An exploration of microbial and associated functional diversityin the OMZ and non-OMZ areas in the Bay of Bengal

SHRIRAM N RAJPATHAK1, ROUMIK BANERJEE

1, PAWAN G MISHRA1, ASMITA M KHEDKAR

1,YUGANDHARA M PATIL1, SURAJ R JOSHI1 and DEEPTI D DEOBAGKAR

1,2*1Molecular Biology Research Laboratory, Centre for Advanced Studies, Department of Zoology, Savitribai

Phule Pune University, Pune 411 007, India2ISRO Cell, Savitribai Phule Pune University, Pune 411 007, India

*Corresponding author (Email, deepti.deobagkar@gmail.com)

MS received 7 August 2017; accepted 14 May 2018; published online 28 July 2018

Depletion of oxygen in certain marine areas creates oxygen minimum zones (OMZs), which can alter the species com-position and abundance. We have carried out high-throughput 16S rRNA gene amplicon profiling from the Bay of Bengal(BOB) OMZ and non-OMZ areas. Typically, a total of 35 families of micro-organisms were identified as biomarkers forOMZ and non-OMZ regions in the BOB. Our analysis has identified families Pseudoalteromonadaceae, OM60 andSynechococcaceae to be abundant in oxygenated water, whereas organisms belonging to families Pelagibacteraceae andCaulobacteraceae, which are involved in sulphur and nitrogen metabolism, were prominent in the OMZ areas. Predictivefunctional analysis for these identified bacteria clearly that suggested an abundance of microbes with assimilatory sulphur-reducing genes (cysl and csH) in the non-OMZ, while bacteria involved in dissimilatory sulphate reduction (known to carryaprA and aprB genes) were enriched in the OMZ areas. Comparative analysis with OMZ areas from Peru and Chilerevealed that OMZ areas in the BOB are characterized by specific and distinctive bacterial diversity. Overall, the currentanalysis provides valuable documentation about the bacterial populations and their characteristics, which can generatepointers for their functional significance in the BOB.

Keywords. Bacterial and functional diversity; BOB; OMZ and non-OMZ water; 16S rRNA gene amplicon analysis;sulphur and nitrogen metabolism

1. Introduction

Microbes are most widely distributed in diverse environ-mental conditions (Whitman et al. 1998) and play a vital rolein the maintenance of different ecosystems, including marineenvironments. Various new technologies have unravelled therole of marine microbes in the maintenance of earth’s bio-geochemical cycle by cycling of carbon, nitrogen, iron andmany other trace elements (Wommack and Ravel 2013). Forinstance, bacteria with zooplankton can remineralizeapproximately 90% of the settling particles in the sea waterproviding nutrients and other resources for marine ecology(Rixen et al. 2009). Marine microbes can carry out chemicaltransformations and impact the flow of energy in water aswell as sediment environment (Arrigo 2005). Additionally,various marine bacteria can act as indicators to accesseffluent discharges (Ramaiah et al. 2007; Selvin et al. 2009),find application in bioremediation of heavy metals (Dash

and Das 2016) and are useful resources for the isolation ofvarious types of enzymes for industrial applications (Ma-halakshmi and Jayalakshmi 2016).

Along with other nutrients, oxygen is a key player inshaping the marine biodiversity from higher organisms tomicrobes. In the marine environment, chemical parameterslike salinity, nutrient concentrations and various physicalparameters such as temperature, light and pressure vary in alocation-specific manner and this is known to influence theconcentration of dissolved oxygen. Human interference inoceans has greatly transformed the marine environmentaffecting several physical and chemical parameters, includ-ing dissolved oxygen (Falkowski et al. 2008). Oxygen-starved regions of the ocean, known as oxygen minimumzones (OMZs), are unique habitats where dissolved oxygenconcentrations at intermediate depths are as low as 0.5 mLl-1 (Kamykowski and Zentara 1990; Stramma et al. 2008) orless than B20 lM (Lam and Kuypers 2011). The abundance

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J Biosci Vol. 43, No. 4, September 2018, pp. 635–648 � Indian Academy of SciencesDOI: 10.1007/s12038-018-9781-2

of aerobically respiring organisms significantly decreases inthe OMZ areas thereby changing the species compositionand food web structure (Rabalais et al. 2010). Micro-or-ganisms from the OMZ areas thus play important roles in thecycling of nutrients such as nitrogen and sulphur and tracegases and this has been suggested to not only influence theocean but also overall earth’s climate (Wright et al. 2012).An assessment of bacterial diversity in the OMZ areas isimportant for the understanding of various aspects of marinebiology.

The Indian Oceans, particularly Arabian Sea (AS) and theBOB, cover 59% of the total global OMZ areas (Helly andLevin 2004).Various studies have been carried out in theArabian Sea with respect to the pelagic or sediment regionsfor the identification of bacterial diversity (Ramaiah et al.1996; Divya et al. 2010, 2011; Anas et al. 2016) andfunctional gene diversity (Woebken et al. 2008; Ward et al.2009; Luke et al. 2016a). The BOB-OMZ is reported to beweaker than the AS-OMZ. The oxygen depletion in the BOBis lesser than the AS and has been shown to be above thedenitrification threshold (Naqvi et al. 2006). Many reasonssuch as short time for oxygen-consuming remineralization,lack of detritus transport (Prasanna Kumar et al. 2004) andreceipt of immense fresh water runoff (Milliman and Meade1983; Milliman and Syvitski 1992) from major rivers havebeen suggested to explain this phenomenon. In addition dueto water evaporation, seasonal advection and mixing ofwaters in BOB, low surface salinity and high surface strat-ification is observed (Prasanna Kumar et al. 2002).

Despite the wide expanse of BOB, very few reports areavailable, which explore the bacterial diversity from variousBOB areas. These reports provide bacterial identificationwhere 16S rRNA-based techniques including pyrosequenc-ing for local areas (Dash and Das 2016; Pramanik et al.2016; Walsh et al. 2016; Wang et al. 2016) and isolation ofindividual bacterial colonies (De et al. 2006; Ramesh et al.2006; Das et al. 2007; Nithya and Pandian 2010; Ruban andGunaseelan 2011; Santhiya et al. 2011) have been used.These studies report bacterial diversity from sediment sam-ples (Das et al. 2007; Nithya and Pandian 2010; Ruban andGunaseelan 2011; Walsh et al. 2016), water samples (Deet al. 2006; Wang et al. 2016), both water and sedimentsamples (Ramesh et al. 2006; Santhiya et al. 2011; Dash andDas 2016) and mangroves (Ghosh et al. 2010; Das et al.2012a, 2012b) in the BOB.

In the current analysis, water samples collected fromdifferent depths from OMZ as well as from non-OMZ areasof BOB are analysed for determination of microbial diver-sity. We have investigated the bacterial community structureby employing next-generation sequencing (NGS)-basedplatform (Illumina HiSeq2500) and in silico approacheswere used for assigning functional significance for the bac-terial taxa identified in this study. The comparative analysisof samples of BOB from OMZ and non-OMZ regions

highlights the influence of various physiochemical parame-ters on bacterial diversity. Similarly, in silico functionalanalysis clearly indicated the differences in the metaboliccapabilities of these organisms thus pointing towards theiradaptability to different environmental conditions. Ouranalysis thus provides a comparative assessment of bacterialas well as functional diversity in BOB-OMZ and non-OMZareas. The rich microbial as well as functional diversityhighlights the significance of these organisms in the IndianOcean.

2. Material and methods

2.1 Sample collection

Water samples were collected during a ‘Sagar Sampada’(Cruise-346) cruise in the BOB from 26th November to 27thDecember 2015. Water samples from the BOB (figure 1A,supplementary table 1) were collected from various loca-tions and depths (100–900 m depth). A conductivity–tem-perature–depth (CTD) system was deployed to recordvarious physical properties of the water column includingdepth, oxygen, turbidity, salinity, density and pressure.Samples (17) were collected from different locations and theoxygen concentration from the CTD data was used to clas-sify samples which include 13 OMZs (samples S1–S12 andS18) and four non-OMZs (S15–S19) samples.

2.2 Sample preparation and DNA extraction

Two litres of water from the BOB was filtered through 0.22lm filter (47 mm in diameter, Whatman, England) and thefilter papers were immediately stored in sterile tubes at -20�C, which were then utilized for DNA isolation usingPower water DNA isolation kit (Catalog ID: 14900-50-NF,MoBio Laboratories Inc., Carlsbad, CA) as per manufac-turer’s instructions. The quality of the DNAwas assessed bygel electrophoresis and the quantity was measured using theNano Drop 2000c spectrophotometer (Thermo Scientific,USA).

2.3 16S rRNA gene amplicon sequencingand bioinformatics analysis

V3 and V4 regions of 16S rRNA genes were amplified withPhusion� High-Fidelity PCR Master Mix (New EnglandBiolabs). Purified PCR products were used to preparesequencing libraries using TruSeq� DNA PCR-Free SamplePreparation Kit (Illumina, USA) following manufacturer’srecommendations and index codes were added to the sam-ples. The library was sequenced on an Illumina HiSeq2500

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platform and 250 bp paired-end reads were generated. Forperforming the microbiome analysis from raw sequencingdata and its representation, an open source bioinformaticspipeline, QIIME was used. These paired-end raw reads (RawPE) were then assembled and quality filtering was performedaccording to the QIIME (V1.7.0) (http://qiime.org/1.7.0/)with specific conditions to obtain Clean Tags. The chimericsequences on Clean Tags were (https://www.drive5.com/usearch/manual/uchime_algo.html) removed to obtain theEffective Tags. The raw sequence data files have been sub-mitted to sequence read archive (SRA) database (accessionnumber SRP096171).

NGS reads of the marker genes (16S rRNA) can be clus-tered into different operational taxonomic units (OTUs) usingUPARSE algorithm. Here in this work, sequences wereanalysed by UPARSE software (UPARSE v7.0.1001, https://www.drive5.com/uparse/) and those with C97% similaritywere assigned to the same OTUs. Representative sequencesfor each OTU were screened for further annotation. For eachrepresentative sequence, the GreenGene Database was usedbased on RDP classifier (Version 2.2 http://rdp.cme.msu.edu/)algorithm to annotate taxonomic information. The abundanceof OTUs information was normalized using a standard of

sequence number corresponding to the sample with the leastsequences. Subsequent analysis including alpha diversity andbeta diversity was carried out using these normalizedsequences. For the statistical analysis of scientific data usingthe functions such as univariate and multivariate statistics,spatial analysis, ecological analysis and so forth, PAST soft-ware is used. The 16S NGS data was subjected to principalcoordinate analysis (PCoA) and canonical correspondenceanalysis (CCA) using the PAST 3 software (Hammer et al.2001). The PCoA with respect to depth and oxygen concen-tration was also carried out using PAST 3 software (Hammeret al. 2001). Further, statistical hypothesis tests and explora-tory plots of taxonomic and functional profiles were analysedby the graphical STAMP software package (statistical analysisof taxonomic and functional profiles). The comparison ofrelative abundance of bacterial genera and families betweenOMZ and non-OMZ sample groups was conducted using theSTAMP software (Parks et al. 2014), analysis of variance(ANOVA), Tukey–Kramer post-hoc correction test and aq value cut off of 0.05.

2.3.1 Detection of bacterial markers: The linear discrimi-nate analysis (LDA) effect size (LEfSe) method is used to

Figure 1. (A) Details of the collection sites in the BOB, red circle – OMZ areas and green circle – non-OMZ area samples. (B)–(G) Details of various physical and chemical parameters at each collection site.

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characterize the differences and identifying the unique bac-teria between two or more microbial communities (Segataet al. 2011). The output files generated using QIIME analysiswas used for the identification of biomarker bacterial fami-lies in BOB-OMZ and non-OMZ areas. Annotated familiesfrom OMZ and non-OMZ samples were used and the LEfSeanalysis with LDA score threshold of 2 was carried out usingonline Galaxy version 1.0. GraPhlAn is a software tool forproducing high-quality circular representations of taxonomicand phylogenetic trees. Bacterial families identified byLEfSe analysis were used for generation of a circularcladogram representing unique microbial taxonomic com-positions by using a standalone graphical tool GraPhlAnv0.9.5 (http://huttenhower.sph.harvard.edu/GraPhlAn).

2.4 Functional analysis of uncultured bacterialmetagenome

PICRUSt is a bioinformatics software package designed topredict the functional profiles for the identified metagenome orbacterial community from marker genes (e.g., 16S rRNA) andfull genomes analysis. The 16S rRNA sequencing data setsanalysed by QIIME, were also analysed by the PICRUSt gen-ome prediction software. PICRUSt script normalize_by_-copy_number.py (Langille et al. 2013), was used for copynumber normalization of 16S rRNA data, which encompassesthe IMG database (Markowitz et al. 2012). GreenGenes ver.13.5 database was used for functional prediction. Functionalpredictions were assigned up to KEGG orthology tier 3 for allgenes. To simplify the analysis functions of ‘metabolism’,‘genetic information processing’, ‘environmental informationprocessing’ and ‘cellular processes’were analysed further sincethe categories of ‘organismal systems’ and ‘human disease’ arepoorly relevant to environmental samples.

Prediction of KEGG orthology was carried out using pre-dict_metagenomes.py followed by KEGG pathway predic-tion by categorize_by_functions.py scripts from PICRUStsoftware package (Kanehisa and Goto 2000; Kanehisa et al.2014). KEGG orthology (KO) results were used to differen-tiate the functional features of communities using statisticalanalysis of metagenomic profiles (STAMP) analysis (Parkset al. 2014) (http://kiwi.cs.dal.ca/Software/STAMP).PICRUSt results were analysed with Welche’s t-test and P-value cut off of 0.05 was considered to reject null hypothesisof no difference between samples using STAMP analysis.

2.5 Comparative analysis of Indian and other (Peruand Chile) OMZ bacterial diversity

The annotated bacterial family data including sample name,family, abundance that were available for the OMZ regionsof Chile and Peru was downloaded in text file from the MG-

RAST server (http://www.mcs.anl.gov/project/mg-rast-metagenomics-rast-server). Using Galaxy join, merge andcompare tool, these data sets were compared in order toobtain the differential and common occurrences. The anno-tated families from all three OMZ areas were comparedusing STAMP by t-test with Benjamini–Hochberg multiplecorrection and q value cut off of 0.05. Chile data set mgm idsare 4443316.3, 4442219.3, 4443317.3, 4442145.3,4443777.3, 4443776.3 and Peru data set mgm ids are4460736.3, 4461588.3, 4460677.3, 44606776.3, 4450891.3and 4450892.3.

2.6 Results and discussion

Water samples collected from various locations and depth(figure 1A, supplementary table 1) displayed differentphysiochemical parameters and oxygen concentration, tem-perature, salinity, pressure, density and turbidity and so forthwhich were observed to vary with the collection depth(figure 1B). The oxygen concentration showed a decreasewith depth and at intermediate depths it was the lowest(\0.15 mL/L). Temperature showed an inverse correlationwith depth. However, salinity, density, turbidity and pressureincreased with the depth. The bacterial diversity of thesesamples was assessed by 16rRNA amplicon-based high-throughput sequencing using Illumina Hiseq2500 platform.

2.6.1 Data profile for the 16S rRNA gene ampliconsequencing: The processing of raw reads including theremoval of bad reads and chimeras generated effective tags,which were then used for subsequent analysis. In total,11,62,796 (61,200±6500 per sample) bacterial 16S V3-V4region of high-quality sequences (97% with Q30) with anaverage read length of 412 bps, were obtained (supple-mentary table 1). From the effective tags obtained for OMZsamples more than 95% of tags were classified and in non-OMZs except for S19 (which was 88%), for the remainingthree samples (S15–S17) 98% tags could be classified. Theseclassified tags were then assigned to OTUs in bacterial andarchaeal domains. For majority of samples, more than 70%reads were assigned to bacterial domain while 30% of tagswere assigned to the archaeal domain, indicating that theseregions of BOB harbour a rich and diverse abundance ofmicrobial community structure (supplementary table 2).

2.6.2 Bacterial composition in different samples: To analysethe species diversity within samples, all effective tags wereclustered at 97% similarity. A total of 4206 OTUs wereidentified with varied abundance in different samples (sup-plementary table 3). PCoA with PAST3 indicated that,samples collected at similar depths and oxygen concentra-tions were observed to cluster together emphasizing the

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effect of depth and oxygen concentration on bacterial com-position (figure 2A).

These OTUs were further annotated to different bacterialfamilies, genera and species which were then used for bac-terial diversity assessment. To assess the correlation betweenenvironmental factors and the bacterial diversity CCAanalysis with identified bacterial families was carried out inPAST3 software. In CCA analysis, axis 1 accounted for70.69% of the explained variance and axis 2 for 21.95%.Oxygen concentration and temperature showed a significant(Monte Carlo permutation tests, P=0.001) positive correla-tion (0.7 and 0.89, respectively). This shows that at higheroxygen levels and temperature, bacteria can thrive easily andthus such regions are likely to possess higher bacterialdiversity. While salinity and density showed significant(p=0.001) negative correlation (-0.8 and -0.9, respec-tively) thereby indicating that the salt concentration andwater density adversely influenced the bacterial composition.Sampling depth, turbidity and pressure was not observed tohave a significant contribution (figure 2B). Overall, the CCAanalysis of samples from BOB analysed in our study clearlyindicates that the bacterial diversity is influenced by thecombined effect of physical and chemical parameters.

2.6.3 Annotations of bacteria from OMZ and non-OMZregions: A total of 255 families of microbes (supplementarytable 3) could be identified from the BOB. The top 10families include SUP50 (2–41%), Alteromonadaceae(4–44%), Cenarchaeaceae (6–53%), Moraxellaceae(0.3–12%), SAR324 (2–18%), Halomonadaceae (1–7%),Exiguobacteraceae (0.01–23%), Vibrionaceae (0.1–21%),Pseudoalteromonadaceae (6–1%) and Marine group II(0.5–9%). Various families such as Mycoplasmataceae,Saccharospirillaceae, Thermogemmatisporaceae andFrankiaceae were exclusively present in the samples fromOMZ, while VC21_Bac22 family was present in the non-OMZ samples only. LEfSe analysis identified a total of 35families as biomarkers for these OMZ and non-OMZ groups(figure 3). A total of eight families including Pseudoal-teromonadaceae, OM60, Christensenellaceae, Synechococ-caceae and Veillonellaceae were identified as biomarkers forthe non-OMZ areas. Interestingly, the metagenomic analysisof BOB that we have carried out has led to the identificationof predominant microbial families including the markerorganisms for the OMZ and the non-OMZ regions. FamiliesPseudoalteromonadaceae, OM60 and Synechococcaceae arereported to be widely distributed in marine ecosystem.Groups of bacteria from OM60 have been observed ineuphotic zone and have a close relationship between aerobicanoxygenic photoheterotrophs (Spring et al. 2013). FamilySynechococcaceae represents cyanobacteria with photosyn-thetic and nitrogen-fixing abilities (Komarek et al. 2014),and are also known to produce extracellular cyanotoxins(Chorus and Bartram 1999).

SUP05, Pelagibacteraceae, Oceanospirillaceae, Colwelli-aceae, Haliangiaceae, SAR324, Hyphomonadaceae andCaulobacteraceae were among the other 27 biomarker fam-ilies from the OMZ areas in BOB identified in our analysis(figure 3B). In earlier studies, SUP05 family bacteria, whichare sulphur oxidizing, have been reported to be abundant inthe OMZ areas (Glaubitz et al. 2013). They are also knownto produce nitrite and to consume ammonium under anaer-obic conditions (Shah et al. 2016). A group of bacteria fromfamily Pelagibacteraceae has the capacity to live undernutrient-limited conditions (Ortmann and Santos 2016), andsome of them are known to feed on low abundance of dis-solved organic carbon and nitrogen (Morris et al. 2002).Bacteria belonging to Oceanospirillaceae family have aunique capability for degrading petroleum compounds andare known nitrogen fixers (Satomi and Fujii 2014). Furtherpossible association of SAR324 cluster bacteria in the sul-phur and nitrogen cycle has been proposed (Sheik et al.2014; Cao et al. 2016). In addition, a group of bacteria fromCaulobacteraceae are chemo-organotrophic, denitrifying andform biofilms (Abraham et al. 2014). LEFse analysis hasthus led to the identification of the unique bacterial familiesfrom OMZ areas of the BOB, which are associated withsulphur, nitrogen and petroleum metabolism, underling thespecific difference in the microbial community structurebetween oxygen-rich and -deficient localities.

A detailed analysis of the metagenomic data set hasidentified 293 bacterial genera including 80 different speciesrelated to these annotated families (supplementary table 4) inBOB. The most abundant bacterial genera include Al-teromonas (7–52%), Nitrosopumilus (7–88%), Psychrobac-ter (0.7–15%), Halomonas (1–9%), Photobacterium(0.1–26%), Pseudoalteromonas (2–6%), Synechococcus(0.1–2%), Pseudomonas (0.1–17%), Vibrio (0.1–7%) andNitrososphaera (0.01–17%) (figure 4A). A recent study onsediments from Odisha has reported the occurrence ofBacteroidetes, Firmicutes, Spirochaetes, Nitrospirae (Pra-manik et al. 2016) and mercury-resistant bacteria belongingto Bacillus, Exiguobacterium and Vibrio sp. (Dash and Das2016). Another study has also identified the presence ofvarious heterotrophic bacteria showing heavy metal toler-ance belonging to Halomonas, Marinobacter, Bacillus,Ruegeria and Ferrimonas genera from Palk Bay sediments(Nithya and Pandian 2010). In addition, bacteria belongingto various lineages, including OP9, OP8, Nitrospirae andPlanctomycetes were shown to be related to rate of degra-dation of organic compounds in the sediments from BOB(Walsh et al. 2016). Bacterial flora from the sediments of theKrishna Godavari basin from BOB include various generasuch as Legionella, Pseudomonas, Microbacterium, Kleb-siella, Yersinia, Corynebacterium, Morganella and Nesaria(Ruban and Gunaseelan 2011).

Further, our analysis showed that a total of 22 bacterialgenera involving Roseivivax, Robiginitalea, Reinekea,

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Nitrospira and Erysipelothrix were present in the OMZ areasexclusively while Nitrosopumilus, Alteromonas, Desulfo-coccus, Nitratireductor and Faecalibacterium were foundacross all the samples. Additionally, STAMP-based analysisfor OMZ and non-OMZ areas revealed that microbesbelonging to 40 genera are present differentially (q\0.05) inthese data sets (supplementary table 5). Microbial generaincluding SargSea-WGS, SGSH944, Erythrobacter, Nitro-spina and Hyphomonas were represented with a relativelyhigh abundance in OMZ areas. This strongly points to theeffect of oxygen depletion on the microbial diversity anddynamics. None of these bacterial genera were observed inthe non-OMZ areas. A recent study (Wang et al. 2016) hasreported the presence of bacterial lineages namely SAR46,Bacillariophyta and Thermodesulfobacteria from the watersof the eastern Indian Ocean. Interestingly, these phyla werenot observed in the current study of the metagenomes of theBOB. Similarly when species level analysis was carried out,out of 80 identified bacterial species, nine showed (q\0.05)

the differential presence in the OMZ and non-OMZ groups(supplementary table 5).

Overall, this comparative analysis highlights the vari-ability in bacterial composition in OMZ and non-OMZ watersamples. The observed differences in the microbial compo-sition could be influenced by the collection site, seasonalvariations and time of collection. In addition it has beensuggested that some of these microbes can get adverselyaffected by heavy metal concentration (De et al. 2006) ornatural disasters such as tsunami (Ramesh et al. 2006). It canbe speculated that due to the accumulation of pollutants (dueto industrialization and anthropomorphic activities) andocean dynamics, microbial community composition shows adynamic nature in the different locations of BOB.

2.6.4 Alpha and beta diversity indices: Alpha diversityanalysis of the data sets from the BOB was carried out,which has revealed the rich taxonomic diversity in thesesamples (table 1). Simpson index for all OMZ and non-

Figure 2. PCoA with respect to (A) oxygen concentration and (B) sample collection depth. (C) CCA identifying the effect of variousenvironmental factors including temperature, salinity and O2 concentration on distribution of bacterial diversity in BOB.

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OMZ samples was 0.9± 0.08 and did not differ significantlybetween OMZ and non-OMZ groups (Mann–WhitneyU test, P=0.065). However, Shannon’s index for OMZ was5.5±0.7 and for non-OMZ it was 4.2 ± 0.7, which wassignificantly different between OMZ and non-OMZ groups(Mann–Whitney U test, P=0.003) indicating a difference inabundance and spread in bacterial diversity in these seasamples. Beta diversity between pairs of samples wasassessed by plotting a square matrix of ‘distance’ or

dissimilarity reflecting the variation between some of thesamples. PCoA based on unweighted Unifrac distanceshowed that non-OMZ and OMZ samples clustered (fig-ure 4B) separately. Among the OMZ samples, some of thesamples such as S10, S11, S12 can be seen to group togetherwhile samples S6, S2, S7, S1 and S18 clustered at distinctlydifferent locations on the graph. This clearly suggests that adistinct variation between microbial communities isobserved within the OMZ samples.

Figure 3. Taxonomic cladogram showing distinctive families in OMZ and non-OMZ areas identified by LEfSe analysis. Enlarged circlesin colour are the differentially abundant taxa identified to be meta-genomic biomarkers and the circle colour is corresponding to OMZ ornon-OMZ areas (red – OMZ and green – non-OMZ).

Microbial and functional diversity of BOB 641

2.7 Predictive functional profiling of microbialcommunities

Having identified and catalogued the microbial communitystructure and divergence, bioinformatics and pathway anal-ysis can enable one to derive information about metaboliccapabilities of the microbial biomass. We have carried outbioinformatics analysis to unravel the possible metabolic

repertoire that these microbes harbour. Functional predictionby PICRUSt software of BOB genomic data sets identified12 KO terms associated with metabolism which includexenobiotics biodegradation, glycan biosynthesis, carbohy-drate and lipid metabolism clearly indicating that bacteriainhabiting the BOB have metabolic pathways associatedwith degradation of xenobiotics in addition to lipids andcarbohydrates. The per cent OTUs associated with theseterms are as follows: xenobiotics biodegradation (3–2%),glycan biosynthesis (1–2.8%), carbohydrate (10–12%) and(2–4%) for lipid metabolism. Three terms under environ-mental information processing contain membrane transport(8–13%), signal transduction (1–5–2%) and signallingmolecules and interaction (0.14–0.18%) (supplementarytable 6).

KEGG orthology terms predicated by PICRUSt wereanalysed by STAMP, which showed that 697 KO terms aredifferentially enriched in the OMZ and non-OMZ areas(figure 5A, supplementary table 6). This observation high-lights the distinct metabolic differences in organisms in theOMZ and the non-OMZ areas of BOB. Organisms thatharbour genes hdrA, fwdA, fwdE, mttC and cdhE, which areassociated with methanogenesis were observed to be sig-nificantly (P\0.01) enriched in OMZ in comparison to non-OMZ areas. Methanogens can obtain energy for growth byconverting substrates such as CO2, methanol and acetate tomethane under anaerobic conditions. Our analysis hasidentified micro-organisms that are capable of participatingin methanogenesis. In the AS, higher concentrations of

Table 1. Alpha diversity indices for BOB OMZ and non-OMZsamples

Group Sample name Shannon Simpson Chao1

OMZ S1 6.812 0.962 1917.337OMZ S2 4.844 0.866 1357.737OMZ S3 5.824 0.925 1535.07OMZ S4 5.975 0.941 1298.59OMZ S5 4.885 0.84 1144.481OMZ S6 6.761 0.96 1850.225OMZ S7 5.641 0.946 1222.957OMZ S8 5.422 0.911 1018.083OMZ S9 6.053 0.953 1135.616OMZ S10 5.554 0.94 1063.232OMZ S11 5.172 0.915 880.476OMZ S12 3.736 0.689 792.25OMZ S18 5.858 0.899 1848.186NOMZ S15 3.801 0.793 857.899NOMZ S16 3.393 0.682 937.81NOMZ S17 4.421 0.805 1138.29NOMZ S19 5.473 0.929 1083.627

Figure 4. (A) Relative abundance of top 10 bacterial genera in different OMZ and non-OMZ samples. (B) Beta diversity analysis byPCoA graph.

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methane have been reported at depths of 150–200 m(Jayakumar et al. 2001); however, not much is known aboutthe processes or the bacteria involved in OMZ methano-genesis. Recent reports have suggested that in the OMZareas, methanogenesis can coexist with sulphate reductionthrough the utilization of non-competitive substrates (Maltbyet al. 2016). Another study (Luke et al. 2016b) has reportedthat conclusive evidence for active methane cycling couldnot be obtained in OMZs of the AS. In this study, the insilico analysis has led to the identification of microbes inthese OMZ areas with significant enrichment of variousgenes involved in methanogenesis from CO2 or methy-lamine substrates. Methanogens play a vital ecological roleby removing excess hydrogen and fermentation products andgenerate methane. They are useful in organic waste treat-ments and are key agents of organic remineralization. Our

observation of occurrence of these methanogenic bacteriaassumes significance in this perspective. Further isolationand characterization of methanogenic bacteria and relatedfunctional genes by advanced genomic tools will beimportant in ecological prospective.

In addition, organisms specifically harbouring genesinvolved in sulphate reduction were present in BOB. In thebioinformatics analysis of our samples (BOB), assimilatorysulphate-reducing genes cysl and csH were enriched in non-OMZ areas (P\0.01) indicating the presence of microbeswith sulphate-reducing capabilities, while microbes pos-sessing genes aprA and aprB involved in dissimilatory sul-phate reduction were detected with high abundance in theOMZ areas. Earlier studies in other locations have uncov-ered the abundant and diverse sulphur-oxidizing microbialcommunity and genes (aprA and aprB) related to

Figure 5. (A) Heat map depicting distinctly (P\0.05) enriched KO terms associated with methanogenesis, sulphate reduction andnitrogen metabolism in OMZ and non-OMZ samples. (B) Differentially enriched KEGG pathways in OMZ and non-OMZ areas.

Microbial and functional diversity of BOB 643

dissimilatory sulphate reduction in OMZ areas. Some reportshave shown that sulphur cycle fuels nitrate reduction bysupplying additional substrates such as nitrite and ammonia(Ulloa et al. 2012). Microbes possessing genes involved innitrogen metabolism, namely, nifK, ntrY, ntrX, nrfA, haoand norC are enriched in the OMZ areas (P\0.05) in theanalysis of BOB samples (this study). Such areas with lessoxygen concentration are also known to harbour bacterialspecies involved in anaerobic N-cycling processes, whichare responsible for 20–40% of the total loss of oceanicnitrogen (Lam et al. 2009; Lam and Kuypers 2011). Thegenes involved in nitrogen fixation and related to nitrogencycling were reported to be abundant in the OMZ areas(Luke et al. 2016a). The observed enrichment of microbeswith nitrogen metabolism related genes in our analysis ofBOB emphasizes the presence of active nitrogen cycle inBOB-OMZ areas. Industrial and agricultural practices haveresulted in increased heavy metal concentration in Indianoceans (Kibria et al. 2016). Earlier reports have identifiedheavy metal-tolerant microbes in BOB (Chari et al.; De et al.2006; Nithya and Pandian 2010). This study has identifiedthe presence of microbial communities carrying two genes,namely, zraS and zraR involved in metal tolerance to beabundant (P\0.001) in OMZ areas. This can work as apointer to the possible accumulation of pollutants in BOB-OMZ areas. The metal degrading microbes/genes from theseextreme environments can be a valuable resource and sincethese organisms from extreme environment are well adaptedthey can find applications in bioremediation processes. Inaddition, we have also observed enrichment of microbescarrying anoxygenic photosystem II genes, pufM and pufL(P=0.0039) in the OMZ areas in this analysis. Obligateanaerobes including green sulphur bacteria, red and greenfilamentous phototrophs carry out anoxygenic photosynthe-sis where the by-product generated is elemental sulphurinstead of molecular oxygen (Johnston et al. 2009). Thepresence of bacteria possessing genes which support anaer-obic metabolism specifically in OMZ areas highlights thedifferences in BOB-OMZ and non-OMZ areas.

In addition, KEGG pathways predicted by PICRUSt wereanalysed by STAMP software to identify the differentmetabolic potential of the OMZ and non-OMZ areas. Ouranalysis identified 54 pathways which were significantly(P\0.05) different in OMZ and non-OMZ groups (supple-mentary table 6). This revealed and suggested that functionaldifferences exist in the metabolic capabilities of these richand diverse microbial communities. KEGG pathways anal-ysis revealed that specific pathways involved in nitrogenmetabolism and one carbon pool were enriched in OMZsamples. Carbohydrate metabolism, chloroalkane, chlor-oalkene, chlorocyclohexane and chlorobenzene degradationpathways were represented at relatively high frequency(figure 5B) in the non-OMZ samples. It is tempting tospeculate that this enrichment of aromatic and chlorinated

hydrocarbon degradation pathways in non-OMZ areas pointstowards the increased presence of such compounds in theseareas. The non-OMZ samples (except S19) were collected atlower depths (20–70 m) and the presence of micro-organ-isms having potential for bioremediation may serve asindicators of possible pollution when supported by corre-sponding chemical analysis in future. A previous study atOrange beach has reported that beach oiling led to enrich-ment of genes related to chlorinated hydrocarbon degrada-tion (Newton et al. 2013). The observation that in shallowwaters in BOB, organisms with hydrocarbon degradationpathways are abundant, raises very important concerns andcan be used to isolate genes involved in their bioremediationand metabolism.

2.8 Comparative analysis of Indian (BOB) OMZto other OMZ areas

Our analysis has generated baseline data for the biodiversityand microbial community structure in the BOB. This anal-ysis has also led to the identification of biomarkers andspecific differences in the metabolic capabilities of themicrobial communities. Previous data sets from Peru andChile related to OMZ areas which are available in the lit-erature were utilized for a comparative evaluation. The ini-tial comparative analysis led to the identification of 127common microbial families in Indian and Peru OMZs,whereas 139 bacterial families were common between Chileand Indian OMZs (supplementary table 7). The PCoAanalysis revealed that Chile OMZ has a similar microbialcomposition to Indian OMZ but samples from Peru clusterseparately indicating discrete microbial diversity than IndianOMZ (figure 6A and B). STAMP analysis has revealed thata total of 95 bacterial families were differentially (q\0.05)present in Indian and Chile OMZ areas (figure 6C). Familiessuch as Dermabacteraceae, Flavobacteriaceae, Clostridi-aceae, Enterobacteriaceae, Comamonadaceae and Bradyrhi-zobiaceae were enriched in Chile OMZ area while familiesCenarchaeaceae, Piscirickettsiaceae and Erythrobacteraceaeare seen to be abundant in the Indian OMZ areas (BOB).Similarly, comparative analysis for Peru and Indian OMZsidentified 116 families to be differentially (q\0.05) enrichedin these OMZ areas (figure 6D). In Indian OMZ areas,Nitrospinaceae, Cenarchaeaceae, Piscirickettsiaceae andErythrobacteraceae were abundant, while Rhodobacteraceae,Flavobacteriaceae, Pseudomonadaceae and Shewanellaceaewere enriched in the OMZ areas from Peru. Seven familiesincluding Methylococcaceae, Brucellaceae, Methanosarci-naceae and Marinilabiaceae were exclusively present inOMZ areas from Peru.

Further detailed analysis at the family level has identified129 families as unique to Indian OMZs and 304 familiesunique to Chile OMZs (supplementary table 7).

644 S N Rajpathak et al.

Sulfolobaceae, Thermoproteaceae, Picrophilaceae, Beuten-bergiaceae were exclusive to Chile OMZs, while Turi-cibacteraceae, Dehalobacteriaceae, AKIW874 and SC3-41and A0-023 represent families that were present only in theIndian OMZ areas (figure 6C). In addition, it can be seenthat various microbial families (706) including Pythiaceae,Polymastigidae and Cafeteriaceae were unique to PeruOMZs, while SHA-31, Oleiphilaceae, Ellin6075 and Tre-bouxiophyceae (total 117) were observed exclusively inIndian OMZ when compared with Peru (figure 6D, supple-mentary table 7). Overall, this analysis ascertains the dif-ferences and similarities across the OMZ areas in differentparts of oceans. OMZ area from Peru has a distinct bacterialcomposition in comparison with Indian samples and Chile.

Although Chile and Indian OMZ areas do share somecommon features, they possess various differences andspecific enrichments of certain communities, thus indicatinglocation specificity in OMZ bacterial composition. Samplesused for the current comparative analysis differ from thesamples from Peru and Chile in the sequencing platform aswell as bioinformatic pipeline. Although, this can influencethe depth of sequencing and bacterial identification to someextent, a comparative analysis provides basic informationand assessment of the bacterial diversity for different oceanicOMZ. In a similar manner, detailed analysis of communitydynamics and their metabolic activities at different locationsand various OMZ areas in different seasons would providevaluable insights into special features and their significance

Figure 6. PCoA analysis of families from (A) Chile-Indian OMZ and (B) Peru-Indian OMZ areas. (C) and (D) Venn diagram depictingfew differentially (q\0.05) and uniquely (marked with *) present bacterial families in Chile-India, Peru-India OMZ areas, respectively.

Microbial and functional diversity of BOB 645

in shaping the marine ecosystem. In addition, detailedmultidimensional analysis involving several parameters frommultiple locations would provide information about thecontribution of these specific communities in elementalcycles, food chain and even climate change.

3. Conclusions

Using high-throughput NGS-based 16S rRNA gene ampli-con analyses, this study has unravelled the rich bacterialdiversity and community composition in the BOB withrespect to the OMZ and non-OMZ areas. The bacterialcomposition in BOB differs significantly in relation to theavailability of dissolved oxygen, temperature and salinity.Further functional analysis of OMZ and non-OMZ samplesclearly demonstrated that the microbial communities havesignificant differences in their metabolic capabilities andgenetic diversity. OMZ areas were enriched with microbesharbouring sulphur and nitrogen metabolism pathways,whereas the non-OMZ areas, microbial communities pos-sessing hydrocarbon or pollutant degradation pathways wereabundant. In addition, this study highlights the uniqueness ofmicrobial communities of the BOB OMZ in comparisonwith Peru and Chile OMZ. Our analysis has generatedbaseline data highlighting the microbial diversity and theirprobable functional role in Indian eastern OMZ areas. Thiscataloguing of bacterial diversity is important in under-standing ocean ecology and the ecosystem. It has alsorevealed the importance of anaerobic sulphur and nitrogenmetabolism along with photosynthetic abilities andmethanogenesis in this region. It would be very interesting toharvest these novel metabolic capabilities through functionalgenomic analysis and isolation of genomic resources. It willbe important to collect and analyse ocean samples fromwater and sediments across different locations and climaticconditions in order to explore the structure and dynamics ofthe rich microbial community.

Acknowledgements

This work was funded by Ministry of Earth Sciences,Government of India under the Microbial Oceanographyscheme to Dr. Deepti Deobagkar. The authorsacknowledge Dr. Mandar Paingankar for help in initi-ating studies on OMZ. The funding agency had no rolein sequencing data generation, analysis and preparationof manuscript. The project was coordinated throughCMLRE which also provided support for the cruise andsample collection. We acknowledge help and supportfrom Dr Saravannane, Dr Cherrayi, Dr Shivaji and DrSudhakar, Director, CMLRE.

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Corresponding editor: R GEETA

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