Paper_Bioresource

Bioresource Technology xxx (2015) xxx–xxx

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Bioresource Technology

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Comparative metagenomics demonstrating different degradativecapacity of activated biomass treating hydrocarbon contaminatedwastewater

http://dx.doi.org/10.1016/j.biortech.2015.01.1410960-8524/� 2015 Published by Elsevier Ltd.

⇑ Corresponding author. Tel.: +91 712 2249883.E-mail address: [email protected] (A. Kapley).

Please cite this article in press as: Yadav, T.C., et al. Comparative metagenomics demonstrating different degradative capacity of activated biomass thydrocarbon contaminated wastewater. Bioresour. Technol. (2015), http://dx.doi.org/10.1016/j.biortech.2015.01.141

Trilok Chandra Yadav a, Rajesh Ramavadh Pal a, Sunita Shastri b, Niti B. Jadeja a, Atya Kapley a,⇑a Environmental Genomics Division, National Environmental Engineering Research Institute, (CSIR-NEERI), Nehru Marg, Nagpur 440 020, Indiab Environment Impact and Risk Assessment Division, CSIR-NEERI, India

h i g h l i g h t s

� Study reports comparative metagenomics of activated biomass samples.� Study explores phylogenetic and functional diversity of microbial community.� In silico metagenome analysis was performed using MG-RAST.� Effect of seasonal and TDS content change was examined on microbial composition.� Study aids in understanding microbial metabolic pathways for aromatics degradation.

a r t i c l e i n f o

Article history:Received 28 November 2014Received in revised form 27 January 2015Accepted 29 January 2015Available online xxxx

Keywords:Activated biomassComparative metagenomicsMicrobial diversityMetabolic pathwaysWastewater treatment plant

a b s t r a c t

This study demonstrates the diverse degradative capacity of activated biomass, when exposed to differ-ent levels of total dissolved solids (TDS) using a comparative metagenomics approach. The biomass wascollected at two time points to examine seasonal variations. Four metagenomes were sequenced on Illu-mina Miseq platform and analysed using MG-RAST. STAMP tool was used to analyse statistically sig-nificant differences amongst different attributes of metagenomes. Metabolic pathways related todegradation of aromatics via the central and peripheral pathways were found to be dominant in lowTDS metagenome, while pathways corresponding to central carbohydrate metabolism, nitrogen, organicacids were predominant in high TDS sample. Seasonal variation was seen to affect catabolic gene abun-dance as well as diversity of the microbial community. Degradation of model compounds using activatedsludge demonstrated efficient utilisation of single aromatic ring compounds in both samples but cycliccompounds were not efficiently utilised by biomass exposed to high TDS.

� 2015 Published by Elsevier Ltd.

1. Introduction

Aromatic hydrocarbons such as benzene, toluene, xylene andnaphthalene which find application in manufacture of syntheticfibre, explosives, pesticides, dye and dye intermediates, detergents,resins, etc., are primarily produced from petroleum refineries. Thepetrochemical plants located in Western India, manufacture ethy-lene oxide, mono–ethylene glycol, vinyl chloride monomer, polyvinyl chloride, high density polyethylene, polyester complex,polypropylene, naphtha cracker, and purified terephthalic acidusing naphtha as the main feedstock. A huge amount of waste-water is generated during different stages of oil processing to

manufacture above mentioned chemicals. Approximately, 2.5 gal-lons of water are required for every gallon of crude oil processedresulting into generation of 8–10 MGD wastewater from petro-chemical plants (Kujawski, 2009).

This wastewater contains toxic compounds that have deleteri-ous effects impacting the ecosystem and human health (Akporand Muchie, 2013). Reclamation and reuse of this huge amountof wastewater would help conserve the diminishing fresh waterresources. The green route for such treatment is via the activatedsludge process wherein bioremediation occurs with the help ofthe microbial diversity of the activated biomass (Das andChandran, 2010; Yang et al., 2011; More et al., 2014). Hence, thekey to understanding this treatment process lies in understandingthe microbial diversity of such niches. A major constraint in biore-mediation of hydrocarbon contaminated wastewater has been the

reating

http://dx.doi.org/10.1016/j.biortech.2015.01.141

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2 T.C. Yadav et al. / Bioresource Technology xxx (2015) xxx–xxx

limited understanding of the phylogenetic diversity (Vartoukianet al., 2010). Metagenomics is increasingly being used to bridgethis gap and has been reported in analysing the microbial diversityof different activated sludge niches (Silva et al., 2012; Jadeja et al.,2014; Yadav et al., 2014). Specific reference to the microbial diver-sity analysis of hydrocarbon contaminated niches has been report-ed in oil spill affected mangrove area in Brazil (Dias et al., 2012),deep-sea sediments from gulf of Mexico (Kimes et al., 2013) andpetroleum muck (Joshi et al., 2014).

Besides analysing the taxonomic diversity of the niche, metage-nomics allows us to map the degradative pathways present in theactivated biomass and gain important information related to theperformance of the wastewater treatment plant. The microbialdiversity is governed by numerous environmental factors (Gilbertet al., 2010). This study reports the comparative metagenomics ofactivated biomass samples from two different wastewater treat-ment plants in the same geographical area, treating wastewatergenerated from a petrochemical complex. A comparative assess-ment of variation in microbial community composition and itsfunctional characteristic with respect to effect of change in seasonhas been analysed using the Metagenome Rapid Annotation usingSubsystem Technology (MG-RAST) server (Meyer et al., 2008). Allfour metagenomes were sequenced on the MiSeq platform (Illumi-na) and FragGeneScan algorithm was used to predict codingregions. Statistical Analyses of Metagenomic profiles (STAMP) soft-ware (Parks et al., 2014) was employed to study statistically sig-nificant differential abundance of taxonomic and functionalfeatures among all four metagenomes.

2. Methods

2.1. Sampling

A petrochemical complex in Western India operates two waste-water treatment plants using the activated sludge process. Onereceives effluents from polyester, poly propylene, poly vinyl chlo-ride and ethylene glycol manufacturing units that are low in con-centration of total dissolved solids (TDS) (<1000 mg/l), while theother receives effluent stream from other processing sections suchas steam cracking process including process water and spent caus-tic, cooling or boiler water blow down, surface or maintenancewater that are high in dissolved salts concentration (>5000 mg/l)and contaminated with oil. Activated biomass was collected fromboth wastewater treatment plants as described earlier (Kapleyand Purohit, 2009). The samples were kept at 4 �C and brought tothe laboratory within 12 h. In order to study seasonal variation inmicrobial community composition, sampling was performed attwo different seasons: (i) Monsoon season (October 2013) and(ii) winter season (February 2014). Operational parameters of bothtreatment plants are listed in Table 1. Samples are designated asfollows:

(1) L1: activated biomass from treatment plant with low TDSwastewater stream in monsoon season.

Table 1Characteristic features of wastewater corresponding to low and high TDS wastewater.

Parameter Monsoon season

Low TDS wastewater High TDS w

Dissolved Oxygen (mg l�1) 2.5 2.9pH 6.5–7.0 7.7–8.0MLVSS (%) 90 70MLSS (mg l�1) 3000 4000SVI 100 300Phosphate (mg l�1) 1.0–2.0 0.5–1.0Ammonia (mg l�1) 1.0–1.5 1.9–2.5

Please cite this article in press as: Yadav, T.C., et al. Comparative metagenomicshydrocarbon contaminated wastewater. Bioresour. Technol. (2015), http://dx.d

(2) L2: activated biomass from treatment plant with low TDSwastewater stream in winter season.

(3) H1: activated biomass from treatment plant with high TDSwastewater stream in monsoon season.

(4) H2: activated biomass from treatment plant with high TDSwastewater stream in winter season.

2.2. Assessment of hydrocarbon degradation potential of activatedbiomass

The degradative potential of the activated biomass towardshydrocarbons was assessed by spectrophotometry using biphenyl,sodium benzoate, phenol, toluene and xylene as model compounds.3000 mg l�1 MLSS was inoculated in 10 ml modified synthetic was-tewater media (SWM) with following composition; 66 mg l�1

(NH4)2SO4, 30 mg l�1 urea, 28 mg l�1 K2HPO4, 2 mg l�1 CaCl2�2H2O,2 mg l�1 of MgSO4�7H2O, 1 ml per litre of trace element solution(0.75 g l�1 FeCl3�6H2O, 0.075 g l�1 H3BO3, 0.015 g l�1 CuSO4�5H2O,0.09 g l�1 KI, 0.06 g l�1 MnCl2�4H2O, 0.03 g l�1 NaMoO4�2H2O,0.06 g l�1 ZnSO4�7H2O, 0.075 g l�1 CoCl2�6H2O, 0.5 g l�1 EDTA, and1 ml per litre concentrated hydrochloric acid) (Bellucci et al.,2011). Each model compound was used as sole source of carbon ata concentration 200 ppm. Degradative capacity of activated bio-mass was assessed after 72 h incubation at 30 �C and 120 rpm.The cell-free extracts were filtered through 0.25 l PTFE filter beforeanalysis on a spectrophotometer as reported (Jadhav et al., 2013).Controls used were pure substrates without the addition of activat-ed biomass. All experiments were carried out in triplicates.

2.3. Metagenome isolation, sequencing and assembly

The metagenomic DNA was prepared from 500 mg of compositesamples using FastDNA Soil Kit (MP Biomedicals, CA, USA) as per themanufacturer’s protocol. The DNA quality and quantity was esti-mated using NanoDrop spectrophotometer (ND-1000, Nano-DropTechnologies Thermo Scientific, USA). Subsequently, high-through-put sequencing was performed on NextSeq500 platform. The pairedend sequencing library was prepared using Illumina TruSeq NanoDNA HT Library Preparation Kit. The mean size of libraries rangedbetween 850 and 950 bp. Overall 1.5 GB of sequencing data wasgenerated for each sludge DNA sample. De novo assembly of highquality data was accomplished using CLC genomics workbench6.0 at default parameters. Table 2 illustrates metagenome data char-acteristics of four samples. The assembled data was further filteredfor submission to NCBI. The contigs <199 nt and containing three ormore ‘‘N’’ or contaminated with adaptors were removed and theremaining clean. Ns from the beginning or end of each sequencewere removed using in house Perl scripts. All four Whole GenomeShotgun projects have been deposited at DDBJ/EMBL/GenBankun-der the accession JRYG00000000, JRYF00000000, JRYK00000000,and JRYJ00000000, for L1, H1, L2 and H2 respectively. Sequence datacan also be viewed in NCBI with following biosample numbers;SAMN03074218, SAMN03074217, SAMN03074220, SAMN03074219.

Winter season

astewater Low TDS wastewater High TDS wastewater

2.8 37.0–7.5 7.0–7.590 703000 4000150 3501–5 1–55–15 5–15

demonstrating different degradative capacity of activated biomass treatingoi.org/10.1016/j.biortech.2015.01.141


Table 2Summary of annotation results from MG-RAST for four metagenome datasets.

Metagenome Monsoon season Winter season

L1 H1 L2 H1

Total sequence (M bases) 124 119 121 99Total reads 1,56,524 1,32,311 1,59,253 1,32,486Average read length (bases) 794 905 761 747Average GC content (%) 60 ± 9 58 ± 11 61 ± 11 61 ± 9QC passed reads 1,34,009 1,14,062 1,47,486 1,21,711(% of total reads) 85.6 86.2 92.6 91.86Average read length after QC (bases) 498 541 617 567Predicted protein features 1,52,336 1,31,900 1,78,769 1,43,875Predicted rRNA features 14,836 12,101 15,925 12,400Identified protein features 84,904 72,939 97,557 77,964Identified functional categories 68,421 58,917 79,159 62,680

T.C. Yadav et al. / Bioresource Technology xxx (2015) xxx–xxx 3

2.4. Bioinformatics analysis

2.4.1. Data pre-processing in MG-RASTSequencing data set in FastQ format of Samples L1, H1 L2 and

H2 were uploaded to MG-RAST server (http://metagenomics.nmp-dr.org/) version 3.5 (Meyer et al., 2008). Sequences were processedthrough quality control (QC) which includes dereplication (remov-ing artificial replicate sequences produced during sequencing),ambiguous base filtering and length filtering. Artificial duplicatereads (ADRs) were analysed using duplicate read inferred sequenc-ing error estimation (DRISEE). MG-RAST also filters near exactmatches to genome of fly, mouse, cow and human. After filteringthe data gene calling was performed by FragGeneScan algorithmto predict coding regions. Clusters of proteins were generatedusing uclust programme in QIIME followed by BLAT analysis. Theannotation mapping was done against M5nr which provides nonredundant integration of many data such as Genebank, SEED,IMG, Uniprot, KEGG and eggNOGs.

2.4.2. MG-RAST analysis pipelineThe functional and taxonomy annotation of four metagenomes

were conferred using default parameters in MG-RAST tool. LCA(Lowest Common Ancestor) (Yu and Zhang, 2012) algorithm imple-mented in MG-RAST was employed to assign taxonomy to metage-nomic reads by targeting NCBI taxonomy database. The data wascompared using a maximum e-value of 1e � 5, a minimum identityof 60%, and a minimum alignment length of 15 bp. Further analysisof taxonomy table generated at each taxa level was analysed inMS-office excel datasheet. To derive sample specific bacterial gen-era, a Venn diagram was generated using online Venny tool(Oliveros, 2009). For functional annotation SEED based subsystemwas used as annotation source and data was compared to Subsys-tems using a maximum e-value of 1e � 5, a minimum identity of60%, and a minimum alignment length of 15 amino acids for pro-teins. To assess the depth of sequencing performed for each sam-ple, comparative rarefaction curves were generated using MG-RAST. To derive Inter-relationship among sludge samples, PrincipalComponent Analysis (PCA) was also performed using MG-RASTanalysis pipeline with default statistical parameters. The genesinvolved in Ortho cleavage and Meta cleavage pathways for phenoldegradation were mapped and the corresponding number of genehits at respective reaction was tabulated. The degradation path-ways for compounds xylene, toluene, biphenyl and benzoate weremapped as per KEGG pathways (Kanehisa and Goto, 2000) for pres-ence of genes in four metagenomes represented by L1, H1, L2, H2.The number of gene hits in all four metagenomes were obtained byanalysing hierarchical classification of subsystems in MG-RASTusing default parameters.


2.5. Statistical analysis

STAMP software v2.0 was employed to study statistically sig-nificant differential abundance of taxonomic and functional fea-tures among four metagenomes. The phylogenetic table of fourmetagenomes comprising of abundance profile at each taxonomiclevel based on LCA method was imported to STAMP tool. Similarly,functional abundance profile based on hierarchical classificationmethod was generated for metagenome dataset and subsequentlyimported to STAMP. Extended error bar plots were generated forfour combinations considering seasonal and TDS parameters; (i)L1–H1, (ii) L2–H2 (iii) L1–L2 and (iv) H1–H2. The statistical analy-sis involving fisher’s exact test with storey FDR correction wereapplied on these datasets to derive statistically significant differen-tial features among four metagenomes. Taxonomic data was anal-ysed at genus level to look at bacterial genera, selective to anysingle metagenome sample. For functional abundance profile, thestatistical tests were applied at level 3 of functional annotations.Extended error bar plots were generated for both, illustrating pecu-liar dissimilar features among four datasets. The results were fil-tered using q-value of 0.05 and effective size of 0.05 threshold inSTAMP.

2.6. Relative quantification using real time PCR

Seasonal variation of gene abundance was analysed real-timePCR (qPCR). Four target genes chosen for this study; aromatic ringhydroxylating dioxygenase (HDO), chlorocatechol 1,2 dioxygenase(tcbC), catechol 2,3 dioxygenase (xylE), and catechol 2,3 dioxyge-nase (dmpB) genes. The primer details are listed in Table 4. Primersfor HDO and xylE were designed using LAserGene softwar, DNAS-TAR (USA). qPCR reaction was carried out in iCycler (Bio-Rad,USA) using SYBR Green chemistry. Each 25 ll reaction comprisedof 50 pmol of respective primer, 12.5 ll of Maxima™ SYBR GreenqPCR Master Mix (Biorad) which includes SYBR�Green I dye, dNTP,KCl, (NH4)2SO4 and Maxima™ Hot Start Taq DNA Polymerase. PCRwas carried out in three replicates for each gene target.

The Comparative CT Method was used to study relative quantifi-cation of catabolic genes between samples from two time points(method described online by Applied Biosystems at this linkhttp://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_042380.pdf). The difference of16S rRNA gene level and target gene level was calculated usingthe following formula,

DCT ¼ CT target � CT reference

where CT target represents CT value of target gene, and CT reference rep-resents the CT value of 16S rRNA gene.


http://metagenomics.nmpdr.org/

http://metagenomics.nmpdr.org/

http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_042380.pdf

http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_042380.pdf



Further the DCT values of samples was compared for betweensamples L1–L2 and H1–H2 and the difference yields DDCT valueswere plotted.

3. Results and discussion

The microbial community in the activated biomass is responsi-ble for the biodegradation of pollutants in the wastewater. Thiscommunity survives under various stress conditions and diverseenvironmental factors (Kapley and Purohit, 2009; Li et al., 2013).The petrochemical industry wastewater has the additional factorof total dissolved solids (TDS) in the form of minerals and differentsalts that have an adverse effect on microbial activities owing to itsplasmolytic effect caused by increased salinity (Ng et al., 2014).Based on different stages in oil refining process and manufactureof related chemicals, two types of wastewater are generated: lowTDS and high TDS, requiring a separate treatment for each. To ana-lyse the microbial response to TDS, we analysed the metagenomeof two different wastewater treatment plants in the same geo-graphical region. The study also analyses the change in diversitywith seasons.

3.1. Metagenome analysis

An in silico analysis using MG-RAST server revealed a largerepertoire of taxonomic and functional diversity for four sludgesamples. Table 2 shows a summary of metagenome datasets char-acteristic and annotation results. After quality filtering andremoval of artificial replicates, the number of reads in metagenomedataset ranged from 1,21,711 (H2) to 1,47,486 (L2) with averageread lengths between 498 bases (L1) and 617 bases (L2). 84,904(55.7%), 72,939 (55.3%), 97,557 (54.5%) and 77,964 (54.18%)sequences contained predicted proteins with known functions inL1, H1, L2 and H2 samples respectively. Furthermore, 14,836(9.47%), 12,101 (9.1%), 15,925 (9.9%) and 12,400 (9.3%) sequencescorresponding to ribosomal RNA features belonged to L1, H1, L2and H2 samples respectively. Additionally, 68,421 (80.58%),58,917 (80.77%), 79,159 (81.14%), and 62,680 (80.39%) reads were

Table 4Details of primers used in analysing seasonal change of gene expression.

Name Primer sequence Func

Chlorocatechol 1,2 dioxygenase (tcbC) F-GTITGGTACTCGAGGCCCGAIG RingR-GCAAGCTTCGAAGTAGTAITGTG

Aromatic ring hydroxylatingdioxygenase (HDO)

F-CAGGTCCGGATGGTATTGATG AromR-ATGTCGCACCTTTCCGGCTTTCTG

Catechol 2,3 dioxygenase (xylE) F-TCTATCCGGCCGAACAGGTG RingdegrR-CCGGACGGGTCGAAGAAGTAG

Catechol 2,3 dioxygenase (dmpB) F-CGACCTGATCTCCATGACCGA RingdegrR-TCAGGTCAGCACGGTCA

16S rRNA gene F-CTGGTAGTCCACGCCGTAAA 30SR-CGAATTAAACCACATGCTCCAC

Table 3Distribution of metagenomic sequences into microbial domains. Percentages werecalculated from total number of annotated features.

Monsoon (% abundance) Winter (% abundance)

L1 H1 L2 H2

Bacteria 99.09 97.63 98.5 98.92Archaea 0.41 1.83 0.39 0.41Eukaryota 0.33 0.32 0.93 0.45Viruses 0.03 0.07 0.02 0.05Unclassified

sequences0.11 0.12 0.14 0.13


categorised as functional genes in L1, H1, L2 and H2 samplesrespectively.

3.2. Taxonomic analysis

Table 3 lists the microbial domains in all four metagenomes. Itcan be seen that the microbial community in the activated biomasswas dominated by Bacteria, with abundance between 97.62% and99.09%. The less abundant fraction of the microbial communitywas represented by the Archaeal community, with representationranging from 0.3% to 0.4% with the exception of H1 that showedthe presence of 1.83% Archaea%. A lesser fraction of the sequenceswere representing Viruses (0.02–0.07%). Supplementary Fig. S1demonstrates the rarefaction curve for bacterial diversity of fourmetagenomes, indicating that sufficient sequencing depth wasachieved in this study. Based on rarefaction curves it can beinferred that H2 sample possess highest bacterial diversity amongfour samples. The taxonomic diversity in all four metagenomes canbe seen in Fig. 1. Proteobacteria, were the dominant phyla, repre-sented by 43.89% in L1, and 46.43%, 45.99% and 48.23% in H1, L2and H2 samples respectively. These results are different from pre-vious reports, wherein, Joshi et al. (2014) reported 99% of bacterialsequences belonging to Proteobacteria phyla and Silva et al. (2012)reported Proteobacteria to represent 66% of the diversity, in theirstudies involving microbial diversity of petroleum muck sampleand phenol enriched activated biomass from oil refinery waste-water respectively. The divergence in results with respect to previ-ous reports can be attributed to difference in sample. As can beseen in Fig. 1, there is no major change due to seasonal variationsin the wastewater treatment plant operating at low and high TDS,with reference to the top five bacterial phyla. However, differencescan be observed with total percentage abundances. There is a con-sistent increase of about 2.2% in Proteobacteria phylum betweenL1–L2 and H1–H2 due to seasonal variation. On the contrary Bac-teroidetes phylum records a decrease of about 2% owing to season-al change. A major difference was observed in the case of theChloroflexi phylum between H1 and H2 samples, suggesting it tobe a key group of bacteria for high TDS waste treating biomass.Taxonomical changes could be observed when diversity was anal-ysed based on ‘‘Class’’. The bacterial community in all four meta-genomes was dominated by Proteobacteria; a phyla reported toplay a major role in degradation (Barragán et al., 2009; Hedrichet al., 2011).

Within the Proteobacteria phylum, Alphaproteobacteria wasfound to be most prevalent in low TDS samples while in case ofhigh TDS samples Betaproteobacteria were predominant. It sug-gests that Betaproteobacteria class might tolerate/respond wellto high TDS conditions as compared to Alphaproteobacteria at tar-geted niche. However, these two bacterial classes showed oppositetrends in response to seasonal change. Alphaproteobacteriaincreased in proportion by 5% while Betaproteobacteria classshowed decrease of 11% between two seasons. Deltaproteobacteria

tion References

opening of chloro catechols Kapley and Purohit(2009)

atic ring cleaving dioxygenase Designed in thisstudy

opening in meta-cleavage pathway of xyleneadation

Designed in thisstudy

opening in the meta-cleavage pathway of phenol/cresoladation

Kapley and Purohit(2009)

small subunit of bacterial ribosomes Sagarkar et al. (2014)




exhibited similar trend among two kind of samples (L–H), showingan increase of 5% in its proportion as effect of seasonal variation.Supplementary Table 1 lists the top 10 bacterial genera from Pro-teobacteria phylum and top 10 genera belonging to remaining bac-terial phyla in all four samples. The abundance of unclassifiedsequences was not considered in calculating percentage of eachgenus.

The number of shared and unique genera between all fourmetagenomes can be seen in Fig. 2. The Venn diagram representsall bacterial genera based on their occurrence in each sample anddemonstrates an inter relationship amongst the four samples in

Fig. 1. Classification of bacterial community corresponding to L1, L2, H1 and H2 in term ototal effective bacterial sequences in a sample.


terms of shared and unique genera. Results demonstrate that avery large community is shared between all four metagenomes,irrespective of season or type of wastewater. 401 genera werefound to be common in all the samples suggesting a common rolein the aromatic degradation pathway that is common to aerobicdigestion and/or house-keeping bacterial community for hydrocar-bon contaminated wastewater treating biomass. The figure alsodemonstrates the number of bacterial genera that are unique toeach sample. Principal component analysis plots (PCA) reiteratedthe diversity of the microbial community in all four metagenomes(data not shown).

f relative abundance. The abundance has been represented in terms of percentage of




Statistically differential features with regard to seasonal varia-tion (L1–L2 and H1–H2) and change in TDS content (L1–H1 andL2–H2) corresponding to four activated biomass samples wereanalysed using STAMP (Statistical Analysis of Metagenomic Pro-files). Results are demonstrated in Fig. 3, illustrating statisticallydifferential representation of bacterial genera between four sam-ples. The extended error bar plot considers bacterial genus abun-dance profile derived from the four metagenomes. In order todisplay major bacterial genera with significant abundance and dif-ferential representation, the results were curated keeping filter of0.05% difference between proportions of two genera. As can beseen, various bacterial genera responded to seasonal variation. Incase of high TDS sample (Fig. 3A), Hyphomicrobium, Gemmata,Opitutus, Candidatus Solibacter, Haliangium and Candidatus Accu-mulibacter were found to be dominant in winter season as com-pared to monsoon. Plesiocystis, Clostridium, Leptothrix andMethylibium were dominant in the monsoon season correspondingto high TDS samples. Similarly, for low TDS samples (Fig. 3B), bac-terial genera such as Sorangium, Acinetobacter, Planctomyces andHyphomycrobium were dominant in winter season, whereas Candi-datus, Solibacter, Anaerolinea, Gemmata and Nitrosomonas weredominating in the monsoon season. Variation between TDSstreams also demonstrated signification differences in microbialdiversity (Fig. 3C and D). Novel bacteria like Conexibacter werefound predominantly in activated sludge treating low TDS waste-water, while uncultured representatives like Anaerolinea werefound in high TDS wastewater.

3.3. Functional analysis

The complete metabolic profile of the microbial community inall four metagenomes can be viewed in Supplementary Fig. S2. Sea-sonal variation does not seem to play a major role in housekeeping

L1: Genera found iwastewater stream L2: Genera found iwastewater stream H1: Genera found iwastewater stream iH2: Genera found iwastewater stream i

n activated biomassin monsoon season n activated biomassin winter season n activated biomassin monsoon season n activated biomassin winter season

Fig. 2. Venn diagram representing bacterial genera distribution among four samplescorresponding samples.


activities of the microbial community. Changes are observedbetween wastewater strength. Genes responsible for metabolismof aromatic compounds were observed to be present in higherabundance in the low TDS samples and more so in the winter sea-son (data not shown).

3.3.1. Aromatic hydrocarbon degradationThe degradative capacity of the activated biomass collected

from different TDS conditions was analysed against model petro-leum hydrocarbons namely, sodium benzoate, biphenyl, phenol,toluene, xylene and naphthalene (Zappi et al., 1996; Garcı́aSánchez et al., 1997). Fig. 4 demonstrates the different cataboliccapacity of the biomass. As can be seen in the figure, the low TDSbiomass degraded benzoate with 20% extra efficiency, biphenylwith 30%, and toluene with 32% better efficiency. However, highTDS biomass was found to be more efficient in utilising phenolby 3.4% and xylene by 14%. Naphthalene could not be degradedat all by activated biomass exposed to high TDS wastewater.

The catabolic gene profile was also analysed using MG-RAST forall four metagenomes. Results demonstrated the dominance of theperipheral pathways of aromatic compound catabolism (data notshown).

3.3.2. Seasonal change of gene expressionHydrocarbon contaminated wastewater is laden with aromatic

compounds that follow different routes of biodegradation. A widevariety of peripheral pathways are involved in bacterial catabolismof aromatic compounds, wherein structurally diverse aromaticsubstrates are transformed into a limited number of commonintermediates. Subsequently, these intermediates are brought tothe central metabolism of the cell via metabolism of central aro-matic intermediates (Carmona et al., 2009). The most commonroute of degradation occurs via the central aromatic pathway,

from treatment plant with l

from treatment plant with l

from treatment plant with h

from treatment plant with h

ow TDS

ow TDS

high TDS

high TDS

. Number represents count of the bacterial genera being shared or exclusive to



Fig. 3. (A–D) Extended error bar plots considering abundance profile of bacterial genera in four metagenome data. Statistically differential features with regard to seasonalvariation (L1–L2 and H1–H2) and change in TDS content (L1–H1 and L2–H2) corresponding to four activated biomass samples were analysed using STAMP and aredemonstrated.

Fig. 4. Qualitative assessment of activated biomass for removal of representativearomatic compounds. (Compound removal has been shown in terms of % degra-dation after 72 h incubation).


wherein the organic compounds are converted to catechols orsubstituted catechols; followed by ring fission that helps in com-plete mineralisation (Kapley and Purohit, 2009). For this reason,


we targeted analysis of genes responsible for ring opening; cate-chol 2,3 dioxygenase from xylene degradation pathway (xylE)and from phenol degradation pathway (dmpB), catechol 1,2 dioxy-genase; tcbC, coding for chloro catechol 1,2 dioxygenase and anaromatic-ring hydroxylating dioxygenases, where consensus pri-mers constructed from gene fragments of dioxyxgenases targetingbenzene derivatives. Results are demonstrated in Fig. 5. The com-parative CT method (DDCT) demonstrates relative quantification.In this study, the 16S rRNA gene was used as internal control. Allfour target genes were found to be more abundant in the lowTDS biomass in the L2 sample, suggesting better degradative effi-ciency in the winter season. However, both catechol 2,3 and cate-chol 1,2 dioxygenases were more abundant in the H1 sample inhigh TDS wastewater, demonstrated by the negative histogram,suggesting a better degradative capacity in the H1 sample. The aro-matic-ring hydroxylating dioxygenases however, were more abun-dant in H2. We could hypothesise that salt stress is relatively lesserin the monsoon season due to natural dilution and hence the HighTDS biomass is more efficient in the monsoon season. However,more on-site analysis will be required to corroborate this.

3.3.3. Pathway mappingMetagenome analysis not only provides a detailed information

on the taxonomic and catabolic gene abundances in the biomassbut also allows pathway mapping and provides insight into theactual degradative capacity for specific pathways. Fig. 4 demon-strated the highest degradative capacity of the biomass of bothTDS streams towards phenol and hence we mapped all the stepsin the phenol degradation pathway in all four metagenomes.



Fig. 5. Seasonal variation of target genes in activated biomass. The graph shows the 1/DDCt value for target genes. The target gene level in samples from second time point(L2, H2) is compared to samples of first time point, (L1, H1) indicated as L2–L1, and H2–H1 respectively.


Phenol is a single ring aromatic compound that present universallyin hydrocarbon contaminated sites. Results are demonstrated inSupplementary Fig. S3A, where seasonal variation as well as geneabundance across TDS stress is distinctly observed. Biomassexposed to high TDS wastewater shows a better phenol degrada-tion capacity (Fig. 4) which may be explained by the higher genecopy number of the first enzyme of phenol degradation, phenolhydroxylase. The H1 sample also possesses a larger number genescoding for ring opening enzymes.

Similarly, mapping of other aromatic compounds can be carriedout to assess the degradative efficiency of the activated biomass ina treatment plant. Supplementary Fig. S3B, demonstrates a samplestudy for xylene, toluene, biphenyl and benzoate indicating sea-sonal variation.

4. Conclusion

This study presents a practical and uniquely informativemethod for understanding microbial communities and their envi-ronments. The comparative metagenomics approach used in thisstudy demonstrates the effect of seasonal variations and environ-mental conditions on the composition of the microbial community.The details of community structure and degradative pathways gen-erated in this study can be used to define better bioremediationstrategies.

Acknowledgements

The authors acknowledge the Council of Scientific and Industri-al Research, India, CSIR-network project ESC-0108-MESER, for sup-porting this research. Trilok Chandra Yadav (JRF) and RajeshRamavadh Pal (SRF) are grateful to UGC. The management of theCETP is acknowledged for providing activated biomass and waste-water samples used in this study.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.biortech.2015.01.141.


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