTCTTTGGGTGGGGGACAACCGCGGGAAACTGCGGCTAATACCGCATGACGAGCCTGGCCGTGGGGTCAGGTTCT

AAAGCTTTTACGAGCGCCTGGAGAGGAGCCTGCGTCCGATTAGCTGGTTGGTGGGGTAACGGCCTACCAAGGCGTCGATCGGTAG

CTGGTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACA

ATGGGCGCAAGCCTGATCCAGCAATGCCGCGTGGGTGAAGAAGGTCTTCGGATTGTAAAGCCCTTTCGGCGGGGACGATGATGAC

GGTACCCGCAGAAGAAGCCCCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGGCTAGCGTTGCTCGGAATGACTGG

GCGTAAAGGGCGCGTAGGCGGATTGCACAGTCGGGCGTGAAATTCCTGGGCTTAACCTGCGGGCTGCGTTCGAGACGTGTGGTCT

GGAGTGCGGAAGAGGGTCGTGGAATTCCCAGTGTAGAGGTGAAATTCGTAGATATTGGGAAGAACACCGGTGGCGAAGGCGGCG

ACCTGGTCCGTGACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGAT

GTGCGCTGGATGTTGGGCGGCCTAGCCGTTCAGTGTCGTAGCCAACGCGATAAGCGCACCGCCTGGGGAGTACGGCCGCAAGGTT

GAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGATGATGTGGATTAATTCGATGCAACGCGAAAAACCTTACCTACC

CTTGACATGTCCAGAACCCTGCAGAGATGCGGGGGTGCCCGAAAGGGAATTGGAACACAGGTGCTGCATGGCTGTCGTCAGCTCG

TGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCATTAGTTGCTACGCAAGGGCACTCTAATGAGGCTGCCG

GTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGTAGGGCTTCACACGTCATACAATGGTCGGTA

CAGAGGGTTGCCAAGCCGCGAGGTGGAGCCAATCCCAGAAAGCCGATCGTAGTCCGGATTGTTCTCTGCAACTCGAGAGCATGA

AGTCGGAATCGCTAGTAATCGCGGATCAGCATGTTGCGGTGAATACGTTCCCGGGTCTTGTACACACCGCCCGTCACACCATGGG

AGTGGAATCTGGCAGAAGTAGGTAGCCTAACCGCAAGGAGGCAGCCGGCCACGGTAGGGTCAGCGACTGGGGTG

>DMS24 FJ536894 [organism=uncultured Thiomonas sp. DMS24]

ATTGAACGCTAGCGGCATGCTTTACACATGCAAGTCGAACGGCAGCGCGGGGCAACCTGGCGGCGAGTGGCGAACGGGTGAGTA

ATACATCGGAACGTGCCCTGTGATGGGGGATAACTACGCGAAAGCGTAGCTAATACCGCATACGACCCAAGGGTGAAAGTGGGG

GATCGCAAGACCTCACGTCATAGGAGCGGCCGATGGCGGATTAGCTAGTTGGTGAGGTAAAGGCTTACCAAGGCGACGATCCGT

AGCTGGTCTGAGAGGACGACCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATCTTGGA

CAATGGGGGCAACCCTGATCCAGCAATGCCGCGTGTGGGATGAAGGCCTTCGGGTTGTAAACCACTTTTGGCGGGGGCGAAATGT

CGAGTGCTAATACCATTCGGTGATGACGGTACCCGCAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGG

GTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTAAGTAAGACAGATGTGAAATTCCCGGGCTCAACCTG

GGAACTGCATTTGTGACTGCTTGACTAGAGTGCGGCAGAGGGGAGTGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGG

AGGAACACCGATGGCGAAGGCAACTCCCTGGGCCTGCACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGATAC

CCTGGTAGTCCACGCCCTAAACGATGTCGACTAGTTGTTGGACGGGTTACTGTTCAGTAACGTAGCTAACGCGTGAAGTCGACCG

CCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGTTTAATTCGAT

GCAACGCGAAAAACCTTACCTACCCTTGACATGCCAGGAACTTACCAGAGATGGTTTGGTGCTCGAAAGAGAGCCTGGACACAG

GTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCATTAGTTGCTACGC

AAGAGCACTCTAATGAGACCGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGTAGGGC

TACACACGTCATACAATGGGCGGTACAGAGGGTTGCCAACCCGCGAGGGGGAGCCAATCCCTTAAAACCGCTCGTAGTCCGGATT

GTAGTCTGCAACTCGACTGCATGAAGTCGGAATCGCTAGTAATCGCGGATCAGCTTGCCGCGGTGTATACGTTCCCGGGTCTTGTA

CACACCGCCCGTCACACCATGGAAGTGGGGTTTACCAGAAGTAGTTAGCTTAACCGCAAGGGGGGCGATTACCACGGTAGGCTTC

ATGACTGGGGTG


Appendix B 154



ATCTGCCCTCAGGTGGGGGATAGCAGACCGAAAGGTCTATTAATACCGCATATGTACATATCTCGACAGAGGAGTGTATGAAAGG

AGTAATCCGCCTATGGATGAGCCTGCGTCCGATTAGCTAGTTGGTGGGGTAAAGGCCTACCAAGGCGACGATCGGTAGCTGGTCT






GGGCCGTAACTGACGCTGAGGAACGAAAGCCAGGGGAGCGAATGGGATTAGATACCCCAGTAGTCCTGGCCGTAAACAATGGAT

ACTAGGCGTAGTGGGTATCGACCCCTACTGTGCCGCAGCAAACGCGATAAGTATCCCGCCTGAGTAGTACGGCCGCAAGGTTGAA

ACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAACATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGGCTT

GACATGTGAGGAACCTTTCGGAAACGAGAGGGTGCCCGCAAGGGAGCCTCAACACAGGTGGTGCATGGCTGTCGTCAGCTCGTGT

CGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCCGTTGTTAGTTGCCATCAGGTAAAGCTGGGCACTCTAGCGAGACT

GCCGGTGACAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGCCCCTTATGTCCTGGGCTACACACGTGTTACAATGGCT

AGGACAATGTGATGCAAACCCGCGAGGGGGAGCGAATCGCCAAACCTAGTCTCAGTTCGGATCGCAGGCTGCAACTCGCCTGCG

TGAAGTCGGAATCGCTAGTAACCGCCGATCAGCACGCGGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACGTCAT

GGGAGCTGGTCACGCCCGAAGTCGGTATGCCAACCGTAAGGAAGCAGCCGCCTAAGGCAGGGCCGGTGACTGGGACG

>DMS26 FJ536896 [organism=uncultured Thiomonas sp. DMS26]

GTTGAACGCTAGCGGCATGCTTTACACATGCAAGTCGAACGGCAGCGCGGGGCAACCTGGCGGCGAGTGGCGAACGGGTGAGTA

ATACATCGGAACGTGCCCTGTGATGGGGGATAACTACGCGAAAGCGTAGCTAATACCGCATACGACCCAAGGGTGAAAGTGGGG

GATCGCAAGACCTCACGTCATAGGAGCGGCCGATGGCGGATTAGCTAGTTGGTGAGGTAAAGGCTTACCAAGGCGACGATCCGT

AGCTGGTCTGAGAGGACGACCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATCTTGGA

CAATGGGGGCAACCCTGATCCAGCAATGCCGCGTGTGGGATGAAGGCCTTCGGGTTGTAAACCACTTTTGGCGGGGGCGAAATGT

CGAGTGCTAATACCATTCGGTGATGACGGTACCCGCAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGG

GTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTAAGTAAGACAGATGTGAAATTCCCGGGCTCAACCTG

GGAACTGCATTTGTGACTGCTTGACTAGAGTGCGGCAGAGGGGAGTGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGG

AGGAACACCGATGGCGAAAGCAACTCCCTGGGCCTGCACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGATAC

CCTGGTAGTCCACGCCCTAAACGATGTCGACTAGTTGTTGGACGGGTTACTGTTCAGTAACGTAGCTAACGCGTGAAGTCGACCG

CCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGATGATGTGGTTTAATTCGAT

GCAACGCGAAAAACCTTACCTACCCTTGACATGCCAGGAACTTACCAGAGATGGTTTGGTGCTCGAAAGAGAGCCTGGACACAG

GTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCATTAGTTGCTACGC

AAGAGCACTCTAATGAGACCGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTATGGGTAGGGC

TACACACGTCATACAATGGGCGGTACAGAGGGTTGCCAACCCGCGAGGGGGAGCCAATCCCTTAAAACCGCTCGTAGTCCGGATT

GTAGTCTGCAACTCGACTGCATGAAGTCGGAATCGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTCTTGTA

CACACCGCCCGTCACACCATGGAAGTGGGGTTTACCAGAAGTAGTTAGCTTAACCGCAAGGGGGGCGATTACCACGGTAGGCTTC

ATGACTGGGGTG






AATATTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCAGGA

ACGAAATCTGCACGGTTAATACCTGTGTAGTCTGACGGTACCTGAGGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTA

ATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCAGTTTGTTAAGTCTGTTGTGAAATCCCCGAG

CTCAACTTGGGAATTGCAATGGATACTGGCAAGCTGGAGTATGGTAGAGGAAGGTGGAATTCCCGGTGTAGCAGTGAAATGCGTA

GAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTTCTGGACCAATACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGG

ATTAGATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGGTACATTACGGTACTCAGTGTCGAAGCTAACGCGT

TAAGTTCGCCGCCTGGGGAGTACGGTCGCAAGACTGGAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTATGTGG

TTTAATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATCTGTCGAATCCTGCAGAGATGCGGGAGTGCCGCAAGGAACGA

CAAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCCTTAGT

TGCCAGCACGTAATGGTGGGAACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGC

CCTTACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCGAAGCCGCGAGGTGGAGCCAATCCCAGAAAGCC

GATCCCAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTTGGAATCGCTAGTAATCGCGAATCAGCTATGTCGCGGTGAAT

ACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGCTGCACCAGAAGCAGGTAGTCTAACCGCAAGGAGGACG

CCTACCACGGTGTGGTCAATGACTGGGGTG


ATTGAACGCTGGCGGCATGCCTAACACATGCAAGTCGAACGGCAGCACAGGTAGCAATACCGGGTGGCGAGTGGCGGACGGGTG

AGTAATGCATCGGGATCTACCCAAACGTGGGGGATAACGTAGGGAAACTTACGCTAATACCGCATACGTCCCATGGGAGAAAGC

GGGGGCTTGCAAGACCTCGCGCGGTTGGACGAACCGATGTGCGATTAGCTAGTTGGTGGGGTAATGGCCCACCAAGGCGACGATC

GCTAGCTGGTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATT

Appendix B 155


GGACAATGGGCGCAAGCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCAGGAGCGAAA

TACTGCGGGTTAATACCCTGCGGGGCTGACGGTACCTGAGGAATAAGCACCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGA

AGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGTTCGTTAAGTCTGTCGTGAAATCCCCGGGCTCAAC

CTGGGAATGGCGATGGATACTGGCGGGCTAGAGTGTGTCAGAGGATGGTGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGAT

CGGGAGGAACATCAGTGGCGAAGGCGGCCATCTGGGACAACACTGACGCTGAAGCACGAAAGCGTGGGGAGCAAACAGGATTA

GATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGTCTCAACTCGGAGATCAGTGTCGAAGCTAACGCGTTAAG

TTCGCCGCCTGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTATGTGGTTTA

ATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCTTCGGGAATCGGAAC

ACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCCTTAGTTGCC

AGCGAGTAATGTCGGGAACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTT

ACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCAATACCGCGAGGTGGAGCCAATCCCAGAAAGCCGATC

CCAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGCGGATCAGCTATGCCGCGGTGAATACGT

TCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTTCTACCCTAAAACGGTGCGCTAACCGCAAGGAGGCAGCCGG

CCACGGTAAGGTCAGCGACTGGGGTG

>DMS29 FJ536899 [organism=uncultured Planctomycetaceae DMS29]

AACGAACGTTGGCGGCGTGGATTAGGCATGCAAGTCGAACGAATCTCATCTGGGTAACTGGATGGGGGAAGTGGCGAAAGGGGC

AGTAAGGCGTGGGTAACCTACCTCGGGGACTGGGATAGCCGTCCTAACGGACGGGTAATACCGGGCGATCTGGCGAAGAGGCAT

CTCTTTGCCAGGAAATGAATCTCGCCTCGAGAGGGGCTCACGTAGTATTAGCTTGTTGGCGGGGTAACGGCCCACCAAGGCCAAG

ATGCTTAGCGGGTGTGAGAGCACGACCCGCGCCACTGGCACTGAGACACTGGCCAGACTCCTACGGGAGGCTGCAGTCGAGGAT

CTTCGGCAATGGGCGCAAGCCTGACCGAGCGACGCCGCGTGTGCGATGAAGGCCTTCGGGTTGTAAAGCACTGTCGAGGGGGAG

AAAAGCCCACAAGGGTCTGATCTATCCCTGGAGGAAGCACGGGCTAAGTTCGTGCCAGCAGCCGCGGTAAGACGAACCGTGCGA

ACGTTGTTCGGATTCACTGGGCTTAAAGGGCGCGTAGGCGGACAATCAAGTCTAGGGTGAAATTTTTCAGCTTAACTGGAAACGT

GCCTTGGATACTGGTTGTCTCGAGGGAGGCAGGGGCATGCGGAACTTCCGGTGGAGCGGTGAAATGCGTAGATATCGGAAGGAA

CGCCGGTGGCGAAAGCGGCGTGCTGGACCTCTTCTGACGCTGAGGCGCGAAAGCTAGGGGAGCGAACGGGATTAGATACCCCGG

TAGTCCTAGCCCTAAACGATGGGTACTAGATTGTGGACTTAACATGGGTTCCCAATCGAAGCTAAAGTGTTAAGTACCCCGCCTG

GGGAGTATGGTCGCAAGGCTGAAACTCAAAGGAATTGACGGGGGCTCACACAAGCGGTGGAGCATGTGGCTTAATTCGAGGCTA

CGCGAAGAACCTTATCCTGGACTTGACATGTGCGAAAGCGGTAGGAAGTAGGGAGCGGAAACGTTTCTCCAACGGTATCCAGTCC

GGAACCTACTACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGAAACCCTTGTC

TCCAGTTGCCAGCGGGTCATGCCGGGGACTCTGGAGAGACTGCCGGTGTTAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTC

ATGGCCTTTATGTCCAGGGCTGCACACGTGCTACAATGGCGTACACAAAGGGAAGCCAACCCGCGAGGGGGAGCCAATCCCAAA

AAATACGCCTCAGTTCAGATCGAAGGCTGCAACTCGCCTTCGTGAAGTTGGAATCGCTAGTAATCGCAGGTCAGCAACACTGCGG

TGAATGTGTTCCTGAGCCTTGTACACACCGCCCGTCAAGCCACGAAAGGGAGGAGCGCCCGAAGTCGCCTCACCAAGTAGGCGCC

GAAGGCGAAACTCCTGATTGGGACT










GTCACGACCTGACGCTGAGGCGCGAAAGCGTGGGTAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGGATA

CTAGGCGTAAGAGGTATCGACCCCTCTTGTGCCGCAGCTAACGCATTAAGTATCCCGCCTGGGGAGTACGACCGCAAGGTTGAAA

CTTAAATGAATTGACGGGGACCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATACTAACCGAAGAACCTTACCCAGACTTGA

CATCGCGTGACAACCCACGAAAGTGGGCCTTCCCAAAAGGACACAAAGACACTTGTTGCATGGCTGTCGTCAGCTCGTGCCGTGA

GGTGTACGGTTAAGTCCGCCAACGAGCGCAACCCTCGTCCTGTGTTACCAGCATGTAATGGTGGGGACTCGCAGGAGACCGCCGG

TGTAAGCCGGAGGAAGGTGAGGATGATGTCAAGTCAGCATGGCAGTTACGTCTGGGGCTACACACATGCTACAATGGACGAAAC

AAAGGGCAGCAATACCGCGAGGTGGAGCCAATCCCAAAAATACGTCCTCAGTTCAGATTGCAGTCTGCAACTCGACTGCATGAA

GTCGGAATCGCTAGTAAACGCAGGTCAGCTATACTGCGTTGAATACGTTCCCGGGTCTTGTACACACTGCCCGTCAAGTCACCTGA

ATTGTCTTCACCCGAAGTCCGTGGCCTAACCGTAAGGAGGGAGCGGCCGAGGGTGAGGGGAGTAAGGGGGACT










Appendix B 156


GTCACGACCTGACGCTGAGCGCGAAAGCGTGGGTAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGGATAC

TAGGCGTAAGAGGTATCGACCCTCTTGTGCCGCAGCTAACGCATTAAGTATCCCGCCTGGGGAGTACGACCGCAAGGTTGAAACT

TAAATGAATTGACGGGGACCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATACTAACCGAAGAACCTTACCCTGACTTGACA

TCGCGTGACAACCCACGAAAGTGGGCCTTCCCAAAAGGACACAAAGACACTTGTTGCATGGCTGTCGTCAGCTCGTGCCGTGAGG

TGTACGGTTAAGTCCGCCAACGAGCGCAACCCTCGTCCTGTGTTACCAGCATGTAATGGTGGGGACTCGCAGGAGACCGCCGGTG

TAAGCCGGAGGAAGGTGAGGATGATGTCAAGTCAGCATGGCAGTTACGTCTGGGGCTACACACATGCTACAATGGACGAAACAA

AGGGCAGCAATACCGCGAGGTGGAGCCAATCCCAAAAATACGTCCTCAGTTCAGATTGCAGTCTGCAACTCGACTGCATGAAGTC

GGAATCGCTAGTAAACGCAGGTCAGCTATACTGCGTTGAATACGTTCCCGGGTCTTGTACACACTGCCCGTCAAGTCACCTGAATT

GTCTTCACCCGAAGTCCGTGGCCTAACCGTAAGGAGGGAGCGGCCGAAGGTGAGGGGAGTAAGGGGGACT

>DMS32 FJ536902 [organism=uncultured Dyella sp. DMS32]

ATTGAACGCTGGCGGCATGCTTAACACATGCAAGTCGAACGGCAGCACAGCAGAGCTTGCTCTGTGGGTGGCGAGTGGCGGACG

GGTGAGTAATGCATCGGGACCTACCCAGACGTGGGGGATAACGTAGGGTAACTTACGCTAATACCGCATACGTCCTACGGGAGA

AAGCAGGGGACCTTCGGGCCTTGCGCGGTTGGACGGACCGATGTTCGATTAGCTTGTTGGTGAGGTAACGGCTCACCAAGGCGAC

GATCGATAGCTGGTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAA

TATTGGACAATGGGCGCAAGCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCAGGAGC

GAAACGCTGTCGGCTAATACCCGGCGGAACTGACGGTACCTGAGGAATAAGCACCGGCTAACTTCGTGCCAGCAGCCGCGGTAA

TACGAAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTTTCTTAAGTCTGCTGTGAAATCCCCGGGC

TCAACCTGGGAATGGCAGTGGATACTGGGAGGCTAGAGTGTGTCAGAGGGTGGTGGAATTCCCGGTGTAGCGGTGAAATGCGTA

GAGATCGGGAGGAACATCAGTGGCGAAGGCGGCCACCTGGGACAACACTGACGCTGAGGCACGAAAGCGTGGGGAGCAAACAG

GATTAGATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGTCTCAACTCGGAGATCAGTGTCGAAGCTAACGCG

TTAAGTTCGCCGCCTGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTATGTG

GTTTAATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCTTCGGGAATC

GGAACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCCTTA

GTTGCCAGCACGTAATGGTGGGAACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATG

GCCCTTACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCAATACCGCGAGGTGGAGCCAATCCCAGAAAG

CCGATCCCAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGCAGATCAGCTATGCTGCGGTGA

ATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGAGTTGCTCCAGAAGCCGTTAGCCTAACCGCAAGGAGGG

CGACGACCACGGAGTGGTTCATGACTGGGGTG

>DMS33 FJ536903 [organism=uncultured Firmicutes bacterium DMS33]

GACGAACGCTGGCGGCGTGCCTAATACATGCAAGTCGTGCGAGACCTTCGGGTCTAGCGGCGGACGGGTGAGTAACACGTGGGT

AACCTGCCTGATCGACCGGGATAACGCTTGGAAACGAGTGCTAATACCGGATAATCTCCTGCACCGCATGGTGCGGGAGTAAAAG

GAGCTTTTGCTTCACGATCAGATGGACCCGCGGCGCATTAGCTAGTTGGTGAGGTAACGGCTCACCAAGGCGACGATGCGTAGCC

GACCTGAGAGGGTGACCGGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTAGGGAATCTTCCGCAAT

GGGCGCAAGCCTGACGGAGCAACGCCGCGTGAGTGATGAAGGCCTTCGGGTTGTAAAACTCTGTCTTCTGTGAAGAACCATCCTG

TGCAGAGAAAGCTCAGGACCTGACGGTAACAGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGG

GCAGGCGTTGTCCGGAATCACTGGGCGTAAAGCGCGCGCAGGCGGTCTTTCACGTCCGGGGTGAAAGCCCAGAGCTCAACTCTGG

GACTGCCTTGGATACGGAGAGACTTGAGGGTCGGAGAGGCAAGGGGAATTCCACGTGTAGCGGTGAAATGCGTAGAGATGTGGA

GGAACACCTGTGGCGAAGGCGCCTTGCTGGCCGACTACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGGATTAGATACC

CTGGTAGTCCACGCCGTAAACGATGAGTGCTAGGTGTTAGGGGGCCCACCCCTTAGTGCCGAAGCTAACGCATTAAGCACTCCGC

CTGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCAGTGGAGCATGTGGTTTAATTCGAAG

CAACGCGAAGAACCTTACCAGGACTTGACATCCCGCTGACCGGTTTAGAGATAAACCTTCCCTTCGGGGCAGCGGTGACAGGTGG

TGCATGGTTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATGTTGTGTTGCTACCATTCA

GTTGAGCACTCACAACAGACTGCCGGTGACAAACCGGAGGAAGGCGGGGATGACGTCAAATCATCATGCCCCTTATGTCCTGGGC

TACACACGTGCTACAATGGGCGGTACAAAGGGATGCCAAGCCGCGAGGCGGAGCCAATCCCAGAAAGCCGTTCGTAGTTCGGAT

TGCAGGCTGCAACTCGCCTGCATGAAGCCGGAATTGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATTCGTTCCCGGGCCTTG

TACACACCGCCCGTCACACCATGGGAGTTGGCAACACCCGAAGTCGGTGAGGTAACTGCTTGCAGAGCCAGCCGCCTAAGGTGG

GGTCGATGACTGGGGTG


ATTGAACGCTGGCGGCATGCCTAACACATGCAAGTCGAACGGCAGCACAGGTAGCAATACCGGGTGGCGAGTGGCGGACGGGTG

AGTAATGCATCGGGATCTACCCAAACGTGGGGGATAACGTAGGGAAACTTACGCTAATACCGCATACGTCCTATGGGAGAAAGC

GGGGGCTCGCAAGACCTCGCGCGGTTGGACGAACCGATGTGCGATTAGCTAGTTGGCGGGGTAATGGCCCACCAAGGCGACGAT

CGCTAGCTGGTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATAT

TGGACAATGGGCGCAAGCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTATCAGGAGCGAA

ATGCCATGGGCTAATACCCCGTGGAGCTGACGGTACCTGAGGAATAAGCACCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACG

AAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGTTATTTAAGTCTGCCGTGAAATCCCCGGGCTCAA

CCTGGGAATGGCGGTGGATACTGGATAGCTAGAGTGTGTCAGAGGATGGTGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGA

TCGGGAGGAACATCAGTGGCGAAAGCGGCCATCTGGGACAACACTGACGCTGAAGCACGAAAGCGTGGGGAGCAAACAGGATT

AGATACCCTGGTAGTCCACGCCCTAAACGATGCGAACTGGATGTTGGTCTCAACTCGGAGATCAGTGTCGAAGCTAACGCGTTAA

GTTCGCCGCCCGGGGAGTACGGTCGCAAGACTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGTATGTGGTTT

AATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATGTCCGGAATCCTGCAGAGATGCGGGAGTGCCTTCGGGAATCGGAA

Appendix B 157


CACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGTCCTTAGTTGC

CAGCACGTAATGGTGGGAACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCT

TACGGCCAGGGCTACACACGTACTACAATGGTCGGTACAGAGGGTTGCAATACCGCGAGGTGGAGCCAATCCCAGAAAGCCGAT

CCCAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGCAGATCAGCTATGCTGCGGTGAATACG

TTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGAGTTGCTCCAGAAGCCGTTAGTCTAACCGCAAGGGGGACGACG

ACCACGGAGTGGTTCATGACTGGGGTG

>DMS35 FJ536905 [organism=uncultured Gp1 sp. DMS35]

AATCAACGCTGGCGGCGTGCCTAACACATGCAAGTCGAACGAGAAAGTGGAGCAATCCATGAGTAAAGTGGCGCACGGGTGAGT

AACACGTGACTAACCTACCTTTTAGTGGGGGATAACCTAGGGAAACCTGGGCTAATACCGCATAAAACTCACGAGTCAAAGCAGT

AATGCGCTGAAAGAGGGGGTCGCGGTCGATTAGTTAGTTGGCAGGGTAATGGCCTACCAAGACTGTGATCGATATCCGGCCTGAG

AGGGCGCACGGACACACTGGAACTGAAACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATTTTGCGCAATGGGGGAAA

CCCTGACGCAGCAACGCCGCGTGGAGGATGAAGTCCCTTGGGACGTAAACTCCTTTCGATCGGGACGATTATGACGGTACCGGAA

GAAGAAGCCCCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAGGGGGGCAAGCGTTGTTCGGAATTATTGGGCGTAAAGGG

TGCGTAGGTGGTTCGGCAAGTCTTGTGTGAAATCTTCAGGCTCAACTTGAAGTCTGCACAAGAAACTGCCGGGCTTGAGTGTGGG

AGAGGTGAGTGGAATTCCGGTGTAGCGGTGAAATGCGTAGATATCGGAAGGAACACCTGTGGCGAAAGCGGCTCACTGGACCAT

TACTGACACTGAGGCACGAAAGCTAGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCTAGCCCTAAACGATGATCGCTTGGTG

TGGCGGGTATCCAACCCTGCCGTGCCGCAGCTAACGCGTTAAGCGATCCGCCTGGGGAGTACGGTCGCAAGGCTGAAACTCAAAG

GAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCTGGGCTCGAAATGTA

GTGGACTGGGGTAGAAATATCCCTTCCTAGCAATAGGCTGCTATATAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGT

TGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCTCCAGTTGCTACCATTTAGTTGGGCACTCTGGCGAAACCGCCTCGGATAACG

GGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCTTTATGTCCAGGGCTACACACGTGCTACAATGGCCGGTATAAACCGCT

GCGAACCCGCGAGGGGGAGCTAATCGGAAAAAGCCGGCCTCAGTTCGGATCGCAGTCTGCAACTCGACTGCGTGAAGCTGGAAT

CGCTAGTAATCGCAGATCAGAATGCTGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCAAATCACGAAAGTGGGTTG

CACTAGAAGCCGGTGCGCTAACCGCAAGGAAGCAGCCGTCCAAGGTGTGATTCATGATTGGGGTT












GCACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCAGCTCTTGACATTCACTGATCGCCTGGAGGGA

TTCGGGAGTCCCAGCAATGGGCAGTGGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGC



GGCGCTAATCGCAAAAAGCCGTCTCAGTTCAGATTGAGGTCTGCAACTCGACCTCATGAAGTCGGAATCGCTAGTAATCGCGCAT

CAGCATGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTCTACCAGAAGTAGGTAGG

CTAACCGCAAGGAGGCCGCTTACCACGGTAGGATTCATGACTGGGGTG


AACGAACGCTGGCGGCAGGCCTAACACATGCAAATCGAACGCCCCGCAAGGGGAGTGGCAGACGGGTGAGTAACACGTGGGAA

CCTTCCCTATAGTACGGAATAGCCCAGGGAAACTTGGAGTAATACCGTATACGCCCGAAAGGGGAAAGATTTATCGCTATAGGAT






CCTAGTGTAGAGGTGAAATTCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCCCACTGGATCGGTACTGACACTGAGGT

GCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGGATGCTAGCCGTCGGATAGCTCGCT

ATTCGGTGGCGCAGCTAACGCATTAAGCATCCCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAAGGAATTGACGGGGGCC

CGCACAAGCGGTGGAGCATGTGGTTTAATTCGACGCAACGCGAAGAACCTTACCAGCTCTTGACATTCACTGATCGCCTGGAGAG

ATCCGGGAGTTCCAGCAATGGACAGTGGGACAGGTGCTGCATGGCTGTCGTCAGTTCGTGTCGTGAGATGTTGGGTTAAGTCCCG

CAACGAGCGCAACCCTCGCCATTAGTTGCCATCATTTAGTTGGGCACTCTAGTGGGACTGCCGGTGAAAAGCCGGAGGAAAGTGG

GGATGACGTCAAGTCATCATGGCCCTTACGGGCTGGGCTACACACGTGCTACAATGGCGGTGACGATGCGCAGCCACCTAGCAAA

AGGGCGCTAATCGCAAAAAGCCGTCTCAGTTCAGATTGAGGTCTGCAACTCGACCTCATGAAGTCGGAACGCTAGTAATCGCGCA

TCAGCATGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTGGTCTTACCCTAAAACTGTGC


Appendix B 158




CCATCCCTATAGTACGGAATAGCCCAGGGAAACTTGGAGTAATACCGTATACGCCCGAGAGGGGAAAGATTTATCGCTATAGGAT




AACTTCGTGCCAGCAGCCGCGGTAATACGAGGGGGACTAGCGTTGTTCGGAATCACTGGGCGTAAAGCGCACGTAGGTGGATTTG






TTCGGGAGTCCCGGCAATGGGCAGTGGGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGC

AACGAGCGCAACCCTCGCCATTAGTTGCCATCATTAAGTTGGGCACTCTAGTGGGACTGCGGGTGATAAGCCGGAGGAAGGTGGG





Appendix C 159


APPENDIX C: BIOENERGETICS CALCULATIONS

C.1 Theoretical biomass yield from dimethyl sulphide (DMS)

1 – Formation of Biological Half Reaction:

→

2 – Balanced Half Reaction:

3 – Free Energy Calculation: (Appendix A: Rittman and McCarty)

DMS free energy of formation estimated from Mavrovouniotis, 1991

4 – Bioenergetics Calculations:

if N is available as NH4+

if N is available as NO3-

,

Appendix C 160


Case 1: N is available as NH4+

Case 2: N is available as NO3-

Appendix C 161


C.2 Theoretical biomass yield from methanol


→






,


Appendix C 162



Appendix C 163


C.3 Theoretical biomass yield from formaldehyde


→






,


Appendix C 164



Appendix C 165


C.4 Theoretical biomass yield from hydrogen sulphide


→






,


Appendix C 166



Appendix D 167


APPENDIX D: BIOMASS YIELD ESTIMATE CALCULATIONS

D.1 Biomass yield of Hyphomicrobium spp. from dimethyl sulphide (DMS)

1 - Biovolume Estimation:

Members of the Hyphomicrobium genus are small, gram-negative, rod-shaped cells (0.3-1.2 μm

x 1-3 μm) (Hirsch, 1989).

where and

2 – Cellular Dry Mass Estimation:

Biovolume to biomass (carbon content) conversion factor used: 0.22 g C cm-3

(Bratbak and

Dundas, 1984).

3 – 16S rDNA Copy Yield:

Assume 1 16S rRNA gene copy per cell (no information available).

Appendix D 168


D.2 Biomass yield of Hyphomicrobium spp. from methanol


Members of the Hyphomicrobium genus are small, gram-negative, rod-shaped cells (0.3-1.2 μm

x 1-3 μm) (Hirsch, 1989).

where and



(Bratbak and

Dundas, 1984).


Assume 1 16S rRNA gene copy per cell (no information available).

Appendix D 169


D.3 Biomass yield of Thiobacillus spp. from dimethyl sulphide (DMS)


Members of the Thiobacillus genus have been described as small, gram-negative, rod-shaped

cells (~0.5 x 1.0-4.0 μm) (Kelly and Harrison, 1989).

where and



(Bratbak and

Dundas, 1984).


Assume 2 16S rRNA gene copies per cell (Beller et al., 2006).

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