THE FORENSIC CHARACTERISATION OF THE
SOIL MICROBIAL COMMUNITY IN RESPONSE TO
CADAVER DECOMPOSITION
Kerith-Rae Dias (BSc, GDipForSci)
Centre for Forensic Science
University of Western Australia
This thesis is presented in partial fulfilment of the requirements for the
Master of Forensic Science
2011
i
ABSTRACT
The cadaver undergoes a complex and dynamic breakdown process after
death, known as decomposition. Taphonomy is the study of these processes;
their mechanisms, agents and interactions with the environment. As
decomposition progresses, nutrient-rich products are released from the
cadaver into the surrounding area that may include soil. Soil is a complex
medium, within which, its diverse community of microbiota is significantly
influenced by edaphic and environmental factors. The soil microbial
community is known to be affected by changes to its immediate environment.
The concept of resource-driven succession, as applied in entomology using the
succession of colonising insects, could theoretically also be applied to the
microbes involved in decomposition. The advent of molecular technology has
revolutionized the field of microbial ecology by providing culture-independent
methods of examining the diversity of a soil microbial community in any
ecosystem.
The primary aim of this research was to investigate if the soil microbial
community changed in response to the presence of a decomposing cadaver.
The objective was to determine that if these changes did occur, could they be
detected by the selected methodologies. Phospholipid fatty acid analysis and
fungal terminal restriction fragment length polymorphism community profiling
were used to provide a qualitative and quantitative analysis of these
transformations in soil microbial populations.
Soils were analysed from two previous experiments. A controlled laboratory
experiment was conducted, where replicate juvenile rat cadavers were
interred for incubation, in microcosms containing two types of tropical
savanna soils from Queensland, Australia. The cadavers were interred as
complete cadavers, incised and sown-up cadavers or eviscerated cadavers and
compared with control soils to determine how these treatments would alter
the soil microbial communities. A second experiment consisted of analysing
ii
soil that had been sampled periodically beneath two human cadavers during
the process of decomposition and associated control sites at the Forensic
Anthropology Center at the University of Tennessee, Knoxville.
The structure of the soil bacterial and fungal communities was affected
significantly by the presence of the decomposing rat and human cadavers.
Both PLFA and fungal T-RFLP were able to detect the alteration of the soil
microbial community, with respect to the different treatments of the rat
cadaver and along a temporal axis of the human decomposition period.
Furthermore, potential patterns of fungal succession were observed with the
human cadaver experiment.
The current study has demonstrated that the introduction of a cadaver into
the soil ecosystem has a significant effect on the surrounding soil microbial
community. The process is affected by environmental variables, the soil in
which the cadaver is placed and the characteristics of the cadaver. The
preliminary evidence demonstrated by this research holds potential for the
development of a novel tool for the estimation of post-mortem and post-
burial intervals based on soil microbial community succession. An accurate
estimation of time since death is an important aim of every medico-legal
investigation and its determination can direct an entire forensic case.
iii
ACKNOWLEDGEMENTS
I would like to thank my brilliant supervisors, Mark Tibbett, Jacqui Horswell
and David Carter for the opportunity to undertake a project I was passionate
about, lending their technical expertise whenever I needed it and providing
me with encouragement and guidance throughout the project. A sincere
thank you to Dr Ian Dadour for enabling me to undertake the Forensics
program, his troubleshooting skills and coordinating all administrative
responsibilities.
I am deeply indebted to Dr Richard Cookson who was extremely patient and
had a great sense of humour during my early PCR days at UWA, Dr Catriona
MacDonald for the strong work ethic she instilled in me at ESR, Dr Paul
Greenwood who‟s door was always open to me for technical advice and
counsel in general and Dr Natasha Banning who never failed to help me with
the most pedantic of my questions. A further thank you to Mark Tibbett for
implementing the multivariate statistics and to Bob Clarke for his guidance
with it.
I wish to express gratitude to a number of people who were involved in the
successful completion of this project: Dr Susan Barker, Dr Suman George, Dr
Kevin Murray, Dr Krystyna Haq and the fabulous ESR team in New Zealand. A
huge thank you to Rachel Parkinson without whom this project would not have
been possible or fun for that matter, for giving me something to aspire to and
being so generous with her soils, time and advice.
Endless thanks to the Centre of Forensic Science, Centre of Land
Rehabilitation and the Graduate Research School at UWA and The Institute of
Environmental and Science Research in New Zealand for generously covering
the costs of my project and for giving me the opportunity to attend and
present at my first conference.
iv
A big thanks to the “Decomp Divas”, Kathryn, Taryn and Natascha for all the
laughs, the encouragement, commiserating all through equipment failure,
experiment flops and writer‟s block as only a fellow student knows how and
for especially making my time in Perth memorable.
Finally, I would like to thank my family, who gave me everything.
v
DECLARATION
I declare that the research presented in this 36 point thesis, as part of the 96
point Master degree in Forensic Science, at the University of Western
Australia, is my own work. The results of the work have not been submitted
for assessment, in full or part, within any other tertiary institute, except
where due acknowledgement has been made in the text.
…………………………………………………
Kerith-Rae Dias
vi
Table of Contents
Abstract ................................................................................ i
Acknowledgements ................................................................... iii
Declaration .............................................................................. v
List of Figures ........................................................................... x
List of Tables ........................................................................ xviii
Chapter 1: INTRODUCTION ............................................. 1
1.1 Cadaver decomposition .......................................... 1
1.2 Soil Microbial Communities ..................................... 1
1.3 Post Mortem Interval ............................................. 2
1.4 Purpose of the current research ............................... 2
1.5 Studies in decomposition and microbiology .................. 3
1.6 Aims of the research ............................................. 5
1.7 Research approach ............................................... 6
Chapter 2: REVIEW OF THE LITERATURE ............................ 7
2.1 Decomposition .................................................... 7
2.1.1 The process of decomposition ....................................... 7
2.1.2 Factors affecting decomposition .................................... 9
2.1.3 The microbiology of decomposition .............................. 10
2.2 Soil ................................................................ 12
2.2.1 The microbiology of soil ............................................ 13
2.2.1.1 Bacteria ...............................................................13
2.2.1.2 Fungi ..................................................................14
2.2.1.3 Other inhabitants ....................................................15
2.2.2 Properties affecting the soil microbial community ............ 16
2.2.2.1 Physical and chemical properties .................................16
2.2.2.2 Nutrient availability.................................................18
2.2.2.3 Soil depth .............................................................18
2.2.2.4 Human activity .......................................................19
vii
2.3 Post-Mortem Interval Estimation ............................. 19
2.3.1 Pathology and Anthropology ....................................... 20
2.3.2 Entomology ............................................................ 21
2.4 Microbial Community Analysis ................................ 23
2.4.1 Phospholipid Fatty Analysis ........................................ 24
2.4.1.1 Structure and function of PLFAs .................................. 24
2.4.1.2 Significance of PLFAs............................................... 25
2.4.1.3 PLFA method ........................................................ 26
2.4.1.4 PLFA Studies ......................................................... 26
2.4.2 Terminal Restriction Fragment Length Polymorphism Analysis
........................................................................... 27
2.4.2.1 T-RFLP method ...................................................... 27
2.4.2.2 Target genes ........................................................ 28
2.4.2.3 T-RFLP Studies ...................................................... 31
2.5 Data handling and statistical analysis ....................... 32
Chapter 3: RAT CADAVER EXPERIMENT ............................ 35
3.1 Introduction ..................................................... 35
3.2 Aims and Objectives ............................................ 35
3.3 Experimental Background ..................................... 35
3.4 Materials/Methods and Results ............................... 36
3.4.1 Phospholipid Fatty Analysis ........................................ 36
3.4.1.1 Extraction ............................................................ 36
3.4.1.2 Fractionation ........................................................ 37
3.4.1.3 FAME Derivitisation ................................................. 37
3.4.1.4 Gas Chromatography/Mass Spectrometry ....................... 38
3.4.1.5 Statistics ............................................................. 39
3.4.2 Terminal Restriction Fragment Length Polymorphism Analysis
........................................................................... 41
3.4.2.1 DNA Extraction ...................................................... 41
3.4.2.2 DNA Quantification ................................................. 42
3.4.2.3 Polymerase Chain Reaction ....................................... 42
viii
3.4.2.4 PCR Product Clean-up ..............................................48
3.4.2.5 Restriction Enzyme Digestion ......................................48
3.4.2.6 T-RF Analysis .........................................................49
3.5 Data Handling and Statistical Analysis ....................... 56
3.5.1 PLFA Datasets ......................................................... 56
3.5.2 T-RFLP Datasets ...................................................... 58
3.6 Discussion ........................................................ 64
3.6.1 PLFA Results .......................................................... 64
3.6.2 T-RFLP Profiling Results ............................................ 65
3.6.2.1 Controls ...............................................................65
3.6.2.2 Bacterial T-RFLP Profiling Results ................................66
3.6.2.3 Fungal T-RFLP Profiling Results ...................................68
3.6.3 Other Considerations ................................................ 69
Chapter 4: HUMAN CADAVER EXPERIMENT ........................ 71
4.1 Introduction ..................................................... 71
4.2 Aims and Objectives ............................................ 71
4.3 Experimental Background ..................................... 72
4.4 Materials/Methods and Results ............................... 76
4.4.1 Accumulated degree-days .......................................... 76
4.4.2 Phospholipid Fatty Analysis ........................................ 76
4.4.3 Fungal Terminal Restriction Fragment Length Polymorphism .
........................................................................... 77
4.4.3.1 DNA Extraction .......................................................77
4.4.3.2 DNA Quantification ..................................................77
4.4.3.3 Polymerase Chain Reaction ........................................77
4.4.3.4 PCR Product Clean-up ..............................................82
4.4.3.5 Restriction Enzyme Digestion ......................................82
4.4.3.6 Fungal Community Profile Generation ...........................83
4.4.3.7 Fungal ITS-TRF Detection ........................................ 102
4.5 Data Handling and Statistical Analysis ...................... 103
4.5.1 PLFA Dataset ......................................................... 103
4.5.2 Fungal Community Dataset ........................................ 107
ix
4.6 Discussion ....................................................... 109
4.6.1 Method Comparison ................................................ 109
4.6.2 PLFA Results ......................................................... 110
4.6.3 Fungal T-RFLP Profiling Results ................................. 111
4.6.3.1 Controls ............................................................. 111
4.6.3.2 Cadavers ............................................................ 111
Chapter 5: CONCLUSION .............................................. 119
References ............................................................... 127
Appendices ............................................................... 140
I PowerSoil™ DNA Isolation Kit .......................................... 140
II FastDNA SPIN kit for Soil Protocol with added Plant DNAzol Protocol
.............................................................................. 141
III DNA Visualisation Protocol with Sybr SAFE ......................... 142
IV Pico Green Assay ......................................................... 143
V QIAquick PCR Purification Kit Protocol .............................. 143
VI Bacterial Digestion Protocol ........................................... 144
VII Fungal Digestion Protocol .............................................. 144
VIII Source of Materials ...................................................... 145
IX Temperature data and ADD calculation for cadaver P and R .... 146
X Phospholipid fatty acid peak area data of Pallarenda and
Wambiana soil microbial communities………………………………………..148
XI Phospholipid fatty acid peak area data of control O and cadaver P
…………………………………………………………………………………………………………149
XII Phospholipid fatty acid peak area data of control Q and cadaver
R………………………………………………………………………………………………………150
x
List of Figures
Figure 2.1: Common microbial species that colonise a human body during life
(Jawetz, Melnick and Melnick, 1982). ......................................... 10
Figure 2.2: The soil textural triangle. The basic soil textural classes
consisting of percentages of clay silt and sand (Murray and Tedrow,
1992). ............................................................................... 13
Figure 2.3: Arrangement of phospholipids in the membrane of a living cell
(Kaur 2005). ....................................................................... 25
Figure 2.4: Overview of the T-RFLP method (Applied Biosystems, 2005). ..... 28
Figure 2.5: The rRNA Operon. It consists of three rRNA molecules: 16S, 23S
and 5S, which are separated by internal transcribed spacer (ITS) regions
(Flechtner et al., 2002). ......................................................... 29
Figure 2.6: The 16S rRNA secondary structure. Primary sequence with near
universal conservation (thick lines), intermediate conservation (normal
lines) and hypervariability (dashed lines) is shown (Ward et al., 1992).
Arrows and black lines indicate the region of the gene amplified by PCR.
The grey regions at the 3‟ and 5‟ ends are not amplified. ................. 30
Figure 2.7: Internal transcribed spacer (ITS) region map. The ITS regions
exist in two segments, the ITS1 and ITS2, which bracket the 5.8S rDNA. 31
Figure 3.2: Optimising the effect of soil weight on DNA yield. Three weights
tested: 0.2 g, 0.4 g, 0.6 g. 1 = Pallarenda soil (A) control (C) 1 (0.2g), 2 =
AC 2 (0.4g), 3 = AC 3 (0.6g), 4 = Wambiana soil (B) incised (IN) 1 (0.2g), 5
= BIN 2 (0.4g), 6 = BIN 3 (0.6g), L = 200 bp ladder. .......................... 41
Figure 3.3: Optimising the DNA concentration used for the polymerase chain
reaction protocol. Three concentrations were tested: 1/5 dilution, 1 µL
of pure DNA extract and 2 µL of pure DNA extract. 1 = AC1 - Pallarenda
soil (A) control (C) 1 (1/5 dilution), 2 = AC2 (1/5 dilution), 3 = AC3 (1/5
dilution), 4 = AC1 (1 µL), 5 = AC2 (1 µL), 6 = AC3 (1 µL), 7 = AC1 (2 µL), 8
= AC2 (2 µL), 9 = AC3 (2 µL), 10 = Wambiana soil (B) incised (IN) (1/5
dilution), 11 = BIN (1/5 dilution), 12 = BIN (1/5 dilution), 13 = BIN (1 µL),
14 = BIN (1 µL), 15 = BIN (1 µL), 16 = BIN (2 µL), 17 = BIN (2 µL), 18 = BIN
(2 µL), P = positive control (E. coli gDNA), N = negative control (reagent),
L = 200 bp ladder. ................................................................ 43
xi
Figure 3.4: Polymerase chain reaction product of bacterial DNA from
Pallarenda soil (soil A) samples using 30 cycles. 1 = control (C) 1, 2 = C1
(duplicate), 3 = C2, 4 = C2 (duplicate), 5 = complete cadaver (CC) 1, 6 =
CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate),
12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 = EV4, P =
positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp
ladder. Duplicate samples are labelled with an asterisk. ................... 44
Figure 3.5: Polymerase chain reaction product of bacterial DNA from
Pallarenda soil (soil A) samples using 25 cycles. 1 = control (C) 1, 2 = C1
(duplicate), 3 = C2, 4 = C2 (duplicate), 5 = complete cadaver (CC) 1, 6 =
CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate),
12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 = EV4, P =
positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp
ladder. .............................................................................. 45
Figure 3.6: Polymerase chain reaction product of fungal DNA from Pallarenda
soil (soil A) samples. 1 = control (C) 1, 2 = C1 (duplicate), 3 = C2, 4 = C2
(duplicate), 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 =
incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate), 12 = IN3, 13 = eviscerated
(EV) 1, 14 = EV2, 15 = EV3, 16 = EV4, L = 200 bp ladder. ................... 45
Figure 3.7: Polymerase chain reaction product of bacterial DNA from
Wambiana soil (soil B) samples. 1 = control (C) 1, 2 = C2, 3 = C2
(duplicate), 4 = C3, 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 =
CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN3, 12 = IN4, 13 = eviscerated (EV)
1, 14 = EV2, 15 = EV2 (duplicate), 16 = EV3, P = positive control (E. coli
gDNA), N = negative control (reagent), L = 200 bp ladder. ................. 46
Figure 3.8: Polymerase chain reaction product of fungal DNA from Wambiana
soil (soil B) samples. 1 = control (C) 1, 2 = C2, 3 = C2 (duplicate), 4 = C3,
5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN)
1, 10 = IN2, 11 = IN3, 12 = IN4, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV2
(duplicate), 16 = EV3, P = positive control (C. albicans DNA), N = negative
control (reagent), L = 200 bp ladder. .......................................... 47
Figure 3.9: Bacterial terminal restriction fragment (T-RF) profile of control
sample 1 (top) and 2 (bottom) of Pallarenda soil. The blue peaks
represent the FAM-labelled T-RFs from the 5‟ end, the green peaks are
xii
the HEX-labelled T-RFs from 3‟ end, and the orange peaks represent the
LIZ500 size standard. ............................................................. 51
Figure 3.10: Fungal terminal restriction fragment profile of control sample 1
(top) and 2 bottom of Pallarenda soil. FAM = blue, LIZ500 size standard =
orange. ............................................................................. 51
Figure 3.11: Bacterial terminal restriction fragment profile of complete
cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver
1 (bottom) of Pallarenda soil. FAM-labelled 5‟ end = blue, HEX-labelled
3‟ end = green, LIZ500 size standard = orange. .............................. 52
Figure 3.12: Fungal terminal restriction fragment profile of complete cadaver
sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1
(bottom) of Pallarenda soil. FAM = blue, LIZ500 size standard = orange. 53
Figure 3.13: Bacterial terminal restriction fragment profile of complete
cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver
1 (bottom) of Wambiana soil. FAM-labelled 5‟ end = blue, HEX-labelled 3‟
end = green, LIZ500 size standard = orange. ................................. 54
Figure 3.14: Fungal terminal restriction fragment profile of complete cadaver
sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1
(bottom) of Wambiana soil. FAM = blue, LIZ500 size standard = orange. 55
Figure 3.15: Multi-dimensional scaling plot of phospholipid fatty acid profiles
of soil microbial communities of Pallarenda (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
) and Wambiana (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
) soils
containing control and treatments soils. Profiles that share at least 60%
similarity are circled in green. PR = Pallarenda, WB = Wambiana. ....... 57
Figure 3.16: Multi-dimensional scaling plot comparing the bacterial 3' end
terminal restriction fragment abundances, labelled with the fluorescent
dye HEX, for both soils. Soil A=Pallarenda, soil B=Wambiana. BH1, 3, 17,
18, 20 = control, BH5, 6, 7, 8, 21, 22, 23, 24 = complete cadaver, BH9,
10, 12, 25, 26, 2, 28 = incised cadaver, BH13, 14, 15, 16, 29, 30, 32 =
eviscerated cadaver. ............................................................. 58
Figure 3.17: Multi-dimensional scaling plot comparing the bacterial 5' end
terminal restriction fragment abundances, labelled with the fluorescent
dye FAM, for both soils. Soil A=Pallarenda, soil B=Wambiana. C =
control, CC = complete cadaver, IC = incised cadaver, EC = eviscerated
cadaver. ............................................................................ 60
xiii
Figure 3.18: Multi-dimensional scaling plot comparing the fungal restriction
fragment abundances for Pallarenda and Wambiana soils. Soil
A=Pallarenda, soil B=Wambiana. F1, 3, 17, 18, 20 = control, F5, 6, 7, 8,
21, 22, 23, 24 = complete cadaver, F9, 10, 12, 25, 26, 27, 28 = incised
cadaver, F13, 14, 15, 16, 29, 30, 32 = eviscerated cadaver. ............... 61
Figure 3.19: Multi-dimensional scaling plot of fungal terminal restriction
fragments of the Pallarenda and Wambiana soil containing C = control
soils (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
), CC = complete cadaver (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
), IC = incised cadaver (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
), and EC =
eviscerated cadaver (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
) samples. ........................................... 63
Figure 4.1: Cadaver P at ADD 106 (day 3) of decomposition. Sloughing of the
skin and some maggots visible. Orange plastic mesh is used to assist in
collection of soil samples from under the cadaver and to preserve its
integrity. ........................................................................... 73
Figure 4.2: Cadaver P at ADD 1092 (day 52) of decomposition. Cadaver is in
the skeletonised stage. .......................................................... 74
Figure 4.3: Cadaver R at ADD 23 (day 0) of decomposition on the day of
placement. ......................................................................... 74
Figure 4.4: Cadaver R at ADD 684 (day 38) of decomposition. Cadaver is in
the „bloat‟ stage. ................................................................. 75
Figure 4.5: Polymerase chain reaction product of fungal amplification from
control O soil samples. 1 = Control O (O) sampled on day 0, 2 = O3, 3 =
O6, 4 = O8, 5 = O10, 6 = O14, 7 = O16, 8 = O20, 9 = O23, 10 = O27, 11
O29= , 12 = O31, 13 = O35, 14 = O38, 15 = O42, 16 = O45, 17 = O49, 18 =
O52, 19 = O58, 20 = O62, 21 = O69, N = negative control (reagent), P =
positive control (C. albicans DNA), L = 100 bp DNA ladder. ................ 79
Figure 4.6: Polymerase chain reaction product of fungal amplification from cadaver P
samples. 1 = Cadaver P (P) sampled at ADD 27, 2 = P106, 3 = P238, 4 = P286, 5 = P376, 6
= P420, 7 = P512, 8 = P573, 9 = P660, 10 = P695, 11 = P730, 12 = P808, 13 = P854, 14 =
P927, 15 = P985, 16 = P1053, 17 = P1092, N = negative control (reagent), P = positive
control (C. albicans DNA), L = 100 bp DNA ladder. .................................... 80
Figure 4.7: Polymerase chain reaction product of fungal amplification from
re-extracted cadaver P samples. 1 = Cadaver P (P) sampled at ADD 185, 2
= P1172, 3 = P1212, 4 = P1285, N = negative control (reagent), P =
positive control (C. albicans DNA), L = 100 bp DNA ladder. ................ 80
xiv
Figure 4.8: Polymerase chain reaction product of fungal amplification from
re-extracted cadaver P samples. 1 = Cadaver P (P) sampled at ADD 106, 2
= P238, 3 = P420, 4 = P1212, N = negative control (reagent), P = positive
control (C. albicans DNA), L = 100 bp DNA ladder. .......................... 81
Figure 4.9: Polymerase chain reaction product of fungal amplification from
control Q samples (top) and cadaver R samples (bottom). Top: 1 =
Control Q (Q) sampled on day 0, 2 = Q3, 3 = Q7, 4 = Q9, 5 = Q11, 6 = Q15,
7 = Q18, 8 = Q22, 9 = Q25, 10 = Q29, 11 = Q32, 12 = Q38, 13 = Q42, 14 =
Q49. Bottom: 1 = Cadaver R (R) sampled at ADD 23, 2 = R85, 3 = R171, 4
= R207, 5 = R242, 6 = R319, 7 = R366, 8 = R438, 9 = R497, 10 = R564, 11 =
R603, 12 = R684, 13 = R724, 14 = R797, N = negative control (reagent), P
= positive control (C. albicans DNA), L = 100 bp DNA ladder. .............. 81
Figure 4.10: Polymerase chain reaction product of fungal amplification from
re-extracted cadaver R samples. 1 = Cadaver R (R) sampled at ADD 85, 2
= R366, 3 = R497, 4 = R684, N = negative control (reagent), P = positive
control (C. albicans DNA), L = 100 bp DNA ladder. .......................... 82
Figure 4.11: Soil fungal profiles from control O sampled on days 0, 6, 8 and 10. The grey bars represent the regions
(120–170 bp and 320-400 bp) where the dominant peaks occur in the control O profiles. Fluorescence intensity
is expressed in relative fluorescence units (RFU) to account for intra-instrument variation. ............. 84
Figure 4.12: Soil fungal profiles from control O sampled on days 14, 16, 20
and 23. Fluorescence intensity is expressed in relative fluorescence units
(RFU) to account for intra-instrument variation. ............................ 85
Figure 4.13: Soil fungal profiles from control O sampled on days 27, 29, 31
and 35. Fluorescence intensity is expressed in relative fluorescence units
(RFU) to account for intra-instrument variation. ............................ 86
Figure 4.14: Soil fungal profiles from control O sampled on days 38, 42, 45
and 49. Fluorescence intensity is expressed in relative fluorescence units
(RFU) to account for intra-instrument variation. ............................ 87
Figure 4.15: Soil fungal profiles from control O sampled on days 52, 58, 62
and 69. Fluorescence intensity is expressed in relative fluorescence units
(RFU) to account for intra-instrument variation. ............................ 88
Figure 4.16: Soil fungal profiles from cadaver P sampled at ADD 27, 106, 376
and 512 (days 0, 3, 14 and 20 respectively). Fluorescence intensity is
expressed in relative fluorescence units (RFU) to account for intra-
instrument variation. ............................................................. 90
xv
Figure 4.17: Soil fungal profiles from cadaver P sampled at ADD 730, 927, 985
and 1053 (days 31, 42, 45 and 49 respectively). Fluorescence intensity is
expressed in relative fluorescence units (RFU) to account for intra-
instrument variation. ............................................................. 91
Figure 4.18: Soil fungal profiles from cadaver P at ADD 1092, 1172, 1212 and
1285 (days 52, 58, 62 and 69 respectively). Fluorescence intensity is
expressed in relative fluorescence units (RFU) to account for intra-
instrument variation. ............................................................. 92
Figure 4.19: Soil fungal profiles from control Q sampled on days 0, 3, 7 and 9. The grey bars represent the regions
(130–170 bp and 320-370 bp) where the dominant peaks occur in the control Q profiles. Fluorescence intensity
is expressed in relative fluorescence units (RFU) to account for intra-instrument variation. ............. 94
Figure 4.20: Soil fungal profiles from control Q sampled on days 11, 15, 18
and 22. Fluorescence intensity is expressed in relative fluorescence units
(RFU) to account for intra-instrument variation. ............................ 95
Figure 4.21: Soil fungal profiles from control Q sampled on days 25, 29, 32
and 38. Fluorescence intensity is expressed in relative fluorescence units
(RFU) to account for intra-instrument variation. ............................ 96
Figure 4.22: Soil fungal profiles from control Q sampled on days 42 and 49.
Fluorescence intensity is expressed in relative fluorescence units (RFU) to
account for intra-instrument variation. ....................................... 98
Figure 4.23: Soil fungal profiles from cadaver R at ADD 23 and 85 (days 0 and
3 respectively). The grey bars represent a peak that appears at ADD23
but disappears at ADD 85. ....................................................... 98
Figure 4.24: Soil fungal profiles from cadaver R sampled at ADD 171, 207, 242
and 366 (days 7, 9, 11 and 18 respectively). The grey bars represent a
reduction in peak height of the same peak from ADD 242 to ADD366..... 99
Figure 4.25: Soil fungal profiles from cadaver R at ADD 438, 497, 564 and 603
(days 22, 25, 29 and 32 respectively). Fluorescence intensity is expressed
in relative fluorescence units (RFU) to account for intra-instrument
variation. ......................................................................... 100
Figure 4.26: Soil fungal profiles from cadaver R sampled at ADD 684, 724, and
797 (days 38, 42 and 49 respectively). Fluorescence intensity is expressed
in relative fluorescence units (RFU) to account for intra-instrument
variation. ......................................................................... 101
xvi
Figure 4.27: Multi-dimensional scaling plot of phospholipid fatty acid profiles
for cadaver P (●) and control O (▪). Accumulated degree-days denote the
stage of decomposition when the sample was collected. 0 = day of
placement/first day of sampling. The boundaries of the ellipses are
defined by the samples within having profiles at least 80% similar to each
other. .............................................................................. 104
Figure 4.28: Multi-dimensional scaling plot of phospholipid fatty acid profiles
for cadaver R (●) and control Q (▪). Accumulated degree-days denote the
stage of decomposition when the sample was collected. 0 = day of
placement/first day of sampling. The boundaries of the ellipses with
dotted lines are defined by the samples within having profiles at least
65% similar to each other. The boundaries of the ellipses with smooth
lines are defined by the samples within having profiles at least 85%
similar to each other. ........................................................... 105
Figure 4.29: Abundance of fungal marker from phospholipid fatty acid
analysis for control O and cadaver P. A temporal profile of the fungal
marker, C18:26 (peak 27) is calculated as a percentage of total
phospholipids. 0 = day of placement/first day of sampling............... 106
Figure 4.30: Abundance of fungal marker from phospholipid fatty acid analysis
for control Q and cadaver R. A temporal profile of the fungal marker,
C18:26 (peak 27) is calculated as a percentage of total phospholipids. 0
= day of placement/first day of sampling. ................................... 106
Figure 4.31: Multi-dimensional scaling plot of fungal internal transcribed
spacer-terminal restriction fragment profiles for cadaver P ( ) and
control O (●). Accumulated degree-days are used to denote the stage of
decomposition when the sample was collected. 0 = day of
placement/first day of sampling. The cadaver samples have been circled
to show their separation from the control samples. ....................... 107
Figure 4.32: Multi-dimensional scaling plot of fungal internal transcribed
spacer-terminal restriction fragment profiles for cadaver P. Accumulated
degree-days denote the stage of decomposition when the sample was
collected. Grey line separates early, mid and late phase fungi. 0 = day of
placement. The black arrow shows potential temporal trend. .......... 108
Figure 4.33: Multi-dimensional scaling plot of internal transcribed spacer-
terminal restriction fragment profiles for cadaver R and control Q.
xvii
Accumulated degree-days denote the stage of decomposition when the
sample was collected. The black arrow shows possible fungal pattern of
succession. 0 = day of placement/first day of sampling. The cadaver
samples have been circled to show their separation from the control
samples. .......................................................................... 109
xviii
List of Tables
Table 3.1: Polymerase chain reaction mastermix used for bacterial
amplification of soil microbial communities of the Pallarenda and
Wambiana soils. Amplification was conducted in a 50 µL reaction volume.
...................................................................................... 43
Table 3.2: Polymerase chain reaction mastermix used for fungal amplification
of soil microbial communities of the Pallarenda and Wambiana soils.
Amplification was conducted in a 50 µL reaction volume. ................. 46
Table 3.3: Amount of bacterial DNA (ng/L) present in Pallarenda and
Wambiana soil samples after polymerase chain reaction amplification. A
= Pallarenda soil, B = Wambiana soil, C = control, CC = complete cadaver,
IN = incised cadaver, EV = eviscerated cadaver, 1A/1B, 2A/2B = duplicate
samples. ............................................................................ 47
Table 3.4: Amount of fungal DNA (ng/L) present in Pallarenda and
Wambiana soil samples after polymerase chain reaction amplification. A
= Pallarenda soil, B = Wambiana soil, C = control, CC = complete cadaver,
IN = incised cadaver, EV = eviscerated cadaver, 1A/1B, 2A/2B = duplicate
samples. ............................................................................ 48
Table 3.5: Definition of significance levels using p-values. ...................... 56
Table 3.6: Significance results of pairwise test conducted on phospholipid
fatty acid profiles between all treatment groups and across both soil
types. ............................................................................... 57
Table 3.7: Significance results of pairwise test conducted on 3‟ end of
bacterial terminal restriction fragments between all treatment groups
and across both soil types. ...................................................... 59
Table 3.8: Significance results of pairwise test conducted on 5‟ end of
bacterial terminal restriction fragments between all treatment groups
and across both soil types. ...................................................... 60
Table 3.9: Significance results of pairwise test conducted on fungal terminal
restriction fragments between all treatment groups and across both soil
types. ............................................................................... 62
Table 3.10: Summary of the significance results of pairwise tests conducted
between all treatment groups, across both soil types and over all
xix
methods. HS = highly significant, S = significant, MS = marginally
significant, NS = not significant ................................................. 63
Table 4.1: Details of two human cadavers used in experiment. ................ 73
1
Chapter 1 : INTRODUCTION
1.1 Cadaver decomposition
A cadaver can be considered of as a source of sequestered nutrients, energy
and a microbial inoculum, which can be released into an ecosystem via the
process of decomposition. The unhindered decomposition of a cadaver
involves the consecutive processes of autolysis, putrefaction and decay (Vass,
2001). These processes initiate the breakdown of the cadavers‟ constituents
into the basic building blocks of organic matter, by the action of cellular
enzymes, microorganisms and a range of environmental variables (Evans,
1963; Clark, Worrell and Pless, 1997). The rate of decomposition is
influenced by the interplay of the intrinsic factors of the cadaver (sex, age,
physique, cause of death) and extrinsic factors (temperature, pH, moisture,
substrate type, feeding) of the environment (Mann, Bass and Meadows, 1990).
The breakdown of this high-quality resource (narrow carbon:nitrogen ratio,
high water content) introduces a concentrated and localised pulse of water,
carbon, nitrogen and other nutrients into the surrounding environment
(Carter, Yellowlees and Tibbett, 2007).
1.2 Soil Microbial Communities
Cadavers can be found in a range of enclosed (dwellings, cars) and exposed
environments (aquatic, terrestrial). They are often discovered in or on soil
and the processes of decomposition will invariably have an effect on the soil
ecosystem (Carter, Yellowlees and Tibbett, 2007). This ecosystem is
composed of a complex community of plants, animals, and microbes, which
include fungi, bacteria, actinomycetes and protozoa. Soil microbial
communities are the main decomposers of organic matter in soil (Coleman,
Crossley and Hendrix, 2004). These communities can respond to the addition
of nutrients or a modification in the environment, like the introduction of
cadaver to the soil environment, by changing in abundance and diversity
(Hopkins, Wiltshire and Turner, 2000). The different components of the
cadaver decompose at different stages, depending on their constitution,
2
metabolic activity during life, enzyme concentration or their exposure to the
environment (Megyesi, Nawrocki and Haskell, 2005). Decomposition may
begin with the intestines, stomach and heart due to their high rates of energy
transformation and end with the autolysis of skeletal muscle and connective
tissue (Vass et al., 2002). The exposed remains, therefore, present an
ephemeral and progressively changing habitat and food source to microbes.
These nutrients may be utilised by a succession of microbes, each wave
colonizing and altering the microhabitat and making way for the next
population of microbes. The dynamics of soil microbial communities in
response to cadaver decomposition might be associated with a microbial
pattern of succession, much like what is seen with the succession of insects on
a cadaver (Payne, 1965; Anderson, 2001).
1.3 Post Mortem Interval
An accurate estimation of time since death or the „post-mortem interval‟
(PMI) is one aim of every medico-legal investigation, equal in importance to
victim identification and cause of death. Determination of the PMI can direct
or re-orient an entire investigation by serving to validate or reject a suspect‟s
alibi or elucidate the victim‟s peri-mortem activities. Rarely, in a forensic
investigation, is a post-mortem estimate based on a single variable or
method. Consequently, new PMI estimation tools are constantly being sought,
as it is appreciated that the use of an array of methods will lead to a more
accurate estimation of the PMI.
1.4 Purpose of the current research
The soil microbial community is recognized as „the eye of the needle‟ through
which all organic matter must eventually pass (Jenkinson, 1977). One gram of
soil may contain up to 10 billion microorganisms of possibly thousands of
species and it is widely accepted that less than 1% of soil microorganisms have
been cultivated and characterized (Torsvik and Ovreas, 2002). Culture-
dependent methods have therefore restricted the accurate examination of the
soil microbial community. Furthermore, the study of human cadaver
decomposition is understandably restricted by the difficulty of obtaining
3
cadavers to experiment with, lack of suitable areas for the placement and
study of these processes, negative public opinion and ethical impositions
(Mann, Bass and Meadows, 1990). As a result, there is very little
understanding of the relationships and interactions between that of a
decomposing cadaver upon soil and its associated microbiology. The current
research project investigates the potential of two culture-independent
methods for soil microbial characterisation in order to study the dynamics of
soil microbial communities associated with cadaver decomposition upon soil;
deoxyribonucleic acid- (DNA) based terminal restriction fragment length
polymorphism (T-RFLP) analysis and lipid-based phospholipid fatty acid (PLFA)
analysis. These methods have revolutionized the study of microbial diversity
and community analysis by eliminating the need for culturing the soil
microbes.
1.5 Studies in decomposition and microbiology
Previous decomposition studies have provided some insight into the
relationships between decomposition, microbiology and the burial
environment, and this information has been gathered from primarily empirical
observations and anecdotal summaries. Many studies have acknowledged the
role (Child, 1995; Campobasso, Di Vella and Introna, 2001; Tibbett and Carter,
2003; Okoth, 2004) and contribution (Micozzi, 1986; Hopkins, Wiltshire and
Turner, 2000; Tibbett et al., 2004; Carter, 2006; Franicevic, 2006) of
microbes to the cadaver decomposition process but little research has focused
on advancing the understanding of how decomposition affects microbes in the
burial environment.
A handful of studies have commented on the effect of cadaver/tissue
decomposition on soil microbiota. Soil microbial biomass has been estimated
in relation to soft tissue decomposition by using substrate-induced respiration
(Hopkins, Wiltshire and Turner, 2000; Fiedler, Schneckenberger and Graw,
2004; Tibbett et al., 2004; Carter, 2006). Soil microbial activity has been
measured in response to soft tissue decomposition using CO2 respiration
(Putman, 1978a; Putman, 1978b; Child, 1995; Tibbett et al., 2004; Petkovic,
Simic and Vujic, 2005; Carter, 2006; Rapp et al., 2006; Carter, Yellowlees and
4
Tibbett, 2007; Wilson et al., 2007;). The extent of current knowledge is
limited to the understanding that cadaver decomposition can prompt the
growth and activity of the soil microbial biomass. The use of DNA-based
methods can add to this knowledge by providing insights into microbial
composition, abundance and diversity. Phospholipid fatty acids are found in
all living cells and rapidly metabolise after cell death (Frostegard, Tunlid and
Baath, 1993). The distribution of PLFAs, may represent a phenotype of extant
soil microbial communities based on the variability of fatty acids of various
microbial organisms (Zelles, 1992).
The microbial decomposition and degradation of various elements of the
cadaver have been studied. Research has demonstrated fungal tunneling and
bacterial alteration of bone and teeth in human skeletal samples (Yoshino et
al., 1991; DeGaetano, Kempton and Rowe, 1992; Child, 1995; Bell, Skinner
and Jones, 1996) and the microbial degradation of adipocere (Pfeiffer, Milne
and Stevenson, 1998), hair (Griffin, 1960; Collier, 2005; Edwards et al., 2007)
and skin (Micozzi, 1986). The bacteria and fungi known as decomposers,
breakdown organic nitrogen which is a constituent of protein, to produce
ammonium ions. Nitrifying bacteria utilize energy sources derived from the
chemical conversion of these ammonium ions to nitrite (ammonia oxidizers) or
nitrite to nitrate (nitrite oxidizers) (Dent, Forbes and Stuart, 2004). Similarly,
the transformation of unsaturated into saturated fatty acids by bacterial
enzymes (Fiedler and Graw, 2003) has been documented. Many bacterial
species are ethanol producers, therefore post-mortem microbial fermentation
can result in the production of ethanol that can affect the accurate
interpretation of blood alcohol concentration in toxicological analysis in a
forensic case (Ziavrou, Boumba and Vougiouklakis, 2005). The utilization of
microbial growth and succession in relation to the cadaver decomposition
process has been suggested as tools for estimating post-mortem interval
(Vass, 2001; Tibbett and Carter, 2003; Parkinson, 2004; Carter, Yellowlees
and Tibbett, 2007). The migration of enteric microbiota through the wall of
the small intestine in mice was evaluated as a model for assessing time of
death (Melvin et al., 1984). Janaway (1996) described the succession from
predominantly aerobic to anaerobic microbes of the enteric community during
autolysis of the cadaver. The relationship between fungal growth and
5
changes in proximal end morphology of human head hair has been evaluated
towards estimating a PMI (Collier, 2005). Griffin (1960) describes an order of
fungal succession on hair in contact with soil, from highly saprotrophic fungi
to fungi with less saprotrophic ability and finally to keratinophilic fungi. More
recently, the potential of using the successive colonization of fungi on human
cadavers for estimating the PMI, was explored (Tibbett and Carter, 2008),
using case studies and culture-dependent methods (Hitosugi et al., 2006; Ishii
et al., 2006; Sagara, Yamanaka and Tibbett, 2008).
1.6 Aims of the research
The broad aim of this work is a preliminary evaluation of the potential for
using soil microbial communities as a tool to estimate post mortem interval.
This will be achieved by determining if the exposure of a soil to a cadaver
alters soil microbial community structure, and furthermore, if this can be
developed to provide a novel line of evidence for post mortem (or burial)
interval. The specific research questions of the project were to address the
following:
(i) Does the structure of the soil microbial community change in the
presence of a decomposing cadaver?
(ii) Does this change show any predictable pattern or temporal
trend?
Subsidiary to these questions, the project was also designed to compare which
one of the two leading methodologies of determining microbial communities
(TRFLP and PLFA) give the best resolution to changes in the soil community
structure. To accomplish this I aimed to detect changes in the patterns of the
chemically diverse phosphilipid fatty acids found in membrane components of
all organisms, and the molecular profiles generated by the fluorescently-
labeled terminal restriction fragments of the 16S ribosomal RNA gene that
probes the eubacterial population and the internal transcribed region of the
fungal ribosomal RNA gene to detect the fungal population in soils from the
detritusphere of decomposing human and mouse cadavers.
6
1.7 Research approach
In this study, PLFA analysis and T-RFLP community profiling was used to
provide a qualitative and quantitative assessment, respectively, of soil
microbial communities at different stages of cadaver decomposition. These
analyses were conducted on existing experimental materials from two unique
experiments. The first experiment involved the decomposition of rat (Rattus
rattus) cadavers in two types of tropical savanna soils of Queensland,
Australia. The cadavers were treated to allow quantification of the influence
of the cadaver-derived enteric microflora on the changes observed in the soil
microbial community. The second experiment investigated soil that has been
sampled periodically from under human cadavers during the process of
decomposition, and associated control sites at the Forensic Anthropological
Centre at the University of Tennessee.
The concepts and techniques of microbiology, soil science, molecular
microbial ecology and forensic taphonomy were combined to advance the
understanding of the microbiology in a decomposition environment. With
further research, a significant application could be the development of a
model for estimation of the post-mortem and/or post-burial intervals based
on soil microbial community succession.
7
Chapter 2 : REVIEW OF THE LITERATURE
2.1 Decomposition
The human body undergoes a complex and dynamic, but successive breakdown
process that is influenced by a range of factors, after death occurs. Many of
these factors are uncontrollable mechanisms, which are difficult to simulate
in a laboratory environment and as a result there is little understanding of the
interactions that take place between soft tissue decomposition and its
surrounding environment. The study of human decomposition is
understandably restricted by ethical impositions. Nonetheless, a few
controlled experiments have been undertaken at the Forensic Anthropology
Centre in Knoxville, Tennessee. There, crime scenes are recreated in the
field, where bodies can decompose naturally and the actions of insects, soil
microorganisms, chemical interactions and various environmental conditions
are studied.
Taphonomy, (Greek taphos: grave, nomos: law) (Efremov, 1940; Aturaliya and
Lukasewycz, 1999) originally a branch of palaeontology, has recently been
associated with forensic science as a way to understand processes associated
with cadaver decomposition; its mechanisms, agents and interactions with the
surrounding burial environment. Experimental taphonomy involves exposing
the soft tissue of an organism to variables or processes that might alter the
decomposition process and then examining the effects of this exposure, to
better understand the progression of decomposition. The applications of
these studies impact a forensic investigation in numerous ways which include
development of systems for estimation of the post-mortem and/or post-burial
interval, the determination of the cause and manner of death and assistance
in the location of clandestine graves (Haglund and Sorg, 1997).
2.1.1 The process of decomposition
Classically, the process of decomposition has been divided into five stages:
fresh, bloat, decay, dry and skeletonisation which do not necessarily imply a
8
sequence of events but instead represent the overall condition of the cadaver
(Goff, 2000). However, stages may not always be observed or may even be
absent depending on the taphonomy and the environment of the cadaver
(Vass, 2001).
Human decomposition begins approximately four minutes after death (Vass,
2001). As the tissues are deprived of oxygen, carbon dioxide levels increase,
pH decreases and wastes start to accumulate in the cells. Simultaneously,
cellular enzymes start to digest cells, causing them to rupture and release
nutrient-rich fluids through a process called autolysis. This process begins in
tissues with high enzyme activity such as the brain and liver and proceeds to
the rest of the body‟s tissues. The appearance of fluid-filled blisters and the
slippage of the skin is the external indication of this stage (Vass, 2001). The
body settles to the ambient temperature (algor mortis), the blood settles in
the body due to gravity (livor mortis) and cellular cytoplasm solidifies due to
increased acidity, causing the stiffening of muscles (rigor mortis) (Vass, 2001).
The process of putrefaction follows, with microbially mediated catabolism of
the soft tissues into gases, liquids and simple molecules. A visible sign of
putrefaction, is a greenish discolouration of the skin attributed to the
formation of sulfhaemoglobin in the blood (Vass, 2001). Following this, a
distension of tissues associated with the formation of various gases, results in
the characteristic bloating of the cadaver. They are the by-products of
anaerobic activity primarily located in the gut. The gases and accumulated
fluid purge from the body‟s natural openings and also burst the skin, releasing
the products of decomposition into the external environment (Vass, 2001).
When the gases have been eliminated, active decay begins where protein is
broken down into amino acids and fats to glycerols. These degradation
products are in turn broken down by bacteria (Vass, 2001). The alternatives
to putrefaction is adipocere formation or mummification (Campobasso, Di
Vella and Introna, 2001). In warm, moist environments, the formation of
adipocere, a yellowish-white wax-like substance, develops as a result of fat
hydrolysis (Forbes et al., 2004). At the end of the active decay, the
dehydration and desiccation of tissue leaves behind parchment-like skin and
renders it unavailable to microbes as a source of nutrition. Finally bone
decomposes via the process of diagenesis, where collagen, hydroxyapatite,
9
magnesium and calcium are exchanged, deposited adsorbed and leached into
the environment (Vass, 2001).
2.1.2 Factors affecting decomposition
The process of decomposition is affected by numerous variables. Research
using case studies have identified that cadaver-specific characteristics (sex,
age, physique, cause of death, integrity of the cadaver), environmental
variables (temperature, oxygen availability, humidity, pH, soil properties) and
other variables (clothing, predator effects) can impact the progression of
decomposition (Mann, Bass and Meadows, 1990). Ambient temperature may
be the most important variable affecting the rate of decomposition. The
reduction of a cadaver to its skeletal elements has been observed in one fifth
of the time in summer compared to the time taken in winter conditions
(Galloway et al., 1989). The availability of oxygen is another important factor
affecting the decomposition process. An oxygenated environment will speed
up the decomposition process (Dent, Forbes and Stuart, 2004). Obese
cadavers decompose more rapidly due to the greater amount of liquid in the
tissues which favours the development and dissemination of bacteria
(Campobasso, Di Vella and Introna, 2001). The location of a cadaver
determines its exposure to insects, carnivores and moisture. Exposure in a
desert environment results in rapid bloating, dehydration and finally
mummification of the cadaver whereas, the confinement of the cadaver to a
closed structure such as a house or a trailer, results in a slower onset of
decomposition which is later accelerated due to the retention of moisture
(Galloway et al., 1989). The effects of clothing, position of cadaver and soil
interment on decomposition rates were studied using rat cadavers (Aturaliya
and Lukasewycz, 1999). It was found that body water loss was enhanced by
clothing or a horizontal versus a vertical position and that desiccation was
equally effective by soil interment as by air exposure. Carnivore activity can
accelerate cadaver decomposition. In southern Arizona, it has been shown
that, coyotes, bear, javelinas and packrats scavenge and consume portions of
the cadaver, thereby increasing the rate of decomposition (Galloway et al.,
1989). This study also found seasonal differences, elevation, latitude,
clothing and wounds to have an effect on decomposition.
10
2.1.3 The microbiology of decomposition
From the moment delivery commences, the newborn child leaves the sterile
environment of the womb and enters a world in which microbes abound.
These microbes mainly inhabit its skin, mucous membranes and
gastrointestinal tract (see Fig 2.1). A delicate and complex relationship
ensues between man and microbe throughout his life. However, the role of
microbes continues well after the cessation of life.
Figure 2.1: Common microbial species that colonise a human body during life (Jawetz,
Melnick and Melnick, 1982).
There are literally hundreds of microbial species involved in the
decomposition process and decomposition would not progress without them
(Vass, 2001). The following species are major colonisers of cadavers:
Clostridium perfringens and other Clostridium spp., enterobacteria
(particularly, Escherichia coli and Proteus spp.), micrococcaeae (mainly
Staphylococcus aureus), streptococci and Bacillus spp. (Vass, 2001). Fungi
Nose
Staphylococcus sp.
Branhamella catarrhalis
Haemophilus influenzae
Streptococcus
pneumoniae
Corynebacteria sp.
Mouth
Streptococcus sp.
Veillonella sp.
Fusobacterium sp.
Actinomyces sp.
Leptotrichia sp.
Stomach
Lacticos
Leveduras
Helicobacter pylori
Small Intestine
Candida albicans
Lactobacillus
Enterococcus
Bacteroides sp.
Throat
Streptococcus sp.
Staphylococcus sp.
Branhamella catarrhalis
Corynebacterium sp.
Neisseria sp.
Mycoplasma sp.
Large Intestine
Bacteroides sp. Klebsiella sp.
Enterobacter Candida albicans
Escherichia coli Proteus sp.
Lactobacillus sp. Fusobacterium sp.
Streptococcus sp. P. aeruginosa
Clostridium sp.
Skin
Staphylococcus sp.
Propionebacterium sp.
Micrococcus sp.
Acinetobacter sp.
Bacillus sp.
Urethra
Streptococcus sp.
Mycobacterium sp.
Bacteroides sp.
11
such as C. albicans and other Candida spp. and Saccharomyces cerevisiae and
Saccharomyces spp. may be found on cadavers too. Vass (2001) isolated many
microbial species in the very early stages of decomposition such as
Staphylococcus, Candida, Malasseria, Bacillus and Streptococcus species, but
indicated many more were involved in cadaver decomposition. As
decomposition progressed, he observed that putrefactive bacteria such as
Escherichia coli were introduced, and followed by anaerobic bacteria such as
the Clostridium spp. and also micrococci, coliforms, diptheroids and
clostridial species. He also noticed the presence of Serratia, Klebsiella,
Proteus, Salmonella, Cytophaga, pseudomonads and flavobacteria species.
Many of the organisms that were isolated originated from the bowel and
respiratory tract of the cadaver. When a cadaver is associated with soil, the
cadaver-derived microflora, mostly habitual saprophytic hosts of the intestine
(Campobasso, Di Vella and Introna, 2001), can intermingle with thousands of
soil microorganisms such as Agrobacterium, amoebae and fungi (Vass, 2001),
as well as airborne aerobic bacteria (Campobasso, Di Vella and Introna, 2001).
It is also suspected that microbes are deposited on the corpse by the visiting
insects and arthropods.
The role of microbes in decomposition begins with the putrefaction of the
cadaver. During putrefaction, the aggressive intervention of exogenous and
endogenous microbial factors can add to the lesser effects of autolysis
(Campobasso, Di Vella and Introna, 2001). Temperatures ranging between
25°C and 35°C are optimal for the development of bacteria (Campobasso, Di
Vella and Introna, 2001), therefore very high or low temperatures inhibit
bacterial proliferation and slows down decomposition. Dry and windy
conditions which dehydrate the cadaver will impair bacterial growth. It has
been demonstrated that decomposition of buried cadavers by bacteria can
result in a temperature differential with cadavers buried at a depth of 30cms
up to 10°C higher than the adjacent soil (Rodriguez and Bass, 1985). The
microbial species associated with decomposition may be present for months or
even years after death, depending on local conditions. A DNA-based study
was able to identify intestinal microbiota of a 12,000 year-old mastadon, an
extinct mammal, from excavated remains in ancient sediments in Ohio and
Michigan (Rhodes, 1998). Recently, the analysis of DNA was able to identify
12
intestinal microbiota from two glacier mummies from the Alps, where there
was possible colonisation of the cadaver by microbes from the outer
environment (Rollo, 2007).
2.2 Soil
Soil is a physically, chemically and biologically complex medium consisting of
minerals, organic matter, animals, plants, and a diverse community of
microbiota (bacteria, fungi, algae, yeast) including its residues, that are
distributed between liquid and gas phases (Andrasco, 1981; Stotzky, 1997). It
is formed and forever changes, due to five major factors: the parent material,
time, climate, the living organisms and topography. The specific composition
of soil varies widely due to the varying proportions of these components
present at different geographical locations (see Fig 2.2) (Liesack et al., 1997).
This milieu can also differ spatially due to the influence of vegetation and
human activity (Prosser, 2002). Inorganic mineral particles in soil can range
in a continuum from 1% to 99% across the planet. The smallest of these
mineral particles are defined as clays (< 0.002 mm), the intermediate size as
silt (0.002-0.05 mm) and the coarser particles, as sand (0.05 - 2 mm) and
stones (> 2 mm) (Murray and Tedrow, 1992). The organic component of soil is
relatively small and rarely exceeds 5% by volume (Andrasco, 1981). It consists
of: plant root systems; animal, plant and microbial residues in various stages
of decay; humus, a heterogeneous complex of organic residues; and the active
microbiota.
13
Figure 2.2: The soil textural triangle. The basic soil textural classes consisting of percentages of clay silt and sand (Murray and Tedrow, 1992).
2.2.1 The microbiology of soil
Like a cadaver, the soil is inhabited by a diverse array of organisms ranging
from microfauna (fungi, bacteria and viruses) but also includes mesofauna
(acari and collembola) and the macrofauna (arthropods, nematodes,
earthworms, millipedes and amoeba). These soil inhabitants form complex
relationships with each other and plant systems.
2.2.1.1 Bacteria
Bacteria represent the largest biological component of soil. It has been
estimated that there may be as many as 109 bacterial cells per gram of soil
(Harris, 1994) and that bacteria in the top two to three centimetres of the soil
represents half of the total biomass on earth (Thornton, 1986). Soil bacteria
have adapted morphologically and physiologically to utilise the complexity of
the soil habitat effectively. One example is a thick mucilaginous capsule
surrounding the bacterial cell which is not found in laboratory grown isolates
of the same species (Coleman, Crossley and Hendrix, 2004). This capsule
protects the bacterium from desiccation, toxic compounds and may affect
adhesion to soil particles (Riley et al., 2001). Many soil bacteria have the
ability to slow their metabolism in response to low levels of available
nutrients and then increase it when nutrient levels rise (Wood, 1995). This
14
adaptation allows the persistence of microbial species over time in
nutritionally poor soil (Stotzky, 1997). Bacteria have numerous functions
within soil. The most common members of the bacterial community are
heterotrophic bacteria, or those that derive energy from organic carbon.
They carry out the decomposition of animal, plant and microbial residues
(Coleman, Crossley and Hendrix, 2004). Heterotrophs also perform nitrogen
fixation, a role mainly exhibited by the Rhizobium species, which inhabits
legume root nodules. The chemoautotrophic bacteria in soil, or those that
derive energy by oxidation of inorganic substances, are largely nitrifiers and
sulphur oxidisers.
Winogradsky (1949) divided soil microbial populations into two distinct classes
based on their response to nutrients and described the classes as
autochthonous and zymogenous microbial populations. Autochthonous
populations are the indigenous soil organisms, which persist actively in the
soil for long periods of time and at relatively constant levels. They are the
most competitive at low substrate concentrations and use the soil carbon
sources that are more resistant to degradation, such as humus. The genus
Arthrobacter is an example of autochthonous soil microbes that can maintain
their presence in soil even when available carbon is limited. The zymogenous
soil microbial populations proliferate when substrates such as plant or animal
residues are introduced into the soil. They have the ability to multiply rapidly
and can form resistant spore structures once the substrate is consumed. It is
likely that these general groups of microbial populations exist within the soil,
even though they may not be as distinct as suggested by Winogradsky
(Killham, 1994). Competition between organisms is likely to lead to
specialisation in terms of rate of growth and substrate utilization.
2.2.1.2 Fungi
Fungi are significant contributors to the biomass of soil microbiota and are the
primary decomposers in all terrestrial ecosystems (Bruns, White and Taylor,
1991). Differing soil conditions can influence the diversity of fungi, however
an average population has been estimated at 10-20 million individual colony
forming units per gram of soil (Griffin, 1960). Soil fungi hold important roles
related to water dynamics, nutrient cycling and disease suppression however,
15
their most important role is the decomposition or organic matter, ranging
from simple sugars to the most resistant polymers such as lignin and complex
humic acids. Soil fungi can be grouped into three general functional groups
based on how they derive their energy. Saphrotrophic fungi convert dead
organic material into fungal biomass, carbon dioxide and small molecules such
as organic acids (Griffin, 1972). These fungi are important for immobilising
nutrients in the soil and they also help increase the accumulation of humic-
acid rich organic matter in the soil. The widely studied mycorrhizal fungi
colonise the roots of plants and form a symbiotic relationship with them
(Sagara, Yamanaka and Tibbett, 2008). The majority of plants have these
associations, in which the fungi provide nutrients and protection from drought
stress and plant pathogens (Coleman, Crossley and Hendrix, 2004). The third
group of fungi are pathogenic or parasitic fungi, which cause reduced
production or death when they colonise roots and other organisms. Fungi
such as Verticillium and Rhizoctonia cause major damage to agriculture each
year, whereas nematode-trapping fungi parasitise disease-causing nematodes
and may be useful as biocontrol agents (Paul, 2007).
Higher fungi usually grow as hyphae, and these filaments confer advantages
over bacteria in some environments. In dry conditions, hyphae help bridge
gaps between pockets of moisture, and so the fungi can persist where
bacteria cannot (Paul, 2007). Soil fungi are also able to use nitrogen from the
soil that allows them to decompose surface residue low in nitrogen (Paul,
2007). They are more tolerant of acidic soils than bacteria and are
consequently predominant in decomposition processes under these conditions
(Thorn, 1997). Fungi are aerobic organisms and the anaerobic conditions
encountered in waterlogged soil and compacted soil generally loses its fungal
component (Paul, 2007).
2.2.1.3 Other inhabitants
Other components of the soil microbial community include actinomycetes,
algae and protozoa. Actinomycetes are bacteria but have a mycelial
morphology that resembles fungi. They degrade an array of carbonaceous
substrates such as chitin, celluloses and hemicelluloses (Wood, 1995). Algae
are photoautotrophic, meaning they use sunlight as an energy source. They
16
are often primary colonisers as they synthesise their own carbon compounds
using photosynthesis and are involved in soil formation and maintaining the
structural stability of degraded soils. Soils commonly support between 103
and 104 algae per gram of soil, although as many as 108 algae per gram have
been documented (Metting 1981). Protozoa are predators of the soil
microbial population and can be found in the order of ten million per gram of
soil (Bardgett and Griffiths, 1997). The larger soil organisms comprise
oligochaetes (earthworms), nematodes, arthropods (millipedes, centipedes
and mites) and molluscs (slugs and snails). Their main ecological role is the
processing and vertical and horizontal mixing of the soil through their
burrowing activity as well as contributing to organic material decomposition
(Wood, 1995; Griffiths and Bardgett, 1997).
2.2.2 Properties affecting the soil microbial community
Microbial communities vary considerably between soils (Liesack et al., 1997;
Zhou, 2003). Soil microbial diversity has been demonstrated not only
between sampling sites but also within sites due to localized variations in the
soil environment that can influence the population at this level (Coleman,
Crossley and Hendrix, 2004). Numerous properties of the soil can influence
the microbial diversity within this environment.
2.2.2.1 Physical and chemical properties
The water/atmosphere content, clay content, pH and temperature contribute
strongly to the heterogeneity of microbial communities in soil (Liesack et al.,
1997). Water makes up about 20-30% of the average soil volume and is
essential to all life within the soil (Wood, 1995). The soil atmosphere is the
gaseous component of the soil. The water content/atmosphere ratio
fluctuates in response to rainfall and temperature and the physical
composition of the soil also affects the amount of available water. Clays and
organic matter tend to retain water, whereas silt and sand allow rapid
drainage to the local water table. In situations where water content is low,
microbial life is reduced whereas if the water content is too high, oxygen is
reduced and only anaerobic life can flourish. The rate of decomposition is
greatly reduced in anaerobic soils, resulting in high organic content (Stotzky,
1997). Additionally, most bacteria are not motile and so their dispersion
17
primarily depends on water movement as well as root growth and the activity
of other organisms (Lavelle and Spain, 2001).
The proportion and type of clay minerals present can greatly influence
microbial activity, by modifying physiochemical characteristics of
microhabitats such as pH, nutritional status, the activity of toxic substances
and water availability (Killham, 1994; Stotzky, 1997). Clay minerals retain
water and are therefore essential for microbial life (Stotzky, 1997). Their
negative charge attracts nutrient cations such as NH4+, Ca2+, Mg2+ and K+.
However, some organic molecules such as amino acids and toxic compounds
can bind to clay minerals reducing their bioavailability (Stotzky, 1997).
Additionally, clay minerals can form aggregates which retain water, causing
the formation of microhabitats which can exclude predators due to small pore
sizes thereby protecting the bacteria within (Killham, 1994; Griffiths and
Bardgett, 1997).
The pH of a soil can significantly affect microbial community structure. Most
soil bacteria and fungi prefer a neutral pH, but their responses to alkali (pH
7.5-8.5) and acidic conditions (pH 4 – 6.5) vary noticeably. Fungi generally
predominate in acidic soils, although postputrefaction fungi are known to
proliferate at the high pH values found around cadavers (Sagara, Yamanaka
and Tibbett, 2008). Whereas bacteria and actinomycetes dominate in near-
neutral or moderately alkaline soils (Stotzky, 1997). Bacteria are efficient
competitors at mid-high pH values, most are intolerant to low pH values, with
the exception of species such as acidophiles (Wood, 1995). The pH of soil also
affects the solubility, availability and toxicity of mineral nutrients that can
significantly affect microbial populations (Coleman, Crossley and Hendrix,
2004).
The average soil temperature greatly affects the composition of the microbial
population. Psychrophilic microbes, with a low temperature preference,
inhabit sub-freezing polar and mountain soils, whereas thermophilic microbes,
with a high temperature preference, flourish in geothermal areas. Mesophilic
microbes, which prefer moderate temperatures predominate the majority of
soils worldwide (Stotzky, 1997). In well established soils, microbial
18
populations reach an equilibrium that is generally resistant seasonal
temperature fluctuations (Wood, 1995). A study presenting a continental–
scale description of the soil bacterial communities from 98 sites across North
and South America showed that microbial biogeography is influenced primarily
by edaphic variables, for example soil moisture and carbon availability, rather
than site temperature, latitude etc that typically predict plant and animal
diversity (Fierer and Jackson 2005). Interestingly, it showed that the degree
of similarity between soil bacterial communities was largely unrelated to
geographical distance.
2.2.2.2 Nutrient availability
The decomposition of vegetation and animals results in an uneven distribution
of substrates on the surface of the soil. This produces localised zones of
nutrients thereby creating niches within the soil where microbes are
concentrated (Stotzky, 1997). Within the rhizosphere, rhizobacteria
proliferate in response to stimulation by root exudates. However, the high
microbial numbers associated with the rhizosphere do not correlate with high
microbial diversity. This is because a selective pressure only allows species
that can utilize carbon most efficiently to proliferate (Coleman, Crossley and
Hendrix, 2004). Similarly, spatial variability in vegetation directly affects
spatial distribution of soil microbes.
2.2.2.3 Soil depth
The number of microbes found in soil is known to decrease with depth (Hurt
et al., 2001) which is mainly due to the reduction of the quality and quantity
of nutrients with depth. Surface soils have a more heterogenous microbial
population, whereas deeper soils show a dominance of a few microbial
groups. Gram negative bacteria, fungi and protozoa are higher in the upper
levels of soil, whereas Gram positive bacteria predominate at depth (Fierer et
al., 2005). Two theories suggest a cause for the differences seen in
biodiversity. The theory of spatial isolation suggests that if microbial groups
are separated, diversity is maintained, but if microbes are allowed to
interact, competition occurs and the fittest dominate. The second theory is
based on carbon availability, where variation in the types of carbon and their
levels, prevent competition and maintain high diversity. Therefore under low
19
carbon condition, fewer microbial species exist and biomass and diversity
decrease (Coleman, Crossley and Hendrix, 2004).
2.2.2.4 Human activity
Human activity such as agriculture, building development, waste disposal and
mining all has profound effects on soil microbial populations. The burning of
fossil fuels has led to the acidification of soils worldwide (Galloway, 2001).
High salinity due to poor irrigation techniques, high concentrations of heavy
metals, fertilizers, pesticides and radioactivity have complex effects on soil
microbial communities that are not fully understood. Many studies show that
these occurrences reduce total biomass and species diversity and impact
heavily on microbial processes (Frostegard, Tunlid and Baath, 1993). This
upsets the balance of the biological community and causes shifts in microbial
community structure. Exotic microbes can be introduced into soil by direct
inoculation to help promote crop growth or for the purpose of biological
control and when impacted by microbially hosted material such as sewage
sludge. More recently, the use of genetically engineered organisms in
agriculture, and horizontal gene transfer between transgenic plants and soil
microbes have potential implications for soil microbial communities. These
implications include disruptive effects, such as elevated or reduced biomass,
activity and diversity of soil microbial communities. Transgenic plants might
express antimicrobial compounds which could confer antibiotic resistance due
to microbial adaptation (Lukow, Dunfield and Liesack, 2000).
2.3 Post-Mortem Interval Estimation
An accurate estimation of time since death or the postmortem interval (PMI)
is one key aim of every medicolegal investigation, equal in importance to
victim identification and cause of death. Determination of the PMI can direct
or re-orient an entire investigation by serving to validate or reject a suspect‟s
alibi or elucidate the victim‟s perimortem activities. Pathology, anthropology
and entomology have developed criteria to fine-tune the estimation of PMI.
20
2.3.1 Pathology and Anthropology
Forensic anthropologists and pathologists have traditionally relied on
observations of the decay of soft tissues to estimate PMI. Cadaver events are
divided into abiotic phenomena and transformative phenomena (Campobasso,
Di Vella and Introna, 2001). Abiotic phenomena can be instant, like loss of
consciousness and absence of breathing or consequential like body cooling and
acidification. These consequential phenomena can be used in the estimation
of time since death. Transformative phenomena can be destructive like the
processes of autolysis and putrefaction (Campobasso, Di Vella and Introna,
2001). The four classical stages of putrefaction: discolouration, bloating,
liquefaction and active decay or skeletonisation follow a timeline and
established diagnostic markers can be used to indicate time elapsed since
death (Campobasso, Di Vella and Introna, 2001). These stages should not be
regarded as clearly defined events, but rather a sequence of overlapping
events until the organic matter is completely destroyed. Putrefactive
changes can only be used for estimating PMI when they are integrated with
environmental and circumstantial elements. Decomposition varies from
cadaver to cadaver, environment to environment and one part of the same
cadaver to another (Campobasso, Di Vella and Introna, 2001).
During the first 24 hours postmortem, the cooling of the cadaver or algor
mortis is the most useful indicator of the time of death. The assessment is
made on the basis of body core temperature, requiring a direct measurement
of the rectal or intra-abdominal temperature. However it is imperative to
consider all the possible variables which influence the rate of cadaver heat
loss. Several additional innovative techniques have been developed to
estimate PMI. Exploiting the degradation of a protein to estimate the PMI has
been investigated using cardiac troponin I (Sabucedo, 2003) and various
proteins found in cerebrospinal fluid (Madea et al., 1994). The levels of
creatinine and serum uric acid have been used to estimate PMI (Zhu et al.,
2002). Creatinine is a by-product of active muscles and is produced at a fairly
constant rate by the body. The relationship between the potassium
concentration in the vitreous humour and the postmortem interval has been
studied by several authors (James, Hoadley and Sampson, 1997; Munoz et al.,
2001). Volatile fatty acids (VFA) and various anions and cations from human
21
decomposition have been used for PMI estimations (Vass et al., 1992). This
study found a direct correlation between the decomposition stages and the
VFA production.
2.3.2 Entomology
The most successful estimation of PMI relates to the developmental biology of
insects such as the blowfly by the field of entomology. Insects arrive at a
cadaver in a pattern that is characteristic and identifiable. The concept of
arthropod succession allows the association of a species or group to a well-
established decomposition stage, thus, estimating the post mortem interval.
Arthropods are classified by the feeding habits of its members and are divided
into five distinct ecological groups – necrophages, necrophiles, omnivores,
opportunists and accidentals. The former three are considered most
important for forensic purposes. The necrophages are useful for establishing
the time of death, as they arrive in a predictable sequence. Necrophagous
arthropods have highly specialized sense organs specifically stimulated by
organic putrefaction odours and gases, which help them locate the cadaver
soon after death (Campobasso, Di Vella and Introna, 2001). Omnivores that
appear practically at the same time as necrophiles and remain through all the
decomposition stages can provide information about the cadaver itself, any
manipulation of it and the arthropod community (Arnaldos et al., 2005).
The decomposition of a cadaver is a sequential but continuous process.
Entomology has replaced the use of broad, qualitative categories to define
decomposition, with an analytical technique known as accumulated degree-
days (ADD). It describes decomposition more precisely as a continuous
variable, by assigning point values to express decomposition. This results in
an increased statistical power of hypothesis testing and could provide more
information about the relationship between decomposition and the PMI
(Megyesi, Nawrocki and Haskell, 2005). Accumulated degree-days represent
heat energy units, known as degree-days (D), available to drive a biological
process such as bacterial or fly larvae growth. Upper and lower
developmental thresholds are the temperatures at which development stops
and needs to be separately determined for each organism being studied.
22
Most entomological evidence has been collected from research done on the
decomposition of various animals. An early study of the entomofauna of 43
dog carcasses was conducted in Knoxville, Tennessee. It reported on the
occurrence and abundance of 240 species, attracted to the carcasses over a
period of a year (Reed Jr, 1958). Arthropod colonisation on 39 small mammal
carcasses in Illinois, Indiana were found to have a “fairly regular successional
pattern”, which depended upon the season of the year (Johnson, 1975). Pig
carcasses are popular analogues for the human cadaver. A five month study
of three pigs buried in Essendon, United Kingdom showed the impact of soil
characteristics, such as pH, tannin levels and the clay component of the soil,
on the decomposition process and how they may confound results when using
blowfly larvae to estimate PMI (Turner and Wiltshire, 1999). A study
conducted in Russia observed the effects of 13 biotypes (environments) on the
entomofauna involved in the decomposition of 211 animals (Marchenko,
2001). It identified the Diptera families as the leading species involved in the
decomposition of a cadaver, followed by Coleptera families.
However, few researchers have had the advantage of testing the validity of
human cadaver entomological evidence for estimating PMI. A two-year study
was conducted on buried human cadavers with respect to a handful of
variables, one being arthropod activity, to contribute to a more accurate
estimation of PMI. It found the burial of a cadaver restricts the access of
many arthropods to the cadaver, thereby slowing down decomposition rates.
Entomological evidence from literature and experimental studies were
applied to seven real forensic cases in the Iberian Peninsula. The importance
of constructing regional specific databases based on different geographical
situations and different habitats is observed, in order to be useful to a
forensic case (Arnaldos et al., 2005).
Theoretically, this concept of succession could also apply to the microbes
involved in decomposition. A cadaver has come to be viewed as a source of
sequestered nutrients and energy that is returned to the wider ecosystem
upon decomposition (Carter, Yellowlees and Tibbett, 2007). When a cadaver
is introduced into an environment and decomposition begins, the
environmental equilibrium is disrupted due to modifications in the soil,
23
addition of nutrients etc. Cadaver-derived enteric microflora may be
introduced and their metabolic processes may modify the environment.
These modifications may result in conditions that are not ideal for the
introduced species, but rather may favour a secondary species, which may
then become dominant. This occurs in continuous succession and as nutrients
are consumed, species are enabled or competitively excluded. This concept
of „resource selects community‟ (Beijerinck, 1913; Connell and Slatyer, 1977)
provides the underlying principles for a microbial PMI estimation model. An
early study into postmortem change in field conditions used traditional
culturing techniques to show a „microbial succession sequence‟, which was
observed from enteric to soil organisms over a period of 6 days of
decomposition (Micozzi, 1986). The microbiological interactions associated
with decomposition may be extremely useful in the development of post-
mortem interval estimation tools and may also be relevant to victim
identification and locating human remains or clandestine graves.
2.4 Microbial Community Analysis
The living component of the soil was first recognized as being useful in
forensic science by Thornton and McLaren (1975). They suggested that the
biochemical properties arising from the metabolic processes of microbes in
the soil could impart uniqueness to a soil. They tested soils from different
sites in close proximity and proved they could be successfully distinguished by
their enzyme activity patterns. However, it has since been shown that drying
and storage can influence enzyme activity (Lorenz et al., 2006). Another
attempt to compare soils based on microbial functional diversity was made,
using a multi-substrate testing method (Omelyanyuk, Alekseev and Somova,
1999). The method was based on functional characterization of microbial
communities, using eleven sources of organic carbon, while observing the
change of colour of tetrazolium salt from yellow to purple. This method was
applied effectively to a real forensic case study.
The advent of molecular technology has revolutionized the field of microbial
ecology by providing nucleic acid-based techniques of examining the diversity
of a soil microbial community in any ecosystem. However, our understanding
24
of soil microbial communities has been limited by the use of culture-based
methods and morphological techniques. It is well known that less than 10% of
soil microbes can be cultured using existing techniques (Dierksen et al.,
2002). Due to these restrictions, available data from culture related methods
provide a selective and biased look at microbial diversity. A number of
approaches that do no rely on culturing and isolating, such as lipid
biomarkers, have been developed which can contribute to microbial
community characterisation. The chemical diversity and cellular abundance
of nucleic acids and lipids make them potentially useful chemical targets for
investigating microbial communities. A strong data correlation was seen
between polymerase chain reaction (PCR)-based methods and lipid-based
methods for comparison of microbial diversity (Ritchie et al., 2000). These
analytical tools are sufficiently sensitive and robust to be used for forensic
sample analysis and as evidence in a court of law.
2.4.1 Phospholipid Fatty Analysis
2.4.1.1 Structure and function of PLFAs
Phospholipid fatty acids (PLFA) are essential membrane components of all
living cells. They consist of a single molecule of glycerol. Two OH groups of
the glycerol are bound to a fatty acid chain (hydrophobic tail) and one OH
group is bonded to a phosphate group (hydrophilic head). The asymmetric
lipids form a bilayer in membranes, with hydrophilic ends towards the outer
surface of the membrane and hydrophobic towards each other (see Fig 2.3)
(Kaur et al., 2005).
25
Figure 2.3: Arrangement of phospholipids in the membrane of a living cell (Kaur 2005).
These compounds degrade rapidly upon cell death and are not found in
storage products and are therefore good indicators of the living microbial
community (Drenovsky et al., 2004). Additionally they are chemically diverse
and abundant in soil and the relative abundance of certain PLFAs can identify
specific groups of soil microorganisms (Zelles 1999, Drenovsky et al 2004).
2.4.1.2 Significance of PLFAs
PLFAs make up a relatively small but constant proportion of the biomass of
organisms (Zelles, 1999). Studies have indicated that rapid changes in
microbial community structure can be detected by changes in PLFA patterns
(Bossio, 1998; Kaur et al., 2005). Certain PLFAs can be used as biomarkers for
specific populations, taxonomic or functional groups. For example, bacteria
contain the unique β–OH, cyclopropane and branched-chain fatty acids which
are not common to other organisms (Zelles, 1999). However, there is limited
indication that individual lipids can serve as unique biomarkers for a specific
microbial species. This is due to overlap in the PLFA composition of different
microbes, where an individual species can have numerous fatty acids, some of
which may occur in many other organisms (Bossio, 1998). Furthermore, the
determination of signature PLFAs for specific microbes requires their isolation
in pure culture and PLFA patterns for individual populations can vary in
response to environmental stimuli (Ramsey et al., 2006). For these reasons,
26
PLFAs are more reliable as reflections of the composition and dynamics of the
microbial community and are not used to calculate microbial diversity
(Lechevalier, 1977; Bossio, 1998).
2.4.1.3 PLFA method
PLFA analysis of microbial communities, originally developed by Bligh and
Dyer (1959) and later modified by White et al. (1979) and Zelles (1992), is a
biochemical method, which provides a culture-independent and broad-scale
analyses of the abundance and change in microbial community structure. It is
the quantitative measurement of ester-linked fatty acids in phospholipids that
has been known to be the most sensitive and reliable measure of microbial
biomass and community structure (Zelles, 1992; Drenovsky et al., 2004). The
PLFA profile of a sample is derived from the whole viable microbial
community and each species contributes to the profile in proportion to its
biomass (White et al., 1979; Zelles, 1999).
The fatty acid nomenclature used is as follows; total number of carbon
atoms:number of double bonds, followed by the position of the double bond
from the methyl end of the molecule. Cis and trans geometry are indicated
by the suffixes c and t. The prefixes a and i refer to anteiso- and iso-
branching (Bossio, 1998). In general, fatty acids most commonly used to
indicate bacteria are 15:0 and 17:0, whereas a good indicator of fungi is
18:26 (Kaur et al., 2005). Due to a limited number of fungal-specific
markers, PLFA signatures only serve to provide an estimate of total fungal
biomass in soil (Anderson, 2004). Generally, odd-number and branched-chain
fatty acids are produced by Gram-positive bacteria, while even number
straight-chain and cyclopropyl fatty acids are from Gram-negative bacteria
(Zelles, 1992).
2.4.1.4 PLFA Studies
PLFAs were measured in soils with differing farming systems in terms of the
source of fertilizer and the presence of a winter cover crop. The importance
of environmental variables on PLFA profiles was determined as following: soil
type > time > spatial variation (Bossio, 1998). Fatty acids observed in a study
of human putrefactive fluids (Cabirol et al., 1998) in a liquid and gelled form,
27
showed solidification of putrefactive fluid may decrease the rate of
decomposition due to declining substrate availability (Cabirol et al., 1998). In
addition to community profiling, PLFAs have been used as indicators of:
environmental stress, high temperature, organic compound toxicity and
osmotic stress, starvation, low pH, and the presence of heavy metals (Kaur et
al., 2005). Differences in the metabolic status and microbial composition of
estuarine microbial mats were monitored by PLFA analysis to determine
changes in the physiological status, biomass and microbial composition. The
study revealed an increase in biomass in the morning hours and a decrease in
growth rate in the deeper layers of the mat (Villanueva et al., 2004).
2.4.2 Terminal Restriction Fragment Length Polymorphism Analysis
The polymerase chain reaction (PCR) heralded the molecular era by enabling
researchers to amplify the large amounts of DNA required by many molecular
techniques. One such technique is terminal restriction fragment length
polymorphism (T-RFLP) analysis (Avaniss-Aghajani et al., 1994), which is an
automated profiling method used to study complex microbial communities.
2.4.2.1 T-RFLP method
In the T-RFLP method (see Fig 2.4), the target gene is amplified with the
polymerase chain reaction. A fluorescent-labelled primer is used to allow
detection of this fragment. A restriction enzyme recognises and cleaves the
PCR product at a particular sequence. In the variable regions, the restriction
sites occur at different places resulting in different length fragments. The
more diverse the microbial community is the greater the range of fragments.
In principle, each fragment represents a unique operational taxonomic unit
(OTU) of the sample (Liu et al., 1997; Marsh, 1999; Osborn, Moore and
Timmis, 2000; Blackwood et al., 2003). The relative quantitative distribution
within a profile can be determined, since the fluorescence intensity of each
peak is proportional to the amount of genomic DNA present for each OTU in
the sample. This results in distinct profiles, known as electropherograms,
dependent on the species composition of the communities of the samples.
The profiles can be visually compared and the semi-quantitative data
statistically compared and used for generating information of the relative
abundance of operational taxonomic units (Liu et al., 1997).
28
Figure 2.4: Overview of the T-RFLP method (Applied Biosystems, 2005).
Despite the advantages of T-RFLP analysis, it is subject to problems such as
the systematic biases caused by PCR and restriction enzyme digestion
efficiency. Preferential annealing to particular primer pairs can cause the
amplification of particular sequences and an increase in PCR cycles may lead
to an increase in the incidence of chimeric PCR products (Egert, 2003;
Lueders, 2003). Enzymatic lysis favour the recovery of DNA from Gram-
negative bacteria, whereas mechanical lysis is considered to give a more
representative sample (Ward et al., 1992). The extraction of DNA, use of
replicates, pooling of samples, dilution effects, choice of polymerase and
reaction annealing temperature may all effect the final profile of the
microbial community (Osborn, Moore and Timmis, 2000). Additionally, operon
heterogeneity can cause the copy number of the 16S gene to vary between 1
and 14 in different bacterial species with some variation between them,
artificially increasing the diversity seen in a profile (Crosby and Criddle,
2003).
2.4.2.2 Target genes
Any gene of interest that has both conserved and diverse regions of genetic
information can be used in T-RFLP analysis. The most widely used genes are
those that code for the RNA component of the small subunit of the cellular
29
ribosomal machinery (see Fig 2.6): the 16S ribosomal RNA (rRNA) gene for
prokaryotes (see Fig 2.5) and the 18S rRNA for eukaryotes (see Fig 2.7).
16S rRNA gene
The 16S rRNA gene encodes for the ribosomal RNA small subunit, which makes
up part of the bacterial ribosome and has the largest representation of any
gene in the public databases (Liesack et al., 1997). Ribosomes are the protein
synthesizing machines of the cell and are made up of two subunits, which are
composed of large protein complexes and rRNAs. Bacteria have three types of
rRNA molecules: the 16S rRNA is part of the small subunit and the 5S and 23S
rRNAs are part of the large subunit.
Figure 2.5: The rRNA Operon. It consists of three rRNA molecules: 16S, 23S and 5S, which are separated by internal transcribed spacer (ITS) regions (Flechtner et al., 2002).
The essential function of the 16S rRNA molecule means that it is evolutionarily
conserved across all known bacterial species (Ward et al., 1992). The gene
has nine regions of high variability interspersed with regions of less variability.
Less conserved regions have changed through evolution without negatively
impacting the organism. Different bacterial species and even strains of the
same species can have very dissimilar 16S rRNA sequences, however if the
species have a close phenotypic relationship, their 16S rRNA sequences can be
similar (Ward et al., 1992). This variability can be used to assess the diversity
of a soil bacterial community.
30
Figure 2.6: The 16S rRNA secondary structure. Primary sequence with near universal conservation (thick lines), intermediate conservation (normal lines) and hypervariability (dashed lines) is shown (Ward et al., 1992). Arrows and black lines indicate the region of
the gene amplified by PCR. The grey regions at the 3’ and 5’ ends are not amplified.
Internal transcribed spacer region
The internal transcribed spacer (ITS) regions of fungal ribosomal RNA genes
are suitable targets for molecular analysis of fungal communities (Buchan et
al., 2002). The ITS regions are stretches of DNA between the 18S, 5.8S and
28S rRNA genes with intervening sequence (IS) regions that do not encode
structural products.
31
Figure 2.7: Internal transcribed spacer (ITS) region map. The ITS regions exist in two segments, the ITS1 and ITS2, which bracket the 5.8S rDNA.
The ITS regions encode structural genes for tRNA, but the IS regions are where
the sequence divergence exists. Their high sequence variability relative to
the flanking rRNA genes makes them valuable for genus- and species-level
identification. In fungi, the rRNA operons, are often found as tandem repeats
of up to 100 copies, hence the possibility exists of significant interspecies
differences in ITS copy number (Buchan et al., 2002).
2.4.2.3 T-RFLP Studies
The T-RFLP method has been used to characterise bacterial (Liu et al., 1997)
and fungal populations (Jones and Bessemer, 2004) in natural habitats and has
been identified as a reproducible and accurate tool for community profiling
(Egert, 2003). Four soil communities were able to be differentiated based on
their 16S rRNA terminal restriction fragment (TRF) profiles, where T-RFLP
analysis was very effective at showing similarity relationships with good
detection sensitivity, but not at comparing community richness and evenness
(Dunbar, Ticknor and Kuske, 2000). The T-RFLP method has been
preliminarily tested to produce soil bacterial community profiles for
comparative forensic purposes (Horswell et al., 2002). Mock crime scenes
were set up, soil evidence collected and the bacterial community profiled.
Soil samples from the same site produced similar profiles, whereas profiles
from different sites produced significantly different profiles. Persistence of
the soil bacterial structure was exhibited with a similar profile to the original
observed at one site after an eight-month period. The presence and
quantification of fungi taken from organic and non-organic vineyard soils was
32
compared using the T-RFLP method and the ITS region of rDNA. Non-organic
soil showed a greater and more diverse amount of fungal rDNA. However DNA
samples were harder to retrieve from the organic soil and only two vineyards
were sampled (Jones and Bessemer, 2004). Similarly, four diverse bacterial
communities from termite hind-gut, aquifer sediment and two activated
sludge samples were distinguished by Liu et al. (Liu et al., 1997). The
selection of a restriction endonuclease is very important and the enzyme HhaI
is shown to provide a greater insight into estimates of biodiversity when
compared with the enzyme CviJI (Marsh, 1999). This was reinforced when
HhaI was compared with eight other restriction enzymes (Osborn, Moore and
Timmis, 2000). A multiplex T-RFLP technique was developed by Singh et al.
(2006) where up to four microbial taxa were analysed simultaneously and
differentiated from rhizosphere bacterial, fungal and rhizobial/agrobacterial
communities in three environments.
2.5 Data handling and statistical analysis
The most simplistic approach in comparing the PLFA and T-RF profiles is to
visually compare traces for the presence or absence of different peaks.
However, more information can be gleaned from quantitative analyses of
these data sets. In particular, multivariate statistical methods have been
extremely valuable in analyzing complex data sets, which can comprise a
variety of variables and need not be limited to species lists (Rees et al.,
2004).
The analysis of the PLFA data began with the generation of chromatograms by
the GC-MS. The integration and handling of peak data was performed by the
GC-MS software, ChemStation (Agilent Technologies, Paolo Alto, CA). The
chromatograms were tentatively identified by comparison of retention times
and mass spectra of fatty acid standards run under the same conditions.
Peaks were manually integrated and peak area data quantified using standard
calibration curves. The quantification data was then imported to an
electronic spreadsheet for statistical analysis.
33
The first step in analyzing the T-RF data is to use an appropriate method for
aligning peaks. This is particularly important since peak size discrimination is
to 1 base pair (Fierer et al., 2005). The position and height of individual
peaks in the microbial community profile indicate the presence and relative
abundance of different ribotypes. Fragment lengths usually range from 0 to
1500 bp, but in reality, the number of base pairs belongs to a discrete series
(eg 1 bp, 2bp). However, automatic sequencers produce values on a
continuous scale to two decimal places and so have to be rounded to the
nearest integer and assigned to the ribotype associated with that integer
(Scallan et al., 2008). The RiboSort program eliminates the need to manually
sort and manipulate the data into the desired format for statistical analyses,
which can be tedious and error-prone (Scallan et al., 2008). The RiboSort
program was used to generate information on ribotype abundances, ribotype
proportions and sequencer detections for the current research.
The next step is to analyse the PLFA and T-RF data sets using statistical tools.
The current research used the multivariate software package, Primer V6
(Primer-E Ltd, Plymouth, UK). Cluster analysis is the method of choice when
relationships between objects are expected to be discontinuous and where
defined groups of objects are expected (Ramette, 2007). The basic aim of
ordination and cluster analysis is to represent the (dis)similarity between
objects, so that similar objects are depicted near to each other and dissimilar
objects are found further apart (Ramette, 2007). A similarity matrix is
calculated using the Bray-Curtis coefficient. The Bray-Curtis coefficient is an
ideal coefficient for the construction of similarity matrices because, it has the
ability to deal with data sets containing multiple blocks of zeros in a
meaningful manner (Rees et al., 2004). Non-metric multidimensional scaling
(MDS) is used to ordinate the similarity data and prepare visual interpretations
of the microbial community as represented by T-RFLP and PLFA data. MDS
uses an algorithm that takes the multidimensional data of a similarity matrix
and presents it in typically two dimensions, although three dimensional plots
can be employed to visualise group differences (Clarke and Ainsworth, 1993).
The result of an MDS ordination is a map where the position of each sample is
determined by its distance from all other points in the analysis. The units on
axes are usually not included, as they would only serve the purpose of
34
indicating the relative positions of the objects, but not have any real
meaning. An important component of an MDS plot is a measure of „goodness
of fit‟ termed the „stress‟ of the plot. A stress value greater than 0.2
indicates that the plot is close to random and a stress value less that 0.2
indicates a useful two dimensional picture (Clarke and Ainsworth, 1993).
An Analysis of Similarity (ANOSIM) routine is used to examine statistical
significance between samples (Clarke and Ainsworth, 1993). ANOSIM tests the
null hypothesis that the average rank similarity between objects within a
group is the same as the average rank similarity between objects between
groups. It produces a test statistic (R) which can range from -1 to 1, which is
a relative measure of separation of the a priori-defined groups. Objects that
are more dissimilar between groups than within groups will be indicated by an
R statistic greater that 0. An R value of 0 indicates that the null hypothesis is
true. The significance level statistic is produced as a percentage which can
be converted into the p-value. It is a measure of how much evidence there is
against the null hypothesis and has a probability ranging from zero to one. A
small p-value (<0.05) is evidence against the null hypothesis while a large p-
value (>0.05) means little or no evidence against the null hypothesis.
35
Chapter 3 : RAT CADAVER EXPERIMENT
3.1 Introduction
Animal models have long been used to study the process of decomposition
(Reed Jr, 1958; Payne, 1965; Wilson et al., 2007). The current research
involved the use of soils from the decomposition of juvenile rat cadavers in
Pallarenda and Wambiana tropical savanna soils from Queensland, Australia
(Stokes et al., 2005). The cadavers were originally used to determine if the
evisceration of a cadaver would decrease the rate of decomposition (Carter,
2005). Putrefaction is predominantly driven by the enteric microflora (Vass,
2001) and therefore the removal of it and the internal organs may slow down
the rate of decomposition. To address this issue, Carter (2005) used four
treatments that included a complete cadaver, an eviscerated cadaver, a
cadaver with a sown up incision only and a control (soil without cadaver) were
investigated. The current research analysed the gravesoils using lipid-based
phospholipid fatty acid analysis and nucleic acid-based terminal restriction
fragment polymorphism analysis to characterise the differences between the
soil microbial communities of two soil types and four treatments.
3.2 Aims
The aim of this experiment was to determine whether there are changes in
the soil microbial communities associated with decomposition in the two
distinct soils types and four cadaver treatments, and if this could be
distinguished with the aforementioned analyses.
3.3 Experimental Background
Existing experimental soils from an earlier research project (Carter, 2005),
were used to pursue the aims of this experiment. Soils from the Pallarenda
and Wambiana sites in tropical savanna ecosystems in Queensland were
collected in September 2003. They were calibrated to a matric potential of -
0.05 megapascals (MPa) and equilibrated for seven days at 22ºC. The
36
experiment used four treatments of juvenile rat cadavers aged 8-10 days
(Rattus rattus), which included a complete cadaver, an eviscerated cadaver,
an incised and sown cadaver and a control (soil without cadaver). The rats
were killed with carbon dioxide. Incisions, where needed, were made from
the anus to the sternum and the internal organs of the lower respiratory tract
and accessory digestive organs were removed for the eviscerated cadavers.
Following evisceration, the abdominal and thoracic cavities were rinsed with
sterile distilled water and sown up whereas the incised cadavers were incised
and stitched without evisceration. The cadavers were buried in soil (500 g dry
weight) and placed into soil microcosms (Tibbett et al., 2004). The
experiment was replicated four times with four sequential harvest events on
days 7, 14, 21 and 28. At each harvest event cadavers were exhumed, along
with the soil adhered to them, and the soil directly surrounding the cadaver.
Harvested soils samples were weighed into sterile culture tubes and
immediately stored at -20C. Subsequently, only the replicates from day 14
were shipped to Western Australia in an icebox and stored in a freezer at -
20C with no freeze-thaw cycle, therefore analysis could only be carried out
for this harvest.
3.4 Materials/Methods and Results
3.4.1 Phospholipid Fatty Analysis
The PLFA analysis of the gravesoils followed the protocol of White et al.
(1979) and Zelles et al. (1992) with a few modifications. It consists of the
three main preparation steps of extraction, fractionation and derivitisation
followed by gas chromatography-mass spectrometric analysis.
3.4.1.1 Extraction
The following replicate soil samples were available for testing: Pallarenda
samples (2 control soils, 4 complete cadaver soils, 3 incised cadaver soils and
4 eviscerated cadaver soils) and Wambiana samples (3 control soils, 4
complete cadaver soils, 4 incised cadaver soils and 3 eviscerated cadaver
soils). Frozen soils were left to thaw for 40 mins to an hour. Lipid extraction
was performed using 2 g (±0.1 g) of thawed moist soil. A solution of
37
phosphate buffer (8.7g K2HPO4/L, neutralised with 1 N HCl to pH 7.4)
methanol (99%) and chloroform (99%; v/v/v 2 ml, 5 ml, 2.5 ml) was added to
the sample. The mixture was sonicated for 15 minutes in an ultrasonic bath
and then centrifuged for 5 minutes at 3500 rpm. The supernatant was
decanted and chloroform and deionised (Milli Q) water were added (3 ml
each). After standing for 30 minutes on ice, the chloroform phase (bottom
phase) was isolated and dried down under a stream of nitrogen gas (N2).
3.4.1.2 Fractionation
The total lipid extract (TLE) was remobilised in chloroform (2 ml) and was
separated into polarity-based fractions by successive solvent elutions through
silica-bonded columns (Supelclean LC-Si-SPE, Sigma-Aldrich (Supelco), Poole,
UK). The silica powder columns were first conditioned with 2 ml of methanol
and 2 ml of chloroform pulled through with a vacuum followed by an
additional 1 ml of chloroform allowed to drip through without the vacuum.
The TLE was then added to the column and successively eluted with
chloroform (2 ml) to remove the neutral fatty acids, acetone (2 ml) to remove
the free glycol fatty acids and methanol (2 ml) to remove the phospholipid
fatty acids.
3.4.1.3 FAME Derivitisation
The phospholipid fatty acid fraction was resuspended in a solution of
methanol and toluene (1:1, v/v, 0.2 ml) and vortexed. A mixture of
potassium hydroxide and methanol (0.2 M, 0.5 ml) was added and heated on a
hot block to 75ºC for 10 minutes with intermittent agitation. On cooling to
room temperature, the fraction was neutralised with acetic acid (0.2 M, 0.5
ml). Chloroform and deionised water (1 ml each) was added, the sample was
allowed to stand and the bottom chloroform phase was removed. The
remaining top aqueous phase was re-extracted and the two chloroform phases
were combined and concentrated under a stream of nitrogen gas. The
remaining sample was aliquoted into a glass capillary placed inside an amber
GC-MS vial along with a methylnonadecanate or C19:0 fatty acid (20 µL, 10 ng
µL-1, Sigma-Aldrich) internal standard for quantitative GCMS purposes and
stored at -20ºC. Analytical grade solvents and chemicals were used and
38
stored at 4ºC. All glassware was acid washed prior to use to remove possible
contaminants.
3.4.1.4 Gas Chromatography/Mass Spectrometry
The fatty acid methyl ester (FAME) fractions were analysed by an Agilent
6890/5973 Gas Chromatography-Mass Spectrometer. A 60 m x 0.25 mm a 0.25
µm ZB-5 (Phenomenex) capillary column was used. The GC was used in
pulsed-splitless mode and the oven was programmed from an initial
temperature of 70ºC held isothermal for 1 minute and then increased at a rate
of 10ºC min-1 to 150ºC, followed by 3ºC min-1 to 300ºC and held isothermal for
a final 20 minutes. Helium carrier gas was maintained at a constant flow of
1.1 mL min-1. Full scan (50-550 Da) data were acquired. Other standard mass
spectral conditions were applied including an electron energy of 70 eV; source
temperature of 230ºC. Product identifications were based on comparison to
library mass spectra. The concentration of each fatty acid was determined
relative to the C19:0 internal standard and was calculated as:
µg individual fatty acid g-1 soil = (PFAME x µg Std) / (PISTD x W)
where PFAME stands for the peak area of the fatty acid samples and PISTD
stands for the peak area of the internal standards; µg Std is the concentration
of the internal standard (µg uL-1 solvent); and W is the dry weight cm-3.
The GCMS data was processed using ChemStation software (Agilent
Technologies, Palo Alto, CA). PLFA peak assignments were based on retention
time and mass spectral correlation (see Fig. 3.1). Identification was aided by
corresponding GCMS analysis of a standard mixture of authentic PLFA
products. The peak area of selected PLFAs in either the total or m/z 74 ion
chromatograms were calculated by peak integration, quantified by
comparison to the measured area of the C19:0 internal standard (see Table
3.1) and statistically processed.
39
Peak Nomenclature Full Compund Name
4 i-15:0 Me.13-methyltetradecanoate
5 a-15:0 Me.12-methyltetradecanoate
6 15-0 Me.pentadecanoate
8 i-16:0 Me.14-methylpentadecanoate
10 16:1(w7c) Me.cis-?-hexadecenoate
11 16:1(9) Me.cis-9-hexadecenoate
12 16-0 Me.hexadecanoate
13 ?-17:0 Me.?-methylhexadecanoate
14 ?-17:0 Me.?-methylhexadecanoate
15 i-17:0 Me.15-methylhexadecanoate
16 a17:0 Me.?-methylhexadecanoate
17 17-0(D) Me.cis-9,10-methylenehexadecanoate
18 17-0 Me.heptadecanoate
21 18:2(9,12) Me.cis-9,10-octadecadienoate
22 18:1(9) cis Me.cis-9-octadecenoate
23 18:1(9) trans Me.trans-9-octadecenoate
24 18-0 Me.octadecanoate
25 i19:0 Me.?-methyloctadecanoate
26 19-0(D) Me.cis-9,10-methyleneoctadecanoate
27 19-0 Me.nonadecanoate
Figure 3.1: An example of a typical phospholipid fatty acid distribution with the assignment of major peaks. Peaks that have been further identified are shown in the
table above.
40
3.4.1.5 Statistics
The Primer 6 software package (Primer-E Ltd., Plymouth, United Kingdom)
was used to generate multi-dimensional scaling (MDS) plots and analysis of
similarity (ANOSIM) calculations.
41
3.4.2 Terminal Restriction Fragment Length Polymorphism Analysis
3.4.2.1 DNA Extraction
A trial was conducted with the UltraClean™ Soil DNA Isolation Kit (Mo Bio
Laboratories, Inc.), but it did not remove contaminating humic acids
effectively (yellow to brown extractions) and the resulting DNA product was
very poor. The Powersoil™ DNA Isolation Kit (Mo Bio Laboratories, Inc.)
trialled next, resulted in clear extractions and consistent, good quality DNA
product, and therefore was used to extract the DNA from the rat cadaver soil
samples. Throughout the protocol, some samples were duplicated to measure
the reproducibility of the method. The manufacturer‟s protocol was followed
with slight modifications to enhance DNA yield and decrease co-extraction of
humic substances (Appendix I). Three weights of soil (0.2 g, 0.4 g and 0.6 g)
were tested, in selected samples from each soil suite, in an attempt to
increase DNA yield (see Fig 3.2). A soil weight of 0.4 g gave a consistent and
good quality yield of DNA.
Figure 3.2: Optimising the effect of soil weight on DNA yield. Three weights tested: 0.2 g, 0.4 g, 0.6 g. 1 = Pallarenda soil (A) control (C) 1 (0.2g), 2 = AC 2 (0.4g), 3 = AC 3
(0.6g), 4 = Wambiana soil (B) incised (IN) 1 (0.2g), 5 = BIN 2 (0.4g), 6 = BIN 3 (0.6g), L = 200 bp ladder.
The extraction protocol used 0.4 g of soil added to a solution (C1) that
dispersed the soil particles, began dissolving humic acids and broke down the
lipids that are associated with cell membranes. Mechanical lysis of the
microbial cells was performed with a bead beater used for 2 minutes at
2500 rpm. The resulting supernatant was separated from the pellet of cell
debris, soil, beads and humic acids. Consecutive solutions (C2) that
1 2 3 4 5 L L 6
10000 bp
1500 bp
42
precipitated non-DNA organic and inorganic material including humic acid,
cell debris and proteins, (C3) precipitated additional humic acids and other
DNA inhibitors, and (C4) bound the DNA to the silica membrane were added to
the supernatant. An ethanol-based wash solution (C5) was used to further
clean the DNA bound to the silica filter by removing salt, humic acid and
other contaminants. A final sterile elution buffer (C6) released the DNA from
the membrane and eluted it into the tube ready for further application. All
centrifugation steps were carried out at 10,000 x g. The DNA from all samples
was successfully extracted. The DNA was stored between -20ºC to -30ºC until
ready to use.
3.4.2.2 DNA Quantification
The success of each extraction was determined by visualisation on a 2%
agarose gel stained with ethidium bromide. The gels were run with the
molecular weight marker Hyperladder I (Bioline, NSW, Australia), a
quantitative DNA marker ranging from 200 to 10,000 bp.
3.4.2.3 Polymerase Chain Reaction
PCR reagents were defrosted on ice prior to use and reactions were put
together in a laminar flow cabinet using filter tips and pre-labelled tubes.
Positive (DNA) and negative (reagent) controls were included in every PCR
reaction suite. Separate PCR reactions were performed to amplify the
bacterial and fungal communities in the soil. A fluorescent-labelled forward
primer FAM63F (5‟-CAG GCC TAA CAC ATG CAA GTC-3‟) (Marchesi et al.,
1998) and a fluorescent-labelled reverse primer HEX1087R (5‟-CTC GTT GCG
GGA CTT ACC CC-3‟) (Singh et al., 2006), which generates a 1,064-base
product of the 16SrRNA gene, were used to amplify conserved eubacterial 16S
ribosomal RNA gene sequences present in the soil extracts. Bacterial
amplification was conducted in 50 µL reaction volumes (see Table 3.2) that
contained PCR buffer (Bioline, Alexandria, NSW, Australia), MgCl2 (Bioline,
Alexandria, NSW, Australia), dNTPs (Bioline, Alexandria, NSW, Australia),
BIOTAQ™ DNA polymerase (Bioline, Alexandria, NSW, Australia) and primers
(GeneWorks, Hindmarsh, SA, Australia).
43
Table 3.1: Polymerase chain reaction mastermix used for bacterial amplification of soil microbial communities of the Pallarenda and Wambiana soils. Amplification was
conducted in a 50 µL reaction volume.
Reagent Concentration Per reaction (L)
Buffer 10 X 5
MgCl2 50 mM 3
dNTPs 25 mM each 1
Taq 5 U/L 0.25
H2O - 37.75
DNA Template 1:1 1
FAM63F 10 M 1
HEX1087R 10 M 1
Three concentrations of bacterial DNA (1/5 dilution, 1 µL of pure extract and
2 µL of pure extract) were tested for each soil type, in an attempt to optimise
the PCR protocol (see Fig 3.3). A concentration of 1 µL of pure extracted DNA
was used for subsequent PCR reactions.
Figure 3.3: Optimising the DNA concentration used for the polymerase chain reaction protocol. Three concentrations were tested: 1/5 dilution, 1 µL of pure DNA extract and 2 µL of pure DNA extract. 1 = AC1 - Pallarenda soil (A) control (C) 1 (1/5 dilution), 2 = AC2 (1/5 dilution), 3 = AC3 (1/5 dilution), 4 = AC1 (1 µL), 5 = AC2 (1 µL), 6 = AC3 (1 µL), 7 =
AC1 (2 µL), 8 = AC2 (2 µL), 9 = AC3 (2 µL), 10 = Wambiana soil (B) incised (IN) (1/5 dilution), 11 = BIN (1/5 dilution), 12 = BIN (1/5 dilution), 13 = BIN (1 µL), 14 = BIN (1 µL), 15 = BIN (1 µL), 16 = BIN (2 µL), 17 = BIN (2 µL), 18 = BIN (2 µL), P = positive control (E.
coli gDNA), N = negative control (reagent), L = 200 bp ladder.
L 1 2 3 4 5 6 7 8 9 P N L
L 10 11 12 13 14 15 16 17 18 P N L
10000 bp
1500 bp
1/5 1µL 2 µL
1/5 1µL 2 µL
44
The thermal cycling protocol was modified from Osborn et al. (2000). A trial
of 30 cycles produced some smearing, probably due to the amplification of
non-specific products (see Fig 3.4) and so 25 cycles were used (see Fig 3.5). A
two minute denaturation step at 94ºC was followed by 25 cycles of 94ºC for
one minute, 55ºC for one minute and 72ºC for two minutes. A final extension
step was conducted at 72ºC for ten minutes. The amplification of bacterial
DNA was successful for all samples (see Fig 3.5, 3.7). Three replicate PCR
reactions were performed under the same conditions and the products were
pooled.
Figure 3.4: Polymerase chain reaction product of bacterial DNA from Pallarenda soil (soil A) samples using 30 cycles. 1 = control (C) 1, 2 = C1 (duplicate), 3 = C2, 4 = C2
(duplicate), 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate), 12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 =
EV4, P = positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp ladder. Duplicate samples are labelled with an asterisk.
L 1 2* 3 4* 5 6 7 8 P N L
9 10 11* 12 13 14 15 16
Control Complete cadaver
Eviscerated
cadaver
Incised cadaver
45
Figure 3.5: Polymerase chain reaction product of bacterial DNA from Pallarenda soil (soil A) samples using 25 cycles. 1 = control (C) 1, 2 = C1 (duplicate), 3 = C2, 4 = C2
(duplicate), 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate), 12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 =
EV4, P = positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp ladder.
Figure 3.6: Polymerase chain reaction product of fungal DNA from Pallarenda soil (soil A) samples. 1 = control (C) 1, 2 = C1 (duplicate), 3 = C2, 4 = C2 (duplicate), 5 = complete
cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate), 12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 = EV4, L = 200 bp
ladder.
A fluorescent-labelled forward primer FAM ITS-1F (5‟-CTT GGT CAT TTA GAG
GAA GTAA-3‟) (Gardes and Bruns, 1993) and an unlabelled reverse primer
ITS4R (5‟-TCC TCC GCT TAT TGA TAT GC-3‟) (White et al, 1990) were used to
amplify the highly variable internal transcribed spacer region of fungal
ribosomal RNA gene. Fungal amplification was conducted in 50 µL reaction
volumes (see Table 3.3) that contained PCR buffer (Bioline, Alexandria, NSW,
Australia), MgCl2 (Bioline, Alexandria, NSW, Australia), dNTPs (Bioline,
L 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
P N L
L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 L
Control Complete cadaver
Incised cadaver Eviscerated cadaver
46
Alexandria, NSW, Australia), BIOTAQ™ DNA polymerase (Bioline, Alexandria,
NSW, Australia) and 2 L of each primer (GeneWorks, Hindmarsh, SA,
Australia).
Table 3.2: Polymerase chain reaction mastermix used for fungal amplification of soil microbial communities of the Pallarenda and Wambiana soils. Amplification was
conducted in a 50 µL reaction volume.
Reagent Concentration Per reaction (L)
Buffer 10 X 5
MgCl2 50 mM 3
dNTPs 25 mM each 1
Taq 5 U/L 0.25
H2O - 35.75
DNA Template 1:1 1
ITS4R 10 M 2
ITS-1F (FAM) 10 M 2
The Wambiana soil (soil B) showed a lower concentration of DNA overall in
comparison with the Pallarenda soil (soil A). Therefore, to achieve a higher
concentration of DNA, 30 cycles instead of 25 cycles were used. A 5 minute
denaturation step at 95ºC was followed by 30 cycles of 94ºC for 45 seconds,
55ºC for 45 seconds and 72ºC for one minute. A final extension step was
conducted at 72ºC for 20 minutes. The amplification of fungal DNA was
successful for all samples (see Fig 3.6, 3.8). Three replicate PCR reactions
were performed under the same conditions and the products were pooled.
Figure 3.7: Polymerase chain reaction product of bacterial DNA from Wambiana soil (soil B) samples. 1 = control (C) 1, 2 = C2, 3 = C2 (duplicate), 4 = C3, 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN3, 12 = IN4, 13 =
eviscerated (EV) 1, 14 = EV2, 15 = EV2 (duplicate), 16 = EV3, P = positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp ladder.
L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 P N L
47
Figure 3.8: Polymerase chain reaction product of fungal DNA from Wambiana soil (soil B) samples. 1 = control (C) 1, 2 = C2, 3 = C2 (duplicate), 4 = C3, 5 = complete cadaver (CC)
1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN3, 12 = IN4, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV2 (duplicate), 16 = EV3, P = positive control (C.
albicans DNA), N = negative control (reagent), L = 200 bp ladder.
Both bacterial and fungal DNA was successfully amplified for all samples.
Amplified DNA was imaged and quantified (see Tables 3.4 and 3.5) using
TotalLab software, v. 1.10 (Nonlinear Dynamics, Durham, NC). The amount of
DNA after amplification was variable between the samples and their
replicates.
Table 3.3: Amount of bacterial DNA (ng/L) present in Pallarenda and Wambiana soil samples after polymerase chain reaction amplification. A = Pallarenda soil, B = Wambiana soil, C = control, CC = complete cadaver, IN = incised cadaver, EV = eviscerated cadaver,
1A/1B, 2A/2B = duplicate samples.
Pallarenda Samples
DNA amount (ng/L) Wambiana Samples
DNA amount (ng/L)
AC1A 8.3 BC1 11.7
AC1B 11.3 BC2A 8.2
AC2A 10.2 BC2B 3.8
AC2B 7.0 BC3 3.9
ACC1 8.7 BCC1 2.2
ACC2 12.5 BCC2 5.8
ACC3 10.6 BCC3 18.6
ACC4 10.7 BCC4 21.3
AIN1 15.1 BIN1 12.8
AIN2A 20.7 BIN2 70.7
AIN2B 11.0 BIN3 8.2
AIN3 76.7 BIN4 5.8
AEV1 18.2 BEV1 3.4
AEV2 7.5 BEV2A 44.9
AEV3 22.8 BEV2B 75.5
AEV4 50.2 BEV3 13.8
L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 P N L
48
Table 3.4: Amount of fungal DNA (ng/L) present in Pallarenda and Wambiana soil samples after polymerase chain reaction amplification. A = Pallarenda soil, B = Wambiana soil, C = control, CC = complete cadaver, IN = incised cadaver, EV = eviscerated cadaver,
1A/1B, 2A/2B = duplicate samples.
Pallarenda Samples
DNA amount (ng/L) Wambiana Samples
DNA amount (ng/L)
AC1A 7.4 BC1 2.2
AC1B 5.9 BC2A 3.8
AC2A 5.7 BC2B 5.2
AC2B 5.9 BC3 3.8
ACC1 20.7 BCC1 1.9
ACC2 25.4 BCC2 4.4
ACC3 4.5 BCC3 95.6
ACC4 15.0 BCC4 110.4
AIN1 22.4 BIN1 82.6
AIN2A 54.6 BIN2 111.6
AIN2B 57.1 BIN3 41.1
AIN3 90.9 BIN4 15.0
AEV1 7.0 BEV1 6.1
AEV2 9.0 BEV2A 108.2
AEV3 57.6 BEV2B 204.0
AEV4 103.1 BEV3 30.8
3.4.2.4 PCR Product Clean-up
All PCR products were purified from primers, nucleotides, polymerases and
salts with the QIAquick PCR Purification Kit (Qiagen) using QIAquick silica-
gel membrane spin columns in a microcentrifuge using the manufacturer‟s
directions (Appendix V).
3.4.2.5 Restriction Enzyme Digestion
The amplified and cleaned bacterial DNA was digested with MspI enzyme (Liu
et al., 1997) (Sigma-Aldrich, St Louis, MO) (Appendix VI) and the fungal DNA
was digested with HhaI enzyme (Osborn, Moore and Timmis, 2000) (Promega,
Madison, WI) (Appendix VII). Digest (without DNA) and enzyme (without
enzyme) blanks were included with every digestion reaction suite. The
reactions were digested at 37ºC for 3 hours followed by 65ºC for 20 minutes.
49
3.4.2.6 T-RF Analysis
The digested products were sent to the Australian Genome Research Facility
in Adelaide, Australia for T-RFLP analysis, where the fluorescently labeled
terminal restriction fragments were separated using the automated
sequencing platform AB3730xl. The output data was imported into Excel for
further statistical analysis. The output of the T-RFLP analysis is generated in
two forms. An electropherogram shows the profile of the microbial
community as a series of coloured peaks of varying heights. The second
output is numerical and consists of a table, which includes the fragment size
measured in base pairs, and the area and height of each peak, measured in
fluorescence units. Peaks generated by fragments less than 50 bp and more
than 500 bp were omitted in order to avoid the T-RFs caused by primer-dimers
and to obtain fragments within the linear range of the internal size standard
(LIZ500). The RiboSort package (Scallan et al., 2008) for the statistical
software R automatically assigned the fragments and their respective peak
heights to appropriate ribotypes. Three separate spreadsheets were produced
containing data from the fungal profiles, the bacterial profiles from the 3‟
fragment end and the bacterial profiles from the 5‟ fragment end. These data
were then inputed into the Primer 6 software package (Clarke and Ainsworth,
1993) for generating MDS plots and ANOSIM calculations.
The selection of T-RF profiles generated has been shown below. Five dyes
have been detected and are shown as different coloured peaks: FAM dye in
blue, HEX dye in green, PET dye in red, the size standard in orange and NED in
yellow. The FAM and HEX dyes indicate the peaks of importance, whereas the
PET, NED and size standard peaks are ignored for the visual comparison of the
profiles. The bacterial profiles from two duplicate controls of the Pallarenda
soil show a high degree of similarity to each other, with a similar number of
peaks and only a slight difference in peak heights (see Fig 3.9). The fungal
profiles of the same duplicate controls of the Pallarenda soils show a slight
difference in the number and height of peaks, but overall show some degree
of similarity (see Fig 3.10). The bacterial T-RF profiles of the complete,
incised and eviscerated cadavers of the Pallarenda soils have been illustrated
for comparison (see Fig 3.11). The complete cadaver samples show more
peaks and higher peak heights than the incised and eviscerated cadaver
samples. The incised and eviscerated profiles seem more similar to each
50
other than the cadaver samples in terms of low peak number and height. The
fungal profiles of the same samples (see Fig 3.12) are very different to the
bacterial profiles, with a higher number of peaks and peak heights overall.
Once again the incised and eviscerated cadaver samples tend to be more
similar to each other than when compared to the complete cadaver.
However, the incised cadaver sample seems to have the most number of
peaks and higher peak heights overall. A dominant peak of approximately 600
bp in size is present in the complete cadaver sample, but disappears in the
incised and eviscerated cadaver samples. However, this fragment is outside
the parameters for T-RF analysis. The bacterial T-RF profiles of the
complete, incised and eviscerated cadavers of the Wambiana soils have been
shown for reference (see Fig 3.13). These profiles look very different to those
of the Pallarenda soils as they seem to have a higher number of peaks with
higher peak heights. The profiles also differ in number of peaks, fragment
lengths and the heights of the peaks. A significant observation is an increase
in the number of HEX-labelled fragments for the incised cadaver sample
compared to the rest of the samples. The fungal profiles of the same samples
have been included (see Fig 3.14). The profiles are once again different to
each other in terms of peak numbers, fragment sizes and peak heights. In
general, FAM-labelled fragments have a higher frequency compared to the
HEX-labelled fragments.
51
Figure 3.9: Bacterial terminal restriction fragment (T-RF) profile of control sample 1 (top) and 2 (bottom) of Pallarenda soil. The blue peaks represent the FAM-labelled T-RFs from the 5’ end, the green peaks are the HEX-labelled T-RFs from 3’ end, and the orange
peaks represent the LIZ500 size standard.
Figure 3.10: Fungal terminal restriction fragment profile of control sample 1 (top) and 2 bottom of Pallarenda soil. FAM = blue, LIZ500 size standard = orange.
Rela
tive f
luore
scence u
nit
s (r
fu)
Fragment size (bp)
Rela
tive f
luore
scence u
nit
s (r
fu)
Fragment size (bp)
52
Figure 3.11: Bacterial terminal restriction fragment profile of complete cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1 (bottom) of Pallarenda soil.
FAM-labelled 5’ end = blue, HEX-labelled 3’ end = green, LIZ500 size standard = orange.
Rela
tive f
luore
scence u
nit
s (r
fu)
Fragment size (bp)
53
Figure 3.12: Fungal terminal restriction fragment profile of complete cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1 (bottom) of Pallarenda soil. FAM
= blue, LIZ500 size standard = orange.
Rela
tive f
luore
scence u
nit
s (r
fu)
Fragment size (bp)
54
Figure 3.13: Bacterial terminal restriction fragment profile of complete cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1 (bottom) of Wambiana soil. FAM-
labelled 5’ end = blue, HEX-labelled 3’ end = green, LIZ500 size standard = orange.
Rela
tive f
luore
scence u
nit
s (r
fu)
Fragment size (bp)
55
Figure 3.14: Fungal terminal restriction fragment profile of complete cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1 (bottom) of Wambiana soil. FAM
= blue, LIZ500 size standard = orange.
Rela
tive f
luore
scence u
nit
s (r
fu)
Fragment size (bp)
56
3.5 Data Handling and Statistical Analysis
The two-way crossed analysis of similarities produces a „significance level of
sample statistic‟. For ease of interpretation of the data in this chapter this
statistic will be converted to the p-value using the following formula:
Significance level of sample statistic/100 = p-value
The significance levels will be interpreted as defined in Table 3.5.
Table 3.5: Definition of significance levels using p-values.
p-value Significance Level
<0.01 Highly significant
<0.05 Significant
<0.1 Marginally significant
>0.1 Not significant
3.5.1 PLFA Datasets
The PLFA peak area data was used to construct a similarity matrix using the
Bray-Curtis coefficient and generate Multi Dimensional Scaling (MDS) plots.
The MDS plot (see Fig 3.15) shows a general separation of phospholipid fatty
acid profiles between soil microbial communities of the Pallarenda and
Wambiana soils, with overlap of a small number Pallarenda with Wambiana
soil profiles at 60% similarity. The profiles that share at least 60% similarity
are circled in green. A two-way crossed analysis of similarities (ANOSIM)
conducted on the differences between the two soil types and across the four
treatment groups resulted in a p-value of 0.0006. This indicates a highly
significant difference between the microbial communities of the two soils.
57
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
SoilPR
WB
Similarity60
2D Stress: 0.02
Figure 3.15: Multi-dimensional scaling plot of phospholipid fatty acid profiles of soil
microbial communities of Pallarenda (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
) and Wambiana (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
) soils containing control and treatments soils. Profiles that share at least 60% similarity are circled in green. PR =
Pallarenda, WB = Wambiana.
A two-way crossed analysis testing for differences between the treatment
groups across both soil types resulted in a p-value of 0.018, which indicates a
significant difference between the microbial communities between all the
treatment groups. A further pairwise test (see Table 3.6) was carried out and
resulted in the following statistics:
Table 3.6: Significance results of pairwise test conducted on phospholipid fatty acid profiles between all treatment groups and across both soil types.
Pairwise Test p-value Significance
Control, Complete 0.093 Marginally significant
Control , Incised 0.066 Marginally Significant
Control, Eviscerated 0.093 Marginally significant
Complete, Incised 0.019 Significant
Complete, Eviscerated 0.322 Not significant
Incised, Eviscerated 0.451 Not significant
58
3.5.2 T-RFLP Datasets
The bacterial dataset was separated into the two sets of terminal restriction
fragments (T-RFs) of the digested product, that is, one from the 5‟ end
labelled with FAM and the other from the 3‟ end labelled with HEX. A
similarity matrix was constructed using the Bray-Curtis coefficient. The data
was transformed using the square root function for the 3‟ end fragment data
and fourth root function for the 5‟ end fragment data.
An MDS plot has compared the 3‟ fragments labelled with HEX (see Fig 3.16),
of both soils. Based on this plot, soil A and soil B have formed separate
clusters from each other with no overlap. This suggests the soil bacterial
community of the Pallarenda soil is considerably different to the Wambiana
soil. Carter Bacterial (HEX) T-RFLP AbundancesTransform: Square root
Resemblance: S17 Bray Curtis similarity
soilA
B
BH1
BH3
BH5
BH6
BH7
BH8
BH9
BH10
BH12
BH13BH14
BH15
BH16BH17
BH18
BH20
BH21
BH22
BH23
BH24
BH25
BH26
BH27
BH28
BH29
BH30
BH32
2D Stress: 0.16
Figure 3.16: Multi-dimensional scaling plot comparing the bacterial 3' end terminal restriction fragment abundances, labelled with the fluorescent dye HEX, for both soils. Soil A=Pallarenda, soil B=Wambiana. BH1, 3, 17, 18, 20 = control, BH5, 6, 7, 8, 21, 22, 23, 24 = complete cadaver, BH9, 10, 12, 25, 26, 2, 28 = incised cadaver, BH13, 14, 15,
16, 29, 30, 32 = eviscerated cadaver.
59
A two-way crossed ANOSIM was conducted on the T-RF data from the 3‟ end to
test for differences between the two soil types and across all treatment
groups. This resulted in a p-value of 0.0001 which indicates a highly
significant difference between the soil bacterial communities of the two soils.
A two-way crossed ANOSIM was conducted on the T-RF data from the 3‟ end to
test for differences between the treatment groups and across both soil types.
This resulted in a p-value of 0.009 and indicates a highly significant difference
between the soil bacterial communities of the treatment groups. A further
pairwise test (see Table 3.7) revealed the following statistics:
Table 3.7: Significance results of pairwise test conducted on 3’ end of bacterial terminal restriction fragments between all treatment groups and across both soil types.
Pairwise Test p-value Significance
Control, Complete 0.048 Significant
Control , Incised 0.003 Highly Significant
Control, Eviscerated 0.207 Not Significant
Complete, Incised 0.081 Marginally Significant
Complete, Eviscerated 0.539 Not significant
Incised, Eviscerated 0.082 Marginally Significant
The second set of T-RFs from the 5‟ end, labelled with the fluorescent dye
FAM was compared between the soil types. There is a distinct separation of
bacterial communities based on these fragments between the Pallarenda and
Wambiana soil types (see Fig 3.17).
60
Carter Bacterial (FAM) T-RFLP AbundancesTransform: Fourth root
Resemblance: S17 Bray Curtis similarity
SoilA
B
C
C
CC
CC
CC
CC
IC
IC
IC
EC
EC
EC
EC
C
C
C
CC
CC
CCCC
ICIC
ICIC
EC
EC
EC
2D Stress: 0.15
Figure 3.17: Multi-dimensional scaling plot comparing the bacterial 5' end terminal restriction fragment abundances, labelled with the fluorescent dye FAM, for both soils. Soil A=Pallarenda, soil B=Wambiana. C = control, CC = complete cadaver, IC = incised
cadaver, EC = eviscerated cadaver.
A two-way crossed ANOSIM was conducted on the T-RF data from the 5‟ end to
test for differences between the two soil types and across all treatment
groups. This resulted in a p-value of 0.0001 which indicates a highly
significant difference between the soil bacterial communities of the
Pallarenda and Wambiana soils. A second two-way crossed ANOSIM was
conducted on the T-RF data from the 5‟ end to test for differences between
the treatment groups and across both soil types. This resulted in a p-value of
0.013 and indicates a significant difference between the soil bacterial
communities of the treatment groups. A pairwise test (see Table 3.8) shows
the following statistics:
Table 3.8: Significance results of pairwise test conducted on 5’ end of bacterial terminal restriction fragments between all treatment groups and across both soil types.
Pairwise Test p-value Significance
Control, Complete 0.004 Highly Significant
Control , Incised 0.009 Highly Significant
Control, Eviscerated 0.053 Marginally Significant
61
Complete, Incised 0.04 Significant
Complete, Eviscerated 0.819 Not significant
Incised, Eviscerated 0.428 Not significant
The fungal T-RF data was transformed using the log(X+1) function. A MDS has
compared the fungal T-RF profiles for the Pallarenda and Wambiana soil
treatments (see Fig 3.18). It showed a separation of Pallarenda samples from
the Wambiana samples. There is almost no overlap seen between the two soil
sample suites and this suggests that the fungal populations are different
between the Pallarenda and Wambiana soils. Carter Fungal T-RFLP AbundancesTransform: Log(X+1)
Resemblance: S17 Bray Curtis similarity
SoilA
B
F1
F3
F5F6
F7
F8
F9F10
F12F13
F14
F15
F16
F17
F18
F20
F21
F22
F23F24
F25
F26F27
F28
F29
F30
F32
2D Stress: 0.16
Figure 3.18: Multi-dimensional scaling plot comparing the fungal restriction fragment abundances for Pallarenda and Wambiana soils. Soil A=Pallarenda, soil B=Wambiana. F1, 3, 17, 18, 20 = control, F5, 6, 7, 8, 21, 22, 23, 24 = complete cadaver, F9, 10, 12, 25,
26, 27, 28 = incised cadaver, F13, 14, 15, 16, 29, 30, 32 = eviscerated cadaver.
A two-way crossed ANOSIM was conducted on the fungal T-RF data to test for
differences between the two soil types and across all treatment groups. This
resulted in a p-value of 0.0001 which indicates a highly significant difference
between the soil fungal communities of the Pallarenda and Wambiana soils. A
62
second two-way crossed ANOSIM was conducted on the fungal T-RF data to
test for differences between the treatment groups and across both soil types.
This resulted in a p-value of 0.0001 and indicates a highly significant
difference between the soil bacterial communities of the treatment groups. A
pairwise test (see Table 3.9) shows the following statistics:
Table 3.9: Significance results of pairwise test conducted on fungal terminal restriction
fragments between all treatment groups and across both soil types.
Pairwise Test p-value Significance
Control, Complete 0.002 Highly Significant
Control , Incised 0.003 Highly Significant
Control, Eviscerated 0.007 Highly Significant
Complete, Incised 0.003 Highly Significant
Complete, Eviscerated 0.005 Highly Significant
Incised, Eviscerated 0.264 Not significant
A MDS was used to compare the fungal T-RFs between all treatment groups of
both soil types (see Fig 3.19). It showed a separation of the control samples
away from the three cadaver treatments. The complete cadaver samples
separated away from the incised and eviscerated cadaver treatments. The
incised and eviscerated samples mostly separated out with some overlap seen
between them. This suggests that the fungal populations are different
between the control soils and the soils that contained the cadaver. It also
shows that the fungal population of the complete cadaver is different to the
two other cadaver treatments.
63
Carter Fungal T-RFLP AbundancesTransform: Log(X+1)
Resemblance: S17 Bray Curtis similarity
TreatmentC
CC
IC
EC
3D Stress: 0.11
Figure 3.19: Multi-dimensional scaling plot of fungal terminal restriction fragments of the
Pallarenda and Wambiana soil containing C = control soils (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
), CC = complete cadaver
(
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
), IC = incised cadaver (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
), and EC = eviscerated cadaver (
Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity
TreatmentCont
Comp
Incis
Evis
3D Stress: 0.01
) samples.
A summary of the significance results produced cross the four methods used is
shown in the table below (see Table 3.10). It showed that the T-RFLP method
using the fungal population of the soil community results in the greater
proportion of significant results, followed by bacterial T-RFLP using FAM
fragments, HEX fragments and PLFA.
Table 3.10: Summary of the significance results of pairwise tests conducted between all treatment groups, across both soil types and over all methods. HS = highly significant, S =
significant, MS = marginally significant, NS = not significant
Pairwise
tests of
treatments
METHODS
PLFA Bacterial T-RFLP
(HEX fragments)
Bacterial T-RFLP
(FAM fragments) Fungal T-RFLP
Control,
Complete MS S HS HS
Control,
Incised MS HS HS HS
Control,
Eviscerated MS NS MS HS
Complete,
Incised S MS S HS
64
Complete,
Eviscerated NS NS NS HS
Incised,
Eviscerated NS MS NS NS
3.6 Discussion
A soil microbial community responds much faster to disturbances and
perturbations in the soil than physiochemical attributes such as pH and
organic carbon measures (Atlas, 1984). Therefore, the dynamics of a soil
microbial community can closely mirror ecosystem dynamics associated with
environmental change. The current study investigates the effects of
decomposition with and without the internal cadaver microflora on the soil
bacterial and fungal communities in a sandy (Pallarenda) and a clayey
(Wambiana) soil.
3.6.1 PLFA Results
In the current research, PLFA analysis was not used to identify functional or
taxonomic groups of microbes, nor provide a measurement of diversity. The
primary aim of this research was to determine if the presence of a
decomposing rat cadaver in soil would change the dynamics of the soil‟s
indigenous microbial community. The PLFA analysis successfully provided
profiles of the soil microbial communities, which allowed comparisons to be
made between the two soils types and the four cadaver treatments within
each soil type. Quantitative assessment of a subset of common PLFAs was
used to detect differences in the overall community structure of the soil
samples studied. Therefore, the differences discussed here relate to
microbial structural change rather than diversity.
There were highly significant differences (p = 0.0006) between the PLFA
abundances of soil microbial communities between the Pallarenda and
Wambiana soils. The differences in the soil microbial communities were more
significant between the two soil types and across the treatment groups than
between the treatment groups and across the two soil types using the PLFA
and bacterial T-RFLP methods. Physical and chemical properties of soil
65
contribute strongly to the heterogeneity of microbial communities in soil
(Liesack et al., 1997). The Pallarenda soil was classified as a Rudosol with a
sand texture, which can be described as a well-drained moist seasonal
savanna soil with a pH of 5.0. The Wambiana soil was classified as a Grey
Vertosol with a medium clay texture (>30% clay) with a pH of 6.1 (Carter,
2005). It is known that clays tend to retain water whereas sand allows rapid
water drainage and in situations where water content is low, microbial life is
reduced (Stotzky, 1997).
The slight scatter in the replicated data of the control samples could be
attributed to soil heterogeneity, due to localised variations in the soil
environment that influence microbial populations (Coleman, Crossley and
Hendrix, 2004). Microbial biomass was calculated, using substrate-induced
respiration, for the soils in the dry season (when they were collected) in the
original experiment. It was calculated at 839 g g-1 soil for the Pallarenda
soil and 766 g g-1 soil for the Wambiana soil (Carter, 2005). It is unknown if
this difference in microbial biomass contributed to the variation seen in the
PLFA abundances of the Wambiana cadaver treatments.
3.6.2 T-RFLP Profiling Results
The bacterial and fungal T-RFLP analysis successfully produced profiles that
could be compared between the two soil types and their respective cadaver
treatments. These profiles were analysed based on the number of fragments
produced in each sample due to their differences in fragment lengths and not
their abundance indicated by peak height. Therefore, these profiles could be
used to compare microbial diversity.
3.6.2.1 Controls
The Pallarenda soils were more diverse and have a larger microbial population
than the Wambiana soils which coincides with the higher Pallarenda soil
biomass estimates (Carter, 2005). A greater cumulative CO2-C, an indicator of
microbial activity, was observed in the cadaver samples when compared with
the control samples (Carter, 2005). This signified a larger microbial biomass
in the cadaver samples than the control samples. This was reflected in the
66
cadaver profiles by a greater diversity and higher abundance overall. The
bacterial profiles of the Pallarenda replicate control soils showed a high
similarity in terms of the number of FAM- and HEX-labelled fragments and
their abundances. However, the replicate control profiles of the Wambiana
soils were very different to each other, with many differences in species type,
number and abundance. The fungal profiles showed an overall similarity
between replicate control samples for the Pallarenda soil, however there
were slight differences in terms of the appearance and disappearance of
species and species abundance. The differences between replicates of the
control Wambiana soils were considerable. This may indicate that the soil
sample was not representative of the total fungal population or exhibit the
effects of spatial variation (Prosser, 2002) and soil heterogeneity (Coleman,
Crossley and Hendrix, 2004). Other factors that may contribute to variations
of soil microbial communities seen in control or replicate soil samples are
floristic and soil physicochemical composition (Kennedy et al., 2005), meio- or
macrofaunal abundance at the centimetre scale, topography at the metre
scale and temporal variation in terms of temperature and seasons (Scala and
Kerkhof, 2000).
3.6.2.2 Bacterial T-RFLP Profiling Results
PCR-induced artefacts resulting from PCR errors and PCR bias can contribute
to an inaccurate estimation of microbial diversity. PCR errors occur due to
the formation of chimeric molecules, heteroduplex molecules and Taq DNA
polymerase error (Acinas et al., 2005). PCR bias occurs due to intrinsic
differences in the amplification efficiency of templates or to the inhibition of
amplification by the self-annealing of the most abundant templates (Acinas et
al., 2005). To minimise these artefacts, it is recommended that several
replicate PCR amplifications should be combined and to minimise chimeras
and Taq DNA polymerase errors, the smallest possible number of PCR
amplification cycles should be carried out (Acinas et al., 2005). In view of
this, three replicate PCR reactions that amplified the bacterial and fungal
DNA were carried out and pooled and 30 cycles of PCR amplification were
reduced to 25 cycles.
67
The replicate samples of the cadaver treatment profiles for the Pallarenda
and Wambiana soil exhibited variability. In most of the profiles the FAM-
labelled fragments dominated the HEX-labelled fragments in number and
abundance. The complete cadaver profiles had more diversity overall than
the control and eviscerated soils, and a common dominant peak (111 bp) were
seen in all of its replicates in the Pallarenda soil. The dominant FAM-labelled
peak (111 bp) was seen in three replicate incised cadaver profiles and the
same peak was seen in all replicate eviscerated cadaver profiles of the
Pallarenda soil. It was present in the control soils at much lower abundance
and could represent a bacterial species that could be associated with the
decomposition process. The HEX-labelled fragments seemed to dominate the
FAM-labelled fragments in number in the Wambiana complete and incised
cadaver treatment profiles. The Wambiana eviscerated samples had two
dominant FAM-labelled fragments (111 and 124 bp) and one dominant HEX-
labelled fragment (421 bp) in all four replicates.
The bacterial analysis utilising the FAM-labelled 3‟ end of the restriction
fragment resulted in a significant difference between the bacterial
community of the Pallarenda and Wambiana soil types, as did the HEX-
labelled 5‟ end of the restriction fragment. However, the separation of the
two clusters was greater in the 5‟ end analysis, as demonstrated by the MDS
plots. It is likely that the greater discrimination observed, results from a
greater number of FAM-labelled T-RFs and is a consequence of the length
heterogeneities at the 5‟ end of the gene, within the V1, V2 and V3 regions
(Osborn, Moore and Timmis, 2000).
The differences in soil bacterial communities between the incised and
eviscerated cadaver soils were mostly not significant. An incision in the
ventral region of the cadaver would have introduced oxygen into the
abdominal cavity and may have disturbed the otherwise anaerobic
environment of the cadaver‟s internal microbiota. This may have led to the
destruction of the cadaver‟s internal microbiota, in much the same way as
evisceration.
68
3.6.2.3 Fungal T-RFLP Profiling Results
Overall, the fungal profiles from the replicate samples were more similar to
each other than the bacterial profiles. The complete cadaver profiles from
the Pallarenda soils exhibited one dominant peak (331 bp) in all four
replicates, the incised profiles had two dominant peaks (318 and 330 bp) in all
the replicates and the eviscerated profiles had one dominant peak (330 bp)
found in all the replicates. The peak at 330 bp was present in the control
profiles but at very low abundances in comparison. This peak could represent
a fungal species that is present in the soil but whose growth is stimulated and
proliferates in the presence of the decomposing cadaver. A dominant peak
(330 bp) was present in high abundance in all four replicates of the complete
cadaver profiles, in three of the incised cadaver replicate profiles and all four
replicates of the eviscerated cadaver profiles. This peak was common to both
soil types and its growth seems to be stimulated by the presence of the
decomposing cadaver.
The fungal T-RFLP analysis resulted in a significant difference between the
fungal community of the Pallarenda and Wambiana soil types, although the
separation seems less distinct than between the soil bacterial communities.
This might be due to the higher diversity seen in the bacterial community.
The fungal analysis showed highly significant differences (p = 0.0001) between
the fungal populations of the Pallarenda and Wambiana soils. It also showed
highly significant differences (p = 0.0001) between the fungal communities of
the cadaver treatments in both soil types. There was a clustering of control
profiles and separation away from the treatment profiles which was the
expected outcome. The separation of the complete cadaver profiles from the
incised and eviscerated profiles confirmed that the cadaver‟s internal
microbiota contribute to the modification of the indigenous soil fungal
population. Some overlap was seen with the incised and eviscerated profiles
which indicated their effects are similar on the soil fungal community.
Furthermore, the fungal T-RFLP method was the only method that detected a
highly significant difference between the complete cadaver and eviscerated
profiles. A significant difference between these profiles would be expected
as the complete cadaver would release the cadaver‟s internal microbiota into
the soil and modify it, whereas the eviscerated cadaver would not.
69
Consequently, the fungal T-RFLP analysis appeared to be more effective at
separating out the different sample suites than the bacterial T-RFLP analysis.
These results may suggest that the T-RFLP method has greater discrimination
than the PLFA method in this experiment.
3.6.3 Other Considerations
It is important to note that the endogenous cadaveric microbial population of
the juvenile rats used in the original experiment, might not have developed
into the complex community associated with adults. As putrefaction is
initiated by the cadaver‟s internal microbiota, this could affect the rate of
decomposition. Additionally, when the products of decomposition are
released into the environment, it can be assumed that this includes a purge of
internal microbiota as well. If this microbiota was not completely developed
in the juvenile rats, this could account for lesser differences seen between
the microbial communities of the control and cadaver soils within the soil
incubation chambers.
The samples were all harvested on day 14 of the decomposition. The cadaver
decomposition period was divided into three stages and day 14 was
categorised as the mid phase of the decomposition. By this stage, a drop in
microbial activity was observed by measuring the CO2-C evolution and enzyme
activity of the eviscerated cadavers in both soils (Carter, 2005) when
compared with the other samples. This reduction of microbial activity seen in
the eviscerated samples was corroborated by the bacterial and fungal T-RF
profiles, by the decrease in the number of peaks observed. This was most
likely due to the fact that the eviscerated samples did not have its internal
microflora to contribute to the soil microbial community diversity.
Furthermore, the removal of the internal organs would reduce the amount of
organic material available to the soil microbial community for growth and
proliferation. The complete cadavers exhibited an overall increased diversity
compared to the other profiles. This was because, by day 14 the soil would
have contained the cadaver‟s internal microflora released through the purging
of decomposition fluids as well as the indigenous soil microbial community.
The incised and sown up cadavers would have allowed a gradual release
70
rather than a burst of decomposition fluids and internal microflora into the
soil. The incision may have also allowed the introduction of oxygen inside the
abdominal cavity, which might have altered the succession of the endogenous
microbes that takes place after death (Vass, 2001).
The biomass estimates increased in complete and incised cadaver samples by
day 14 in the Pallarenda soil (Carter, 2005). However, an increase in biomass
of the eviscerated cadavers only increased after day 14 (Carter, 2005). This
was reiterated, upon observing a low microbial diversity in the T-RF profiles
of the eviscerated samples. This may be explained by the slower rate of
decomposition due to the removal of the internal microflora. Consequently,
there would be a delay in the proliferation of the soil microbial community. A
variety of hydrolytic enzymes and microbes are associated with the organs
that are removed during evisceration. The absence of these and the nutrients
the organs present, may have contributed to the variation seen in the
eviscerated profiles. The greatest biomass increase occurred in the
Wambiana soil. This may reflect the low biomass that the Wambiana soil had
to start with. The soil microbial biomass may play a more important role in
cadaver decomposition in clay soils while enteric microflora may play a more
prominent role in sandy soil (Carter, 2005). This could be due to many
reasons such as dominance of desiccation, removal of viscera as a source of
enteric microbes and as an organic resource for the soil microbial
communities. In general, fungal diversity was lower than bacterial diversity
on visual inspection of the profiles. If fungal activity was predominant in the
later stages of decomposition due to competition with the overwhelming
bacterial population during the early stage of decomposition, this would be
seen later than day 14 of decomposition.
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Chapter 4 : HUMAN CADAVER EXPERIMENT
4.1 Introduction
Human decomposition is a complex process involving large numbers and great
diversity of species of microbes (Vass, 2001). Previous research (Parkinson,
2004) has investigated the soil bacterial community associated with human
decomposition using bacterial molecular biological techniques. This was
achieved by sampling soil from under two human cadavers that were laid on
the soil surface to decompose at the Forensic Anthropology Center in
Knoxville, Tennessee. The current research has analysed these gravesoils
using lipid-based phospholipid fatty acid analysis and fungal terminal
restriction fragment polymorphism analysis, to characterise the differences in
the soil microbial community as decomposition progresses. The underlying
concept is that as decomposition proceeds, a sequence of microbial
succession will occur in response to the succession of cadaver-derived
nutrients released from the cadaver into the underlying soil. Also
contributing to the dynamics of the soil microbial community, is the cadaver‟s
internal microflora that will be released into the soil when the cadaver fluids
begin to be purged. Decomposition has been defined in this research, using
accumulated degree-days (ADD) (section 4.4.1) instead of the classical stages
that are based on the visual cues of decomposition. If recurring shifts in the
microbial community can be correlated with the ADD measurements, the
microbial succession may present evidence to estimate the post mortem
interval (PMI). Understanding decomposition-associated microbiology may
eventually lead to the development of a new technique to estimate PMI.
4.2 Aims
The aim of this research tests the hypothesis that the presence of a
decomposing human cadaver upon a soil substrate changes the dynamics of
the surrounding indigenous soil microbial community. It investigates if these
changes can be visualised and compared as decomposition proceeds by
utilising the microbial PLFA and fungal T-RFLP methodologies. Furthermore,
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it evaluates the potential of developing these methods as tools to estimate
PMI using microbial succession.
4.3 Experimental Background
The ethical impositions of using human cadavers in decomposition
experiments have limited the acquisition of data in this unique environment.
The Forensic Anthropology Centre at the University of Tennessee-Knoxville,
Tennessee, USA provides a unique opportunity to investigate the human
decomposition processes in a natural setting. An opportunity to investigate
the objectives of this research was presented when soils from a previous
decomposition experiment conducted by Rachel Parkinson, were made
available. A caveat of carrying out this type of research is that cadavers are
accepted as they are donated and therefore replication of the experiment, in
effect, of the cadavers is impossible. In this instance, the soils from two
cadavers chosen at random out of six cadavers were analysed. The two
cadavers, arbitrarily named P and R were placed at separate sites on the
facility within 20 days of each other (see Table 4.1). These sites had not
previously been used for decomposition studies and were chosen for similarity
based on soil content and vegetation. Respective control sites were selected
a few metres away from the decomposition site and soil samples were
collected at the same time as for the cadaver soil samples. Both cadavers
were in the fresh stage of decomposition and presented with no visual signs of
decay. The cadavers differed in size immensely with cadaver P weighing 159
kg (see Fig 4.1) and cadaver R (see Fig 4.3) only 44 kg. An area, of
approximately 1 m x 60 cm x 10 cm of soil at the sites, was prepared by
removing stones, plant, leaf and root material and loosening and
homogenising by raking and mixing. Each cadaver was placed on a plastic
mesh sheet to allow for the cadaver to be rolled to the side during sample
collection, ensuring minimal damage to the cadaver and minimum disruption
to the decomposition process. Approximately 50 g of soil was collected from
the surface by dragging the lip of a plastic centrifuge tube in an S-shape
pattern. Soil is known to be heterogenous across very small distances,
therefore different soil strata were not sampled over time, and a grid-like
sampling pattern was not used. Insect larvae were removed from samples
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before storing at 4°C. Soil samples were transported at -20°C to ESR in New
Zealand for further analysis.
Table 4.1: Details of two human cadavers used in experiment.
Body P Body R
Official ID UT61-06 UT66-06
Study ID Body P Body R
Date of Birth 1936 13-09-1943
Date of Death 18-08-2006 09-09-2006
Cause of Death Natural Renal Failure
Age 69 62
Sex Male Female
Race Caucasian Caucasian
Height (cm) 184 162.5
Weight (kg) 159 44
Autopsied No No
Placement Date 22-08-2006 11-09-2006
Substrate Soil Soil
Figure 4.1: Cadaver P at ADD 106 (day 3) of decomposition. Sloughing of the skin and some maggots visible. Orange plastic mesh is used to assist in collection of soil samples from
under the cadaver and to preserve its integrity.
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Figure 4.2: Cadaver P at ADD 1092 (day 52) of decomposition. Cadaver is in the skeletonised stage.
Figure 4.3: Cadaver R at ADD 23 (day 0) of decomposition on the day of placement.
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Figure 4.4: Cadaver R at ADD 684 (day 38) of decomposition. Cadaver is in the „bloat‟ stage.
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4.4 Materials/Methods and Results
4.4.1 Accumulated degree-days
The decomposition of a human cadaver is a sequential but continuous
variable. It is generally described in broad, qualitative stages relying on the
gross observations of the decay of soft tissues. These stages are often used to
provide a rough estimate of PMI depending on environmental conditions.
However, for this research, the application of a quantitative approach using
assigned values to express decomposition seemed more reasonable. A greater
number of quantified stages would increase the statistical power of
hypothesis testing and could provide more information about the relationship
between decomposition and the PMI (Megyesi 2005). Ambient temperature
(minimum and maximum) and precipitation data were collected at all four
sites on every day for the duration of the two decompositions. A daily
average was calculated by averaging the maximum and minimum
temperatures for that day. Accumulated degree-days represent heat energy
units needed to drive a biological process such as bacterial or fly larvae
growth (Megyesi 2005). The ADDs were calculated by adding together all the
average daily temperatures from placement of the cadaver until the end of
the experiment (Appendix IX). In order to differentiate samples in this study,
cadaver data points will be labelled with ADDs and control data points will be
labelled with the day of decomposition the samples were collected.
4.4.2 Phospholipid Fatty Analysis
The PLFA analysis of the human cadaver soils follows the method described in
Chapter 3. The extractions were carried out at ESR and transported to UWA
for the subsequent steps of fractionation, derivitisation and GCMS analysis.
PLFA profiles were successfully detected for 18 of 21 control O samples, 19 of
21 cadaver P samples and all 14 Q and R samples. The PLFA peak area data
has been shown for control O and cadaver P (see Appendix XI) and control Q
and cadaver R (see Appendix XII). An internal standard, methylnonadecanate,
represented by peak 35, was used for quantification.
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4.4.3 Fungal Terminal Restriction Fragment Length Polymorphism
4.4.3.1 DNA Extraction
The FastDNA® SPIN kit for Soil (QBiogene, CA) protocol (Martin-Laurent et al.,
2001) was used to extract DNA from the human cadaver soil samples. The
manufacturer‟s protocol was followed with slight modifications to enhance
DNA yield and decrease co-extraction of humic substances (Appendix II). This
kit involves DNA extraction via mechanical lysis of fungal cells, followed by
DNA purification using a silica matrix. The frozen samples were homogenized
by thorough mixing before sub-sampling. A DNAzol (Invitrogen) wash step
was incorporated into the DNA extraction procedure to remove any remaining
contaminants. DNAzol is a guanidine-detergent based lysing reagent
commonly used for selective isolation of genomic DNA from various sample
types. The extraction protocol used 300 mg of soil instead of the 500 mg
recommended as this gave a better yield of DNA. The tubes were processed
in the FastPrep instrument for 90 seconds at a speed of 5.5. The Plant DNAzol
step was added after the first centrifugation step. The FastDNA SPIN kit
protocol is then adhered to through to the final step where the DNA is eluted
into the catch tube and stored at -20°C. All centrifugation steps were carried
out at 14,000 x g (13,200 rpm) unless otherwise indicated.
4.4.3.2 DNA Quantification
The extractions were run on a 2% Seakem® LE agarose (Cambrex, Rockland,
ME, USA) gel stained with Sybr SAFE™ (Invitrogen Molecular Probes) (Appendix
III). The fungal DNA was quantified using the Pico Green Assay (Appendix IV)
and read at a wavelength of 485 nm. Standard solutions were made up and
used to calculate a calibration curve. The sample values were then
correlated to calculate the amount of DNA present in each sample using the
FluroStar Galaxy Fluorescence Instrument and software. The amounts of DNA
were variable and measured in the range of ~2-200 ng.
4.4.3.3 Polymerase Chain Reaction
PCR reagents were defrosted on ice prior to use and reactions were put
together in a laminar flow cabinet using filter tips and pre-labelled tubes.
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Positive and negative controls were included in every PCR reaction suite. The
analysis of the bacterial component of these samples was carried out as part
of another research project (Parkinson, 2004) and was therefore not
duplicated here.
PCR Optimization
The PCR product was low for most samples and so the concentration of DNA,
concentration of MgCl2 and the annealing temperatures were optimised. The
following concentrations of DNA were tested: 1 µL of neat DNA extract, 1/10
dilution, 1/20 dilution, 1/50 dilution, 1/100 dilution and 1/200 dilution. A
concentration of 1 µL of neat DNA extract gave the best yield for both
cadaver P and R and their control samples. The following volumes of MgCl2
were tested: 0 µL, 0.5 µL, 1 µL, 2 µL, 3 µL, and 4 µL. A volume of 2 µL was
adequate at producing a good quality DNA yield. The following annealing
temperatures were tested using a PCR gradient program on the thermalcycler:
50.0°C, 51.0°C, 51.9°C, 52.9°C, 53.8°C, 54.6°C, 55.4°C, 56.3°C, 57.1°C,
58.1°C, 59.0°C and 60.0°C. An annealing temperature of 57°C produced the
best DNA yield.
Fungal amplification was performed using a fluorescently labelled forward
primer FAM ITS-1F (5‟-CTT GGT CAT TTA GAG GAA GTA A-3‟) (Gardes and
Bruns, 1993) and an unlabelled reverse primer ITS4R (5‟TCC TCC GCT TAT TGA
TAT GC 3‟) (White et al, 1990). A 50 µL reaction volume was used containing
5 ng – 20 ng of extracted DNA, PCR buffer (Qiagen), MgCl2 (Qiagen), dNTPs
(Qiagen), Taq polymerase (Qiagen) and 2 µL of each primer. The
thermalcycling protocol (Osborn, Moore and Timmis, 2000) included a 5
minute denaturation step at 95ºC was followed by 30 cycles of 94ºC for 45
seconds, 57ºC for 45 seconds and 72ºC for 1 minute. A final extension step
was conducted at 72ºC for 20 minutes. If DNA was not amplified using the
original mastermix (see Table 4.2), the concentration was either increased
(more template DNA) or decreased (removal of PCR inhibitors) until DNA was
amplified. If amplification was still unsuccessful, DNA was re-extracted from
the soil samples. Eventually, the amplification of fungal DNA was successful
for all control O samples (see Fig 4.5), and sample O8 was successfully
amplified on the second attempt, none of cadaver P samples, with the
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exceptions of soil samples at ADD 27, 376, 512, 730, 927, 985, 1053, 1092 (see
Figs 4.6, 4.7 and 4.8), all control Q samples (see Fig 4.9), all R cadaver
samples except for sample R15 (see Fig 4.9 and 4.10). The unsuccessful
amplification could be either due to an insufficient amount of DNA being
extracted from the soil, or due to the presence of PCR inhibitors co-extracted
along with the DNA.
Table 4.2: Polymerase chain reaction mastermix used in fungal amplification of soil DNA.
Reagent Concentration Per reaction L (final conc.)
Buffer 10 X 5 (1X)
MgCl2 25 mM 2 (1mM)
dNTPs 10 mM each 1 (0.2 M)
BSA 50 µg/µL 1 (1 µg/µL)
Taq 5 U/L 0.25 (0.025 U/L)
H2O - 35.75
DNA Template 2-20 ng/L 1
ITS4R 10 M 2 (0.4 M)
ITS-1F (FAM) 10 M 2 (0.4 M)
Figure 4.5: Polymerase chain reaction product of fungal amplification from control O soil samples. 1 = Control O (O) sampled on day 0, 2 = O3, 3 = O6, 4 = O8, 5 = O10, 6 = O14, 7 =
O16, 8 = O20, 9 = O23, 10 = O27, 11 O29= , 12 = O31, 13 = O35, 14 = O38, 15 = O42, 16 = O45, 17 = O49, 18 = O52, 19 = O58, 20 = O62, 21 = O69, N = negative control (reagent), P = positive
control (C. albicans DNA), L = 100 bp DNA ladder.
L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 N P L
300bp
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Figure 4.7: Polymerase chain reaction product of fungal amplification from re-extracted cadaver P samples. 1 = Cadaver P (P) sampled at ADD 185, 2 = P1172, 3 = P1212, 4 = P1285, N = negative control (reagent), P = positive control (C. albicans DNA), L = 100 bp DNA ladder.
Figure 4.6: Polymerase chain reaction product of fungal amplification from cadaver P samples. 1 = Cadaver P (P) sampled at ADD 27, 2 = P106, 3 = P238, 4 = P286, 5 = P376, 6 = P420, 7 = P512, 8 = P573, 9 = P660, 10 = P695, 11 = P730, 12 = P808, 13 =
P854, 14 = P927, 15 = P985, 16 = P1053, 17 = P1092, N = negative control (reagent), P = positive control (C. albicans DNA), L = 100 bp DNA ladder.
L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 N P L
L 1 2 3 4 N P L 300bp
200bp 100bp
L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 N P L
100bp
1000bp
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Figure 4.8: Polymerase chain reaction product of fungal amplification from re-extracted cadaver P samples. 1 = Cadaver P (P) sampled at ADD 106, 2 = P238, 3 = P420, 4 = P1212, N = negative control (reagent), P = positive control (C. albicans DNA), L = 100 bp DNA ladder.
Figure 4.9: Polymerase chain reaction product of fungal amplification from control Q samples (top) and cadaver R samples (bottom). Top: 1 = Control Q (Q) sampled on day 0, 2 = Q3, 3 = Q7, 4 = Q9, 5 = Q11, 6 = Q15, 7 = Q18, 8 = Q22, 9 = Q25, 10 = Q29, 11 = Q32, 12 =
Q38, 13 = Q42, 14 = Q49. Bottom: 1 = Cadaver R (R) sampled at ADD 23, 2 = R85, 3 = R171, 4 = R207, 5 = R242, 6 = R319, 7 = R366, 8 = R438, 9 = R497, 10 = R564, 11 = R603, 12 = R684, 13 = R724, 14 = R797, N = negative control (reagent), P = positive control (C. albicans DNA), L =
100 bp DNA ladder.
L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 L
L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 N P L
L 1 2 3 4 N P L
200bp
100bp
400bp
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Figure 4.10: Polymerase chain reaction product of fungal amplification from re-extracted cadaver R samples. 1 = Cadaver R (R) sampled at ADD 85, 2 = R366, 3 = R497, 4 = R684, N =
negative control (reagent), P = positive control (C. albicans DNA), L = 100 bp DNA ladder.
The amplified DNA was covered with foil to prevent degradation of labelled
primers. Once PCR was completed, the products were spun down briefly and
stored at –20C until ready for further use.
4.4.3.4 PCR Product Clean-up
All PCR products were purified from primers, nucleotides, polymerases and
salts with the QIAquick PCR Purification Kit (Qiagen) using QIAquick silica-
gel membrane spin columns in a microcentrifuge using the manufacturer‟s
directions (Appendix V). To further concentrate the DNA, the elution buffer
was warmed on a 70°C heating block prior to use.
4.4.3.5 Restriction Enzyme Digestion
Restriction enzyme digestion was trialled with Taq polymerase but produced
few or no fragments. The amplified and cleaned fungal DNA was digested
with HhaI enzyme (Promega, Madison, WI, USA) (Appendix VII). Digest
(without DNA) and enzyme (without enzyme) blanks were included with every
digestion reaction suite. The reactions were digested at 37ºC for 3 hours
followed by 65ºC for 20 minutes, to heat-inactivate the restriction enzyme, as
per manufacturers instructions. The fungal digestions were quantified with
the Pico Green Assay to ensure at least 5ng/µL of template DNA was available
for T-RFLP analysis.
L 1 2 3 4 N P L
400bp
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4.4.3.6 Fungal Community Profile Generation
The digested products were sent to the Allan Wilson Centre at Massey
University in Palmerston North, New Zealand for the fungal T-RFLP analysis.
Fragment detection was performed using an automated capillary ABI PRISM
310 Genetic Analyser and analysed using the Genescan 3.1 Analysis Software
(Applied Biosystems, Australia). The Genescan software evaluates the raw
data generated by the Genetic Analyser and resolves the size of the
fluorescent-labelled DNA fragments by comparing them to fragments
contained in a size standard. It also quantifies each fragment by measuring
the amount of fluorescence emitted. Fungal profiles (examples seen in Figs
4.7, 4.11, 4.12, 4.13) were produced using the Genetic Analyser and Genescan
software. As the products were amplified from the ITS region of the fungal
DNA, the resulting fragments will be known as internal transcribed spacer-
terminal restriction fragments (ITS-TRFs). The output was imported into
Microsoft Excel for further statistical analysis.
Fungal ITS-TRF profiles were obtained from 20 of 21 samples from control O,
12 of 21 samples from under cadaver P, all control Q samples (14) and 13 of
14 samples from under cadaver R. Of the successful amplifications, profiles
were not generated for the following samples and so they were removed from
further analysis: control O day 3, cadaver P ADD 573 and cadaver R ADD 319.
Cadaver P gave a progression of profiles from ADD 27 to 1302 and the
decomposition to skeletonisation lasted 70 days. Cadaver R gave a
progression of profiles from ADD 23 to 813 and the last sample was collected
49 days into the decomposition, however complete decomposition was not
reached in this case.
The fungal community under both cadavers changed markedly as
decomposition progressed. Shifts in dominant peaks, the appearance and
disappearance of peaks (see Fig 4.11) and the varying peak areas over time
were observed for both cadavers. Determining the stages of decomposition
based on visual observations of the cadaver can be difficult and therefore ADD
measurements were used to semi-quantify decomposition. The ADD value
could be correlated with approximate times when major changes in the
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nutrient release from the body may have occurred and aid in the
interpretation of the profiles.
Figure 4.11: Soil fungal profiles from control O sampled on days 0, 6, 8 and 10. The grey bars represent the regions (120–170 bp and 320-400 bp) where the dominant peaks occur in the control O profiles. Fluorescence
intensity is expressed in relative fluorescence units (RFU) to account for intra-instrument variation.
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Fragment size (bp)
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Figure 4.12: Soil fungal profiles from control O sampled on days 14, 16, 20 and 23. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for
intra-instrument variation.
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Fragment size (bp)
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Figure 4.13: Soil fungal profiles from control O sampled on days 27, 29, 31 and 35. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for
intra-instrument variation.
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Fragment size (bp)
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Figure 4.14: Soil fungal profiles from control O sampled on days 38, 42, 45 and 49. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for
intra-instrument variation.
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Fragment size (bp)
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Fragment size (bp)
Figure 4.15: Soil fungal profiles from control O sampled on days 52, 58, 62 and 69. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for
intra-instrument variation.
Fragment size (bp)
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Control O
The profiles of control O are shown in Figures 4.11 to 4.15. Although the
fungal profiles obtained from control O soils varied in species abundance by
observing the relative fluorescence units of the peaks, a number of dominant
peaks are found in almost all of the profiles between the fragment size ranges
of approximately 120 – 170 bp and 320 – 400 bp (see Fig 4.11). Profiles
generated from samples O23 and O29 exhibit very low fluorescence units,
however the smaller peaks still dominate in the aforementioned ranges.
There is an absence of all peaks except for one in the 120 – 170 bp range for
the profile generated from sample O35. The profile from sample O49
illustrates a bigger spread of peaks over the entire profile and the dominant
peaks are not contained within the above ranges.
90
Figure 4.16: Soil fungal profiles from cadaver P sampled at ADD 27, 106, 376 and 512 (days 0, 3, 14 and 20 respectively). Fluorescence intensity is expressed in relative
fluorescence units (RFU) to account for intra-instrument variation.
131
131
131
410
410
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Fragment size (bp)
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Figure 4.17: Soil fungal profiles from cadaver P sampled at ADD 730, 927, 985 and 1053 (days 31, 42, 45 and 49 respectively). Fluorescence intensity is expressed in relative
fluorescence units (RFU) to account for intra-instrument variation.
133
133
133
133
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Fragment size (bp)
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Figure 4.18: Soil fungal profiles from cadaver P at ADD 1092, 1172, 1212 and 1285 (days 52, 58, 62 and 69 respectively). Fluorescence intensity is expressed in relative
fluorescence units (RFU) to account for intra-instrument variation.
133
133
133
133
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Fragment size (bp)
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Cadaver P
The profiles for cadaver P are shown in Figures 4.16 to 4.18. Fewer profiles,
than the controls, were generated from samples collected under cadaver P
and for this reason the points where changes in the profiles may have first
occurred are not discernible. There are no common peaks observed in any
profiles over the entire decomposition period of cadaver P. The first sample
collected from under cadaver P at ADD 27 (see Fig 4.16) displayed dominant
peaks in the same regions as seen in the associated control O profile, which is
approximately in the ranges of 120 – 170 bp and 320 – 400 bp, although, there
was a decrease in the diversity and abundance in comparison to the control O
soil. This might be due to the effect of handling and movement of the
cadaver during placement or a diminished aerobic population due to levels of
oxygen being restricted by the overlying cadaver. The profile at ADD 106 has
already changed noticeably, in comparison with the control collected at the
same time (O3) and ADD 27, with dominant peaks appearing between the 300
and 400 bp fragment size region that were not present before. The overall
fungal diversity of the profiles (ie number of peaks) decrease suddenly at ADD
376 and this reduced diversity is maintained through to ADD 927. The fungal
diversity increases again from ADD 985 onwards. The peak at fragment size
410 bp is present in 7 of 12 samples. The peaks at fragment sizes 44, 123,
124, 132, 134, 172, 305, 356, 410 and 414 bp are only present from ADD 1053
to ADD 1212. These peaks are not however present in the control samples at
the associated ADDs. The peak with a fragment size of 415 bp occurs in
profiles at ADD 1092 - 1212. This peak is unique to cadaver P and is not seen
in any other profiles generated for the experiment.
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Figure 4.19: Soil fungal profiles from control Q sampled on days 0, 3, 7 and 9. The grey bars represent the regions (130–170 bp and 320-370 bp) where the dominant peaks occur in the control Q profiles. Fluorescence
intensity is expressed in relative fluorescence units (RFU) to account for intra-instrument variation.
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Figure 4.20: Soil fungal profiles from control Q sampled on days 11, 15, 18 and 22. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for
intra-instrument variation.
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Fragment size (bp)
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Figure 4.21: Soil fungal profiles from control Q sampled on days 25, 29, 32 and 38. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for
intra-instrument variation.
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Control Q
The fungal profiles for control Q soils are shown in Figures 4.19 to 4.22.
Similar to the sample collected on the first day at the control O site, QO
exhibits a higher diversity than on any other control collection day. The
dominant peaks for most of the profiles lie in the fragment size ranges of
approximately 130 – 170 and 320 – 360 bp (see Fig 4.19). The profiles
generated from the sample Q7 assumes a reduced diversity and abundance
compared to the earlier profiles. However, these attributes resume in the
next profile from soil sample Q9. Only one peak within the selected relative
florescence unit parameters is detected for the profiles generated from the
sample Q29. A spread of peaks over the entire profile is seen for the profile
from sample Q32, instead of being dominant in the regions mentioned above.
Very low diversity is observed for the profile from sample Q42, where only
two peaks are present at 102 and 328 bp.
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Figure 4.22: Soil fungal profiles from control Q sampled on days 42 and 49. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for intra-instrument
variation.
Figure 4.23: Soil fungal profiles from cadaver R at ADD 23 and 85 (days 0 and 3 respectively). The grey bars represent a peak that appears at ADD23 but disappears at
ADD 85.
298
298
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Figure 4.24: Soil fungal profiles from cadaver R sampled at ADD 171, 207, 242 and 366 (days 7, 9, 11 and 18 respectively). The grey bars represent a reduction in peak height
of the same peak from ADD 242 to ADD366.
298
298
298
298
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Figure 4.25: Soil fungal profiles from cadaver R at ADD 438, 497, 564 and 603 (days 22, 25, 29 and 32 respectively). Fluorescence intensity is expressed in relative fluorescence
units (RFU) to account for intra-instrument variation.
298
298
298
298
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Figure 4.26: Soil fungal profiles from cadaver R sampled at ADD 684, 724, and 797 (days 38, 42 and 49 respectively). Fluorescence intensity is expressed in relative fluorescence
units (RFU) to account for intra-instrument variation.
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Cadaver R
The fungal profiles from cadaver R are shown in Figures 4.23 to 4.26. The
sample collected on the first day of decomposition of cadaver R is visually
similar to its associated control sample Q0 in terms of diversity, abundance
and regions where dominant peaks occur. The profile at ADD 85 of
decomposition shows some reduction in diversity with the disappearance of
some peaks in the region of 130 – 180 bp and 340 – 360 bp. In the next four
profiles (ADD 171 - 319), peaks appear and disappear, however, the dominant
peaks mainly occur in the fragment size regions of approximately 130 – 180
and 320 – 360 bp. For the following ADDs: 438, 603, 684 and 724, almost all
the peaks in the fragment size region of 130 – 180 bp have disappeared,
except for the peak of fragment size 174 bp which occurs in differing
abundances. The peak of fragment size 298 bp occurs in all R profiles but
only in Q0 and Q22. The peak at 328 bp occurs in all R profiles except for ADD
724 and this peak is shared in most of the control profiles as well. The peaks
that some cadaver R profiles have in common with some cadaver P profiles
are at fragment sizes 131, 356 and 410 bp.
4.4.3.7 Fungal ITS-TRF Detection
Only fragments within a size range of 30-500 bp were included in the analysis
because this is the range in which fragments can be accurately sized using the
size calling method available with the Genescan software. Additionally, peaks
outside these parameters were omitted in order to avoid the T-RFs caused by
primer-dimers. The minimum fluorescence unit or peak height cut-off was
arbitrarily set at 50 relative fluorescence units (rfu) to eliminate noise
interference. The RiboSort package for the statistical software R
automatically assigned the fragments and their respective peak heights to
appropriate ribotypes (Scallan et al., 2008). Two separate spreadsheets were
produced containing data from the fungal profiles of O and P samples and Q
and R samples. This data was then used by the Primer 6 package to generate
multi-dimensional scaling (MDS) plots and ANOSIM calculations (Clarke and
Ainsworth, 1993).
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4.5 Data Handling and Statistical Analysis
4.5.1 PLFA Dataset
An MDS plot shows the PLFA profiles obtained from control O and cadaver P
samples (see Fig 4.27). A clear separation is seen between the soil microbial
communities from the control O and cadaver P samples. Upon closer
inspection, the profile of the first day of sampling cadaver P (ADD 27) rests
within the cluster of control O samples and within close proximity to the
control sample collected at the same time. A greater spread of soil microbial
community profiles is seen with the cadaver P samples, relative to the control
O samples. Control O and cadaver P samples that are at least 80% similar with
respect to their phospholipid fatty acids have been separately circled. All the
control O samples were at least 80% similar to each other. Cadaver P samples
at ADD 573, ADD 695 and ADD 1212 were less than 80% similar to the other
cadaver P samples.
An ANOSIM test was conducted on the PLFA profiles of the control O and
cadaver P samples. A one-way analysis resulted in a significance level statistic
of 0.1% or a p-value of 0.001, confirming the significant difference between
the PLFA profiles of control O and cadaver P samples at the 5% level.
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Figure 4.27: Multi-dimensional scaling plot of phospholipid fatty acid profiles for cadaver P (●) and control O (▪). Accumulated degree-days denote the stage of decomposition when the sample was collected. 0 = day of placement/first day of sampling. The boundaries of the ellipses are defined by the samples within having profiles at least 80% similar to each other.
An MDS plot showing the PLFA profiles obtained from control Q and cadaver R
can be seen in Fig 4.28. There is tight clustering of the control Q samples
additionally magnified outside the main plot. The PLFA profile from the first
day of sampling cadaver R (ADD 23) rests within the control Q cluster in close
proximity to the control sample of the same time. The cadaver samples
collected at ADD 85 and ADD 242 clustered with the control samples but
plotted further away from them. A cluster of seven cadaver R samples
grouped together, while the two cadaver R samples that plotted further away
led to a larger spread than the control samples.
An ANOSIM test was conducted on the PLFA profiles of the control Q and
cadaver R samples. A one-way analysis resulted in a significant level statistic
105
of 0.1% or a p-value of 0.001, emphasizing very distinct soil microbial
communities at the 5% level.
Figure 4.28: Multi-dimensional scaling plot of phospholipid fatty acid profiles for cadaver R (●) and control Q (▪). Accumulated degree-days denote the stage of decomposition when the sample was collected. 0 = day of placement/first day of sampling. The boundaries of the
ellipses with dotted lines are defined by the samples within having profiles at least 65% similar to each other. The boundaries of the ellipses with smooth lines are defined by the
samples within having profiles at least 85% similar to each other.
65
85
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Figure 4.29: Abundance of fungal marker from phospholipid fatty acid analysis for control
O and cadaver P. A temporal profile of the fungal marker, C18:26 (peak 27) is calculated as a percentage of total phospholipids. 0 = day of placement/first day of sampling.
Figure 4.30: Abundance of fungal marker from phospholipid fatty acid analysis for control
Q and cadaver R. A temporal profile of the fungal marker, C18:26 (peak 27) is calculated as a percentage of total phospholipids. 0 = day of placement/first day of sampling.
A known fungal marker, C18:26 (Stahl and Klug, 1996) (peak 27 in
Appendices XI and XII) was calculated as a percentage of total phospholipids
in order to assess the growth dynamics of fungi for both cadaver (P and R) and
control samples (O and Q; see Figs 4.29 and 4.30). The temporal profile of
the fungal marker showed no obvious trend with either control or cadaver
samples, although the cadavers do suggest some stimulation, albeit quite
sporadic.
Accumulated degree-days
Accumulated degree-days
Abundance (
%)
Abundance (
%)
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4.5.2 Fungal Community Dataset
An MDS plot visually compares the fungal T-RF profiles obtained from control
O and cadaver P (see Fig 4.31). It shows a separation of profiles detected
from the cadaver and the control samples over the decomposition/sampling
process. A natural variation in the control O samples over time is seen, but
they cluster a lot more closely than the cadaver P samples. This indicates
control O samples are more similar to each other than cadaver P samples are
to each other.
Figure 4.31: Multi-dimensional scaling plot of fungal internal transcribed spacer-terminal
restriction fragment profiles for cadaver P ( ) and control O (●). Accumulated degree-days are used to denote the stage of decomposition when the sample was collected. 0 = day
of placement/first day of sampling. The cadaver samples have been circled to show their separation from the control samples.
A snapshot has been taken of the cadaver P samples in isolation, to depict the
separation of the profiles in another view (see Fig 4.32). It shows a possible
grouping of fungal species in relation to time elapsed during decomposition,
where an arrow has been drawn to indicate a potential temporal trend. The
species of fungi that may be colonising in the early phase (ADD 27 (0), 106,
376 and 512) of decomposition are different to the species that may be
colonising in the mid (730, 927 and 985) and late phase of decomposition (ADD
1053 to 1285).
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Figure 4.32: Multi-dimensional scaling plot of fungal internal transcribed spacer-terminal restriction fragment profiles for cadaver P. Accumulated degree-days denote the stage of decomposition when the sample was collected. Grey line separates early, mid and late phase
fungi. 0 = day of placement. The black arrow shows potential temporal trend.
An MDS plot depicts the fungal T-RFLP profiles obtained from control Q and
cadaver R samples (see Fig 4.33). The majority of control Q samples cluster
together, but four of the controls (ADD 171, 319, 497 and 724) plot further
away from this cluster. The T-RFLP profile sampled from cadaver R on the
first day of decomposition (ADD 0) lies in close proximity to the control Q
sample collected at the same time. An arrow has been drawn to indicate a
potential temporal trend from early (ADD 0, 85,171 and 207) to mid (ADD 366,
438, 497, 603) to late phase fungi (ADD 564, 684, 724, and 797).
Mid
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Figure 4.33: Multi-dimensional scaling plot of internal transcribed spacer-terminal restriction fragment profiles for cadaver R and control Q. Accumulated degree-days
denote the stage of decomposition when the sample was collected. The black arrow shows possible fungal pattern of succession. 0 = day of placement/first day of sampling. The cadaver samples have been circled to show their separation from the control samples.
4.6 Discussion
4.6.1 Method Comparison
The total PLFA distribution reflects the whole soil microbial community and
identifies trends on a large scale, whereas fungal T-RFLP analysis evaluates
solely the fungal component of the soil microbial community. The PLFA
profile comprises of 37 main peaks, which are used to generate a fingerprint
of the soil microbial community for comparative purposes. PLFAs are
biomarkers of bacteria and fungi, but with few exceptions (eg, C18:26)
cannot be used to indicate a single species (Zelles, 1999). The identity of
many PLFA compounds can be identified on the basis of their GC or MS data
(White et al., 1979). However, many of the peaks of this correlation study
are not unequivocally identified. Nevertheless, quantitative differences in
the abundances of the detected PLFAs are used to detect structural changes
in the soil microbial community. While the PLFA method is time-consuming
and not automated except for the GC/MS step, more samples are successfully
profiled than T-RFLP which carries a significantly higher financial expense.
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After fungal T-RFLP parameters are set to exclude instrument noise and
amplification irregularities, all peaks are assumed to be representative of the
total fungal community. T-RFLP data therefore gives a quantitative (in terms
of relative signal intensities) and qualitative (presence or absence of distinct
populations) interpretation of the data.
Although most of the method is automated, time is spent on optimising PCR
reactions and trouble-shooting challenging samples. The PCR reactions, which
failed, are mainly cadaver samples. A cadaver is a complex biological sample,
which contains inhibitory substances that may reduce the amplification
efficiency by obstructing the cell lysis step, inactivation of the thermostable
DNA polymerase and or interfering with nucleic acids (Al-Soud and Radstrom,
2000). Some known PCR inhibitors are bile salts, complex polysaccharides in
faeces, haeme in blood and urea in urine (Al-Soud and Radstrom, 1998). In
addition, Taq (thermus aquaticus) DNA polymerase can be degraded by
proteinases (Rossen et al., 1992), denatured by phenol and detergents and
inhibited by blocking of the active site by the inhibitor (Al-Soud and
Radstrom, 1998). Additional PCR inhibitors such as bilirubin, fulvic acids,
humic acids, tannic acids, NaCl, SDS, TritonX-100 and EDTA have also been
indentified (Kreader, 1996). Furthermore, DNA seems more difficult to
extract than lipids, as fewer samples are successfully profiled. A limitation of
this method is that the estimated abundances may not equal true percentages
in the soil samples. This may be due to DNA extraction bias which can alter
the estimated abundances of certain groups, heterogeneity in ribosomal
operon number and the inability of the primers at amplifying rRNA genes
belonging to all members of the population (Fierer et al., 2005). Like all PCR-
based techniques, T-RFLP is subject to PCR biases, such as preferential
amplification of certain templates and template reannealing with increasing
PCR cycle numbers (Lueders, 2003).
4.6.2 PLFA Results
The PLFA profiles demonstrate a significant separation of soil microbial
communities between the cadavers and control soils. As expected, the soil
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microbial community profiles of cadaver and control soils on the first day of
sampling are similar. Greater temporal variation of the PLFA profiles is
evident for cadaver samples. This is a strong indication that the equilibrium
of the microbial communities is being significantly altered by the presence of
the cadaver as decomposition proceeds. The large fat content of cadaver P
may have released a large amount of fatty acids and nutrients into the soil
upon decomposition, which might stimulate the soil microbial communities.
In the case of cadaver R, a slower rate of decomposition, based on the visual
progression of decomposition, is observed. This might explain why the
profiles from the first few sampling days of cadaver R rest within the control
sample cluster. The delayed release of organic products from cadaver R may
have led to little change in the soil microbial community in the early days of
decomposition.
4.6.3 Fungal T-RFLP Profiling Results
4.6.3.1 Controls
The purpose of the control soils is to provide a „baseline‟ of the indigenous
soil fungal populations, so that any changes in the cadaver soil samples can be
distinguished. Visual inspection of the relative profiles showed all were
relatively similar throughout, however the control soils, especially for control
O, showed higher fungal diversity (more peaks) and concentrations (high
fluorescence units) than the cadaver profiles. Natural variation in the control
soil fungal communities over time, was confirmed by the MDS plots which
show a scatter of both control samples. This is likely due to the effects of
temperature change over time where temperatures decreased from 28ºC in
August to 5ºC in October, spatial variability (Prosser, 2002) and soil
heterogeneity (Coleman, Crossley and Hendrix, 2004). However the changes
are small compared to those associated with the decomposing cadavers.
Therefore, it is likely that decomposition is a major cause of the community
changes observed, although environmental effects may also contribute.
4.6.3.2 Cadavers
The soil fungal community profiles obtained from both cadavers clearly
separated from their associated control samples in the MDS plots. The
cadaver profiles demonstrate that changes in the soil fungal population were
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occurring as cadaver decomposition progressed. Most of the samples
generated profiles, however, some were of low quality, with few peaks
present and/or low total fluorescence. The samples collected from under
cadaver P proved to be especially difficult to amplify resulting in a less than
complete set of samples. This may have been due to contaminants, such as
humic acids, that might have been co-extracted along with the DNA, not
enough template DNA extracted and/or impurities remaining in the digested
DNA that can affect the uptake of DNA by the Genetic Analyser (LaMontagne
et al., 2002).
It is evident that the fungal community was always changing with sporadic
appearance of species throughout the period of decomposition. No major
peaks were consistently detected from cadaver P, and only one recurrent
peak (298 bp) was consistently detected in the cadaver R samples, although at
varying abundances. The same major peaks were detected in the first cadaver
P and R samples and most of the associated control profiles. Few
decomposition products would have yet reached the soil microbial
community. However, these initial cadaver samples do show a decrease in
peaks compared to the control samples. This could be due to the physical
presence of the cadaver on the soil and the microhabitat changes associated
with it. When a cadaver is placed upon the soil, it changes the physical soil
environment. Sunlight is blocked from reaching the soil under the body,
which causes vegetation to die. This leads to an increased nutrient source for
the microbial community, as well as a cessation of rhizobial deposits from
root structures, all altering the surrounding microbial communities. These
effects are likely to be small on the soil microbial community, but due to the
delicate balance of the microhabitats in which they live, it might contribute
to some change in the profiles seen very early on in the decomposition.
A rapid response of the soil fungal community to the presence of the cadaver
is observed even before decomposition products are released. The profile at
ADD 106 for cadaver P sees the appearance of dominant peaks between 300
and 400 bp that were not present before and not seen in the control profiles.
The overall fungal diversity of the cadaver P profiles drops suddenly at ADD
376 and may be maintained through to ADD 927. However, due to the missing
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profiles in the middle of the cadaver P decomposition this cannot be said
conclusively. The same reduced diversity is observed for cadaver R at ADD
85, but an increase in diversity is quickly re-established at ADD 171. This
initial reduced diversity is more likely to be in response to changes in the
fungal physical microhabitat, like soil compaction or disruption of rhizobial
interactions due to the presence of the cadaver, than the release of organic
compounds (Rodriguez and Bass, 1985). Organic decomposition products are
first released in the late „fresh‟ and early „bloat‟ stages, when internal
microflora begin to decompose soft tissues and decomposition products begin
to purge from cadaver orifices (Vass, 2001). Therefore, it may be possible to
correlate ADD 376 with the initiation of this release from cadaver P. This
cannot be elucidated for cadaver R due to the short interval of time before
the fungal diversity is increased again. There is a substantial microbial
population explosion in the early stages of decomposition, mainly consisting of
the cadaver‟s intestinal microflora (Haglund and Sorg, 1997). The drop in
fungal diversity could be explained by the overwhelming presence of the
bacterial community due to this flush of the intestinal microbiota into the soil
as well as the response of the soil bacteria to the release of cadaver-derived
nutrients into the soil. The bacterial population may be out-competing the
fungal community during this early period of decomposition. The increase in
fungal diversity of cadaver P from ADD 985 onwards may be explained by
competition, by which the species that most effectively utilises the nutrients
will proliferate and dominate (Coleman, Crossley and Hendrix, 2004). This
seems to be happening to the fungal population in the later stages of
decomposition of cadaver P (ADD 985 - 1212). This increase in fungal diversity
may also be in response to the release of antibiotics by the maggots present
on the cadaver at this stage. As part of their waste products, maggots
produce ammonia (Nigam et al., 2006). Ammonia increases pH creating
alkaline conditions, which are unfavourable for many bacterial species.
Additionally, larvae of Phaenicia sericata carry the commensal Proteus
mirabilis in their midgut (Nigam et al., 2006). These commensals produce
phenylacetic acid and phenylacetaldehyde acid, which are known
antibacterial agents (Nigam et al., 2006). A more likely explanation is that
the maggots ingest the bacteria, which are killed as they pass through the
maggot‟s digestive tract.
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The results from the cadaver P MDS plots have indicated that there may be
certain species of fungi that colonise at different stages (early, mid and late)
of decomposition (see Fig 4.32). This correlates with the observation from
cadaver R MDS plots, of a potential temporal trend of fungal colonisation with
respect to the progression of decomposition. The eight peaks that proliferate
only between ADD 1053 and 1212 for cadaver P could indicate fungi that
colonise the cadavers‟ components like skin and skeletal remains, in the later
stages of decomposition. The isolation of several species of soil fungi found
on the skin and bones of human cadavers is consistent with this finding (Ishii
et al., 2006). The dominant species was identified as Eurotium repens in the
telomorphic and anamorphic stages, amidst Eurotium rubrum, Eurotium
chevalieri and Gliocladium species.
The appearance and disappearance of major and minor peaks in both cadaver
profiles reflect the proliferation of species that most effectively utilise the
cadaver-derived nutrients and the demise of species that are competitively
excluded from the community. Approximately 12 peaks detected in cadaver P
profiles were not present in the control O profiles and 41 peaks detected in
cadaver R profiles were not present in the control Q profiles. This suggests
that these fungal species may be specifically connected to the decomposition
process and stimulated by the presence of the cadavers. Of these new peaks,
peaks at 141, 237, 251 and 296 bp were common to both cadavers. These
peaks could represent fungal species that were cadaver-derived, previously
dormant until the release of cadaver-specific nutrients or introduced to the
gravesoils by other means. One peak at 415 bp (ADD 1092 - 1212) was found
to be unique to cadaver P and may be specific to cadaver P as it was not
detected in any of the other experimental soils. Due to the gaps in profile
succession for cadaver P and the overall slow rate of decomposition for
cadaver R, it is not possible to determine whether community changes
occurred at around the same ADDs for both cadavers. Therefore, it is difficult
to comment on peaks that might indicate a particular ADD or peaks that are
common to both profiles at a particular ADD and then disappear.
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It is apparent from comparing the profiles from cadavers P and R, that
individual cadavers will produce different profiles even when they are at the
same stage of decomposition. Both cadavers were placed on the same type of
soil and within a short distance of each other, so it is obvious that some other
variables influenced the decomposition process. Environmental conditions,
like exposure to differing amounts of sunlight, shade or rain able may
influence a microbial community (Wood, 1995). Another critical variable is
the cadaver itself. The cadaver‟s internal microbiota are a complex
community of several hundred species and sub species, therefore this
combination is unique to an individual (Jawetz, Melnick and Melnick, 1982). It
is likely that the changes seen in the fungal populations as decomposition
progresses, is a contribution of the indigenous soil microbes and the unique
insect and cadaver-derived microflora to the overall population at different
times and at different abundances. Although, this combination might be
unique to an individual, 30 – 40 common species predominate, such as
Clostridium, Bacteroides, Fusobacterium and Bifidobacterium, due to the
significant roles they play in the intestinal microfloral community. It is not
known whether the internal microflora of the cadaver, mostly consisting of
obligate anaerobes, would survive long in the soil environment. However, the
presence of the body and its decomposition fluid on the soil may temporarily
make conditions in the soil anoxic, thus allowing these species to persist for a
while. Conversely, bacteria originating from the skin of the cadaver may have
higher chances of survival in the soil environment. Recently, research showed
that many bacterial species previously known to inhabit the soil also prefer
human skin (Pennisi, 2008). Almost 60% of the human dermal population is
made up of the Gram-negative bacteria Pseudomonas, which flourishes in soil,
water and decomposing organic debris, followed by Janthinobacterium,
another common Gram-negative soil and water bacteria making up 20% of the
population. Of 113 dermal bacteria identified, just 10 species accounted for
90% of the population indicating the volunteers shared a common core set of
dermal microbes. Every human body also differs in biochemical composition,
proportions of body components and body mass. Fat and muscle proportions
differ in males and females, suggesting the gender of the cadaver may
eventually affect the microbial changes during decomposition. An obese
cadaver would release more breakdown products from lipid than a cadaver
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with little body fat. This would expectedly affect the chemical composition
of the soil environment directly below the cadaver and therefore influence
the surrounding microbial community.
It is noted that the rate of decomposition for cadaver R was a lot slower than
that observed for cadaver P. Cadaver R did not reach skeletonisation and at
the collection of the last sample (ADD 797) cadaver R was still in the „bloat‟
stage (see Fig 4.4). It is known that cadaver R was treated with extensive
medication for illness preceding her death. The slow rate of decomposition
may also be accounted for by a decreased population of internal microflora
due to the effects of the medication. Obese cadavers decompose more
rapidly due to the greater amount of liquid in the tissues which favours the
development and dissemination of bacteria (Campobasso, Di Vella and
Introna, 2001). It may also be possible that a greater body mass may bring
about a greater retention of heat thus resulting in a faster rate of
decomposition. Cadaver P was an obese individual with a body mass index
(BMI) of 47.0 and his high fat content would have contributed to the unusually
rapid rate of decomposition observed (see Fig 4.2). In comparison, cadaver R
was underweight (BMI 15.2) and therefore had a significantly lower fat
content. This would result in a slower onset and rate of decomposition. If
the release of cadaver fluids is slow, species utilizing those nutrients within
the soil may proliferate but perhaps not out-compete other members in the
community.
One of the most important variables in decomposition in Tennessee is ambient
temperature. The placement date for cadaver P was 22nd August 2006 and
cadaver R was placed 20 days later on 11th September 2006. This is the
autumn season in Tennessee and temperatures can get quite low.
Temperatures were in the mid twenties (ºC) at the start of cadaver P
decomposition whereas they were significantly colder for the duration of the
decomposition of cadaver R. This may have also contributed to the slow rate
of decomposition observed for cadaver R. Cadaver microflora prefer
temperatures around 37ºC and when exposed to lower temperatures may
result in their metabolic activity being slowed. It has been documented that
soil temperature under a cadaver rises due to maggot masses and other insect
117
activity (Mann, Bass and Meadows, 1990). This could underestimate ADD
values and result in an increased rate of decomposition.
Soil pH is known to become more alkaline in the presence of a decomposing
cadaver on the surface of the soil (Rodriguez and Bass, 1985). Soil pH
measurements were consistent with this finding (Rachel Parkinson, personal
communication). The pH values significantly increased in alkalinity between
ADD 27 to 420 for cadaver P, followed by a drop in and eventual stabilising of
pH between ADD 512 to 1285 for cadaver P. Cadaver R exhibited the initial
increase of pH, however the pH did not drop later but fluctuated at this level.
Fungi are known to predominate in acidic soils while bacterial and
actinomycete populations dominate in near-neutral or moderately alkaline
soils (Stotzky, 1997; Thorn, 1997). This coincides with the observations of the
fungal profiles especially for cadaver P, where fungal diversity decreases
when the pH is high.
Sampling of soil beneath cadaver P stopped soon after skeletonisation and
cadaver R did not reach this stage. However, it can be expected that once
there is no longer active nutrient release from the cadaver, the soil microbial
community would begin to revert to its original structure. When the cadaver-
derived nutrients are all utilised and the soil environment becomes
oligotrophic, the zymogenous species would die off and in turn become a
nutrient source (Winogradsky, 1949). Cadaver-derived microflora would be
superseded by soil microbes. However, many compounds released from the
body may take time to degrade and so the microbial community may take a
considerable amount of time to return to their original structures. The same
can be said for the physical and chemical characteristics of the soil. This
persistence of change in the soil microbial community structure might provide
information towards estimating PMI long after skeletonisation has occurred.
Similarly, if a cadaver is moved from the site of decomposition, the soil
microbial community is likely to be affected by the decomposition compounds
already in the soil and this could provide information about decomposition in
the absence of the cadaver.
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It is important to note that the microbial succession data have been gathered
from only two cadavers and therefore it is imprudent to draw universal
conclusions about the soil microbial changes observed as decomposition
progresses. Further research using more cadavers would be required to
establish the processes occurring and the variables that contribute to the
changing soil microbial communities.
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Chapter 5 : CONCLUSION
The decomposition of a cadaver is a continuous process, which breaks down
the cadaver‟s constituents into their basic elements. The destruction of the
tissues releases a variety of cadaver-derived nutrients and microflora into the
immediate environment as decomposition proceeds. When this occurs upon or
within a soil medium, it can create considerable modifications in soil
chemistry and biology. A soil microbial community responds to alterations in
its ecological niche by changes in abundance and diversity. Nucleic acid-
based and other culture-independent technology have revolutionised our
capability to detect these dynamics in the soil microbial community
accurately and rapidly. There has been little research into the microbiology
associated with decomposition and even less on the succession of microbial
community changes in response to cadaver decomposition.
The objective of this thesis was to investigate if changes occurred in the soil
microbial community in response to the decomposition of a cadaver on a soil
substrate. Based on this hypothesis, the specific aims were to investigate the
ability of two methods, PLFA and T-RFLP community profiling, to characterise
these dynamics in the soil microbial community and compare and contrast
these methodologies. Furthermore, a preliminary evaluation of the potential
of using soil microbial communities as a tool to estimate post mortem interval
would be conducted. The thesis preliminarily investigated this concept, by
testing the hypothesis that a cadaver would release different nutrients into
the soil at different stages of the decomposition, promoting a succession
pattern of changes in the soil microbial community. These changes would
then be captured in the successive profiles generated by the two
methodologies.
A high variability was seen between PLFA and T-RFLP profiles generated from
replicate extractions of the same soil sample in the rat cadaver experiment.
This could be explained by the fact that soils contain a complex system of
plants and microbes in a heterogeneous solid medium in which chemical and
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physical conditions fluctuate at the level of the molecule and the cell.
However, it also raises the question of the soil sample size required to
comprise a representative sample. A more thorough method of homogenising
the soil may be critical for analytical reproducibility. The rat cadaver
experiment showed that two different soils, differing in characteristics such
as pH and composition, had an effect on the diversity and abundance of the
soil microbial community. This in turn can affect the progression of
decomposition because the bacterial and fungal communities of the soil play a
significant role in the decomposition of a cadaver. This could mean that a soil
with a greater microbial content could lead to a faster onset and rate of
decomposition. This would contrast the findings of Mant (1950), which
suggested soil type would probably have little effect on cadaver
decomposition. It is known that the cadaver‟s internal microflora plays a
significant role in the decomposition process (Vass, 2001). The present
research has demonstrated that upon the disruption of cadaveric tissues, the
introduction of the cadaver‟s internal microflora into the soil alters the
indigenous soil microbial community notably.
The human cadaver experiment led to some fascinating preliminary data on
the important relationship that exists between soil microbiology and the
decomposition of human cadavers. The profiles generated from both methods
showed significant changes in abundance and diversity of the microbial
populations throughout the duration of decomposition. It demonstrated that
considerable alterations occurred in the soil microbial community with
decomposition. In addition, the experiment has presented partial and
potential evidence for a successive colonisation of certain fungal species
during the early, intermediate and late phases of decomposition. This
indication suggests that there may be a temporal pattern of change in the soil
fungal community in association with cadaver decomposition. It is highly
probable that the dynamics seen in the soil fungal communities are in
response to competition for the variety of cadaver-derived products released
into the soil at different phases in the decomposition.
There is significant experimental evidence presented in this thesis that soil
microbial communities are strongly influenced by the decomposition of a
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cadaver. Changes in the community continue throughout the progression of
decomposition and alteration of the community may continue even after
skeletonisation is reached. The implication of these results demonstrates the
potential of profiling soil microbial communities as a tool for post mortem
interval (PMI) estimation. A useful technique will detect changes at genera,
species and sub-species level. The profiling of the soil microbial communities
using the PLFA and T-RFLP methods holds immense potential to be developed
into a robust and reproducible technique that can be used for PMI estimation.
The PLFA results from the rat and human cadaver experiments demonstrated
decomposition of the cadaver led to significant changes in the soil microbial
communities. The PLFA method was successful in detecting and characterizing
changes in the microbial communities between: soils exposed to rat cadavers
and control soils, rat cadaver treatments, and soils adjacent to decomposing
cadavers and control soils. Phospholipids are important biomarkers of the
living microbial biomass. They degrade rapidly after cell death, are not found
in storage lipids or in anthropogenic contaminants and have a high natural
turnover rate. However, few PLFAs have been associated with specific
taxonomic or functional groups. Individual species comprise many different
fatty acids and many fatty acids occur in many different microbes (Bossio,
1998). Environmental factors such as temperature, pressure, pH, water
activity, nutrients, ions and chemicals and enzyme action can influence
microbial lipid composition. Therefore, any significant changes in these
factors must be considered when interpreting phospholipid fatty acid profiles.
Phospholipid fatty acid analysis is a reliable method to detect changes in the
structure of soil microbial communities. Relative to molecular methods it is
more cost- and time-effective without the loss of high precision or quality in
the data obtained.
The T-RFLP technique differentiates microbes according to the patterns
derived from the cleavage of their DNA, and produces a specific fingerprint of
the community based on the polymorphism of the target gene. It is a high-
throughput, reproducible method that allows the semi-quantitative analysis of
the diversity of a particular gene in a community. The T-RFLP results from the
rat and human cadaver experiments identified soil bacterial and fungal
122
community dynamics and demonstrated that significant changes occurred in
the diversity and abundance of soil microbial communities in response to the
decomposition of the cadaver. However, due to the fact that the same T-RFs
were not observed at similar stages of decomposition for the two cadavers,
„markers‟ of a specific decomposition stage could not be picked out. This
could be because the sensitivity of the T-FRLP technique distinguishes profiles
at the sub-species level. To overcome this, genera-specific primers could be
used in a multiplex PCR reaction.
A challenging aspect of successfully profiling the soil microbial community
using the T-RFLP method was the amplification of the DNA using PCR. Soil
contains macromolecules with complex structures that are derived directly
from the alteration of the soil organic matter or formed de novo in the soil by
factors that make up the humic fraction in the soil, such as humic acids
(Lueders, 2003). These molecules are co-extracted along with the DNA and
can operate as contaminants and inhibitors, which block the PCR reaction. It
is unknown whether the loss in overall diversity of the human cadaver
exposed soil samples was due to an increase in these contaminants, which
have yet to be characterised. The PCR reaction had to be additionally
optimised to increase the yield of DNA, which even though time-consuming, is
a necessary step in DNA amplification. This was required especially in the
human cadaver experiment and was accomplished by optimising the dilution
of DNA used, the annealing temperature and the MgCl2 amount, which all have
significant effects on the yield of DNA. Solutions to overcoming the difficulty
of amplifying DNA from complex biological samples such a cadaver, could be
to use alternative thermostable DNA polymerases which may be more
resistant to inhibitors such as Hot Tub, Pwo, rTth and Tfl DNA polymerases
(Al-Soud and Radstrom, 1998) or using amplification facilitators such as bovine
serum albumin which binds haeme, betaine which increases the thermal
unfolding transition temperatures of proteins, single-stranded DNA binding T4
gene 32 protein which protects single-stranded DNA from nuclease digestion,
organic solvents and proteinase inhibitors (Al-Soud and Radstrom, 2000).
The extent to which T-RFLP analysis can be effective is dependent on the
specificity of primer pairs, which should be complementary to all known
123
sequences. However, none of the known universal primers for 16S rDNA have
been shown to hybridize or amplify all sequences from the eukaryal, bacterial
and archaeal domains (Liu et al., 1997). Therefore, the universal primers that
were used in these experiments represent only a portion of the total species
diversity of the microbial world. The use and interpretation of the TRF
profiles is limited by the biases inherent to any DNA and PCR-based
investigation of environmental samples. The templates with good primer
homologies will be preferentially amplified and some templates may not
compete well for primers and therefore be underrepresented or missing from
the profile (Egert, 2003). The estimates of amplicon abundance may be
biased as a result of the heterogeneity of the gene copy number (Crosby and
Criddle, 2003). The range of the rRNA gene copy number in eubacteria is
from 1 to 13 with an average of 3.8 copies per genome (Kitts, 2001). This
operon heterogeneity and the different terminal restriction sites seen
between the gene copies can artificially increase the diversity seen in a
profile. Other PCR-based artefacts such as the formation of chimeric
amplicons, primer-dimers and incomplete digestion of PCR products can result
in additional restriction fragments in the TRF profiles (Lukow, Dunfield and
Liesack, 2000). In this study, the number of PCR cycles was kept within
accepted ranges to minimise some of these effects and three replicate PCR
reactions from a single sample were pooled to ensure random PCR artefacts
were minimised.
The research presented in this thesis clearly demonstrates the potential of
soil microbial community analysis as a promising technique in the post-
mortem interval estimation of a human cadaver. However, there is a need for
extensive research, standardisation of procedures and comprehensive
validation studies that will expand the method development and data analysis
and interpretation of both methodologies. Further research could enable us
to ascertain the particular microbial species or populations that are
stimulated at a particular „stage‟ of decomposition, as well as the effect of
environmental variables that may influence their development.
Standardisation would establish parameters for the method, apparatus and
statistical evaluation of the data that forensic laboratories could adhere to
worldwide. Validation studies would be essential to prove the reproducibility
124
and reliability of the techniques for application to forensic casework. These
qualities are critical to facilitate the evidence to withstand the strict scrutiny
it will encounter in a court of law.
Future research would benefit from experimenting with a larger number of
cadavers in a range of different environments. This would enable researchers
to observe more statistically sound trends in the dynamics of the soil
microbial community in response to decomposition and simultaneously
observe any differences originating from the unique nature of the individual
cadaver. Furthermore, the identification of decomposition stage-specific
„markers‟ would be easier if the microbial succession was of specific genera,
at a specific time and in response to a specific compound. Primers that
target specific groups of microbes such as Archaeabacteria, Eubacteria,
Basidoymycetes and Oomycetes may be useful for this purpose. The soil
microbial community profiling techniques depend strongly on the spatial
variability of the soil microbial communities. If soil varies significantly over
short distances, the sampling strategy employed must be representative of
the soil. Further development of the PLFA and T-RFLP methods will enhance
the amount of data gleaned from the soil microbial community.
In addition to viable biomass and microbial community structure data, PLFA
can help reveal the nutritional and physiological status of the microbial
community. The fatty acids extracted from soil can be used to classify
distinct microbial groups: microeukaryotes (polyunsaturated fatty acids),
aerobic prokaryotes (monounsaturated fatty acids), gram-positive and other
anaerobic bacteria (saturated and branched fatty acids in the range from C14
to C16). The extraction of this data could identify microbial groups involved
at different phases of cadaver decomposition.
Although the primers and restriction enzymes employed in the T-RFLP
technique facilitated successful profiles, the potential to improve this result
can be further investigated. Experimenting with the choice of primer pairs
for the PCR reaction and restriction enzyme for the digestion could determine
combinations that produced the best quality and most discriminatory profiles.
A primer pair that generates shorter PCR products may increase the number
125
of terminal restriction fragments generated from a soil microbial community
and provide greater resolution of a profile. Similarly, the combination of
more than one restriction enzyme will produce a more discriminatory profile.
The T-RFLP technique can be used to characterize functional diversity in soil
microbial communities. Primers with homology to broadly conserved
sequences in functional genes can be used to produce TRF profiles that can
illustrate the measure of diversity in functional genotypes. Specific genes
could be targeted to investigate specific functionality groups such as N2-
fixation (nifH), nitrification (amoA), denitrification (nosZ) and mercury
resistance (merR) (Kitts, 2001). Moreover, this would help elucidate the
microbial processes occurring in response to the decomposition of a cadaver.
In this study, the bacterial and fungal populations were targeted by specific
primers, however by using primers that target other members of the soil
microflora, additional community structure information may be expounded
and contribute further to decomposition microbiology applications.
The identification of microbial species could indicate if dominating species
originate from the indigenous soil community or from the cadaver-derived
intestinal microflora and help recognise key microbial players at certain times
during decomposition. Identification of the key microbes could then be used
to develop a system for PMI estimation, where the presence, absence or
combination of these indicator microbial species in a soil sample could predict
the stage of decomposition. The data produced by TRF patterns can be used
to search databases for matching sequences that might identify individual
microbes in the community profile. However, this must be done with caution,
as database matching of TRF sizes can be imprecise and may not produce
species- or even genera-specific identification. In addition, if an existing
sequence database is incomplete, some TRF peaks in a pattern might not be
represented in the database.
Advances in technology could offer alternatives to the PLFA and T-RFLP
techniques in the future. The recent merging of biotechnology with
nanotechnology has introduced ultra-sensitive and multiplexed technologies
that allow rapid detection of microbes and measurement of genes, proteins
and cells. Microarray technology has the ability to rapidly identify the
126
presence or absence of hundreds of key microbial species in a soil sample.
„Lab-on-a-chip‟ devices perform multiple processes such as microbial sample
preparation, reaction and detection that are required for discovering targeted
microbes at a single-cell level and can be made portable for on-site use.
„Lab-on-a-bead‟ devices achieve biochemical reactions on a bead surface that
contains short strands of DNA. The bead is embedded with quantum dots,
which are tiny light-emitting crystals that recognise certain DNA molecules of
interest and tag them by giving off a particular colour and intensity of light,
when certain genes or proteins are detected. When key microbial species
associated with certain phases of decomposition have been identified, these
technologies could be utilised for their rapid, on-site detection in soil
samples. There is a high possibility that additional species, which are
supported by cadaver substrates and not detected by present techniques,
might be evident utilizing these new technologies.
This preliminary investigation into the soil microbial changes associated with
cadaver decomposition unlocks many possibilities for future research. In
addition to a powerful tool for corroborating PMI, soil microbial community
profiling could provide additional valuable information of forensic relevance.
By using the techniques described in this thesis, samples of soil from suspect
locations could be compared with adjacent or alternative sites to identify
where significant changes or disruptions to the indigenous soil microbial
community might have occurred. This information could then be used to
indicate the location of a cadaver in the absence of any cadaveric material,
either through the removal of the cadaver to another location or the
complete decomposition of the cadaver. Similarly, this technology could be
used to identify the location of clandestine graves. Once fully developed, soil
microbial community profiling is an affordable method, using equipment
already in use in most forensic laboratories and thus places another powerful
tool in the hands of forensic scientists worldwide.
127
References
Acinas SG, Sarma-Rupavtarm R, Klepac-Ceraj V, Polz MF. PCR-indiced
sequence artefacts and bias: insights from comparison of two 16S rRNA
clone libraries constructed from the same sample. Applied and
Environmental Microbiology. 2005; 71:8966-8969.
Al-Soud WA, Radstrom, P. Capacity of nine thermostable DNA polymerases to
mediate DNA amplification in the presence of PCR-inhibiting samples.
Applied and Environmental Microbiology. 1998; 64:3748-3753.
Al-Soud WA, Radstrom P. Effects of amplification facilitators on diagnostic
PCR in the presence of blood, feces, and meat. Journal of Clinical
Microbiology. 2000; 38:4463-4470.
Anderson GS. Insect succession on carrion and its relationship to determining
time of death. In: Forensic entomology: the utility of arthropods in legal
investigations. Boca Raton: CRC Press; 2001.
Anderson IC. Diversity and ecology of soil fungal communities: increased
understanding through the application of molecular techniques.
Environmental microbiology. 2004; 6:769.
Andrasco J. Soil. In: Maehly A, Stromberg L, editors. Chemical Criminalistics:
Springer-Verlag; 1981.
Applied Biosystems. Terminal fragment length polymorphism (T-RFLP) on
Applied Biosystems capillary electrophoresis systems. 2005 [updated.
Available
Arnaldos MI, Garcia MD, Romera E, Presa JJ, Luna A. Estimation of
postmortem interval in real cases based on experimentally obtained
entomological evidence. Forensic Science International. 2005; 149:57-65.
Atlas R. Diversity of microbial communities. Advances in Microbial Ecology.
1984; 7:1-47.
Aturaliya S, Lukasewycz A. Experimental forensic and bioanthropological
aspects of soft tissue taphonomy: 1. Factors influencing postmortem
tissue desiccation rate. Journal of Forensic Science. 1999; 44:893-896.
Avaniss-Aghajani E, Jones K, Chapman D, Brunk C. A molecular technique for
identification of bacteria using small subunit ribosomal RNA sequences.
BioTechniques. 1994; 17:144-149.
128
Bardgett RD, Griffiths BS. Ecology and biology of soil protozoa, nematodes,
and microarthropods. In: van Elsas J, al e, editors. Modern soil
microbiology. New York: Marcel Dekker; 1997.
Beijerinck MW. De infusies en de ontdekking der backterien. Amsterdam:
Muller; 1913.
Bell LLS, Skinner MMF, Jones SSJ. The speed of post mortem change to the
human skeleton and its taphonomic significance. Forensic Science
International. 1996; 82:129-40.
Blackwood CB, Marsh T, Sang-Hoon K, Eldor AP. Terminal restriction fragment
length polymorphism data analysis for quantitative comparison of
microbial communities. Applied and Environmental Microbiology. 2003;
69:926-932.
Bligh E, Dyer W. A rapid method of total lipid extraction and purification.
Canadian Journal of Biochemistry and Physiology. 1959; 37:911-917.
Bossio DA. Determinants of Soil Microbial Communities: Effects of Agricultural
Management, Season, and Soil Type on Phospholipid Fatty Acid Profiles.
Microbial ecology. 1998; 36:1.
Bruns TD, White TJ, Taylor JW. Fungal Molecular Systematics. Annual Review
of Ecology and Systematics. 1991; 22:525-564.
Buchan A, Newell SY, Moreta JIL, Moran MA. Analysis of internal transcribed
spacer (ITS) regions of rRNA genes in fungal communities in a
southeastern US salt marsh. Microbial Ecology. 2002; 43:329-340.
Cabirol N, Pommier MT, Gueux M, Payen G. Comparison of lipid composition in
two types of human putrefactive liquid. Forensic Science International.
1998; 94:47-54.
Campobasso CP, Di Vella G, Introna F. Factors affecting decomposition and
Diptera colonisation. Forensic Science International. 2001; 120:18-27.
Carter DO. Forensic Taphonomy: processes associated with cadaver
decomposition in soil [PhD thesis thesis]: James Cook University; 2005.
Carter DO. Microbial decomposition of skeletal muscle tissue(Ovis aries) in a
sandy loam soil at different temperatures. Soil biology & biochemistry.
2006; 38:1139.
Carter DO, Yellowlees D, Tibbett M. Cadaver decomposition in terrestrial
ecosystems. Naturwissenschaften. 2007; 94:12-24.
129
Child AM. Towards and Understanding of the Microbial Decomposition of
Archaeological Bone in the Burial Environment. Journal of Archaeological
Science. 1995; 22:165.
Clark MA, Worrell MB, Pless JE. Postmortem changes in soft tissues. In:
Haglund WD, Sorg MH, editors. Forensic taphonomy: the postmortem fate
of human remains. Boca Raton: CRC Press; 1997.
Clarke KR, Ainsworth M. A method of linking multivariate community structure
to environmental variables. Marine Ecology Progress Series. 1993; 92:205-
219.
Coleman DC, Crossley DA, Hendrix PF. Fundamentals of soil ecology. 2nd ed.
Boston, MA: Elsevier Academic Press; 2004.
Collier JH. Estimating the postmortem interval in forensic cases through the
analysis of postmortem deterioration of human head hair [Masters thesis
thesis]: Louisiana State University; 2005.
Connell JH, Slatyer RO. Mechanisms of succession in natural communities and
their role in community stability and organisation. The American
Naturalist. 1977; 111:1119-1144.
Crosby LD, Criddle CS. Understanding bias in microbial community analysis
techniques due to rrn operon copy number heterogeneity. Biotechniques.
2003; 34:2-9.
DeGaetano DH, Kempton JB, Rowe WF. Fungal tunnelling of hair from a buried
body. Journal of Forensic Sciences. 1992; 37:1048-54.
Dent BB, Forbes SL, Stuart BH. Review of human decomposition processes in
soil. Environmental Geology. 2004; 45:576-585.
Dierksen KP, Whittaker GW, Banowetz GM, Azevedo MD, Kennedy AC, Steiner
JJ, et al. High resolution characterization of soil biological communities
by nucleic acid and fatty acid analyses. Soil Biology & Biochemistry. 2002;
34:1853-1860.
Drenovsky RE, Elliott GN, Graham KJ, Scow KM. Comparison of phospholipid
fatty acid (PLFA) and total soil fatty acid methyl esters (TSFAME) for
characterizing soil microbial communities. Soil Biology & Biochemistry.
2004; 36:1793-1800.
Dunbar J, Ticknor LO, Kuske CR. Assessment of microbial diversity in four
southwestern United States soils by 16S rRNA gene terminal restriction
130
fragment analysis. Applied and Environmental Microbiology. 2000;
66:2943-2950.
Edwards HGM, Wilson AS, Hassan NFN, Davidson A, Burnett A. Raman
spectroscopic analysis of human remains from a seventh century cist
burial on Anglesey, UK. Annals of Bioanalytical Chemistry. 2007; 387:821-
828.
Efremov IA. Taphonomy: a new branch of paleontology. Pan-American
Geologist. 1940; 74:81-93.
Egert M. Formation of Pseudo-Terminal Restriction Fragments, a PCR-Related
Bias Affecting Terminal Restriction Fragment Length Polymorphism
Analysis of Microbial Community Structure. Applied and environmental
microbiology. 2003; 69:2555.
Evans WED. The chemistry of death. Illinois: Charles C Thomas; 1963.
Fiedler S, Schneckenberger K, Graw M. Characterization of soils containing
adipocere. Archives of environmental contamination and toxicology.
2004; 47:561-8.
Fiedler SS, Graw M. Decomposition of buried corpses, with special reference
to the formation of adipocere. Naturwissenschaften. 2003; 90:291-300.
Fierer N, Jackson JA, Vilgalys R, Jackson RB. Assessment of soil microbial
community structure by use of taxon-specific quantitative PCR assays.
Applied and Environmental Microbiology. 2005; 71:4117-4120.
Flechtner VR, Boyer SL, Johansen JR, DeNoble ML. Spirirestis rafaelensis gen.
et. nov. (Cyanophyceae), A new cyanobacterial genus from acrid soils.
Nova Hedwigia. 2002; 74:1-24.
Forbes SL, Stuart BH, Dadour IR, Dent BB. A preliminary investigation of the
stages of adipocere formation. Journal of Forensic Science. 2004; 49:1-9.
Franicevic B. Inter-tidal decomposition patterns in Croatia: an experiment
using Sus scrofa pedal elements thesis]. Bradford: University of Bradford;
2006.
Frostegard A, Tunlid A, Baath E. Phospholipid fatty acid composition, biomass,
and activity of microbial communities from two soil types experimentally
exposed to different heavy metals. Applied and Environmental
Microbiology. 1993; 59:3605-3617.
131
Galloway A, Birkby WH, Jones AM, Henry TE, Parks BO. Decay rates of human
remains in an arid environment. Journal of Forensic Sciences. 1989;
34:607-16.
Galloway, J. Acidification of the world: natural and anthropogenic. Water, Air
& Soil Pollution. 2001; 17-24
Goff ML. A fly for the prosecution: how insect evidence helps solve crimes.
Harvard University Press; 2000. 159.
Griffin DM. Fungal colonisation of sterile hair in contact with soil.
Transactions of the British Mycological Society. 1960; 43:593-596.
Griffin DM. Ecology of soil fungi. 1st ed. London: Chapman & Hall; 1972.
Griffiths BS, Bardgett RD. Interactions between microbe-feeding invertebrates
and soil microorganisms. In: van Elsas J, al e, editors. Modern soil
microbiology. New York: Marcel Dekker; 1997.
Haglund W, Sorg M. Forensic Taphonomy: the postmortem fate of human
remains. Boca Raton: CRC Press; 1997.
Harris D. Analyses of DNA extracted from microbial communities. In: Ritz K,
editor. Beyond the biomass: Wiley-Sayce; 1994.
Hitosugi M, Ishii K, Yaguchi T, Chigusa Y, Kurosu A, Kido M, et al. Fungi can be
a useful forensic tool. Legal Medicine. 2006; 8:240-242.
Hopkins DW, Wiltshire PEJ, Turner BD. Microbial characteristics of soils from
graves: an investigation at the interface of soil microbiology and forensic
science. Applied Soil Ecology. 2000; 14:283-288.
Horswell J, Cordiner SJ, Maas EW, Martin TM, Sutherland BW, Speir TW, et al.
Forensic comparison of soils by bacterial community DNA profiling.
Journal of Forensic Sciences. 2002; 47:350-353.
Hurt RA, Qiu X, Wu L, Roh Y, Palumbo AV, Tiedje JM, et al. Simultaneous
recovery of RNA and DNA from soils and sediments. Applied and
Environmental Microbiology. 2001; 67:4495-4503.
Ishii K, Hitosugi M, Kido M, Yaguchi T, Nishimura K, Hosoya T, et al. Analysis
of fungi detected in human cadavers. Legal Medicine. 2006; 8:188-190.
James RA, Hoadley PA, Sampson BG. Determination of postmortem interval by
sampling vitreous humour. The American Journal of Forensic Medicine
and Pathology. 1997; 18:158-162.
Jawetz E, Melnick JL, Melnick EA. Review of Medical Microbiology. 15th ed.
Boston: Lange Medical; 1982.
132
Jenkinson DS. The soil biomass. New Zealand Soil News. 1977; 25:213-218.
Johnson MD. Seasonal and Microseral Variations in the Insect Populations on
Carrion. The American Midland Naturalist. 1975; 93:79.
Jones C, Bessemer CR. A comparative study of fungal communities in vineyard
soils using terminal restriction fragment (TRF) analysis. 2004.
Kaur A, Chaudhary A, Kaur A, Choudhary R, Kaushik R. Phospholipid fatty acid
- a bioindicator of environment monitoring and assessment in soil
ecosystem. Current Science. 2005; 89:1103-1112.
Kennedy N, Edwards S, Clipson N. Soil bacterial and fungal community
structure across a range of unimproved and semi-improved upland
grasslands. Microbial Ecology. 2005; 50:463-473
Killham K. Soil Ecology. Cambridge University Press; 1994.
Kitts CL. Terminal restriction fragment patterns: a tool for comparing
microbial communities and assessing community dynamics. Current Issues
in Intestinal Microbiology. 2001; 2:17-25.
Kreader CA. Relief of amplification inhibition in PCR with bovine serum
albumin or T4 gene 32 protein. Applied and Environmental Microbiology.
1996; 62:1102-1106.
LaMontagne MG, Michel FC, Holden PA, Reddy CA. Evaluation of extraction
and purification methods for obtaining PCR-amplifiable DNA from
compost for microbial community analysis. Journal of Microbiological
Methods. 2002; 49:255-264.
Lavelle P, Spain A. Soil ecology. Amsterdam: Kluwer Scientific Publications;
2001.
Lechevalier M. Lipids in bacterial taxonomy-a taxonomist´s view. Critical
Review Microbiology. 1977; 5:109-210.
Liesack W, Janssen P, Rainey F, Ward-Rainey N, Stackebrandt E. Microbial
diversity in soil: the need for a combined approach using molecular and
cultivation techniques. In: van Elsas JD, et al., editors. Modern soil
microbiology. New York: Marcel Dekker; 1997.
Liu WT, Marsh TL, Cheng H, Forney LJ. Characterisation of microbial diversity
by determining terminal restriction fragment length polymorphisms of
genes encoding 16S rRNA. Applied and Environmental Microbiology. 1997;
63:4516-4522.
133
Lorenz N, Lee Y-B, Dick LK, Dick R Impact of soil storage on soil microbial
biomass, total DNA yield, enzyme activities and fatty acid microbial
biomarkers. 18th World Congress of Soil Science; 2006; Philadelphia,
Pennsylvania, USA.
Lueders T. Evaluation of PCR amplification bias by terminal restriction
fragment length polymorphism analysis of small-subunit rRNA and mcrA
genes by using defined template mixtures of methanogenic pure cultures
and soil DNA extracts. Applied and Environmental Microbiology. 2003;
69:320.
Lukow T, Dunfield PF, Liesack W. Use of the T-RFLP technique to asses spatial
and temporal changes in the bacterial community structure within an
agricultural soil planted with transgenic and non-transgenic potato
plants. Federation of the European Microbiological Societies Microbiology
Ecology. 2000; 32:241-247.
Madea B, Kaferstein H, Hermann N, Sticht G. Hypoxanthine in vitreous humor
and cerebrospinal fluid - a marker of postmortem interval and prolonged
(vital) hypoxia? Forensic Science International. 1994; 65:19-31.
Mann RW, Bass WM, Meadows L. Time since death and decomposition of the
human body: variables and observations in case and experimental field
studies. Journal of Forensic Sciences. 1990; 35:103-111.
Marchenko MI. Medicolegal relevance of cadaver entomofauna for the
determination of the time of death. Forensic science international. 2001;
120:89.
Marchesi JR, Sato T, Weightman AJ, Martin TA, Fry JC, S.J. H. Design and
evaluation of useful bacterium-specific PCR primers that amplify genes
coding for bacterial 16S rRNA. Applied and Environmental Microbiology.
1998; 64:795-799.
Marsh TL. Terminal restriction fragment length polymorphism (T-RFLP): an
emerging method for characterizing diversity among homologous
populations of amplification products. Current opinion in microbiology.
1999; 2:323-7.
Martin-Laurent F, Philippot L, Hallet S, Chaussod R, Germon JC, Soulas G, et
al. DNA extraction from soils: old bias for new microbial diversity analysis
methods. Applied and Environmental Microbiology. 2001; 67:2354-2359.
134
Megyesi MS, Nawrocki SP, Haskell NH. Using accumulated degree-days to
estimate the postmortem interval from decomposed human remains.
Journal of Forensic Science. 2005; 50:1-9.
Melvin JR, Cronholm LS, Simson LR, Avrom MI. Bacterial transmigration as an
indicator of time of death. Journal of Forensic Sciences. 1984; 29:412-
417.
Metting B. The systematics and ecology of soil algae. The Botanical Review.
1981; 47:195-312
Micozzi MS. Experimental study of postmortem change under field conditions:
effects of freezing, thawing, and mechanical injury. Journal of forensic
sciences. 1986; 31:953-61.
Munoz JI, Suarez-Penaranda JM, Otero XL, Rodriguez-Calvo MS, Costas E,
Miguens X, et al. A new perspective in the estimation of postmortem
interval (PMI) based on vitreous potassium. Journal of Forensic Sciences.
2001; 46:1-6.
Murray R, Tedrow J. Forensic geology. New Jersey: Prentice Hall; 1992.
Nigam Y, Bexfield A, Thomas S, Ratcliffe NA. Maggot therapy: the science and
implication for CAM Part II-maggots combat infection eCAM. 2006; 3:303-
308.
Okoth SA. An overview of the diversity of microorganisms involved in
decomposition in soils. Journal of Tropical Microbiology. 2004; 3:3-13.
Omelyanyuk GG, Alekseev AA, Somova NG The possibility of application of
the multisubstrate testing method in soil criminalistic investigation.
Proceedings of the 15th International Association of Forensic Science;
1999.
Osborn AM, Moore ER, Timmis KN. An evaluation of terminal-restriction
fragment length polymorphism (T-RFLP) analysis for the study of
microbial community structure and dynamics. Environmental
microbiology. 2000; 2:39-50.
Parkinson R. Forensic DNA profiling of bacterial communities in soil thesis].
Wellington: Victoria University of Wellington; 2004.
Paul EA. Soil microbiology, ecology and biochemistry. Oxford: Academic Press;
2007; p. 532.
Payne JA. A summer carrion study of the baby pig Sus scrofa Linnaeus.
Ecology. 1965; 46:592.
135
Pennisi E. Bacteria are picky about their homes on human skin. Science. 2008;
320:1001.
Petkovic SM, Simic MA, Vujic DN. Postmortem production of ethanol in
different tissues under controlled experimental conditions. Journal of
Forensic Science. 2005; 50:1-5.
Pfeiffer S, Milne S, Stevenson RM. The natural decomposition of adipocere.
Journal of forensic sciences. 1998; 43:368-70.
Prosser JI. Molecular and functional diversity in soil micro-organisms. Plant
and soil. 2002; 244:9.
Putman RJ. Flow of Energy and Organic Matter from a Carcase during
Decomposition: Decomposition of Small Mammal Carrion in Temperate
Systems 2. Oikos. 1978a; 31:58.
Putman RJ. Patterns of Carbon dioxide Evolution from Decaying Carrion
Decomposition of Small Mammal Carrion in Temperate Systems 1. Oikos.
1978b; 31:47.
Ramette A. Multivariate analyses in microbial ecology. Federation of the
European Microbiological Societies Microbiology Ecology. 2007; 62:142-
160.
Ramsey PW, Rillig MC, Feris KP, Holben WE, Gannon JE. Choice of methods for
soil microbial community analysis: PLFA maximises power compared to
CLPP and PCR-based approaches. Pedobiologia. 2006; 50:272-280.
Rapp D, Potier P, Jocteur-Monrozier L, Richaume A. Prion degradation in soil:
possible role of microbial enzymes stimulated by the decomposition of
buried carcasses. Environmental Science and Technology. 2006; 40:6324-
6329.
Reed Jr HB. A Study of Dog Carcass Communities in Tennessee, with Special
Reference to the Insects. The American Midland Naturalist. 1958; 59:213.
Rees GN, Baldwin DS, Watson GO, Perryman S, Nielsen DL. Ordination and
significance testing of microbial community composition derived from
terminal restriction fragment length polymorphisms: application of
multivariate statistics. Antonie van Leeuwenhoek. 2004; 86:339-347.
Rhodes AN. Identification of Bacterial Isolates Obtained from Intestinal
Contents Associated with 12,000-Year-Old Mastodon Remains. Applied and
environmental microbiology. 1998; 64:651.
136
Riley M, Cooper V, Lenski R, Forney L, Marsh T. Rapid phenotypic change and
diversification of a soil bacterium during 1000 generations of
experimental evolution. Microbiology. 2001; 147:995-1006.
Ritchie NJ, Schutter ME, Dick R, Myrold DD. Use of length heterogeneity PCR
and fatty acid methyl ester profiles to characterise microbial
communities in soil. Applied and Environmental Microbiology. 2000;
66:1668-1675.
Rodriguez WC, Bass WM. Decomposition of buried bodies and methods that
may aid in their location. Journal of forensic sciences. 1985; 30:836-52.
Rollo F. Persistence and decay of the intestinal microbiota's DNA in glacier
mummies from the Alps. Journal of Archaeological Science. 2007;
34:1294.
Rossen L, Norskov P, Holmstrom K, Rasmussen OF. Inhibition of PCR by
components of food samples, microbial diagnostic assays and DNA-
extraction solutions. International Journal of Food Microbiology. 1992;
17:37-45.
Sabucedo A. Estimation of post mortem interval using the protein marker
cardiac troponin I. Forensic Science International. 2003; 134:11.
Sagara N, Yamanaka T, Tibbett M. Soil fungi associated with graves and
latrines: toward a forensic mycology. In: Tibbett M, Carter DO, editors.
Soil analysis in forensic taphonomy: chemical and biological effects of
buried human remains. Boca Raton: CRC Press; 2008. p. 67.
Scala DJ, Kerkhof LJ. Horizontal heterogeneity of denitrifyng bacterial
communities in marine sediments by terminal restriction fragment length
polymorphism analysis. Applied and Environmental Microbiology.
2000;66:1980-1986
Scallan U, Liliensiek A, Clipson N, Connolly J. Ribosort: a program for
automated data preparation and exploratory analysis of microbial
community fingerprints. Molecular Ecology Notes. 2008; 8:95-98.
Singh BK, Nazaries L, Munro S, Anderson IC, Campbell CD. Use of multiplex
terminal restriction fragment length polymorphism for rapid and
simultaneous analysis of different components of the soil microbial
community. Applied and environmental microbiology. 2006; 72:7278-85.
137
Stahl PD, Klug MJ. Characterisation and differentiation of filamentous fungi
based on fatty acid composition. Applied and Environmental
Microbiology. 1996; 62:4136-4146.
Stokes C, Ash A, Tibbett M, Holtum J. OzFACE: the Australian Savanna free air
CO2 enrichment facility and its relevance to carbon-cycling issues in a
tropical savanna. Australian Journal of Botany. 2005; 53:677-687.
Stotzky G. Soil as an environment for environmental life. In: van Elsas J, al e,
editors. Modern soil microbiology. New York: Marcel Dekker; 1997.
Thorn G. The fungi in soil. In: van Elsas J, al e, editors. Modern soil
microbiology. New York: Marcel Dekker; 1997.
Thornton J. Forensic soil characterisation. Forensic Science Progress. 1986;
1:3-35.
Thornton JI, McLaren AD. Enzymatic characterisation of soil evidence. Journal
of Forensic Sciences. 1975; 20:674-692.
Tibbett M, Carter DO. Mushrooms and taphonomy: the fungi that mark
woodland graves. Mycologist. 2003; 17:20-24.
Tibbett M, Carter DO. Soil analysis in forensic taphonomy: chemical and
biological effects of buried human remains. Boca Raton, FL: CRC Press;
2008.
Tibbett M, Carter DO, Haslam T, Major R, Haslam R. A laboratory incubation
method for determining the rate of microbiological degradation of
skeletal muscle tissue in soil. Journal of Forensic Sciences. 2004; 49:560-
565.
Torsvik V, Ovreas L. Microbial diversity and function in soil: from genes to
ecosystems. Current Opinion in Microbiology. 2002; 5:240-245.
Turner BD, Wiltshire PEJ. Experimental validation of forensic evidence: a
study of the decomposition of buried pigs in a heavy clay soil. Forensic
Science International. 1999; 101:113-122.
Vass A. Beyond the grave - understanding human decomposition. Microbiology
Today. 2001; 28:190-192.
Vass AA, Barshick S, Sega G, Caton J, Skeen JT, Love JC, et al. Decomposition
chemistry of human remains: a new methodology for determining the
postmortem interval. Journal of Forensic Science. 2002; 47:542-553.
138
Vass AA, Bass WM, Wolt JD, Foss JE, Ammons JT. Time since death
determinations of human cadavers using soil solution. ASTM International.
1992:1236-1253.
Villanueva L, Navarrete A, Urmeneta J, White DC, Guerrero R. Combined
phospholipid biomarker-16S rRNA gene denaturing gradient gel
electrophoresis analysis of bacterial diversity and physiological status in
an intertidal microbial mat. Applied and Environmental Microbiology.
2004; 70:6920-6926.
Ward D, Bateson M, Weller R, Ruff-Roberts A. Ribosomal RNA analysis of
microorganisms as they occur in nature. In: Marshall K, editor. Advances
in microbial ecology. New York: Plenum Press; 1992. p. 219-286.
White DC, Davis WM, Nickels JS, J.D. K, Bobbie RJ. Determination of the
sedimentary microbial biomass by extractable lipid phosphate. Oecologia.
1979; 40:51-62.
Wilson AS, Janaway RC, Holland AD, Dodson HI, Baran E, Pollard AM, et al.
Modelling the buried human body environment in upland climes using
three contrasting field sites. Forensic Science International. 2007; 69:6-
18.
Winogradsky S. Methode dans la microbiologie du sol. Paris: Masson et Cie;
1949.
Wood M. Environmental soil biology. 2nd ed. Glasgow: Chapman & Hall; 1995.
Yoshino M, Kimijima T, Miyasaka S, Sato H, Seta S. Microscopical study on
estimation of time since death in skeletal remains. Forensic science
international. 1991; 49:143-58.
Zelles L. Signature fatty acids in phospholipids and lipopolysaccharides as
indicators of microbial biomass and community structure in agricultural
soils. Soil biology & biochemistry. 1992; 24:317.
Zelles L. Fatty acid patterns of phospholipids and lipopolysaccharides in the
characterisation of microbial communities in soil: a review. Biology and
Fertility of Soils. 1999; 29:111-129.
Zhou J. Microarrays for bacterial detection and microbial community analysis.
Current Opinion in Microbiology. 2003; 6:288-294.
Zhu B, Kaori I, Li Q, Mari T, Shugeki O, Dong-Ri L, et al. Postmortem serum
uric acid and creatinine levels in relation to the causes of death. Forensic
Science International. 2002; 125:59-66.
139
Ziavrou K, Boumba VA, Vougiouklakis TG. Insights into the origin of
postmortem ethanol. International journal of toxicology. 2005; 24:69-77.
140
Appendices
I PowerSoil™ DNA Isolation Kit
1. To the PowerBead Tubes provided, add 0.4 g of soil sample.
2. Gently vortex to mix.
3. Add 60 l of Solution C1 and vortex briefly.
4. Secure PowerBead Tubes horizontally and homogenize the sample in a
bead beater for 2 minutes at 2500 rpm.
5. Transfer the supernatant to a clean 2 ml Collection Tube (provided).
6. Add 250 l of Solution C2 and vortex for 5 seconds. Incubate at 4C for
5 minutes.
7. Centrifuge the tubes at room temperature for 1 minute at 10,000 x g.
8. Avoiding the pellet, transfer up to, but no more than, 600 l of
supernatant to a clean 2 ml Collection Tube (provided).
9. Add 200 l of Solution C3 and vortex briefly. Incubate at 4C for 5
minutes.
10. Centrifuge the tubes at room temperature for 1 minute at 10,000 x g.
11. Avoiding the pellet, transfer up to, but no more than, 750 l of
supernatant into a clean 2 ml Collection Tube.
12. Add 1200 l of Solution C4 to the supernatant and vortex for 5 seconds.
13. Load approximately 675 l onto a Spin Filter and centrifuge at
10,000 x g for 1 minute at room temperature. Discard the flow through
and add an additional 675 l of supernatant to the Spin Filter and
centrifuge at 10,000 x g for 1 minute at room temperature. Load the
remaining supernatant onto the Spin Filter and centrifuge at 10,000 x g
for 1 minute at room temperature.
14. Add 500 l of Solution C5 and centrifuge at room temperature for 30
seconds at 10,000 x g.
15. Discard the flow through.
16. Centrifuge again at room temperature for 1 minute at 10,000 x g.
141
17. Carefully place Spin Filter in a clean 2 ml Collection Tube. Avoid
splashing any Solution C5 onto the Spin Filter.
18. Add 100 l of Solution C6 to the centre of the white filter membrane
and leave for 5 minutes.
19. Centrifuge at room temperature for 30 seconds at 10,000 x g.
20. Discard the Spin Filter. The DNA in the tube is now ready for any
downstream application.
II FastDNA SPIN kit for Soil Protocol with added Plant DNAzol
Protocol
1. Add 0.3 g of soil to the Tissue Matrix E tube.
2. Add 978 L Sodium Phosphate Buffer and 122 L MT Buffer.
3. Secure tubes in FastPrep Instrument and process for 90 seconds at
speed 5.5.
4. Centrifuge Tissue Matrix tubes at 14,000 x g for 1 minute.
5. Follow Plant DNAzol Protocol below:
6. After centrifugation, transfer the supernatant to a clean tube and add
500 L of Plant DNAzol. Mix gently a few times.
7. Add 400 L of 100% ethanol and invert the mixture 6-8 times. Leave at
room temperature for 5 minutes.
8. Centrifuge at 4,000 x g for 4 minutes – repeat if necessary.
9. Tip off the supernatant, air-dry the DNA/soil pellet for ~10 minutes and
re-suspend in 200 L Sodium Phosphate buffer.
10. Continue with FastDNA SPIN kit protocol: Add 250 L PPS reagent and
mix by shaking the tube by hand 10 times.
11. Centrifuge at 14,000 x g for 5 minutes to precipitate the pellet.
12. Transfer the supernatant to a clean 1.5 mL tube. Add 1 ml Binding
Matrix Suspension to the supernatant.
13. Invert by hand for 2 minutes to allow binding of DNA to matrix. Place
tube in a rack for 3 minutes to allow settling of silica matrix.
14. Remove 500 L of supernatant being careful to avoid settled Binding
Matrix. Discard supernatant. Re-suspend the Binding Matrix in the
remaining amount of supernatant.
142
15. Transfer ~600 L of the mixture to a Spin Filter and centrifuge at
14,000 x g for 1 minute. Empty the catch-tube and add the remaining
supernatant to the Spin Filter and spin again.
16. Add 500 L SEWS-M to the Spin Filter and centrifuge at 14,000 x g for 1
minute. Discard flow through and replace Spin Filter in catch-tube.
Centrifuge at 14,000 x g for 2 minutes to „dry‟ the matrix of residual
SEWS-M wash solution.
17. Remove Spin Filter and place in fresh kit-supplied catch-tube. Air-dry
the Spin Filter for 5 minutes at room temperature.
18. Add 50 L DES and gently stir matrix on filter membrane with a pipette
tip to re-suspend the silica for efficient elution of the DNA.
19. Centrifuge at 14,000 x g for 1 minute to transfer eluted DNA to catch-
tube.
20. Discard the Spin Filter. DNA is now application ready.
III DNA Visualisation Protocol with Sybr SAFE
1. Make a 2% agarose gel with 2 g agarose and 100 mls TE Buffer. Heat in
microwave until all particles have dissolved and leave to cool.
2. Add 3 L of Sybr SAFE and mix well
3. Pour agarose into gel holder with teeth in place, making sure there are
no bubbles and let the gel set for 20 minutes
4. Place gel holder and gel into tank filled with TE buffer, ensuring the
buffer covers the gel completely.
5. Load 2 L of DNA ladder to first and last wells
6. Load 5 L of loading dye per sample onto parafilm
7. Add 5 L of amplified DNA to dye and mix both in the tip
8. Add 10 L of the mixture into the well carefully
9. When loading is complete, close lid firmly
10. Set the powerpack to 90 volts and run for 40 minutes.
143
IV Pico Green Assay
Use Nunc back-well, flat bottom plates only
1. Prepare 1 x TE buffer by diluting from 20 x TE provided in kit.
1/20 dilution in DNase/RNase free water.
2. Standard solutions are prepared in 1st row of plate using DNA standard
stock (100 g/mL) and 1 x TE buffer:
To make 1 mL of each standard:
Add 100 L DNA from kit to 4900 L 1 x TE buffer = 2 g/mL
Add 100 L of the above 2 g/mL solution to 900 L 1 x TE buffer
= 0.2 g/mL
200 ng = 1000 L of 2 g/mL solution
100 ng = 500 L of 2 g/mL solution + 500 L 1 x TE
50 ng = 250 L of 2 g/mL solution + 750 L 1 x TE
25 ng = 125 L of 2 g/mL solution + 875 L 1 x TE
12.5 ng = 62.5 L of 2 g/mL solution + 973.5 L 1 x TE
2.5 ng = 125 L of 0.2 g/mL solution + 875 L 1 x TE
1 ng = 50 L of 0.2 g/mL solution + 950 L 1 x TE
0.5 ng = 25 L of 0.2 g/mL solution + 975 L 1 x TE
3. Samples are diluted 2:100 in TE by adding 2 L of sample to 98 L in a
well (add buffer to well first).
4. Dilute PicoGreen quantitation reagent 1:200 in DNase/RNase free water
(100 L needed for each well, including standards and a blank).
5. Add 100 L of diluted PicoGreen to each well. Cover plate with foil
and mix gently. Leave to stand at room temperature for 5 minutes.
6. Read plate at an excitation wavelength of 485 nm and an emission of
538 nm.
7. Using the standards construct a standard curve.
8. Correlate the sample values with the curve after subtraction of a blank
to calculate the amount of DNA present in each sample.
V QIAquick PCR Purification Kit Protocol
1. Add 5 volumes of Buffer PB to 1 volume of the PCR sample and mix.
2. Place a QIAquick spin column in a provided 2ml collection tube.
144
3. To bind DNA, apply the sample to the QIAquick column and centrifuge
for 60 seconds.
4. Discard flow-through. Place the QIAquick column back into the same
tube.
5. To wash, add 0.75 ml Buffer PE to the QIAquick column and centrifuge
for 60 seconds.
6. Discard flow-through and place the QIAquick column back in the same
tube. Centrifuge the column for an additional 1 minute.
7. Place QIAquick column in a clean 1.5 ml microcentrifuge tube.
8. To elute an increased concentration of DNA, add 30 l Buffer EB
(10 mM Tris-Cl, pH 8.5) to the centre of the QIAquick membrane, let
the column stand for 1 minute and centrifuge the column for 1 minute.
VI Bacterial Digestion Protocol
Digest PCR products using the following mastermix:
Reagent Concentration Per reaction (L)
MspI Enzyme 10 U/L 2
Buffer A 10 X 3
DNA 1:1 15
Total - 20
VII Fungal Digestion Protocol
Digest PCR products using the following mastermix:
Reagent Concentration Per reaction (L)
HhaI Enzyme 10 U/L 2
Buffer 10 X 1.5
BSA 10 mg/ml 0.2
H2O - 0.3
DNA 1/20 or 1:1 16
Total - 20
145
VIII Source of Materials
Material Company
Phosphate Ajax Finechem (Labchem), Auburn, NSW,
Australia
Methanol:
Anhydrous, HPLC grade
Biolab (Aust) Ltd. (NZ protocol)
Sigma-Aldrich, Australia (Australia
protocol)
Chloroform:
Alcohol-free, HPLC grade
Mallinckrodt Baker Inc., Phillipsburg, NJ,
USA (NZ protocol)
Sigma-Aldrich, Australia (Australia
protocol)
Hydrochloric acid Sigma-Aldrich, Australia
Hexane:
HPLC grade
Sigma-Aldrich, Australia
Nitrogen gas:
G-size, high purity
BOC, Australia
Acetone:
99.9% acs reagent, HPLC grade
Sigma-Aldrich, Australia
Toluene Sigma-Aldrich, Australia
Potassium hydroxide Ajax Finechem (Labchem), Auburn, NSW,
Australia
Acetic acid Sigma-Aldrich, Australia
GC-MS vial Grace Davison, Deerfield, IL, USA
Glass capillary tube Grace Davison, Deerfield, IL, USA
Pasteur pipettes Crown Scientific
Extraction glass vials Alltech (Adelab Scientific), Australia
Glass vial caps Alltech (Adelab Scientific), Australia
146
IX Temperature data and ADD calculation for cadaver P and R
(collected by Rachel Parkinson at the Forensic Anthropology Centre,
University of Tennessee)
Project
DATE
Min
(F)
Max
(F)
Ave
Ambient
temp
(C)
Day Body P Day Body R
01/08/2006 95 71 28.3
02/08/2006 93 75 28.9
03/08/2006 94 74 28.9
04/08/2006 93 75 28.9
05/08/2006 92 71 27.8
06/08/2006 94 71 28.3
07/08/2006 95 73 28.9
08/08/2006 94 73 28.9
09/08/2006 95 70 28.3
10/08/2006 97 71 28.9
11/08/2006 80 71 24.4
12/08/2006 87 72 26.7
13/08/2006 86 67 25.0
14/08/2006 89 70 26.7
15/08/2006 88 72 26.7
16/08/2006 91 73 27.8
17/08/2006 91 71 27.2
18/08/2006 90 69 26.7
19/08/2006 91 72 27.8
20/08/2006 83 71 25.0
21/08/2006 87 70 26.1
22/08/2006 87 73 26.7 0.0 26.7
147
Project
DATE
Min
(F)
Max
(F)
Average
Ambient
temp
(C)
Day Body P Day Body R
23/08/2006 87 68 25.6 52.3
24/08/2006 90 68 26.1 78.4
25/08/2006 91 70 27.2 3.0 105.6
26/08/2006 90 68 26.1 131.7
27/08/2006 93 67 26.7 158.4
28/08/2006 89 70 26.7 6.0 185.0
29/08/2006 89 71 26.7 211.7
30/08/2006 86 72 26.1 8.0 237.8
31/08/2006 78 71 23.9 261.7
01/09/2006 82 69 24.4 10.0 286.1
02/09/2006 81 68 23.9 310.0
03/09/2006 82 63 22.8 332.8
04/09/2006 79 67 22.8 355.6
05/09/2006 73 64 20.6 14.0 376.1
06/09/2006 80 63 22.2 398.4
07/09/2006 83 59 21.7 16.0 420.0
08/09/2006 83 63 22.8 442.8
09/09/2006 82 62 22.2 465.0
10/09/2006 84 64 23.3 488.4
11/09/2006 84 64 23.3 20.0 511.7 0.0 23.3
12/09/2006 77 63 21.1 532.8 44.4
13/09/2006 75 64 21.1 553.9 65.5
14/09/2006 76 57 19.4 23.0 573.4 3.0 85.0
15/09/2006 80 57 20.6 593.9 105.5
16/09/2006 83 59 21.7 615.6 127.2
17/09/2006 82 57 21.1 636.7 148.3
148
Project
DATE
Min
(F)
Max
(F)
Average
Ambient
temp
(C)
Day Body P Day Body R
18/09/2006 86 60 22.8 27.0 659.5 7.0 171.1
19/09/2006 76 60 20.0 679.5 191.1
20/09/2006 69 50 15.6 29.0 695.0 9.0 206.6
21/09/2006 73 46 15.6 710.6 222.2
22/09/2006 75 58 19.4 31.0 730.0 11.0 241.6
23/09/2006 77 66 22.2 752.3 263.9
24/09/2006 75 62 20.6 772.8 284.4
25/09/2006 71 56 17.8 790.6 302.2
26/09/2006 72 53 17.2 35.0 807.8 15.0 319.4
27/09/2006 76 48 16.7 824.5 336.1
28/09/2006 74 50 16.7 841.1 352.7
29/09/2006 65 44 12.8 38.0 853.9 18.0 365.5
30/09/2006 74 49 16.7 870.6 382.2
01/10/2006 78 56 19.4 890.0 401.6
02/10/2006 78 50 17.8 907.8 419.4
03/10/2006 79 53 18.9 42.0 926.7 22.0 438.3
04/10/2006 82 56 20.6 947.3 458.9
05/10/2006 82 59 21.7 968.9 480.5
06/10/2006 69 52 16.1 45.0 985.0 25.0 496.6
07/10/2006 66 48 13.9 998.9 510.5
08/10/2006 73 47 15.6 1014.5 526.1
09/10/2006 79 54 19.4 1033.9 545.5
10/10/2006 78 54 18.9 49.0 1052.8 29.0 564.4
11/10/2006 77 61 20.6 1073.4 585.0
12/10/2006 63 40 11.1 1084.5 596.1
149
Project
DATE
Min
(F)
Max
(F)
Average
Ambient
temp
(C)
Day Body P Day Body R
13/10/2006 56 34 7.2 52.0 1091.7 32.0 603.3
14/10/2006 61 32 8.3 1100.0 611.6
15/10/2006 65 33 9.4 1109.5 621.1
16/10/2006 55 48 11.1 1120.6 632.2
17/10/2006 67 52 15.6 1136.1 647.7
18/10/2006 76 59 20.0 1156.1 667.7
19/10/2006 65 57 16.1 58.0 1172.3 38.0 683.9
20/10/2006 62 42 11.1 1183.4 695.0
21/10/2006 65 37 10.6 1193.9 705.5
22/10/2006 66 44 12.8 1206.7 718.3
23/10/2006 46 37 5.6 62.0 1212.3 42.0 723.9
24/10/2006 49 32 5.0 1217.3 728.9
25/10/2006 58 30 6.7 1223.9 735.5
26/10/2006 61 45 11.7 1235.6 747.2
27/10/2006 58 51 12.8 1248.4 760.0
28/10/2006 59 41 10.0 1258.4 770.0
29/10/2006 68 40 12.2 1270.6 782.2
30/10/2006 73 43 14.4 69.0 1285.0 49.0 796.6
31/10/2006 72 49 16.1 1301.1 812.7
150
X Phospholipid fatty acid peak area data (%) relative to the internal standard (C19:0) consisting of both soil types with their respective
cadaver treatments. PR = Pallarenda soil, WB = Wambiana soil, C = control, CC = complete cadaver, IN = incised cadaver, EV = eviscerated cadaver. The peaks
have been named according to tentative identification based on PLFA database comparison.
-P[m14:1a] -P[m14:1b] -P[F] -P[15:0] -P[m15:0] -P[16:1a] -P[16:1b] -P[16:0] -P[M] -P[m16:0a] -P[m16:0b] -P[m16:0c] -P[m16:1] -P[17:0] -P[m17:0] -P[u] -P[18:1a] -P[18:1b] -P[18:0] -P[m18:0] -P[m18:0] -P[19:1] -P[19:0]
PR C 56.5 27.6 5.1 0.7 40.9 0.6 6.1 98.1 1.2 9.0 15.5 16.7 2.9 5.0 3.6 0.5 2.3 22.7 37.9 1.0 2.0 10.7 100.0
PR C 17.5 4.6 0.3 2.7 14.4 0.6 0.3 66.6 1.4 5.5 5.3 4.7 0.9 1.9 0.8 0.3 2.9 1.9 32.1 0.8 0.8 2.6 100.0
PR CC 28.3 13.7 3.5 1.5 29.1 2.1 3.7 120.3 0.7 4.2 0.5 10.6 0.8 4.5 2.8 0.4 9.7 10.2 37.2 5.7 1.4 0.5 100.0
PR CC 51.9 25.6 7.5 1.0 47.0 0.3 2.8 167.6 0.8 12.8 17.3 17.4 1.7 0.6 0.8 0.9 10.8 28.0 46.6 8.6 5.1 17.5 100.0
PR CC 41.6 18.0 3.7 0.3 28.7 1.2 0.6 81.9 0.8 7.6 11.4 11.2 1.7 3.5 1.9 0.8 9.3 16.0 25.9 3.5 2.0 8.6 100.0
PR CC 56.5 27.6 5.1 0.7 40.9 0.6 6.1 98.1 1.2 9.0 15.5 16.7 2.9 5.0 3.6 0.5 2.3 22.7 37.9 1.0 2.0 10.7 100.0
PR IN 126.0 51.0 13.5 2.4 101.2 4.0 0.0 360.0 0.0 29.6 40.7 37.8 2.9 9.8 1.2 1.3 20.8 49.8 102.0 14.3 7.7 8.1 100.0
PR IN 166.9 71.2 20.4 0.0 144.9 7.3 0.0 470.3 4.8 31.5 39.4 46.3 7.9 8.0 10.1 2.3 40.4 58.5 113.5 20.3 5.8 34.1 100.0
PR IN 181.0 92.0 19.3 0.0 140.4 5.3 0.0 508.0 2.0 27.8 36.7 49.0 12.4 17.9 8.0 20.6 47.4 61.3 115.3 26.8 13.3 25.4 100.0
PR EV 105.2 37.4 13.9 0.0 101.4 4.8 0.0 356.3 4.9 30.6 31.6 36.6 4.9 10.2 9.5 2.0 28.7 46.3 119.9 17.6 8.8 28.1 100.0
PR EV 93.1 37.5 9.0 7.1 113.1 4.3 3.6 426.3 9.0 30.3 31.2 34.6 5.0 4.4 2.4 1.9 33.7 48.1 111.1 6.8 12.3 12.0 100.0
PR EV 25.6 9.0 1.0 0.0 21.7 1.2 0.9 97.5 1.4 5.5 7.1 6.7 0.9 0.9 0.0 9.5 9.3 12.5 28.0 1.4 1.8 8.4 100.0
PR EV 32.5 10.3 5.2 0.7 43.6 1.6 1.6 166.7 0.7 11.0 16.5 15.0 1.9 5.1 3.5 0.8 10.0 11.5 52.9 5.3 3.5 7.9 100.0
WB C 228.9 57.9 23.1 1.0 86.2 29.1 12.6 303.0 3.4 56.4 58.8 40.7 20.6 22.7 10.8 1.2 39.5 1.8 20.7 25.2 2.2 42.8 100.0
WB C 331.6 78.3 29.5 10.6 114.4 1.6 22.2 44.5 2.3 87.1 78.4 59.6 32.2 30.0 10.4 1.8 76.1 88.2 90.6 43.2 5.5 103.1 100.0
WB C 164.8 39.3 14.0 4.6 58.7 1.3 7.4 208.2 1.1 24.0 37.4 29.6 15.1 13.1 5.2 1.9 27.0 36.3 38.2 10.0 3.4 31.5 100.0
WB CC 285.8 77.2 28.1 6.6 119.2 27.5 0.0 458.5 1.2 56.5 70.9 59.5 26.9 27.5 26.1 1.4 69.1 103.8 94.0 43.9 7.2 115.6 100.0
WB CC 257.0 61.1 22.7 9.1 108.6 1.4 19.0 375.0 2.0 66.0 37.7 47.2 22.3 22.4 28.4 5.6 60.0 98.7 88.9 42.2 2.7 78.3 100.0
WB CC 321.7 78.4 28.7 10.8 138.1 22.3 0.0 506.5 2.4 86.4 78.3 66.5 27.1 33.2 32.0 2.2 39.8 117.7 108.3 58.0 11.9 100.6 100.0
WB CC 467.8 119.8 45.0 16.0 205.8 36.5 0.0 753.2 7.4 87.6 114.3 98.1 40.2 45.0 50.0 4.3 124.0 189.3 158.9 85.1 17.0 149.2 100.0
WB IN 105.2 37.4 13.9 0.0 101.4 4.8 0.0 356.3 4.9 30.6 31.6 36.6 4.9 10.2 9.5 2.0 28.7 46.3 119.9 17.6 8.8 28.1 100.0
WB IN 126.0 51.0 13.5 2.4 101.2 4.0 0.0 360.0 0.0 29.6 40.7 37.8 2.9 9.8 1.2 1.3 20.8 49.8 102.0 14.4 7.7 8.1 100.0
WB IN 461.0 119.3 41.7 11.1 201.2 28.5 0.0 675.3 4.9 73.3 114.5 92.8 42.1 44.0 20.4 10.6 117.9 152.0 146.4 72.0 8.4 120.1 100.0
WB IN 590.9 150.9 58.1 12.7 263.5 2.8 25.8 900.2 5.8 107.6 141.5 113.7 26.4 47.4 32.5 8.3 127.6 166.1 204.8 91.0 19.7 141.9 100.0
WB EV 279.8 92.9 41.5 21.8 151.3 38.4 18.0 416.6 4.4 31.9 110.0 74.6 29.4 31.6 38.4 1.7 74.2 69.1 144.3 62.6 62.6 69.8 100.0
WB EV 399.0 100.1 38.3 9.0 150.9 2.7 22.8 562.9 3.4 24.2 101.3 75.9 21.2 38.4 44.7 3.7 86.8 119.2 123.4 56.1 17.9 90.8 100.0
WB EV 468.9 115.5 42.9 20.5 193.0 36.4 2.6 699.3 4.1 91.6 117.4 88.5 40.7 20.8 51.5 8.0 115.9 155.3 157.3 72.2 11.6 134.2 100.0
SOIL T/MENT
PEAK AREA
151
XI Phospholipid fatty acid peak area data relative to the internal standard (peak 35) of control O and cadaver P.
Sample Peak 1 Peak 2 Peak 3 Peak 4 Peak 5 Peak 6 Peak 7 Peak 8 Peak 9 Peak 10 Peak 11 Peak 12 Peak 13 Peak 14 Peak 15 Peak 16 Peak 17 Peak 18 Peak 19 Peak 20 Peak 21 Peak 22 Peak 23 Peak 24 Peak 25 Peak 26 Peak 27 Peak 28 Peak 29 Peak 30 Peak 31 Peak 32 Peak 33 Peak 34 Peak 35 Peak 36 Peak 37 Peak 38
O0 206.4 64.0 27.0 23.8 358.6 272.4 30.8 22.3 19.7 169.1 94.0 322.3 28.3 238.3 759.6 71.4 149.6 697.4 34.7 122.1 125.1 149.4 86.0 70.7 24.4 14.3 62.6 533.5 1064.5 128.9 185.6 112.7 142.4 528.8 100.0 17.8 3.2 16.9
O03 103.1 29.9 8.3 10.1 208.7 171.4 12.6 9.3 12.3 92.1 46.0 166.9 17.2 125.6 393.4 40.0 85.3 427.6 16.5 71.2 72.8 86.2 56.9 23.3 11.1 6.1 35.8 304.9 534.4 71.6 96.7 59.7 93.0 323.0 100.0 6.9 4.3 5.9
O06 105.5 33.8 13.4 11.2 204.6 159.6 14.3 9.9 11.9 90.1 44.2 174.0 14.8 114.6 400.4 39.3 82.8 381.0 16.2 66.4 64.7 85.0 56.5 24.2 11.9 6.0 35.6 276.3 509.8 61.3 94.8 53.1 81.5 252.7 100.0 7.7 3.8 7.3
O10 172.3 48.5 23.0 19.5 323.6 253.7 21.8 18.2 17.3 135.9 81.9 304.5 19.5 203.8 629.2 65.3 151.2 627.4 27.0 101.6 101.7 144.5 85.0 37.7 17.2 10.7 51.0 457.7 893.5 78.4 137.0 96.6 117.9 444.1 100.0 12.1 2.2 10.2
O14 160.6 53.4 23.4 17.7 315.4 239.8 21.6 15.0 17.2 135.8 74.1 323.1 20.1 192.0 652.4 64.9 126.1 621.9 26.3 104.6 100.6 135.6 80.0 35.0 15.9 7.4 47.7 439.0 881.9 78.4 145.3 86.4 119.4 431.3 100.0 11.0 1.8 9.7
O16 211.3 65.3 21.8 23.7 415.3 328.8 28.3 24.0 22.5 184.3 104.9 362.6 32.5 220.8 794.1 84.6 171.6 797.0 35.8 147.2 143.8 157.8 128.9 54.2 28.3 11.9 64.4 602.8 1076.0 104.2 202.3 133.7 177.5 606.2 100.0 18.6 2.2 23.7
O20 121.4 39.9 17.0 14.4 249.4 196.8 16.3 14.9 13.8 108.3 58.6 211.2 18.3 139.7 469.0 47.3 99.2 450.2 19.8 83.3 78.7 103.6 64.6 26.2 14.9 8.5 43.5 328.6 620.0 66.3 108.5 67.2 105.1 324.7 100.0 10.8 1.8 11.2
O23 87.8 33.6 16.2 12.0 185.0 139.1 12.2 9.5 10.7 68.1 43.7 170.7 11.7 117.5 339.5 33.1 83.5 350.4 12.8 52.4 51.5 82.6 34.5 20.5 7.5 10.2 19.7 236.1 460.9 44.9 73.6 50.7 59.2 210.6 100.0 5.4 1.5 4.8
O27 119.0 32.7 13.0 12.7 229.8 172.8 12.7 9.9 13.0 92.2 51.3 217.3 14.8 150.5 434.7 50.7 101.9 488.5 19.4 75.4 71.4 99.8 60.6 26.8 10.8 5.8 26.9 324.9 638.7 60.0 101.3 74.5 83.0 316.4 100.0 7.5 0.9 6.7
O29 181.3 45.4 29.6 20.7 348.5 274.7 20.6 21.1 17.3 148.5 79.0 303.0 21.4 212.7 615.9 88.7 172.0 708.8 28.4 120.7 118.6 153.1 105.7 44.5 21.5 9.2 40.2 467.5 880.1 90.5 152.4 100.9 150.0 481.9 100.0 13.2 1.8 13.8
O31 156.0 52.0 20.7 18.9 324.4 246.9 21.2 17.3 17.3 133.0 75.2 266.8 22.1 189.2 562.0 62.8 134.6 584.2 26.1 104.5 103.5 128.1 87.2 35.3 19.0 12.0 39.8 405.4 740.8 91.6 138.2 82.8 99.6 361.4 100.0 13.5 2.1 14.4
O35 165.0 56.5 28.9 19.8 364.6 281.3 22.1 21.9 19.8 153.3 81.0 316.8 25.3 198.8 597.7 75.4 144.8 655.8 30.9 118.0 114.3 146.8 104.4 40.7 21.7 11.0 57.8 447.4 801.5 90.0 145.3 91.6 162.4 412.4 100.0 13.3 2.5 13.3
O42 196.2 58.2 33.9 26.8 422.6 327.3 27.5 30.8 23.4 184.4 101.5 339.7 24.0 236.1 692.6 75.9 160.0 745.5 35.3 131.6 134.0 167.3 113.9 60.9 26.1 19.1 72.9 513.2 912.8 108.2 154.4 106.4 163.6 462.5 100.0 14.5 6.1 11.7
O45 209.6 69.9 32.0 29.6 461.5 335.0 31.4 26.9 34.0 196.2 121.3 386.7 30.5 294.7 813.4 85.8 200.5 894.7 41.9 144.5 148.2 187.4 124.9 69.9 28.4 20.5 94.7 644.1 1148.5 133.0 183.7 122.3 132.6 535.1 100.0 17.7 5.3 18.9
O52 162.4 54.1 33.0 22.2 367.5 285.7 24.5 24.6 21.2 149.1 92.5 292.1 25.3 223.0 617.7 67.3 143.4 626.3 32.8 117.5 119.7 150.3 98.2 53.1 18.0 11.4 74.6 476.6 823.0 115.0 140.2 93.4 139.5 410.9 100.0 16.3 5.0 18.6
O58 242.7 76.1 38.2 31.1 524.2 405.5 32.7 32.8 28.5 213.1 124.5 471.0 31.2 288.5 906.6 109.5 201.4 1032.4 47.4 165.7 174.6 186.0 131.7 66.0 28.4 15.2 108.5 633.8 1253.1 165.3 200.9 126.7 197.3 650.1 100.0 15.1 6.4 11.4
O62 216.7 77.7 37.5 32.9 451.7 353.3 35.9 31.5 28.6 184.9 126.7 578.9 36.7 301.2 832.8 83.0 172.8 829.7 39.0 130.8 138.7 175.3 99.8 59.3 23.5 18.5 71.2 593.6 1191.2 132.3 170.9 118.4 149.2 457.2 100.0 16.6 3.8 18.3
O69 163.4 52.4 33.3 20.0 325.4 237.8 23.4 17.9 14.2 131.0 82.7 343.9 18.8 242.0 593.9 40.5 113.9 538.4 26.8 93.1 99.8 117.7 63.2 30.9 13.6 12.8 57.5 415.5 879.1 108.3 131.3 72.3 107.8 331.9 100.0 9.1 1.4 6.5
P0 138.9 73.8 23.6 20.0 428.7 266.7 36.8 21.5 21.8 165.1 107.0 398.0 24.1 218.1 752.1 70.4 150.7 649.8 32.9 133.2 117.8 135.5 89.1 38.5 23.0 15.1 66.0 458.5 929.3 98.0 179.5 105.5 137.3 576.6 100.0 19.0 7.4 21.3
P03 18.0 42.7 2.7 1.5 153.7 40.9 14.2 2.2 6.1 23.9 32.3 28.2 3.4 9.5 253.9 4.2 12.4 28.1 4.8 19.5 14.4 6.8 20.1 9.2 3.3 1.7 109.4 304.8 86.6 8.0 50.3 17.7 17.6 34.1 100.0 6.3 0.7 3.9
P06 15.1 67.6 6.8 2.8 146.1 48.5 10.0 2.0 3.4 23.6 10.8 47.9 4.3 7.1 387.9 3.3 13.7 16.7 13.3 15.6 12.6 3.8 19.9 3.5 2.3 1.6 169.9 550.0 123.1 9.3 103.5 3.9 11.3 26.5 100.0 34.2 4.8 27.6
P08 11.0 75.4 8.6 2.7 84.3 41.6 12.4 1.0 4.5 17.0 25.7 73.3 5.0 5.5 582.1 4.1 2.0 19.0 15.0 10.7 11.4 3.7 14.4 13.2 1.2 7.7 384.4 952.0 224.8 54.5 396.7 4.5 12.3 33.1 100.0 68.0 6.7 54.3
P10 20.7 133.8 2.6 37.9 137.8 83.9 26.3 2.7 0.9 23.8 63.4 101.0 18.0 6.3 957.6 80.8 6.9 33.6 1.0 14.1 16.9 6.4 24.4 19.4 1.4 5.2 406.1 1199.5 314.1 133.1 301.3 2.5 9.2 41.2 100.0 122.9 17.8 93.9
P14 12.5 108.6 1.6 1.6 116.7 54.5 20.8 0.8 9.0 32.7 33.9 81.5 7.3 7.3 737.4 3.2 41.5 41.9 67.3 2.9 17.4 5.2 24.6 10.0 5.5 6.6 326.3 895.3 229.9 2.9 245.1 2.8 2256.9 23.7 100.0 49.6 2.1 39.5
P16 5.0 80.0 1.3 0.7 63.7 23.4 15.0 0.5 0.6 15.5 6.7 20.1 1.0 1.7 654.2 1.7 6.0 7.7 7.2 17.3 8.6 0.8 3.3 0.3 0.5 0.4 15.6 184.0 45.3 0.7 215.2 3.5 58.8 5.6 100.0 65.6 1.3 51.0
P20 17.7 251.9 1.3 1.7 197.4 74.6 48.6 0.5 1.0 31.6 8.8 32.4 7.7 6.8 1527.7 11.8 1.2 4.5 1.2 15.6 18.8 1.8 18.4 3.3 0.3 0.8 15.6 324.5 66.5 2.4 250.9 2.1 7.7 0.7 100.0 105.1 3.3 94.2
P23 9.5 30.9 8.4 9.7 9.0 23.6 5.3 0.4 2.3 5.1 6.6 13.3 5.3 4.0 230.2 2.5 6.5 12.7 16.0 12.9 24.0 13.8 18.7 9.8 4.0 1.1 0.8 54.3 217.5 39.4 2.2 57.3 2.1 25.3 100.0 3.1 0.8 0.5
P27 30.1 259.0 6.8 7.1 368.8 187.9 51.8 6.7 7.1 67.2 32.0 143.8 98.3 32.5 2323.1 1.0 7.5 13.8 7.5 36.5 40.5 9.3 6.4 2.0 2.1 6.7 409.9 2118.8 504.8 347.5 367.6 3.8 310.2 45.7 100.0 161.2 12.5 165.9
P29 7.2 79.0 1.0 0.9 61.8 31.9 13.1 2.9 3.0 10.4 4.3 10.2 3.7 3.0 512.7 1.8 0.4 2.0 0.7 4.7 0.8 0.9 3.1 1.9 1.4 0.3 0.3 7.7 120.3 18.0 89.4 89.6 1.2 4.4 100.0 20.9 0.7 21.2
P35 5.9 39.2 1.5 0.2 37.3 39.6 5.5 3.2 3.2 6.4 6.0 14.3 19.9 9.0 445.6 2.3 1.7 2.5 4.6 11.8 3.5 0.8 4.3 3.5 0.2 0.7 25.3 286.8 59.0 64.6 36.5 0.5 44.5 1.9 100.0 8.1 0.2 7.1
P42 7.8 57.8 0.3 1.2 36.4 39.5 7.5 0.6 6.8 8.1 27.0 22.7 24.3 18.7 319.6 1.7 6.5 6.3 2.7 4.5 4.3 2.9 15.2 15.2 1.2 4.2 56.3 297.7 122.7 10.5 54.3 4.0 20.9 15.7 100.0 11.6 0.9 11.2
P45 26.3 97.9 6.2 2.2 86.2 136.9 11.4 1.7 15.7 17.1 45.5 40.3 20.4 11.2 494.4 2.2 5.4 8.5 4.2 56.1 10.3 5.4 5.7 8.7 1.4 6.5 60.5 490.6 211.6 94.9 94.4 4.0 29.1 40.8 100.0 12.9 3.8 18.0
P49 94.9 393.4 2.7 3.6 329.7 522.8 40.3 2.2 22.8 55.5 94.3 684.9 282.2 22.5 2261.6 9.3 23.0 15.6 8.8 19.2 33.1 34.4 40.0 2.6 3.9 8.0 156.2 2768.8 1013.6 30.7 288.5 12.1 107.9 213.1 100.0 8.7 6.2 11.6
P52 34.8 134.8 2.1 1.4 104.4 188.9 14.5 2.4 11.2 22.4 24.4 123.9 52.3 8.3 720.4 3.8 11.7 9.4 3.3 29.3 12.8 11.6 9.1 7.2 1.2 6.4 61.0 767.5 320.7 10.2 104.7 4.1 31.2 75.2 100.0 7.2 0.7 7.0
P58 45.1 141.1 4.4 4.8 129.2 222.7 20.1 2.1 5.9 23.4 43.2 127.3 59.1 9.2 727.6 1.2 11.0 8.0 7.4 10.8 22.0 18.7 19.8 5.4 4.1 2.9 80.4 839.0 346.5 12.8 93.3 11.7 66.3 84.4 100.0 10.0 5.2 7.0
P62 3.9 10.5 0.4 0.1 19.5 27.8 3.4 1.9 1.9 2.8 0.1 4.7 2.8 0.5 41.2 0.7 1.5 0.4 0.2 0.8 0.7 0.2 1.4 1.9 0.2 0.7 2.3 44.3 20.3 0.2 5.7 1.4 1.4 1.3 100.0 0.2 0.3 0.2
P69 69.0 213.2 1.6 2.1 218.9 291.8 20.3 3.1 10.5 31.2 21.0 140.9 145.2 15.7 843.5 4.0 18.0 8.7 3.5 6.5 16.1 10.8 14.8 19.0 4.8 8.2 73.3 999.4 483.1 13.0 107.6 3.8 43.1 80.5 100.0 6.6 3.8 5.5
152
XII Phospholipid fatty acid peak area data relative to the internal standard (peak 35) of control Q and cadaver R.
Sample Peak 1 Peak 2 Peak 3 Peak 4 Peak 5 Peak 6 Peak 7 Peak 8 Peak 9 Peak 10 Peak 11 Peak 12 Peak 13 Peak 14 Peak 15 Peak 16 Peak 17 Peak 18 Peak 19 Peak 20 Peak 21 Peak 22 Peak 23 Peak 24 Peak 25 Peak 26 Peak 27 Peak 28 Peak 29 Peak 30 Peak 31 Peak 32 Peak 33 Peak 34 Peak 35 Peak 36 Peak 37 Peak 38
Q0 70.0 27.2 15.1 13.3 220.9 163.6 13.6 12.2 11.1 83.1 46.2 149.7 8.2 99.1 336.3 40.4 87.4 368.7 19.7 78.7 80.6 89.5 65.7 25.7 18.7 14.6 32.4 240.1 434.5 57.5 98.4 57.3 62.4 306.1 100.0 17.2 1.7 12.3
Q03 57.7 25.7 15.5 10.0 199.6 137.6 9.9 6.6 8.9 66.7 30.5 145.6 12.4 82.5 343.5 32.6 69.2 325.3 12.8 56.3 52.0 73.9 39.7 15.9 12.5 8.7 25.5 234.3 484.1 38.8 81.0 57.7 95.2 286.8 100.0 9.1 2.7 9.5
Q07 66.1 24.7 10.7 9.0 229.7 166.9 11.4 9.1 11.2 81.7 37.6 136.0 12.6 89.6 325.5 40.6 70.7 387.5 17.3 67.2 63.2 71.9 48.4 20.3 9.7 4.1 21.2 205.9 415.3 46.0 70.4 51.3 80.8 277.9 100.0 4.1 0.1 3.0
Q09 96.5 50.4 19.4 16.0 325.5 234.1 19.4 17.2 20.6 120.4 67.3 217.3 16.0 146.7 509.3 52.5 117.2 573.8 25.2 102.7 93.1 116.7 73.4 46.6 16.6 9.5 35.8 346.4 667.9 68.1 125.2 81.8 86.0 417.6 100.0 10.0 5.2 8.5
Q11 65.7 38.4 13.8 11.4 270.4 179.6 13.7 10.9 13.2 98.6 46.9 183.9 10.6 124.5 416.2 46.7 93.4 430.9 18.9 80.7 72.4 94.1 51.6 22.6 12.8 7.6 22.5 283.5 586.5 59.0 96.2 68.3 96.9 349.2 100.0 6.8 0.5 6.6
Q15 125.0 64.0 32.8 21.1 455.5 302.9 28.2 23.1 21.6 170.4 94.5 323.4 29.1 208.8 734.2 74.6 160.6 688.9 35.1 138.0 124.6 172.9 102.5 55.8 22.4 16.6 82.7 552.6 1003.1 99.1 163.8 116.4 166.2 586.0 100.0 14.0 8.1 11.5
Q18 61.2 27.8 8.3 8.4 243.2 160.1 12.3 7.1 8.7 84.1 36.3 170.5 12.7 97.0 392.3 40.6 72.0 401.2 13.9 68.9 61.8 82.5 48.3 19.8 8.9 4.6 17.7 233.0 528.1 43.5 81.0 50.5 77.6 299.4 100.0 4.3 0.2 3.2
Q22 102.2 47.4 23.3 16.7 367.2 251.9 18.7 16.7 19.2 125.5 72.0 258.2 22.6 162.8 576.9 70.7 129.7 642.1 30.8 110.6 100.5 135.2 75.2 47.2 16.0 14.4 35.1 397.6 804.7 91.0 130.6 86.0 132.4 494.6 100.0 8.7 0.3 7.4
Q25 117.0 59.3 22.4 19.8 398.7 296.8 25.6 21.7 26.5 145.7 93.6 302.0 27.4 168.2 616.8 67.2 139.9 613.4 31.6 118.9 115.2 150.8 89.2 53.6 20.6 31.3 67.2 445.1 755.0 87.0 137.6 88.1 156.5 455.3 100.0 13.7 7.2 13.3
Q29 127.0 92.2 25.3 17.7 427.8 302.6 30.3 22.8 24.4 150.7 92.1 304.4 27.3 199.4 786.1 66.3 150.2 653.5 36.6 126.3 120.3 178.7 83.7 46.0 19.5 12.5 72.4 500.0 1013.0 124.0 170.8 105.5 116.8 637.2 100.0 13.7 3.1 12.9
Q32 139.7 66.8 41.8 22.4 514.0 342.8 32.6 27.5 25.0 196.1 105.9 374.7 27.6 271.7 872.3 95.4 182.7 824.0 41.5 163.0 152.0 199.7 116.2 63.4 26.9 38.0 180.4 609.8 1200.0 140.8 191.5 131.8 134.1 708.6 100.0 16.4 3.5 15.2
Q38 53.2 31.8 13.5 7.5 190.7 129.8 13.4 9.5 8.1 67.4 36.4 235.7 11.8 84.6 362.8 26.7 59.5 277.6 12.2 46.5 46.7 59.9 30.5 13.0 6.9 5.4 44.3 199.0 480.7 45.1 69.1 35.9 55.7 218.2 100.0 4.8 2.2 5.3
Q42 82.9 36.6 18.3 12.1 311.3 212.4 15.9 14.2 13.7 116.8 59.1 288.6 16.0 137.5 528.9 56.5 98.8 519.6 22.9 91.6 87.4 100.4 66.8 25.0 12.6 7.0 44.2 329.5 739.8 77.9 106.6 73.3 98.2 405.5 100.0 7.0 0.7 4.9
Q49 89.1 46.6 20.3 12.7 304.9 208.3 16.7 16.5 15.8 107.6 66.3 265.7 17.3 172.7 522.0 50.4 117.7 506.7 25.3 90.3 88.2 103.6 58.6 27.3 13.0 10.9 46.7 347.4 724.1 97.9 116.2 71.9 70.7 405.2 100.0 9.5 1.6 11.8
R0 103.2 49.8 18.3 15.0 326.1 196.9 22.1 16.0 15.3 122.5 75.0 315.8 15.8 178.4 595.5 54.5 131.8 554.5 24.4 105.0 88.0 119.4 67.7 29.5 16.7 14.5 50.0 363.2 858.7 87.5 144.6 94.9 105.7 512.6 100.0 14.2 0.1 13.0
R03 46.5 18.1 9.5 7.3 145.2 91.9 8.0 6.5 8.3 55.4 30.2 116.9 6.4 72.8 222.0 26.0 58.5 253.5 9.7 42.8 39.3 50.9 33.7 14.9 7.7 5.2 12.4 146.7 320.4 29.8 52.7 36.3 47.8 186.1 100.0 3.9 0.2 4.6
R07 5.5 12.2 0.9 0.8 20.1 13.3 0.9 0.4 2.8 5.6 3.4 15.0 0.8 6.5 31.2 2.4 25.3 0.5 0.5 4.1 3.7 8.9 2.8 0.8 0.5 0.3 1.1 15.0 41.8 2.1 5.2 3.5 4.6 24.5 100.0 0.5 0.1 0.4
R09 0.0 0.1 0.2 0.0 0.1 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.1 0.2 0.6 0.4 0.0 0.3 100.0 0.1 0.1 0.0
R11 49.3 41.5 7.1 5.7 168.3 142.4 15.4 5.2 6.2 64.4 69.4 241.7 21.3 51.2 551.7 18.6 51.2 207.3 8.1 41.2 43.0 102.3 32.2 10.4 9.5 8.8 113.8 680.8 557.5 31.8 92.5 43.8 87.6 278.0 100.0 6.8 2.3 6.4
R15 3.8 5.0 0.2 0.3 18.3 14.4 1.3 0.2 3.5 2.4 2.4 6.7 0.7 1.2 33.1 0.1 3.6 5.4 0.2 1.3 1.3 0.9 1.0 0.9 0.3 0.4 4.2 27.3 16.1 0.9 8.3 0.8 0.9 4.3 100.0 0.4 0.1 0.2
R18 0.1 1.3 0.1 0.0 0.8 0.6 0.2 0.0 0.1 0.2 0.3 0.6 0.3 0.2 7.1 0.0 0.1 0.1 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.2 2.1 8.8 1.1 0.3 1.7 0.2 0.4 0.3 100.0 0.5 0.1 0.5
R22 8.8 16.1 2.1 0.9 44.2 35.4 3.1 0.6 2.1 7.8 8.7 40.3 9.6 4.3 90.2 0.3 3.8 12.1 0.2 3.6 4.1 2.6 2.5 1.7 0.4 1.6 15.9 127.7 63.7 7.3 18.4 2.1 2.8 9.5 100.0 0.7 0.1 0.7
R25 9.1 13.0 1.5 1.7 50.5 35.1 3.1 0.3 1.5 6.2 3.3 15.8 4.8 2.0 60.4 1.3 1.6 8.4 0.3 3.4 3.1 1.7 3.0 1.9 0.7 1.4 7.6 49.1 51.0 4.9 15.4 1.5 3.1 10.8 100.0 1.0 0.5 0.9
R29 4.3 7.9 1.1 0.1 31.3 28.0 3.0 0.3 1.0 2.9 2.3 21.7 5.8 1.8 50.4 0.2 1.6 4.7 0.1 2.1 2.0 1.0 0.4 1.5 0.5 0.7 3.6 50.9 70.2 3.2 7.7 1.4 0.9 6.0 100.0 0.2 0.1 0.2
R32 6.2 10.3 0.7 0.3 33.8 33.6 2.7 0.1 2.1 4.1 2.7 15.1 5.0 1.4 51.6 0.7 2.6 5.1 0.4 2.4 2.1 1.2 1.6 1.1 0.2 0.9 8.3 47.0 44.5 4.0 9.9 0.5 3.9 5.4 100.0 0.2 0.0 0.2
R38 38.2 51.1 3.3 1.6 252.8 203.0 13.3 0.8 2.3 26.9 15.0 143.4 34.8 8.5 318.9 1.0 10.3 30.3 1.8 15.2 11.8 11.3 6.1 1.9 1.0 1.9 31.4 287.8 292.8 2.5 36.0 4.1 20.7 36.4 100.0 1.0 0.5 1.3
R42 31.8 26.9 5.3 3.1 112.5 77.5 10.8 3.1 4.7 33.1 18.7 140.6 33.6 31.0 244.7 10.5 27.8 114.6 4.4 23.6 20.0 31.2 17.9 5.3 2.9 2.8 61.4 207.1 330.2 21.5 43.9 20.5 33.1 118.5 100.0 4.0 1.9 4.4
R49 12.3 23.1 2.1 1.2 81.6 82.4 5.9 0.3 2.4 6.6 2.9 49.8 22.2 2.5 100.7 0.3 5.3 8.1 0.5 4.6 3.2 2.6 3.5 2.3 0.2 1.7 3.0 72.3 152.2 10.5 17.0 1.0 1.9 0.5 100.0 0.3 0.2 0.5
153