Microbial community variation in pristine and polluted nearshore Antarctic sediments Shane M. Powell a ; , John P. Bowman a , Ian Snape b , Jonathan S. Stark b a School of Agricultural Science, University of Tasmania, Private Bag 54, Hobart, Tasmania 7001, Australia b Human Impacts Research Programme, Australian Antarctic Division, Kingston, Tasmania, Australia Received 20 November 2002; received in revised form 29 April 2003; accepted 29 April 2003 First published online 4 June 2003 Abstract Two molecular methods were used to investigate the microbial population of Antarctic marine sediments to determine the effects of petroleum and heavy metal pollution. Sediment samples were collected in a nested design from impacted and non-impacted locations. A detailed description of the diversity of the microbial population in two samples was obtained using 16S ribosomal DNA clone libraries constructed from an impacted and a non-impacted location. The clone libraries were very similar with the exception of two sequence clusters containing clones from only the impacted location. All samples were analysed by denaturing gradient gel electrophoresis. The band patterns generated were transformed into a presence/absence matrix and a multivariate approach was used to test for differences in the locations. Statistically significant differences were observed both between and within locations. Impacted locations showed a greater variability within themselves than the control locations. Correlations between the community patterns and environmental variables suggested that pollution was one of a number of factors affecting the microbial community composition. ß 2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved. Keywords : Denaturing gradient gel electrophoresis ; Antarctic microbial ecology ; Impacted sediment 1. Introduction Despite our relatively recent arrival in the Antarctic, human activities have already had a signi¢cant impact on the environment [1]. Past waste management practices as well as accidents have been responsible for pollution of both the marine and terrestrial environments. Although all waste produced through our activities is now returned to the country of origin, dumping of waste and disposal to sea were common practices that left a legacy of pollution. Petroleum spills have been reported at most scienti¢c re- search stations including Amundsen^Scott at the South Pole [2], Palmer Station on the Antarctic Peninsula [3] and Casey Station in the Windmill Islands [4]. There have also been major marine spills such as the Bahia Pa- riso that ran aground on the Antarctic Peninsula losing over 150 000 gallons of petroleum products [5]. Three successive stations have been in operation in the Windmill Islands region since 1957 and there are well- documented accounts of contamination associated with station activities [1,4,6]. Near the locations of Old and New Casey stations there have been several large spills of petroleum products as well as many smaller spills asso- ciated with day-to-day operations. There is also an aban- doned waste disposal site in the area that is known to be highly contaminated [4]. During the annual melt, water runs through both the waste disposal site and several hy- drocarbon plumes and entrains and transports contami- nants into Brown Bay. Elevated levels of hydrocarbons (up to 200 mg kg 31 of total petroleum hydrocarbons) and heavy metals such as copper, lead and zinc have been measured in Brown Bay sediments [1]. Sediment characteristics such as grain size and total organic carbon (TOC) were found to be variable at all sites, though TOC was more variable at impacted locations than control lo- cations (see Table 1 for examples). Previous work on the benthic fauna in the marine envi- ronment surrounding Casey station has shown di¡erences in the infaunal communities that were correlated with the presence of pollutants, the most important of which were 0168-6496 / 03 / $22.00 ß 2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved. doi :10.1016/S0168-6496(03)00135-1 * Corresponding author. Tel.: +612 (3) 62262776; Fax : +61 (3) 62262642. E-mail address : [email protected](S.M. Powell). FEMS Microbiology Ecology 45 (2003) 135^145 www.fems-microbiology.org
Transcript
Microbial community variation in pristine and polluted nearshoreAntarctic sediments
Shane M. Powell a;�, John P. Bowman a, Ian Snape b, Jonathan S. Stark b
a School of Agricultural Science, University of Tasmania, Private Bag 54, Hobart, Tasmania 7001, Australiab Human Impacts Research Programme, Australian Antarctic Division, Kingston, Tasmania, Australia
Received 20 November 2002; received in revised form 29 April 2003; accepted 29 April 2003
First published online 4 June 2003
Abstract
Two molecular methods were used to investigate the microbial population of Antarctic marine sediments to determine the effects ofpetroleum and heavy metal pollution. Sediment samples were collected in a nested design from impacted and non-impacted locations. Adetailed description of the diversity of the microbial population in two samples was obtained using 16S ribosomal DNA clone librariesconstructed from an impacted and a non-impacted location. The clone libraries were very similar with the exception of two sequenceclusters containing clones from only the impacted location. All samples were analysed by denaturing gradient gel electrophoresis. Theband patterns generated were transformed into a presence/absence matrix and a multivariate approach was used to test for differences inthe locations. Statistically significant differences were observed both between and within locations. Impacted locations showed a greatervariability within themselves than the control locations. Correlations between the community patterns and environmental variablessuggested that pollution was one of a number of factors affecting the microbial community composition.2 2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.
Despite our relatively recent arrival in the Antarctic,human activities have already had a signi¢cant impacton the environment [1]. Past waste management practicesas well as accidents have been responsible for pollution ofboth the marine and terrestrial environments. Although allwaste produced through our activities is now returned tothe country of origin, dumping of waste and disposal tosea were common practices that left a legacy of pollution.Petroleum spills have been reported at most scienti¢c re-search stations including Amundsen^Scott at the SouthPole [2], Palmer Station on the Antarctic Peninsula [3]and Casey Station in the Windmill Islands [4]. Therehave also been major marine spills such as the Bahia Pa-riso that ran aground on the Antarctic Peninsula losingover 150 000 gallons of petroleum products [5].
Three successive stations have been in operation in theWindmill Islands region since 1957 and there are well-documented accounts of contamination associated withstation activities [1,4,6]. Near the locations of Old andNew Casey stations there have been several large spillsof petroleum products as well as many smaller spills asso-ciated with day-to-day operations. There is also an aban-doned waste disposal site in the area that is known to behighly contaminated [4]. During the annual melt, waterruns through both the waste disposal site and several hy-drocarbon plumes and entrains and transports contami-nants into Brown Bay. Elevated levels of hydrocarbons(up to 200 mg kg31 of total petroleum hydrocarbons)and heavy metals such as copper, lead and zinc havebeen measured in Brown Bay sediments [1]. Sedimentcharacteristics such as grain size and total organic carbon(TOC) were found to be variable at all sites, though TOCwas more variable at impacted locations than control lo-cations (see Table 1 for examples).
Previous work on the benthic fauna in the marine envi-ronment surrounding Casey station has shown di¡erencesin the infaunal communities that were correlated with thepresence of pollutants, the most important of which were
0168-6496 / 03 / $22.00 2 2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.doi :10.1016/S0168-6496(03)00135-1
heavy metals [7]. However, no single pollutant or environ-mental variable appeared to be consistently important.Infaunal communities in control locations were more di-verse than Brown Bay communities and several taxa werenot found in any Brown Bay samples. Moreover, somespecies that are commonly found associated with pollu-tion, such as capitellid polychaetes, were present in BrownBay but not at control locations. The intention of ourstudy was to extend the ecological investigations of theregion to include some measure of the microbial popula-tions in both contaminated and non-contaminated sites. Interms of impact assessment, microbial techniques poten-tially o¡er an alternative method to surveys of infaunaand macrofauna for identifying areas a¡ected by pollu-tion.
Molecular techniques are important in microbial ecol-ogy as they are not dependent on the culturability of un-known micro-organisms but instead rely on the extractionof DNA from environmental samples. As for all methods,polymerase chain reaction (PCR), which forms the basis ofmany molecular techniques, is subject to certain biases andlimitations. These include the existence of multiple 16SrRNA gene operons, primer annealing speci¢city, di¡er-ences in the ease of ampli¢cation of DNA from di¡erentorganisms and the need to design primers that will bind toas many target organisms as possible. Despite this, a rel-ative comparison between samples is still possible if allsamples are treated in the same manner.
The construction of clone libraries and the sequencingof 16S rRNA gene sequences is a well-established molec-ular method. It provides high-resolution phylogenetic in-formation on the micro-organisms present in a sample.The higher the number of clones sequenced, the morereliable the estimate of diversity and the more completethe knowledge of community structure. Clone library anal-ysis is, however, very labour-intensive and it is not yetpossible to analyse the large numbers of samples requiredto obtain quantitative, statistically signi¢cant data in aregional survey.
Denaturing gradient gel electrophoresis (DGGE) is arelatively new, but increasingly popular, technique in mi-crobial ecology [8]. One of the advantages of this methodis that it is possible to analyse many samples simulta-
neously so that statistically testable data can be obtained.DGGE relies on the separation of a mixture of 16S rRNAgene fragments with di¡erent sequences. Each sample re-sults in a pattern of bands on a gel. Although theoreticallyeach band represents a unique sequence and therefore aunique species, this is not always the case. Several sequen-ces may co-migrate to form what appears to be a singleband [9,10] and some organisms generate more than oneband [11,12]. In addition, DNA extraction methods mayselect for certain types of organisms [13]. PCR biases areknown to have an e¡ect [14,15] and even sample handlingcan change the result [16]. However, di¡erences in thebanding patterns overall are related to the di¡erences inthe microbial community composition. Statistical analysisof these banding patterns is a potentially powerful tool fordistinguishing signi¢cant di¡erences in microbial commun-ities. Once di¡erences have been identi¢ed it is also possi-ble to excise bands from the gel and sequence them inorder to identify species present. This information o¡ersinsights into species distribution and possible biochemicalprocesses in the sediment. Several approaches have beenused to transform DGGE banding patterns into a quanti-tative format. Some authors have incorporated the inten-sity of the bands as well as their position [17,18]. Alter-natively, the banding patterns have been transformed intoa presence/absence matrix [19]. Recently, ordination meth-ods such as principal components analysis [20], multi-di-mensional scaling [19] and cluster analysis [21] have beenused to graphically display the similarities making inter-pretation of the data easier.
There were several aims in this work. Firstly, we wantedto evaluate DGGE as a tool for following changes inmicrobial populations in large-scale studies. This involvedtesting the reproducibility of the banding patterns andestablishing statistical methods for analysing them. Anoth-er molecular method (the clone libraries) was used to ex-amine broad community composition of an impacted andnon-impacted location in order to verify or refute the con-clusions reached from the DGGE analysis. The secondpart of the work was to investigate variation in microbialcommunities in contaminated marine sediments in com-parison to sediment from control locations in the contextof environmental impact.
Table 1Range of values for some environmental variables
Samples were collected from three locations withinBrown and O’Brien Bays, from one location withinSparkes Bay and one location at the Casey wharf (seeFig. 1). The locations were generally separated by kilo-metres, except for Brown Bay in which the locationswere separated by approximately 300 m. Samples forthis survey were collected in a hierarchical nested design.That is, samples were taken from two sites within eachlocation (approximately 100 m apart) and from two plotswithin each site (approximately 10 m apart). Four repli-cate samples were taken from each plot, one of which wasutilised for microbial analysis. The locations were shallowembayments with a range of sediment characteristics(muddy to sandy) and physical attributes (sea-ice cover,depth, aspect etc.). Sampling locations are described ingreater detail in Stark et al. [7].
2.2. Sampling
The samples were collected by diver using a hand-heldcorer. Details are described in Stark et al. [7]. Samples
were taken back to the laboratory and frozen to 320‡Cwithin 6^8 h of collection.
2.3. DNA extraction
DNA was extracted from sediment samples using afreeze^thaw method based on that described by Rochelleet al. [16]. Approximately 1 g or 1 ml of sediment wassuspended in 2 ml of lysis bu¡er (0.15 M NaCl, 0.1 MEDTA, 4% sodium dodecyl sulfate) with 30 mg lysozymeand ca. 20 mg polyvinylpolypyrrolidone. Samples wereheated in a 55‡C water bath for 10 min and then subjectto three rounds of freezing at 380‡C for 15 min and heat-ing at 55‡C for 10 min. After the ¢nal thaw, samples wereextracted with an equal volume of Tris-equilibrated phenolfollowed by extraction with an equal volume of phenol:chloroform:isoamyl alcohol (25:24:1). The aqueous phasewas removed to a clean tube and 0.7 volumes of isopro-panol added. Extracts were incubated for 1 h at roomtemperature, followed by centrifugation for 30 min at3100Ug. Pellets were air-dried and resuspended in 100 Wlof sterile milli-Q water overnight at 4‡C. Extracts werechecked on a 1% agarose gel before the ¢nal puri¢cationstep on Chromaspin columns (Clontech) following themanufacturer’s directions. The amount of DNA present
Kilometres
N
0 1 2 3 4
Sparkes
Bay
OB1
OB2OB3
O`Brien Bay
Brown
Bay
BB3Wharf.
Newcomb Bay
CASEY
Windmill Islands
Fig. 1. Map of the Windmill Islands region showing the position of sampling locations and Casey Station.
in extracts was measured using the Hoechst £uorometricassay and a Bio-Rad £uorimeter.
2.4. Clone libraries
Two clone libraries were generated using DNA ex-tracted from an O’Brien Bay sample (OB311) and a BrownBay sample (BB222). A fragment of the 16S rRNA genewas ampli¢ed using Advantage 2 Taq (Clontech) with thesupplied 10U bu¡er and the primers 519f (CAG CMGCCG CGG TAA TAC) and 1392r (ACG GGC GGTGTG GRC). These universal primers are expected tobind to the majority of bacteria and archaea. Each 100Wl reaction mix contained 5 Wl of 10U bu¡er, 2 Wl of Taq,1.25 mM of each deoxynucleoside triphosphate, 20 nmolof each primer and 80 ng of template DNA. The followingthermal cycling programme was used: initial denaturing at94‡C for 15 min; 30 cycles of denaturing at 94‡C for 1 min,annealing at 52‡C for 1 min, extension at 72‡C for 1.5min; ¢nal extension at 72‡C for 10 min. The reactionproducts were puri¢ed using the Prep-a-gene kit (Bio-Rad).
The fragment was cloned using the pGEM-T easy vec-tor system (Promega) and transformed into Epicurian coliXL ultracompetent cells (Stratagene) following the manu-facturer’s directions. Transformants were screened usingblue^white screening on Luria agar containing X-gal andisopropyl-L-D-thiogalactose. Approximately 250 white col-onies from each library were sub-cultured.
Ultraclean mini plasmid preps (MoBio) were used toextract the plasmids from the sub-cultured clones. 3 Wlof the extracts were run on a 1% agarose gel alongside amolecular mass marker in order to verify that the plasmidcontained the correct-sized insert.
Positive clones were sequenced with the BigDye Termi-nator Ready Reaction mix sequencing reactions (AppliedBiosystems). 7 Wl of the plasmid extract was used in a 20Wl reaction with 5 pmol of either the M13f or M13r prim-er. This generated sequences of approximately 1000 bp.These sequences are deposited under GenBank accessionnumbers AY133347 to AY133467.
The chimera-check tool of the Ribosomal RNA Data-base Project (http://www.rdp.cme.msu.edu) [22] was usedto check possible chimeric sequences. Sequences werealigned against reference sequences obtained from Gen-Bank (http://www.ncbi.nlm.nih.gov/blast) [23]. DNADISTand NEIGHBOR from the PHYLIP package [24] wereused to generate phylogenetic trees. Cloned sequencesthat were more than 98% similar to each other were con-sidered to be the same phylotype [25] for the purposes ofcalculating diversity statistics. However, all sequences areshown in Fig. 3. Simpson’s index (D=gp2i ) and the Shan-non^Wiener index (H=3gpi ln(pi)) were calculated andthe Chao-1 estimator (http://www2.biology.ualberta.ca/jbrzusto/rarefact.php) was used to calculate species rich-ness. The method of Singleton et al. [26] was used to
compare the similarity of the two libraries directly. Thiscalculation gives a P value that is considered to show asigni¢cant di¡erence at values less than 0.05.
2.5. DGGE
Advantage 2 Taq (Clontech) with the supplied 10Ubu¡er was used for ampli¢cation of a fragment of the16S rRNA gene containing the V3 and V4 regions. Each50 Wl reaction mix contained 5 Wl of 10U bu¡er, 1 Wl ofTaq, 1.25 mM of each deoxynucleoside triphosphate, 20nmol of each primer and either 20 ng of sample DNA or1 ng of the standard control DNA mix. The standardDNA mix consisted of 5 ng Wl31 each of genomic DNApreparations from four strains grown routinely in our lab-oratory and chosen because they denatured at a range ofdi¡erent denaturant concentrations. The primers were907R (CCG TCA ATT CCT TTG AGT TT) and 341Fwith a GC clamp (CGC CCG CCG CGC CCC GCGCCC GGC CCG CCG CCC CCG CCC CCC TACGGG AGG CAG CAG). The touchdown thermal cyclingprogramme consisted of the following steps: initial dena-turing step at 94‡C for 5 min; then 10 cycles of denaturingat 94‡C for 1 min, annealing at 65‡C for 1 min (decreasingby 1‡C each cycle) and extension at 72‡C for 3 min; fol-lowed by 20 cycles of 94‡C for 1 min, 55‡C for 1 min,72‡C for 2 min; ¢nal extension at 72‡C for 4 min and thenheld at 4‡C.
The DGGE was performed using a D-Code UniversalMutation Detection System (Bio-Rad). Half the volume ofthe PCR products were run on 6% acrylamide gels with adenaturing gradient of 30^65% (where 100% denaturant is7 M urea and 40% formamide). Gels were run at 80 V for16 h at 60‡C in 1UTAE (40 mM Tris, 20 mM sodiumacetate, 1 mM EDTA). Standards were run on either sideof the gel and the outside lanes were not used. In order toobtain even heat distribution throughout the tank, theentire tank was placed on a magnetic stirring plate.
Gels were stained in 1:1000 Sybergold (MolecularProbes) in the dark with gentle shaking for approximately20 min. They were then washed once with deionised waterand destained with deionised water for 20 min beforeviewing on a UV transilluminator.
Photos were scanned in and viewed with the UTHSCSAImageTool program (developed at the University of TexasHealth Science Center at San Antonio, TX, USA andavailable from the Internet by anonymous FTP fromftp://maxrad6.uthscsa.edu). The best possible banding pat-tern was obtained by enhancing the contrast and greyscaleof the images. This banding pattern was then transformedinto a presence/absence matrix for statistical analysis. Thestandards were used to check for gradient consistency be-tween gels and to assist in comparing the position ofbands between gels.
A multivariate approach using the Primer5 package(Plymouth Marine Laboratory, UK) was used to investi-
gate the DGGE banding patterns. For some analyses, thebanding patterns from several runs were pooled and thetotal presence/absence of bands recorded. Similarity ma-trices were generated using the Bray^Curtis measure onpresence/absence of bands. Non-metric multidimensionalscaling plots (nMDS) were used to represent the relativesimilarities between the samples. The stress levels of thenMDS plots were generally between 0.1 and 0.2 and clus-ter analysis (hierarchical agglomerative clustering withgroup average linkage) was used to check the groupingsproduced by the nMDS procedure. The analysis of simi-larity (ANOSIM) test (one-way) was used to comparegroups. ANOSIM R values of 1 indicate that replicateswithin a location are more similar to each other than toany samples from another location whereas an R value of0 indicates that there is as much variation within a groupas between the two groups being compared. In decidingwhether locations were di¡erent, both the R value andsigni¢cance level were considered. The signi¢cance levelwas a¡ected by the small number of samples in the Wharfand Sparkes Bay locations. Rather than considering aspeci¢c number (e.g. 5%) to be signi¢cant, levels close tothe minimum possible were taken to be signi¢cant.
The BIOENV procedure in the Primer5 package wasused to investigate correlations between environmentalvariables as reported in [7] and the microbial communitystructure described by the DGGE analysis. TOC, heavymetals (lead, tin, zinc, copper, iron, antimony, cadmium,chromium, manganese, mercury, nickel and silver) andsediment characteristics (skewness, kurtosis, sorted particlediameter and mean particle diameter) were included in theanalyses.
2.6. Nested samples
In order to reduce the e¡ects of PCR and gel bias on thebanding patterns, all the samples were analysed threetimes. All samples were ampli¢ed in the same round ofPCR. This PCR was carried out twice and each samplewas run on a total of three gels. That is, one of the roundsof PCR was run on two gels (half the volume of a PCRreaction is loaded onto a gel) and the second round ofPCR was run only once. These three banding patternswere then added together as described above.
3. Results
3.1. Clone libraries
After discarding sequences that were suspected of beingchimaeric and sequences that were of poor quality, therewere 98 clones from the Brown Bay library and 85 fromthe O’Brien Bay library. Of these, 66 and 64 respectivelywere unique phylotypes. Fourteen phylotypes were foundin both libraries, although many of the other phylotypes
were closely related despite falling outside our de¢nition ofphylotype. This number of unique sequences represents33% (Brown Bay) and 25% (O’Brien Bay) coverage wherecoverage is considered to be the proportion of clonesfound more than once. The species richness was 304 forBrown Bay and 282 for O’Brien Bay as estimated by theChao-1 estimator. Both these ¢gures suggest that the mi-crobial diversity of both bays is much higher than detectedhere. The Simpson diversity index was 0.025 for BrownBay and 0.022 for O’Brien Bay. The Shannon^Wiener in-dex was 4.05 for both Brown Bay and O’Brien Bay. Againthis indicates a high microbial diversity in these sediments.
The sequences obtained from the clone libraries weredivided into seven groups for ease of handling. The mostnumerous sequences belonged to the N and Q proteobacte-ria followed by the £avobacteria. The two libraries arecompared in Fig. 2 on the basis of the number of uniquephylotypes in each group. Both sites have diverse micro-bial populations with similar proportions of the seven phy-logenetic groups. The P values generated by the methoddescribed in [26] were 0.063 (when BB2 is X and OB3 is Y)and 0.372 (when OB3 is X and BB2 is Y). Neither of thesevalues indicates a signi¢cant di¡erence between the twoclone libraries.
When the sequences were aligned onto phylogenetictrees, there tended to be clusters of very similar phylotypesthat generally contained representatives from both libra-ries. However, in both the N and Q proteobacteria, therewere clusters in which there were only Brown Bay clones(Fig. 3). In the N proteobacteria these clones were relatedto Desulfobacula toluolica and Desulfobacterium phenoli-cum, both hydrocarbon-oxidising sulfate reducers. Thecluster of Brown Bay clones in the Q proteobacteria wasrelated to the genus Pseudoalteromonas.
3.2. Evaluation of DGGE reproducibility: PCR and gele¡ects
The ¢rst stage was to evaluate the reproducibility ofDGGE banding patterns in the sediment samples. Ini-
0
2
4
6
8
10
12
14
16
18
Deltab
acte
ria
Chrom
atib
acte
ria
other
pro
teobac
teria
Planct
omyc
etes
Archae
a
Flavo
bacte
ria
Other
s
Nu
mb
er o
f u
niq
ue
ph
ylo
typ
es
Fig. 2. Comparison of Brown Bay (black bars) and O’Brien Bay (greybars) clone libraries by the number of unique phylotypes in various phy-logenetic groups.
tially, three samples (one each from Sparkes, Brown andO’Brien bays) were subject to three simultaneous PCRreactions that were then run on the same DGGE gel toprovide three PCR replicates for each sample. Each of thereplicates was over 95% similar to each other, but thethree samples were clearly di¡erent from each other. ThePCR was repeated on a di¡erent day and these reactionsrun alongside the remaining half reactions from the ¢rstround of PCR. Once again, replicates that had been sub-ject to the same round of PCR and run on the same gelwere over 95% similar. However, for every sample, therewere di¡erences between reactions either ampli¢ed on dif-ferent days or run on di¡erent gels (Fig. 4). The ANOSIMtest was used to compare the similarities between each run(Table 2). In all three samples, replicates run on the samegel were the most similar whereas those subject to thesame PCR reaction but run on di¡erent gels were themost dissimilar. This suggests that di¡erences betweengels are the main reason for di¡erences in banding pat-terns of the same sample.
O36Thiorhodococcus minusRi ft ia pachypti la endosymbiont
B230B193
B140O189
B131O55
O221B135
O62O217
O57O138
O140O172
B126O198
O88B169
B125Pseudomonas cel lulosa
M icrobulbifer sal ipaludisPseudomonas synxanthaB186
M ethylomonas rubraB110
B147Aeromonas bestariumShewanella putr ifaciens
B17B153Pseudoal teromonas tetraodonisB149
B15B178
B69B177
O96thial kalivibrio denitri fi cansO48
B26B137
OO98O163O49B216
B53B163
B200O169
O186B114
B219O75
O117
A BFig. 3. Phylogenetic trees of the N proteobacteria (A) and Gamma proteobacteria (B) in the O’Brien Bay (numbers preceded by O) and Brown Bay(numbers preceded by B) clone libraries. Bracket indicates clusters of Brown Bay clones that are associated with (A) Desulfobacula toluolica and (B) thegenus Pseudoalteromonas.
Fig. 4. nMDS of three samples (one each from Brown Bay, O’BrienBay and Sparkes Bay), each replicated three times in two rounds ofPCR and run on a total of three separate gels (squares, triangles andcircles). In some cases, the banding patterns of replicates were identicaland therefore the symbol appears only once or twice instead of threetimes (e.g. Sparkes Bay, triangles).
The entire nested survey sample set was analysed threetimes. The negative image of one of these gels is shown inFig. 5. The controls in the outside lanes were consistentacross all gels used in the analysis. When the results fromindividual runs were plotted on an nMDS, some groupingby location was observed (not shown) although the nMDSwere di¡erent for each run.
The nMDS obtained from pooling the banding patternsfrom each batch together is shown in Fig. 6 and the ANO-SIM R statistic for each pair of locations is given in Ta-ble 3. Generally, the samples group together within theirlocations although the Brown Bay locations overlap.O’Brien Bay, Sparkes Bay and Wharf area locations areall distinct (ANOSIM values generally over 0.8). InO’Brien Bay, the two locations from the north side ofthe bay are more similar to each other (ANOSIM R valueof 0.448) than to the location from the south side of thebay (ANOSIM R values of 0.844 and 0.948).
In contrast, the ANOSIM R values for each pair of
Brown Bay locations are negative, indicating as much var-iation within each location as between them. In Fig. 6 itcan be seen that the spread of the Brown Bay locations isgreater than the spread of the other locations suggesting agreater heterogeneity in Brown Bay. The two sites withinthe wharf location are di¡erent to each other (ANOSIMR value of 0.8) whereas the two sites within the SparkesBay location are more similar (ANOSIM R value of 0.3).
The BIOENV procedure ¢nds the combinations of en-vironmental variables that best ¢t the microbial commu-nity structure patterns. The environmental data weretransformed by several methods including square root,log and fourth root. The square root transform on theenvironmental data resulted in the best separation of thesamples into their locations as seen in nMDS plots (notshown). The correlations from this transform are pre-sented in Table 4. However, the same variables appearedin much the same order of importance regardless of whichof the above transforms was used. From Table 4 it can beseen that the highest correlation between environmentalvariables and the microbial community structure occurswhen TOC, arsenic, iron and manganese are combined(P=0.411). TOC, arsenic, iron and manganese and cad-mium consistently appear in the highest correlations. Themeasures of sediment size and sorting are much less im-portant and rarely appear in the higher correlations foreach group of variables.
4. Discussion
4.1. Clone library results
The clone libraries described here are the ¢rst recordedfor nearshore Antarctic marine sediments. However, as
Table 2Comparison of banding patterns for each sample over two PCR ampli¢-cations and two DGGE gel runs
A indicates PCR 1/gel 1, B indicates PCR 1/gel 2 and C indicatesPCR2/gel 2. ANOSIM R statistics are shown in parentheses.
Fig. 5. Negative image of a photo of a DGGE gel containing nine ofthe nested samples. The two outside lanes are the standard controlDNA mix, lanes 2 and 10 are from Sparkes 2, lane 3 is from theWharf, lanes 4, 5, 8 and 9 are Brown Bay 3 and lanes 6 and 7 areBrown Bay 4.
Fig. 6. nMDS showing relative similarities between locations: BrownBay 2 (black triangles), 3 (black circles), 4 (black squares) ; O’Brien Bay1 (grey triangles), 2 (grey circles), 3 (grey squares) ; Sparkes Bay (crossedopen squares) ; and Wharf (open down-pointing triangles). All sampleswere analysed three times, this plot resulted from pooling the three pres-ence/absence matrices.
they were constructed from only two samples, it is di⁄cultto say how representative they are of the two bays. Thesequences obtained are only a fraction of those present inthe sediment, estimated at 33 and 25% coverage for BrownBay and O’Brien Bay respectively. Both libraries contain adiverse array of sequences as indicated by both the Shan-non^Wiener and Simpson’s indices. It was expected thatonly very large di¡erences between the microbial commun-ities would be seen in the clone libraries. They both have asimilar diversity, both contain the same major phyloge-netic groups and the large number of closely related phy-lotypes (see for example Fig. 3) suggests that they arequite similar. Other studies comparing marine sedimentclone libraries have found similarities between commun-ities from apparently diverse locations. For example,N proteobacteria, a large group in both the Brown andO’Brien Bay clone libraries, were also a dominant groupin clone libraries from lakes in the Vestfold Hills in East-ern Antarctica [27] and Arctic marine sediment [28]. Bothof these studies also concluded that the diversity of coldmarine sediments was surprisingly high.
The two most interesting phylogenetic groups are shownin more detail in Fig. 3. In both the Q and N proteobac-teria, there are clusters of sequences that only containBrown Bay clones. Although phylogenetically related tohydrocarbon-degrading strains, it is not possible to tell
from the 16S sequence alone whether these clones degradehydrocarbons as well. The fact that cultures of thesestrains are required to determine metabolic capabilities isone of the disadvantages of molecular techniques. Giventhe random nature of the construction and sequencing ofclone libraries, it is possible that these sequences did occurin O’Brien Bay, but were overlooked as they were lessnumerous. One way of testing this is to use primers de-signed speci¢cally to amplify these groups and to probefor them using £uorescence in situ hybridisation.
4.2. DGGE technique
DGGE banding patterns are known to be in£uenced bymany things: the DNA extraction method used; minorvariations in the PCR reaction; the gradient used; gra-dient and acrylamide variations between gels ; the com-plexity of the microbial community present in a sampleand the actual species present. Attempts have been madepreviously to determine the reproducibility of banding pat-terns [10,29]. However, these mainly compared di¡erentrounds of PCR on the same gel. Unfortunately, ecologicalstudies will generate more samples than can be analysedon one gel. In addition, most work attempting to de¢nethe sensitivity and reproducibility of DGGE used con-structed assemblages [8,10]. Whilst this is an e¡ective, con-trolled approach, these assemblages are much more simplethan sediment samples and care should be taken in extrap-olating results from DNA mixes to environmental sam-ples. Generally we had between 20 and 30 distinguishablebands per sample and it is clear from the clone librariesthat there are at least 60 di¡erent phylotypes present. It isassumed that only numerically dominant sequences willappear in a banding pattern, although this is also depen-dent on the DNA extraction method used and PCR biases.We took a step-by-step approach to analysing the contri-bution of these biases to the reproducibility of DGGE
Table 3ANOSIM values comparing the similarity between pairs of locationsbased on data pooled from three analyses
Pairs of locations R statistic Signi¢cance level (%)
Table 4Correlations between environmental variables and microbial communitystructure patterns
All environmental data were subject to a square root transformation.The environmental variables giving the best results when taken k at atime are presented. Correlation coe⁄cients are given in parentheses,bold indicates the highest overall value of P.aTotal organic carbon.bMean sorted particle size.
banding patterns. Our results show that both gel and PCRe¡ects are important (Fig. 4) but that di¡erences betweengels are slightly more important (Table 2). Ferrari andHollibaugh [29] found a similar phenomenon. When thesame sample was subject to several rounds of PCR andrun on the same gel, the patterns were over 90% similar.However, when they repeated a gel, depending on themethod used to compare the banding patterns, di¡erentsamples from the same gel could be more alike than thesame sample run on two di¡erent gels. They concludedthat the ‘gel signature’ could not be completely removedin the image-processing step. Despite this, we have shownthat the banding patterns generated by repeated analysisof the same sample are more similar to each other thanthose from other samples analysed at the same time. Thatis, at least for the samples used in this study, the greatestoverall in£uence on banding patterns was di¡erences with-in the samples themselves. In order to minimise the e¡ectof other factors and allow the sample di¡erences to be-come the dominant factor, we found pooling data frommultiple runs to be an e¡ective solution. Our results sug-gest that analysing a small number of samples only onceis not su⁄cient to determine the relative di¡erences (orsimilarities) between samples or between sampling loca-tions. Furthermore, without determining the extent of var-iation in banding patterns caused by gel and PCR e¡ects,banding patterns from di¡erent gels should not be com-pared.
4.3. DGGE results
The ANOSIM tests (Table 3) as well as the nMDS plot(Fig. 6) showed that the O’Brien Bay locations were allseparate, signi¢cantly di¡erent groups. Within each loca-tion, however, the sites and plots are very similar to eachother. It is also interesting that the two O’Brien Bay loca-tions that are geographically closer to each other are moresimilar. This suggests that natural di¡erences in the envi-ronment at a scale of kilometres are enough to in£uencethe microbial community structure. However, on a smallerscale (e.g. hundreds of metres), the communities are muchthe same, perhaps as a result of homogeneous environ-mental conditions.
The spatial variation in microbial communities in theBrown Bay locations is very di¡erent from the controllocations. The Brown Bay locations overlap to a greaterextent than the O’Brien Bay locations, but this is probablydue to the fact that they are much closer together (300 mapart rather than kilometres apart). The variation withineach Brown Bay location is greater than the variationwithin the control locations. Brown Bay 2, the closest tothe tip site, has two points close together and two outlyingpoints. We interpret this to be the result of heterogeneousenvironmental conditions possibly caused by ‘hot-spots’ ofcontamination. A sample taken next to a battery fragmentfor example will have high levels of heavy metals and this
will probably in£uence the microbial community structure.Environmental factors are more variable in Brown Bay(Table 1) and these too could be contributing to the var-iation in microbial community structure.
The Sparkes and Wharf locations both form distinctgroups (Fig. 6). Despite being a control location, SparkesBay is slightly more similar to the Brown Bay and Wharflocations than the O’Brien Bay locations. This is possiblydue to the occurrence of naturally elevated levels of heavymetals in this bay.
Although the locations generally form distinct groups,there is some separation between impacted (Brown Bayand Wharf) locations and non-impacted locations(O’Brien and Sparkes Bays) with Sparkes Bay (whichhas elevated metal levels) grouping with the impacted lo-cations. The chemistry of the impacted sediments is quitedi¡erent in each location, Sparkes Bay has some elevatedlevels of metals, Brown Bay has high levels of heavy met-als and some petroleum hydrocarbons and the Wharf lo-cation has very high levels of petroleum hydrocarbons.The e¡ect on the microbial communities is not as simpleas impacted and non-impacted but perhaps re£ects speci¢ccomponents of the pollution and the complexity of hetero-geneous environmental conditions. Sampling of more con-trol locations for further comparison of variability be-tween control locations as well as between control andimpacted locations may assist in resolving this. In orderto con¢rm the e¡ect that human activity has had on thebenthic microbial populations at Casey, it would be neces-sary to compare samples taken from the same locations(e.g. Brown Bay) before they became contaminated. Asthis is not possible, in situ experiments with arti¢ciallycontaminated sediment are currently under way.
4.4. Correlation of environmental variables with microbialcommunity patterns
In this study TOC levels, in combination with heavymetals, are correlated (P=0.4) with the microbial commu-nity structure patterns (Table 4). The type and availabilityof carbon is one of the most important factors in£uencingthe development of a microbial community. Unfortunatelydata which distinguish between petroleum hydrocarbonsand other organic carbons are not available for these sam-ples. However, petroleum hydrocarbons contribute to theTOC level which is higher for the impacted locations thannon-impacted locations (Table 1). Heavy metals also ap-pear to have some in£uence on the microbial communities,particularly iron, cadmium, manganese, zinc and arsenic.Naturally elevated levels of some of these metals (e.g. cad-mium) are found in Sparkes Bay, but others such as iron,zinc and arsenic are far higher at impacted locations (Ta-ble 1) and are most likely anthropogenic in origin. How-ever, the correlation between these factors and the micro-bial community structure was not very high. Other factorswhich we have not measured (for example depth of oxygen
penetration into the sediment, hydrocarbon concentra-tions) may also be just as important.
4.5. Conclusions
In this work we attempted to de¢ne the extent to whichgel and PCR biases a¡ected the DGGE banding patterns.With this knowledge, we were able to take steps to reducethese problems when analysing our samples. In order toobtain reliable results from DGGE, we suggest that multi-ple analyses of large numbers of samples should be under-taken and the banding patterns pooled. However, whenused in conjunction with large numbers of samples anda statistical analysis of the results, DGGE can be a veryuseful tool in microbial ecology. By using a second tech-nique we were able to verify the similarity between themicrobial communities found in O’Brien Bay and BrownBay. The presence of two sequence clusters of Brown Bayonly clones suggests that there are also di¡erences.
Sediment microbial communities are very diverse andare subject to a complex array of environmental factorsresulting in complex patterns of community variability. Inthis study, variation was observed between control loca-tions as well as between control and impacted locations.However, as the microbial community structure patternscorresponded to environmental variables which areanthropogenic, it is likely that this variation is in partdue to human activities in the region.
Acknowledgements
This study was supported by funding from the AntarcticScience Advisory Committee.
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