microbial gene functions enriched in the deepwater horizon deep-sea oil plume

ORIGINAL ARTICLE

Microbial gene functions enriched in the DeepwaterHorizon deep-sea oil plume

Zhenmei Lu1,2, Ye Deng2, Joy D Van Nostrand2, Zhili He2, James Voordeckers2,Aifen Zhou2, Yong-Jin Lee2, Olivia U Mason3, Eric A Dubinsky3, Krystle L Chavarria3,Lauren M Tom3, Julian L Fortney3, Regina Lamendella3, Janet K Jansson3,Patrik D’haeseleer3, Terry C Hazen3 and Jizhong Zhou2,3,4

1College of Life Sciences, Zhejiang University, Hangzhou, China; 2Department of Botany and Microbiology,Institute for Environmental Genomics, University of Oklahoma, Norman, OK, USA; 3Earth Sciences Division,Lawrence Berkeley National Laboratory, Berkeley, CA, USA and 4Department of Environmental Science andEngineering, Tsinghua University, Beijing, China

The Deepwater Horizon oil spill in the Gulf of Mexico is the deepest and largest offshore spill in theUnited State history and its impacts on marine ecosystems are largely unknown. Here, we showedthat the microbial community functional composition and structure were dramatically altered in adeep-sea oil plume resulting from the spill. A variety of metabolic genes involved in both aerobic andanaerobic hydrocarbon degradation were highly enriched in the plume compared with outside theplume, indicating a great potential for intrinsic bioremediation or natural attenuation in the deep sea.Various other microbial functional genes that are relevant to carbon, nitrogen, phosphorus, sulfurand iron cycling, metal resistance and bacteriophage replication were also enriched in the plume.Together, these results suggest that the indigenous marine microbial communities could have asignificant role in biodegradation of oil spills in deep-sea environments.The ISME Journal (2012) 6, 451–460; doi:10.1038/ismej.2011.91; published online 4 August 2011Subject Category: microbial ecology and functional diversity of natural habitatsKeywords: oil spill; deep-sea plume; microbial community; metagenomics; functional gene arrays;GeoChip

Introduction

On 20 April 2010, a massive oil leak occurred in theGulf of Mexico’s Mississippi Canyon area at a depthof 1544 m, releasing B4.9 million barrels of crudeoil into the deep ocean before the wellhead wasfinally capped on 15 July 2010 (The FederalInteragency Solutions Group, Oil Budget CalculatorScience and Engineering Team, November 2010).Chemical dispersants, including COREXITEC9500A and COREXIT EC9527A, were used onsite at one of the highest rates in history to accelerateoil dispersal. A deep-water oil plume was initiallydetected at a depth of 1000–1200 m below thesurface (Camilli et al., 2010; Hazen et al., 2010;Mascarelli, 2010a), but at last account (Mascarelli,2010b) could no longer be detected, presumably as aresult of dispersion and microbial degradation(OSAT, 2010). Significant environmental differencesin the deep sea of Gulf of Mexico from other historic

offshore oil spills present an urgent need to betterunderstand the fate and impacts of the oil on thisspecific habitat (Kerr et al., 2010a,b).

In marine ecosystems, microorganisms are knownto have predominant roles in degradation of oilcontaminants (Head et al., 2003; Larter et al., 2003).Therefore, it was expected that the indigenousmicrobial communities would have a significant rolein degradation of the deep oil plume. This hypothesiswas supported by two recent studies that exploredthe microbial and chemical properties of samplescollected from the deep oil plume (Camilli et al.,2010; Hazen et al., 2010). Hazen et al. (2010) used acombination of molecular, chemical and physiologi-cal approaches to investigate the microbial andchemical composition in the deep-sea plume com-pared with uncontaminated water from the samedepth outside the plume. They demonstrated that theoil depletion was due to a combination of mixing,dispersion and biodegradation by microbes residingin the deep sea (Hazen et al., 2010).

In this study, samples from the deep-sea plume,oil-contaminated seawater (hereafter referred to as‘oil plume’ in the following text) and non-plumecontrols (seawater samples at same depth that werenot contaminated with oil) were analyzed witha functional gene microarray, the GeoChip 4.0

Received 3 March 2011; revised 6 June 2011; accepted 6 June2011; published online 4 August 2011

Correspondence: J Zhou, Department of Botany and Microbiology,Institute for Environmental Genomics, University of Oklahoma,Norman, OK 73019, USA.E-mail: [email protected]

The ISME Journal (2012) 6, 451–460& 2012 International Society for Microbial Ecology All rights reserved 1751-7362/12

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(Hazen et al., 2010), to address the followingquestions: (i) How did the oil contamination affectthe marine microbial community functional compo-sition and structure? (ii) How did different microbialfunctional genes involved in key microbial pro-cesses shift in response to the oil spill? (iii) Werefunctional genes specific to hydrocarbon (HC)degradation processes enriched in the oil plume?Our results indicated that the oil spill dramaticallyaltered microbial community functional structure,the marine microbial communities present weremetabolically diverse, and that these communitieswere able to respond to the oil spill.

Materials and methods

The following is the summary of methods used inthis study. More detailed information is provided inSupplementary Data A.

Sample descriptionBetween 27 May and 2 June 2010, seawater sampleswere collected from the Gulf of Mexico during twomonitoring cruises on the R/V Ocean Veritas and R/V Brooks McCall (Supplementary Table S1) aspreviously described (Hazen et al., 2010). Briefly,two colored dissolved organic matter WETstarfluorometers (WET Labs, Philomath, OR, USA) wereattached to a CTD sampling rosette (Sea-BirdElectronics Inc., Bellevue, WA, USA) and used todetect the presence of oil. The fluorometer resultswere subsequently confirmed by laboratory HCanalysis. Niskin bottles attached to the CTD rosettewere used to capture water samples at variousdepths with detected HCs. Eight samples (BM053,BM054, BM057, BM058, BM064, OV201, OV401 andOV501) from the MC252 dispersed oil plume, andfive samples (OV003, OV004, OV009, OV013 andOV014) from non-plume at a depth of 1099–1219 mwere analyzed in this study.

To better define the geochemical properties of theplume and non-plume samples, two sets of variableswere measured: (i) seawater variables (dissolvedoxygen, temperature, small particle counts, totalammonia nitrogen, nitrite (NO2-N), total iron), ortho-phosphate (PO4-P) and acridine orange direct count)and (ii) oil composition variables (fluorometerdetection of oil, benzene, toluene, ethylbenzene,isopropylbenzene, n-propylbenzene, 1,3,5-trimethy-lbenzene, tert-butylbenzene, 1,2,4-trimethylbenzene,sec-butylbenzene, p-isopropyltoluene, n-butylben-zene, naphthalene and o-xylene, m- and p-xylenes)(Hazen et al., 2010).

DNA amplification and labelingApproximately 100 ng of DNA that was previouslyextracted from the samples (Hazen et al., 2010) wasamplified using a modification of the Templiphikit (GE Healthcare, Piscataway, NJ, USA). The

amplified DNA (2 mg) was then labeled with Cy3using random primers and the Klenow fragment ofDNA polymerase I (Wu et al., 2006) and thenpurified and dried in a SpeedVac (45 1C, 45 min;ThermoSavant, Milford, MA, USA) before hybridi-zation.

GeoChip 4.0 hybridization and data pre-processingThe GeoChip 4.0, containing 83 992 50-mer oligo-nucleotide probes targeting 152 414 genes in 410gene categories for different microbial functionaland biogeochemical processes, was synthesized byNimbleGen (Madison, WI, USA). All hybridizationswere carried out at 42 1C with 40% formamide for16 h on a MAUI hybridization station (BioMicro,Salt Lake City, UT, USA). After hybridization, thearrays were scanned (NimbleGen MS200, Madison,WI, USA) at a laser power of 100%. Signalintensities were measured based on scanned images,and spots with signal-to-noise ratios lower than 2were removed before statistical analysis as describedpreviously (He et al., 2010).

Statistical analysisPre-processed GeoChip data were further analyzedwith different statistical methods: (i) microbialdiversity index, the two-tailed t-test and responseratio (Luo et al., 2006); (ii) hierarchical clustering formicrobial community structure and composition(de Hoon et al., 2004); (iii) analysis of similarity,permutational multivariate analysis of varianceusing distance matrices and multiresponse permu-tation procedure analysis of differences of microbialcommunities (Anderson, 2001); (iv) canonical cor-respondence analysis (CCA) for linking microbialcommunities to environmental variables (Rametteand Tiedje, 2007; Zhou et al., 2008); and (v) partialCCA for co-variation analysis of wellhead distanceand environmental variables (variation partitioninganalysis). Details for all methods are provided in theSupplementary Information.

Results

Functional gene changes in response to oil spillTo assess the dynamic changes of microbial com-munities in response to oil spill, microbial commu-nity functional composition and structure wereanalyzed using functional gene arrays (GeoChip4.0). Significantly more functional genes (Po0.01)were detected in the oil plume samples than in non-plume control (Supplementary Table S2). Theoverall microbial functional diversity was alsosignificantly (Po0.01) higher in the plume samplesbased on Shannon–Weiner (H0) and Simpson’s (1/D)indices. Consistent with geochemical ordinationpatterns, hierarchical clustering analysis showedthat all plume samples were clustered together andwell separated from non-plume samples (Figure 1

Marine microbial communities for potential bioremediation of oil spillsZ Lu et al

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and Supplementary Figure S1), as also shown for themicrobial communities at a phylogenetic level(Hazen et al., 2010). However, considerable varia-bility in functional gene distribution was observedamong different samples and some functional geneswere common to all samples, although others wereunique to oil plume samples (Figure 1). Forexample, Group 6, with 1439 or 20.14% of all genesdetected, largely involved in organic remedia-tion, carbon degradation, denitrification, sulfate

reduction, metal resistance and stress response, wasgenerally detected in all samples. Groups 1, 2, 10and 17, with 2.2%, 3.9%, 20.5% and 10.8% of allgenes detected, were mainly detected in the plumesamples (Figure 1). In addition, the microbialcommunity functional structure was significantly(Po0.05) different between the plume and non-plume samples as revealed by the three comple-mentary non-parametric multivariate statisticaltests (analysis of similarity, permutational

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Figure 1 Hierarchical cluster analysis of all genes present in at least two out of the five samples. Results were generated in CLUSTERand visualized using TREEVIEW. Red indicates signal intensities above background, whereas black indicates signal intensities belowbackground. Brighter red coloring indicates higher signal intensities. All oil plume samples clustered together and were well separatedfrom non-plume samples.


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multivariate analysis of variance using distancematrices and multiresponse permutation procedure)(Table 1).

Oil as a predominant factor shaping microbialcommunity functional structureCCA was performed to determine the most signifi-cant environmental variables shaping microbialcommunity structure. On the basis of variance ininflation factors, seven variables were selected:dissolved oxygen, temperature, total volatile HC,total extractable petroleum HC, fluorometer detec-tion of oil, phosphate and iron. The specified CCAmodel was significant (P¼ 0.026). Of these, the totalvolatile HC, extractable petroleum HC, fluorometerdetection of oil and dissolved oxygen were the mostsignificantly correlated with plume samples(Figure 2). To separate the effects of seawatergeochemical variables, geographic distance and oilcomposition on microbial community structure, aCCA-based variation partitioning analysis (Rametteand Tiedje, 2007; Zhou et al., 2008) was performed.Seawater geochemical variables, oil compositionand wellhead distance showed a significant correla-tion (P¼ 0.041) with the functional gene structure ofthe community. Oil composition explained substan-tially more variations (48.34%, P¼ 0.03) than sea-water variables (21.76%, P¼ 0.017), whereasdistance independently explained 9.1% (P¼ 0.43)of the observed variation (Figure 3). About 28% ofthe community functional variation based on Geo-Chip data remained unexplained by the aboveselected variables, which is significantly lower thanthose observed in other systems such as soils

(Ramette and Tiedje, 2007; Zhou et al., 2008). Theseresults indicate that oil contaminants could be adominant factor shaping microbial communityfunctional structure and potentially regulatingassociated microbial functional processes.

Oil spill stimulated increase in functional genes forHC degradationA substantial number of genes involved in HCdegradation were detected in the oil plume samples(Hazen et al., 2010), especially those involved indegrading alkanes, alkynes and cycloalkanes, BTEXand related aromatics, chlorinated aromatics,heterocyclic aromatics, nitroaromatics, polycyclicaromatics and aromatic carboxylic acids. For

Table 1 Significance of the effects of the oil spill on the overallmicrobial community structure and geochemical pattern usingthree statistical analyses

Method Geochemical parametersa Microbial community

Statistic P-value Statistic P-value

MRPPb 233.112 0.037 53.617 0.003ANOSIMc 0.057 0.046 0.501 0.002Adonisd 0.258 0.043 0.192 o0.001

Abbreviations: ANOSIM, analysis of similarity; MRPP, multi responsepermutation procedure.aGeochemical parameters included temperature, DO concentration,fluorometer detection of oil, small particle concentrations, Fe, nitrate,phosphate, benzene, toluene, naphthalene, ethylbenzene, isopropyl-benzene, n-propylbenzene, 1,3,5-trimethylbenzene, tert-butylben-zene, 1,2,4-trimethylbenzene, sec-butylbenzene, p-isopropyltoluene,n-butylbenzene, total xylenes, total volatile HC and total petroleumhydrocarbons—extractable (DRO).bMultiple response permutation procedure, a nonparametric proce-dure that does not depend on assumptions such as normallydistributed data or homogeneous variances, but rather depends onthe internal variability of the data.cAnalysis of similarities.dNon-parametric multivariate analysis of variance (MANOVA) withthe adonis function.All three tests are non-parametric multivariate analyses based ondissimilarities among samples.

Figure 2 CCA compares the GeoChip hybridization signalintensities (symbols) and environmental variables (arrows).Environmental variables were chosen based on significancecalculated from individual CCA results and variance inflationfactors (VIFs) calculated during CCA. The percentage of variationexplained by each axis is shown, and the relationship issignificant (P¼0.026).

Figure 3 Variation partitioning based on CCA for all functionalgene signal intensities. (a) General outline, (b) all functionalgenes. A CCA-based VIF was performed to identify common setsof oil composition and seawater variables important to themicrobial community structure. Oil composition variables in-cluded fluorometer detection of oil, the concentration of totalvolatile HCs, xylenes and petroleum HCs—extractable (DRO).Seawater geochemical variables included temperature, dissolvedoxygen (DO), Fe and phosphate.


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example, gene alkB encoding alkane 1-monooxy-genase, a key enzyme responsible for the initialoxidation of inactivated alkanes, showed a signifi-cantly (Po0.05) higher abundance, with 19–26genes detected in the oil contaminated samples and11–15 detected in the non-oil contaminated sam-ples. The alkB genes derived from Rhodospirillumcentenum SW, Bdellovibrio bacteriovorus HD100,Prauserella rugosa, Roseobacter sp. CCS2, Myco-bacterium bovis AF2122/97, Bacillus sp. BTRH40,Gordonia sp. Cg and Rhodococcus sp. RHA1appeared to be dominant in all oil plume samples(Supplementary Figure S2).

GeoChip analysis also detected many aerobic PAHdegradation genes from a variety of microorganisms(Figure 4 and Supplementary Figure S3). PAHdegradation genes were more abundant in the plumesamples, while some were unique to the plumesamples. Although oxygen was still present in theplume samples (Camilli et al., 2010; Hazen et al.,2010), the gene bbs (beta-oxidation of benzylsucci-nate) for anaerobic toluene degradation was alsoenriched in plume samples. These bbs genes werederived from putative E-phenylitaconyl-CoAhydratase of Azoarcus sp. EbN1 and Thaueraaromatic, and benzylsuccinyl-CoA dehydrogenaseof Azoarcus sp. EbN1 (Figure 5).

Shifts of the genes involved in key biogeochemicalcycling processesCarbon. Among the carbon cycling genes detected,798 genes involved in the degradation of complex

carbon compounds, such as starch, hemicellulose,cellulose, chitin, lignin and aromatics, showedpositive hybridization signals. Most of these genes(for example, pulA, xylA, xynA, lip, limEH andvanA) showed significantly (Po0.05) higherabundance in plume than in non-plume samples(Supplementary Figure S4). These types of genescould also be important in degradation of various oilcomponents and their intermediates.

In this study, 9–14 mcrA genes encoding thea subunit of methyl coenzyme M reductase and 5–8

Figure 4 The normalized signal intensity of the nahA genes (naphthalene 1,2-dioxygenase) for the initial oxidation of naphthalene. Thesignal intensity for each sequence was the average of the total signal intensity from all the replicates. Gene number is the protein IDnumber for each gene as listed in the GenBank database. All data are presented as mean±s.e. ***Po0.01, **Po0.05, *Po0.1.

Figure 5 The normalized signal intensity of bbs (b-oxidation ofbenzylsuccinate) genes for anaerobic toluene degradation. Thesignal intensity for each sequence was the average of the totalsignal intensity from all the replicates. Gene number is the proteinID number for each gene as listed in the GenBank database. Alldata are presented as mean±s.e. ***Po0.01, **Po0.05, *Po0.1.In total, seven probes were designed for bbs genes in GeoChip 4.0and three probes were detected in the samples.


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pmoA genes for methane monooxygenase weredetected in the plume samples. Specifically, mcrAgenes from Methanococcus aeolicus Nankai-3,Methanoculleus marisnigri JR1 and Methanocorpus-culum labreanum Z were detected in all of the oilplume samples, but most of them were fromuncultured microorganisms. Significantly (Po0.05)higher signal intensities were observed for mcrA inthe plume than in the non-plume samples (Supple-mentary Figure S5). However, no significant differ-ences were found for pmoA and mmoX (particulatemethane monooxygenase) between plume and non-plume samples.

Nitrogen. Petroleum generally contains about0.1–2% nitrogen, and given the large quantities ofoil involved, it may act as an N pool in thisecosystem. Interestingly, nasA (nitrate reductase)and nir (nitrite reductase) for assimilatory N reduc-tion, and gdh (glutamate dehydrogenase) for ammo-nia assimilation exhibited significantly (Po0.05 or0.01) higher signal intensities in plume samples(Figure 6). The observed stimulation of N assimila-tion processes could be due to an increase ofmicrobial biomass (Hazen et al., 2010). However,no significant differences were observed for otherN-cycling genes, for example, nitrification, denitri-fication and N fixation (Figure 6).

Sulfur. Sulfite reduction genes were highly abun-dant in the deep-sea plume: 81–102 dsrA/B genes fordissimilatory sulfite reductase, and 8–12 AprAgenes for dissimilatory adenosine-50-phosphosulfatereductase were detected with significantly (Po0.05)higher abundance in the plume than in non-plumesamples (Supplementary Figure S6). Microbialpopulations similar to Alkalilimnicola ehrlicheiMLHE-1, Chlorobium ferrooxidans DSM 13031,Clostridium leptum DSM 753, Desulfomicrobiumthermophilum, Pyrobaculum calidifontis JCM11548, Thermodesulforhabdus norvegica, Magneto-coccus sp. MC-1, Pyrobaculum aerophilum str. IM2,Alkalilimnicola ehrlichei MLHE-1, Desulfohalobiumretbaense DSM 5692, sulfate-reducing bacteriumQLNR1 and Syntrophobacter fumaroxidans MPOBwere frequently detected in each sample, while mostof the genes detected were from uncultured micro-organisms (for example, sulfate-reducing bacteria)from various environments. The results suggest thatsulfate reduction could be enhanced when coupledwith HC degradation.

Phosphorus and iron reduction. As phosphorus isoften a limiting factor for oil bioremediation, it isessential to understand phosphorus cycling inmarine ecosystems. Genes encoding exopolyphos-phatase (ppx) for inorganic polyphosphate degrada-tion and phytase for phytate degradation weredetected with significantly (Po0.01 and Po0.05,respectively) increased abundance in plume sam-ples (Supplementary Figure S7). These results

suggested that organic phosphorus release could bestimulated by oil contamination. In addition, higher(Po0.1) signal intensities for 61 detected cyto-chrome c genes were observed in plume samples(Supplementary Figure S8), suggesting that HCdegradation coupled with metal reduction couldoccur in the deep water.

Metal resistance. A substantial number (917) of thegenes involved in resistance to various metals weredetected, many of which showed significantly(Po0.05) increased abundance in plume samples(Supplementary Figure S9). Genes encoding reduc-tases for As (arsC) and Hg (mer), efflux transportersfor Cd (cadA), Cu, Co and Zn (czcA and czcD), Cr(ChrA), Cu (copA), Hg (merT), Ag (silC) and Zn(zntA), and the proteins involved in Te resistance

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Figure 6 The relative changes of the detected genes involved inthe N cycle in oil plume. The signal intensity for each genedetected was normalized by all detected gene sequences using themean. The percentage of a functional gene in a bracket was thesum of signal intensity of all detected sequences of this genedivided by the grand sum of signal intensity of the detected Ncycle genes, and weighted by the fold change of the signalintensity of this gene in plume to that in non-plume. For eachfunctional gene, red indicates that this gene had a higher signalintensity in plume than in non-plume and their significance wasindicated with two stars (**) at Po0.01, whereas blue indicatesthat this gene had a lower signal intensity in oil-plume than innon-plume. Grey-colored genes were not targeted by thisGeoChip, or not detected in those samples. It remains unknownif nosZ homologs exist in nitrifiers. Description of the genes:(a) gdh, encoding glutamate dehydrogenase, ureC, encodingurease responsible for ammonification; (b) nasA, encoding nitratereductase, NiR, encoding nitrite reductase, responsible forassimilatory N reduction; (c) nifH, encoding nitrogenase respon-sible for N2 fixation; (d) narG encoding nitrate reductase, nirS andnirK-D (with denitrification activity), encoding nitrite reductase;nosZ, encoding nitrous oxide reductase, norB, encoding nitricoxide reducatse, responsible for denitrification (e) napA, encod-ing periplasmic nitrate reductase, nrfA, encoding c-type cyto-chrome nitrite reducatse, responsible for dissimilatory Nreduction to ammonium; (f) hao, encoding hydroxylamineoxidoreductase, and nirK-N encoding nitrite reductase fornitrifiers (an indication of nitrification activity), responsible fornitrification.


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(terC, terD and terZ) were more (Po0.05 or 0.01)abundant in the plume samples.

Bacteriophages were also significantly stimulatedIn total, 52 bacterial phage genes associated withhost recognition, lysis, replication and structurewere observed in all samples. The signal intensitiesfor many of the genes involved in replication weresignificantly (Po0.05) higher in the plume than inthe non-plume samples (Figure 7), supporting thesuggestion by Head et al. (2006) that bacteriophagescould be an important factor for intrinsic bioreme-diation of HCs.

Discussion

The Deepwater Horizon oil spill in the Gulf ofMexico was one of the worst environmental disas-ters in the United State history. The impact of an oilspill of such an unprecedented magnitude anddepth on marine ecosystems is largely unknown.Using the GeoChip-based high-throughput micro-array technology, we showed that diverse microbialfunctional groups (a group of genes involved incertain functional processes), including thoseimportant to HC degradation, carbon metabolism,methanogenesis, nitrogen assimilation, sulfate re-duction, phosphorus release, metal resistance andbacteriophage replication, were more highly repre-sented in the oil plume samples than in non-plumesamples from the same depth. Also, the changesin community functional structure were highlycorrelated to the changes in geochemistry, withoil being the predominant factor shaping the func-tional composition and structure of the microbial

communities. Our results support the phylogeny-based study by Hazen et al. (2010) that the deep-sea marine microbial communities underwent adynamic change in response to the oil spill andassociated geochemical changes. Our results are alsoconsistent with previous studies of oil spill andpetroleum contamination (Harayama et al., 2004;Head et al., 2006; Bordenave et al., 2007), whichshowed that microorganisms able to utilize HCsbecame dominant in oil-contaminated sites. Suchfunctional gene information is useful for assessingthe impacts of oil spills and should facilitate thedesign of appropriate strategies and approaches todeal with petroleum contamination.

The clean-up of the deep-sea oil plume willprimarily depend on the indigenous microbespresent in this environment, as current technologydoes not allow removing the dispersed oil and gas atsuch great depths. One of the critical environmentalquestions is whether microorganisms for degradingvarious HCs exist in the community and whetherthey respond to oil spill. Our GeoChip resultsindicated that many functional genes/populationsinvolved in both aerobic and anaerobic degradationof various oil components are detected and/orenriched in the oil plume, indicating that theindigenous HC-degrading populations are capableof responding to the oil spill. For example, alkB foralkanes, Xamo for alkene, genes bco, ohbAB,GCoADH and pimF for benzoate, genes mdlA, mdlBand mdlC for mandelate, and genes Apc and catB forBTEX metabolic pathway exhibited a significantly(Po0.05) higher abundance in the oil plume than inthe non-oil plume. The changes in relative abun-dance of these genes/populations were significantlycorrelated with the concentrations of various oilcontaminants in the samples (Hazen et al., 2010).

Figure 7 The normalized signal intensity of the replication genes for bacteriophage. The signal intensity for each sequence was theaverage of the total signal intensity from all the replicates. Gene number is the protein ID number for each gene as listed in the GenBankdatabase. All data are presented as mean±s.e. ***Po0.01, **Po0.05, *Po0.1.


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Especially, several genes for PAH degradation wereenriched in the oil plume samples, which could beimportant in determining the long-term effects of theoil spill on the marine ecosystems. Also, consistentwith phylogenetic gene distribution obtained usinga phylogenetic microarray ‘PhyloChip’ (Hazen et al.,2010), functional genes representative of the orderOceanospirillales appeared to have significantlyhigher (Po0.01) abundance in the plume samplesthan in non-plume samples, although the domi-nance of the Oceanospirillum population consum-ing the oil in the plume was based on clone libraryand sequence analysis of 16S rRNA genes (Hazenet al., 2010). GeoChip was not originally designed tolink the detection of functional genes to theexistence of related microbial population and itcontains 567 functional genes derived from theorder Oceanospirillales, with 25 genes detected inthis study. In addition, a large number of metalresistance genes were enriched in plume samples,which are usually linked to organic degradationgenes, for example, on plasmids (Parales andHaddock, 2004; Kunapuli et al., 2007). Our GeoChipresults demonstrated that there is a great potentialfor intrinsic bioremediation of oil contamination inthe deep-sea environment.

Anaerobic HC degradation associated with sulfatereduction, denitrification and methanogenesis haslong been considered the prevailing mechanism forpetroleum biodegradation in the deep subsurface(Head et al., 2003; Aitken et al., 2004; Kniemeyeret al., 2007; Jones et al., 2008). Recent investigationshave demonstrated that several classes of petroleumHCs, including alkanes (So et al., 2003), mono- andpolycyclic aromatic compounds (Meckenstock et al.,2000; Widdel and Rabus, 2001), and short-chain HCs(Kniemeyer et al., 2007), can be degraded anaerobi-cally under nitrate-, iron- or sulfate-reducing condi-tions, or under methanogenic conditions (Harayamaet al., 2004; Jones et al., 2008). Indeed, a substantialnumber of dsrA/B genes for sulfate reduction, mcrAgenes for methanogenesis, narG, nirS, nirK and nosZresponsible for denitrification and populations formetal reduction were detected in this study. Also,dsrA/B and mcrA genes showed significantly(Po0.05 or 0.01) higher abundance in the plumethan in the non-plume samples. In addition, bbsgenes for the strict anaerobic toluene degradationwere detected and enriched in the plume samples. Itis possible that anaerobic HC degradation couldhave most likely occured through microaggregateformation as reported in Hazen et al. (2010).

Hydrocarbon degradation is generally limited bynutrient availability, which can be improved bynutrient recycling through phage-mediated biomassturnover (Jiang et al., 1998; Head et al., 2006; Paul,2008). As significant biomass increase was observed(Hazen et al., 2010) in the plume samples, bacter-iophages could have critical roles in HC degrada-tion. Approximately 43% of marine bacterialisolates have been found to contain prophages (Jiang

et al., 1998; Paul, 2008), which are induced byvarious environmental contaminants, such as fueloil (Cochran et al., 1998). The oil spill may stimulatethe growth of pathogenic bacteria in marine envi-ronments and many pathogens are capable ofefficiently degrading HCs (Rojo and Martı́nez,2010). The research on phages has been heavilyslanted to those that affect human-related activities,health/medical and industry. As no target genes forOceanospirillum phages were designed on GeoChip4.0, the Oceanospirillum phages were not detected.Genes for both iron uptake (iro) and adherence (papand pilin) were significantly (Po0.01 or 0.05)enriched in the plume samples. The increase inthe abundance of microorganisms capable of produ-cing siderophores, highly specific iron-chelatingcompounds, may facilitate microbial acquisition ofiron, a limiting nutrient in marine systems (Barbeauet al., 2001a, b), thereby potentially increasing HCdegradation.

A substantial quantity of methane gas wasreleased together with the oil (The Federal Inter-agency Solutions Group, Oil Budget CalculatorScience and Engineering Team, November 2010;Kessler et al., 2011), which may result in moremethane in the oil plume ecosystem and have thepotential to greatly impact methane metabolism.GeoChip targets three key genes/enzymes involvedin methane metabolism, with mcrA encodingmethyl coenzyme M reductase for methanogenesisand two enzymes/genes (methane monooxygenase/mmoX and particulate methane monooxygenase/pmoA) for methanotrophy (He et al., 2010). In thisstudy, pmoA and mmoX genes for aerobic methaneoxidation did not show a statistically significantchange though their abundance was higher in plumesamples than in non-plume samples. There are twopossible explanations for this: one is that the aerobicmethane oxidation was inhibited owing to thepresence of easier to degrade alkanes in the deepsea, and the other is that the methane gas was movedup to the surface more directly and did notaccumulate in the deep oil plume. Also, unlikepropane, methane may form gas hydrates at the deepplume temperature and pressure, making it unavail-able to microorganisms (Valentine et al., 2010).However, significantly (Po0.05) higher signal in-tensities were observed for mcrA in the plume thannon-plume samples, indicating that those enrichedmcrA genes derived from methanogens likely link toHC degradation rather than plume methane release(Harayama et al., 2004; Jones et al., 2008). Enzymesor genes involved in anaerobic methane oxidation,however, remain unclear; thus, we could not detectthis functional process.

In this study, many functional genes weredetected in the uncontaminated samples that werenot detected in the contaminated samples (Supple-mentary Table S3 and Supplementary Figure S10).These results suggest that oil spills can selectagainst those populations containing these genes,


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or that specific members of the community have aselective advantage if they are capable of HCdegradation and these grow to represent a greaterproportion of the functional gene repertoire.

In conclusion, our results indicate that a variety ofHC-degrading functional genes were enriched inresponse to oil contamination and associated envir-onmental changes. Our results also imply that thereis a great potential for in situ bioremediation of oilcontaminants in the deep-seawater ecosystem, andsuch oil-degrading populations and associatedmicrobial communities may have a significant rolein determining the ultimate fates and consequencesof the spilled oil. However, to further understandand evaluate the potential impacts of this unprece-dented oil spill on the marine ecosystem structureand function, it is essential to launch an integratedand comprehensive monitoring program to track thedynamics and adaptive responses of microbialcommunities together with other physical andchemical analysis of tracing oil contaminants andtheir products.

Acknowledgements

We thank the Captain, crew and science teams aboard theR/V Ocean Veritas and R/V Brooks McCall. This work waspart of ENIGMA, a Scientific Focus Area Programsupported by the US Department of Energy, Office ofScience, Office of Biological and Environmental Research,Genomics: GTL Foundational Science through ContractDE-AC02-05CH11231 between Lawrence BerkeleyNational Laboratory and the US Department of Energy.This study was also supported by University of OklahomaResearch Foundation, the National Key Science andTechnology Project of China: Water Pollution Controland Treatment (No. 2008ZX07101-006), the NaturalScience Foundation of Zhejiang Province (No. R5080124)and the National Key Technologies Research and Develop-ment Program of China during the 11th Five-Year PlanPeriod (No. 2006BAJ08B01).

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http://www.restorethegulf.gov/sites/default/files/documents/pdf/OilBudgetCalc_Full_HQ-Print_111110.pdf



http://www.nature.com/ismej

microbial gene functions enriched in the deepwater horizon deep-sea oil plume

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