bacterial and methanogenic archaeal communities during the single-stage anaerobic digestion of...

Bioresource Technology xxx (2014) xxx–xxx

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Bacterial and methanogenic archaeal communities during thesingle-stage anaerobic digestion of high-strength food wastewater

http://dx.doi.org/10.1016/j.biortech.2014.02.0280960-8524/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding authors. Tel.: +82 54 279 2275; fax: +82 54 279 8659.E-mail address: [email protected] (J.M. Park).

Please cite this article in press as: Jang, H.M., et al. Bacterial and methanogenic archaeal communities during the single-stage anaerobic digestion ostrength food wastewater. Bioresour. Technol. (2014), http://dx.doi.org/10.1016/j.biortech.2014.02.028

Hyun Min Jang a, Ji Hyun Kim a, Jeong Hyub Ha a, Jong Moon Park b,c,⇑a School of Environmental Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Pohang 790-784, South Koreab Department of Chemical Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Pohang 790-784, South Koreac Division of Advanced Nuclear Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Pohang 790-784, South Korea

h i g h l i g h t s

� Applicability of AD process wasevaluated for the treatment of high-strength FWW.� Microbial communities were

examined by barcoded-pyrosequencing and qPCR.� Bacterial communities were highly

affected by the change of organicloading rate.� Methanogenic species shifted from

aceticlastic to hydrogenotrophicmethanogens.

g r a p h i c a l a b s t r a c t

a r t i c l e i n f o

Article history:Available online xxxx

Keywords:High-strength food wastewaterSingle-stage anaerobic digestionBarcoded-pyrosequencingQuantitative real-time PCR (qPCR)Multivariate statistical analysis

a b s t r a c t

Single-stage anaerobic digestion (AD) was operated to treat high-strength food wastewater (FWW) derivedfrom food waste recycling facilities at two different organic loading rates (OLRs) of 3.5 (Phase I) and 7(Phase II) kg COD/m3 d. Changes in composition of microbial communities were investigated usingquantitative real-time PCR (qPCR) and barcoded-pyrosequencing. At the high FWW loading rate, ADshowed efficient performance (i.e., organic matter removal and methane production). Bacterial communi-ties were represented by the phyla Bacteroidetes, Firmicutes, Synergistetes and Actinobacteria. During theentire digestion process, the relative abundance phylum Chloroflexi decreased significantly. The qPCR anal-ysis demonstrated that the methanogenic communities shifted from aceticlastic (Methanosarcinales) tohydrogenotrophic methanogens (Methanobacteriales and Methanomicrobiales) with high increase in theproportion of syntrophic bacterial communities. Canonical correspondence analysis revealed a strongrelationship between reactor performance and microbial community shifts.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Food wastewater (FWW) is an organic-rich byproduct that isproduced in large quantities during recycling of food waste (FW).Korea alone produces more than 9000 tons FWW/d (MOE, 2012).Before 2013, most FWW was discarded by ocean dumping, but this

practice was banned by the London Convention 97 protocol in Jan-uary of 2013. Thus, treatment of FWW is very important for envi-ronmental protection and development of appropriate treatmenttechnology has become an urgent task.

Anaerobic digestion (AD) is widely used to treat organic wastes.Single-stage continuous mesophilic AD reactors are the mostattractive for decentralized, medium or small-scale AD becausethey have high process stability and low cost, and because theydo not require specialized operating skills. Meanwhile, the AD isperformed by diverse microorganisms involved in serial

f high-

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2 H.M. Jang et al. / Bioresource Technology xxx (2014) xxx–xxx

biochemical reactions, i.e., hydrolysis, acidogenesis, acetogenesisand methanogenesis. Therefore, an improved understanding ofmicrobial communities in AD would be useful to optimize reactorperformance (i.e., organic matter removal and methaneproduction).

The microbial community structure in AD can be quantifiedrapidly and inexpensively using quantitative real-time polymerasechain reaction (qPCR) and pyrosequencing. qPCR enables real-timedetection and quantification of specific sequences in a DNA sample,either as an absolute number of copies or as a relative amount.Pyrosequencing enables the sequencing of thousands to millionsof molecules in parallel in a single run, thereby enabling inexpen-sive investigation of microbial community changes during biolog-ical processes (Kim et al., 2013; Lee et al., 2012b; Sundberg et al.,2013). Furthermore, with the help of specific barcode, high-throughput sequences of multiple samples are available in parallelto more fully examine various microbial species (Parameswaranet al., 2007).

In this study, barcoded pyrosequencing was used to identify keyphylotypes of bacteria, and a methanogen-specific qPCR technique(Jang et al., 2014) was used to quantify 16S rRNA gene copy num-bers of total bacteria, archaea and four major methanogen ordersin the single-stage AD of high-strength FWW. Multivariate statisti-cal analysis (canonical correspondence analysis; CCA) and micro-bial community comparison (UniFrac hierarchical clustering andprincipal coordinated analysis; PCoA) were applied to investigatethe relationship between reactor performance and microbial com-munity structure.

2. Methods

2.1. Preparation of feedstock

The feedstock was collected from a FW recycling facility inPohang, Korea. About 50 tons/d of FWW has been generated in thisfacility. We proceeded pretreatment and storage directly aftersampling from this facility as described in previous research (Janget al., 2013). In addition, detailed physico-chemical characteristicsof the FWW used in this study are presented in Table 1.

2.2. Reactor operation

To evaluate reactor performance and microbial communities, alab-scale single-stage anaerobic reactor was used; it was operated

Table 1Physico-chemical characteristics of Seed and Feedstock used in this study.

Parameter Seed (Anaerobicsludge)

Feedstock (Foodwastewater)

pH 7.42 (0.25) 4.31 (0.02)TS (g/L) 24.4 (0.21) 118.49 (3.54)VS (g/L) 12.4 (0.31) 106.52 (3.41)TCOD (g/L) 16.88 (0.12) 139.58 (2.79)SCOD (g/L) 0.52 (0.03) 90.46 (1.53)TN (g N/L) 4.38 (0.45) 1.94 (0.15)NHþ4 –N (g-NH3/L) 0.98 (0.04) 0.57 (0.03)NO�2 (g NO�2 -N/L) – –NO�3 (g NO�3 -N/L) – –Total organic acid

(g COD/L)– 69.47 (1.46)

Lactic acid (g COD/L) – 40.74 (1.04)Acetic acid (g COD/L) – 14.35 (0.59)Propionic acid (g COD/L) – 3.32 (0.31)Butyric acid (g COD/L) – 9.94 (0.51)Succinic acid (g COD/L) – 1.12 (0.04)

Values are expressed as average (standard deviations).‘–’: not detected.

Please cite this article in press as: Jang, H.M., et al. Bacterial and methanogenicstrength food wastewater. Bioresour. Technol. (2014), http://dx.doi.org/10.101

with working volume of 6 L and mesophilic condition (35 ± 0.2 �C)(Fig. 1). The reactor was seeded with mesophilic anaerobic sludge(Table 1) taken from a successfully-operated full-scale mesophilicanaerobic plant (only treated sewage sludge) in Daegu, Korea.The reactor was fed four times a day using a peristaltic pump(Cole-Parmer�) controlled by a timer and relay, and FWW was ap-plied at two different HRTs (40-d, 20-d) at corresponding organicloading rates (OLRs) of 3.5 and 7 kg COD/m3 d sequentially over aperiod of 204 days.

2.3. Physico-chemical analysis

As described in Standard Methods (APHA-AWWA-WEF, 1998),total solids (TS), volatile solids (VS), total chemical oxygen demand(TCOD), total alkalinity (TA), total nitrogen (TN) and total phospho-rus (TP) were measured in samples from the reactor at intervals of3 d. The concentration of soluble organic matter including solubleCOD (SCOD), ammonia (NHþ4 –N), soluble TN (STN), and soluble TP(STP) were measured after filtering through a 0.45-lm pore-size fil-ter (Whatman, USA). The concentrations of nitrite ðNO�2 Þ, nitrateðNO�3 Þ, and orthophosphate (PO3�

4 –P) were determined using anion chromatograph (ICS-1000, DIONEX Co., USA). The pH and oxida-tion reduction potential (ORP) in each reactor were continuouslymeasured and recorded using a pH meter (405-DPAS-SC-K85, MET-TLER TOLLEDO, Switzerland) and ORP meter (Pt-4805, METTLERTOLLEDO, Switzerland). After filtering step through a 0.22-lmpore-size filter (Whatman, USA), organic acids were quantifiedusing a high performance liquid chromatograph (HPLC, AgilentTechnology 1100 series, Agilent Inc., USA) equipped with an organicacid and alcohol analysis column (Aminex HPX-87H, BIORAD Inc.,USA), a refractive index detector (RID), and a diode array detector(DAD). Biogas volume from the reactor was quantified using a waterdisplacement method, and composition was detected using a gaschromatograph (Model 6890 N, Agilent Inc., USA) equipped with apulsed discharged detector (PDD). The quantified organic acid val-ues were converted theoretically to g COD/L by using the followingconversion factors: lactic acid: 1.07, acetic acid: 1.07, propionicacid: 1.51, butyric acid: 1.82, succinic acid: 0.95.

2.4. Microbial community and population analysis

2.4.1. Genomic DNA extraction and qPCR analysisTotal genomic DNA from samples (Seed, Phases I and II) was ex-

tracted and stored as described previously (Jang et al., 2014). To

Fig. 1. Schematic diagram of lab-scale single-stage AD process for high-strengthFWW.

archaeal communities during the single-stage anaerobic digestion of high-6/j.biortech.2014.02.028


H.M. Jang et al. / Bioresource Technology xxx (2014) xxx–xxx 3

quantify 16S rRNA gene copy numbers of total bacteria, archaeaand four major methanogen orders (Methanobacteriales, MBT;Methanococcales, MCC; Methanomicrobiales, MMB; and Methanos-arcinales, MSL), qPCR amplification and fluorescence detectionwere conducted using an Applied Biosystems 7300 real-time PCRsystem (Applied Biosystems, Forster City, USA) with group andorder specific primer sets and representative strain reported inprevious research (Jang et al., 2014).

2.4.2. Pyrosequencing analysisTo investigate in-depth bacterial communities, hypervariable

regions within bacterial 16s rRNA genes were amplified with uni-versal primers (Table 2): Bac27F (50-adaptor A-Barcode-AC-GAGTTT GAT CMT GGC TCA G-30)/Bac541R (50-adaptor B-Barcode-AC-WTT ACC GCG GCT GCT GG-30) (Lee et al., 2014). PCR amplificationwas conducted using a FastStart High Fidelity PCR system (Roche,Branford, CT); the protocol was (1) initial denaturation at 94 �Cfor 4 min; (2) 35 cycles of 94 �C for 15 s, 55 �C for 45 s, and 72 �Cfor 1 min; (3) a final extension at 72 �C for 8 min. All PCR productswere purified using a PCR purification kit (Solgent, Korea) and con-centrations of the nucleic acid were measured using a fluorometerwith Quant-iT™ PicoGreen� dsDNA Assay Kit (Invitrogen TM, Cal-ifornia). After pooling of equal amount of PCR products from eachsample, their sequencings were performed with 454-GS-FLX Tita-nium (Roche, Branford) using the massively parallel pyrosequenc-ing protocol by a sequencing company (Macrogen, Korea).

2.4.3. Pyrosequencing data and multivariate statistical analysisThe raw 16S rRNA gene sequences of bacteria obtained from

pyrosequencing were initially sorted using the RDP Pyrosequenc-ing Pipeline Initial Process (http://pyro.cme.msu.edu/) based onthe barcode and filter criteria as follows: maximum number (N)of ambiguous bases = 1, minimum read quality score = 20 and min-imum sequence length = 400 bp. Then adapters, barcodes andprimers in all raw sequence were trimmed to reduce errors of clus-tering. Extraction of chimeric sequences was conducted using theMOTHUR program (www.mothur.org/) (Lee et al., 2014). The mul-tiple clean sequences were aligned using the fast, secondary-struc-ture aware INFERNAL aligner and were clustered into operationaltaxonomic units (OTUs) defined by 3% max distance (97% similar-ity) using complete linkage-clustering method provided in RDPpyrosequencing pipeline. The Shannon–Weaver index (Shannonand Weaver, 1963), Chao1 richness index (Chao and Bunge,2002) and Evenness were calculated using clustered sequence datausing the Shannon Index and Chao1 estimators of the RDP pyrose-quencing pipeline. Rarefaction curves were also constructed with a3% dissimilarity cut-off value using the RDP pyrosequencing pipe-line. The taxonomic classification of clean sequences obtained fromeach sample was conducted using the RDP classifier 2.5 trained on16S rRNA training set 9 (Wang et al., 2007) with an 80% confidencecut-off.

The comparison of microbial communities was conducted usingUniFrac analysis (http://unifrac.colorado.edu/) based on thephylogenetic information as described previously (Lee et al.,

Table 2Adaptor and barcode sequences used in the pyrosequencing.

Name Sequence (50–30)

Adaptor sequencesAdaptor A CCTATCCCCTGTGTGCCTTGGCAGTCTCAGAdaptor B CCATCTCATCCCTGCGTGTCTCCGACTCAG

Bacterial barcode sequencesSeed (0 d) CATGCTCPhase I (0–102 d) ATACGTACGPhase II (102–204 d) AGACAGTACAG


2012a). Briefly, clean sequences were clustered into OTUs by usingCD-HIT (www.bioinformatics.org/cd-hit/) and representative se-quences from CD-HIT were aligned using NAST based on theGreengenes database. Then, a phylogenetic tree was constructedusing PHYLIP software and the Kimura two-parameter model forthe hierarchical clustering of bacterial communities in the un-weighted UniFrac analysis. Also PCoA was performed to confirmthe multiple communities identified by the UniFrac analysis. Toevaluate the correlations, a multivariate CCA of the relative abun-dance of microbial communities, reactor parameters and reactorperformance was performed using R software (http://cran.r-project.org/) with the vegan library.

3. Results and discussion

3.1. Reactor performance

The reactor showed stable performance and methane produc-tion at 40-d and 20-d HRTs over 204 days (Fig. 2). In general, load-ing of high-strength feedstock on the biological reactor is a majorcause of the reactor performance deterioration due to increase ofenvironment stress, especially unfavorable pH condition (Salminenand Rintala, 2002). Although the feedstock used in this study hadlow pH (�4.3), pH in the reactor was maintained between 6.75and 7.33 (Fig. 2a), which is known to be a favorable pH range fororganic matter degradation and methane production during sin-gle-stage AD. Also, sufficient total alkalinity which is regarded asbuffering capacity in the AD was maintained in the range of 2.23to 3.29 g CaCO3/L throughout digestion.

When HRT was decreased from 40-d (Phase I) to 20-d (Phase II)(OLR increased from 3.5 to 7 kg COD/m3 d), solid (TS and VS) andtotal organic matter (TCOD) concentrations in the reactor in-creased slightly (Fig. 2b and c), but the efficiencies of solid and or-ganic matter removal remained stable: 63–68% of TS, 77–80% of VSand 71–78% of TCOD were removed during the overall digestion.The FWW contained abundant organic matter, in both solid andsoluble forms (SCOD) (Table 1). Of the SCOD, organic acid (con-verted to g COD/L) accounted for over 76%; over 58% was lacticacid, which is produced by partial lactic acid fermentation duringthe recycling process. Overall, significant SCOD removal efficiency(over 96%) was observed (Fig. 2c) and lactic acid was not detectedin the reactor (Fig. 2d). During Phase I, most of the organic acidswere consumed and only acetic acid (<1 g COD/L) was detected.However, during Phase II a relatively low concentration ofpropionic acid (<0.8 g COD/L) was detected along with the slightlyelevated acetic acid concentration (�1.25 g COD/L). This phenome-non coincided with the decrease of the HRT during Phase II;previous research reported propionic acid accumulation in metha-nogenic reactor at short HRT (Shin et al., 2010).

During the startup period methane production rate (MPR) in thereactor fluctuated slightly then stabilized at �0.63 L/L d after 30-dof operation (Fig. 2e). During the initial period of Phase II, MPR in-creased significantly from 0.63 to 1.30 L/L/d as OLRs increasedfrom 3.5 to 7 kg COD/m3 d then stabilized at �1.19 L/L d after145-d of operation. Methane yield was stable at 0.23–0.24 m3

CH4/kg CODremoved and 0.28–0.29 m3 CH4/kg VSremoved. Theseexperimental results demonstrate that single-stage AD achievedboth efficient organic removal and stable methane productionfrom high-strength FWW.

3.2. Microbial community structure

3.2.1. 16S rRNA gene copy numbers of bacteria and archaeaThe number of 16S rRNA gene copies in the reactor can provide

overall information regarding microbial community composition


http://pyro.cme.msu.edu/

http://www.mothur.org/

http://unifrac.colorado.edu/

http://www.bioinformatics.org/cd-hit/

http://cran.r-project.org/

http://cran.r-project.org/


Fig. 2. Reactor performance during the overall digestion: (a) pH and TA, (b) TS and VS, (c) TCOD and SCOD, (d) Organic acids and (e) methane production rate (MPR).


Please cite this article in press as: Jang, H.M., et al. Bacterial and methanogenic archaeal communities during the single-stage anaerobic digestion of high-strength food wastewater. Bioresour. Technol. (2014), http://dx.doi.org/10.1016/j.biortech.2014.02.028



and response to the change of operating conditions (e.g., tempera-ture, OLR, pH and HRT). We estimated the 16S rRNA gene copynumbers of total bacteria, archaea and four major methanogen or-ders (MBT, MCC, MMB and MSL) (Table 4). The number of bacterial16S rRNA gene copies in the seed was 2.85 � 1010 copies/mL; dur-ing Phase I it increased to 6.29 � 1010 copies/mL (a 2.21-fold in-crease), and during Phase II, it increased to 2.15 � 1011 copies/mL(7.54 and 3.42-fold increase compared to the Seed and Phase I,respectively) with further increase of OLR from 3.5 to 7 kg COD/m3 d. The large increase of bacteria population in the reactor mightbe affected by the characteristics of the FWW (i.e., high concentra-tion of organic matter that is readily available to most bacteria)and was consistent with stable reactor performance, especially re-moval of organic matter (Fig. 2). The FWW characteristics mightalso enhance stability of reactor performance because a highmicrobe population can respond quickly to environmentstress (i.e., introduction of high-strength feedstock in this study)(Klappenbach et al., 2000).

Similarly to the bacteria, the number of archaeal 16S rRNA genecopies increased during Phases I (from 1.71 � 109 to 6.9 � 109 cop-ies/mL) and II (to 2.35 � 1010 copies/mL). Typically, archaea popu-lation accounts for �10% of total microbe population in biogasproducing reactors (Wirth et al., 2012). The proportion of archaeato the total microbe amount (the sum of archaea and bacteria)was 5.66% in the seed, and increased during Phase I (9.89%) andII (9.85%); these increases corresponded to the marked increaseof MPR (Fig. 2e). Thus, these results imply that the archaeacommunity mainly related to methane production was well-established in this study.

The four methanogen orders accounted for 95.1–97.8% of totalarchaeal 16S rRNA gene copy number. In this study, the orderMCC was not detected during the overall digestion, aceticlasticmethanogen order MSL accounted for over 81.3%, and hydrogeno-trophic orders MBT and MMB represented �5% and 11% of the ar-chaea population in Seed, respectively (Table 4). Both the numberof 16S rRNA gene copies of all targeted methanogens and the var-iation in relative abundance of methanogens increased signifi-cantly when the FWW was introduced into reactor. During PhaseI, the relative abundance of MSL decreased to �61%, and hydro-genotrophic methanogens including MBT and MMB increased to�17% and 20% of the archaea populations, respectively. DuringPhase II, the relative abundance of MSL decreased continuouslyto �54% and that of MMB increased to �25%. The variation in rel-ative abundance of methanogen populations strongly suggests thata large proportion of the methane generation pathway shifted fromaceticlastic to hydrogenotrophic methanogens during overalldigestion. This marked increase of the hydrogenotrophic methano-gen population (from �16% to �41%) during the overall digestioncan be explained by (1) difference in the specific growth rate be-tween aceticlastic and hydrogenotrophic methanogens; typicallya relatively short HRT provides an environment that is more favor-able for hydrogenotrophic methanogens than for aceticlastic meth-anogens, and (2) bacterial community changes (Fig. 4) caused byan increase of OLR; methanogenic community was highly affectedby the bacterial communities because main carbon sources werederived from bacteria species (Lee et al., 2009). This observationis supported by previous studies, which reported methanogeniccommunity shift from aceticlastic to hydrogenotrophic methano-gens during AD of MSW, although operating conditions and sub-strate were different than in this study (Kim et al., 2013; Janget al., 2014).

3.2.2. Phylotypes and diversity indices of bacterial communitiesTo explore bacterial communities involved in Seed, Phase I

(1–102 d) and Phase II (102–204 d), a total of 14,875 raw se-quences were obtained by pyrosequencing (Table 3). After sorting


and trimming processes, a total of 14,477 clean sequences (97.3%of the raw sequences) were obtained and 16S rRNA gene librariesof Seed, Phase I and Phase II were constructed with 8550 (462),3725 (474) and 2202 (475) clean sequences (average length ofclean sequences; bp), respectively. The clean sequences weregrouped into OTUs using a 3% sequence dissimilarly cut-off valuewhich is commonly used to estimate the species level of phylotypediversity (Stackebrandt and Goebel, 1994).

Observed OTUs and those estimated using the Chao 1 estimator,the number of OTUs in each sample estimated by Chao 1 estimatorshowed similar variation pattern, but the numbers estimated usingthe Chao 1 estimator were significantly higher than that of ob-served OTUs (covering an 49.1–56.5% of estimated richness). Thisdiscrepancy indicates that 1126 (Seed), 305 (Phase I) and 370(Phase II) additional bacterial phylotypes could be observed. Indi-vidual rarefaction curves of each sample (Fig. 3) demonstrated asimilar trend and approached an asymptote, but did not reach asaturation phase. This result indicates that each sample showedhighly diverse bacterial communities and that additional sequenc-ing may be required to describe the full set of bacterial phylotypes.This is probably because environmental samples commonly con-tain numerous rare species (Ashby et al., 2007). Additionally, thecalculated probability of sampling completeness (Good’s coverage)showed coverage in the range of 95.6–97.4% with 97% species levelphylotypes, suggesting that the major bacterial phylotypes presentin each sample were detected in this study.

The ecological diversity indices which provide more informa-tion about community composition than simple species richnesswere also estimated by the Shannon–Weaver index (H0) and even-ness (E0) based on the OTU data set. In general, H0 is positively cor-related with the number of rare phylotypes and E0 in the sample.Low E0 (the value is constrained between 0 and 1 with 1 being com-plete evenness) indicates high variation in the relative abundanceof phylotypes. H0 in each sample showed some variation in therange of 3.61–4.37 with highest H0 in Seed (Table 3). E0 was highest(0.73) in Phase II although H0 in Phase II was slightly lower thanthat of Seed.

3.2.3. Taxonomic distribution of the bacterial communitiesTo investigate the bacterial succession during the AD of high-

strength FWW, relative abundance and taxonomic distribution ofthe bacterial communities in each sample were analyzed at thephylum, class and genus levels including unclassified sequences(Fig. 4). A significant number of bacterial sequences from pyrose-quencing were assigned to unclassified sequences. These resultsare in agreement with previous studies that showed a high propor-tion of unclassified sequences in anaerobic reactors (Lee et al.,2012b; Wirth et al., 2012).

Previous studies of bacteria in AD mainly detected four majorphyla (Chloroflexi, Proteobacteria, Bacteroidetes and Firmicutes),although their relative abundances differed (Rivière et al., 2009;Nelson et al., 2011; Lee et al., 2012b; Wirth et al., 2012; Sundberget al., 2013). In this study, the numbers of sequences classified atthe phylum level were 14 in the Seed, 8 in Phase I and 7 in PhaseII. Most bacteria in Seed were affiliated with one of seven phyla:Chloroflexi (63.95%), Thermotogae (5.56%), Proteobacteria (3.63%),Bacteroidetes (3.22%), Synergistetes (2.63%), Firmicutes (2.41%) andActinobacteria (0.77%), but unclassified phyla were relatively abun-dant (16.84%) (Fig. 4a).

Members of chloroflexi are important in AD of sewage sludge.Rivière et al. (2009) reported that Chloroflexi was more abundantthan other major phyla involved in AD of sewage sludge. A meta-analysis of microbial diversity datasets derived from sewagesludge digesters determined that Chloroflexi is one of the mostabundant phyla (Nelson et al., 2011). Given this information, bac-teria represented by this phylum make a major contribution to



Table 3Summary of the pyrosequencing and statistical data of bacterial communities in the samples at the start up and steady state condition of Phases I and II.

No. of rawsequences

No. of cleansequences

Average length of cleansequences (bp)

OTUa,b Shannon–Weaverindex (H0)b

Chao1b Evenness(E0)b

Good’scoverage (%)c

Seed (0 d) 8704 8550 462 1088 4.37 2214 0.62 96.3Phase I (0–102 d) 3757 3725 474 396 3.61 701 0.60 95.6Phase II (102–204 d) 2414 2202 475 357 4.30 727 0.73 97.4

a Operational taxonomic units.b Diversity indices of the microbial communities were calculated using the RDP pyrosequencing pipeline.c Calculated as G = 1�(n/N), where n is the number of singleton phylotypes and N is the total number of sequences in the sample.

Fig. 3. Rarefaction analysis of 16S rRNA gene sequencing reads of bacterial diversity during AD of high-strength FWW. The rarefaction curves were constructed using RDPpipeline with a 97% sequence similarity cut-off value.

Table 4The number of 16S rRNA gene copies of total bacteria, archaea and four order level of methanogens in the each sample.

Target group 16S rRNA gene copy numbera (relative abundance)b

Seed (0 d) Phase I (0–102 d) Phase II (102–204 d)

Total bacteria (copies/mL) 2.85 � 1010 6.29 � 1010 2.15 � 1011

Total archaea (copies/mL) 1.71 � 109 6.9 � 109 2.35 � 1010

Archaea/total microbec (%) 5.67 9.89 9.85Methanosarcinales (MSL, copies/mL) 1.39 � 109 (81.28%) 4.21 � 109 (61.01%) 1.27 � 1010 (54.04%)Methanobacteriales (MBT, copies/mL) 8.75 � 107 (5.11%) 1.17 � 109 (16.95%) 3.77 � 109 (16.04%)Methanomicrobiales (MMB, copies/mL) 1.88 � 108 (10.99%) 1.38 � 109 (19.85%) 5.88 � 109 (25.02%)Methanococcales (MCC, copies/mL) – – –

‘–’: not detected.a Average of duplicate qPCR reactions.b Calculated based on the 16S rRNA gene copy number of total archaea.c Sum of archaea and bacteria.


sludge digestion, and the high abundance of Chloroflexi in Seedmight be due to its origin (Section 2.2).

Drastic shifts of bacteria composition at the phylum level wereobserved during overall digestion. During Phase I, over 50% ofsequences were assigned to unclassified phyla, followed byChloroflexi (19.38%), Bacteroidetes (13.37%), Firmicutes (8.27%),Synergistetes (6.31%) and Actinobacteria (2.04%). During Phase II,


the relative abundances of unclassified phyla and Chloroflexidecreased to 8.45 and 7.13%, whereas those of Bacteroidetes,Firmicutes, Synergistetes and Actinobacteria increased to 35.38%,15.94%, 27.88% and 4.95%, respectively. These results suggest thatbacteria communities were highly affected by characteristics offeedstock and operating conditions (i.e., OLR). In addition, thebacteria community shift in this study is in general agreement with




the results of an earlier study which found a relationship betweensubstrate and bacteria community (Sundberg et al., 2013).

The affiliation of the bacterial communities was also assessed atthe class level (Fig. 4b). The number of classes detected was 24 inthe Seed, 13 in Phase I, and 8 in Phase II. Although the number ofunclassified bacterial classes increased to 20.44 (Seed), 63.09(Phase I) and 14.76% (Phase II), significant differences of bacterialcomposition were observed during the overall digestion. ClassAnaerolineae (phylum Chloroflexi) decreased significantly to19.38% and 7.08% at Phase I and II, respectively. Class Bacteroidia(phylum Bacteroidetes) were uncommon (0.36%) in Seed, but in-creased to 1.29% in Phase I and became the most abundant class(32.52%) in Phase II. Typically, most bacteria that belong to Bacter-oidetes can produce various lytic enzymes and acetic acid duringthe degradation of organic materials (Chen and Dong, 2005; Robertet al., 2007; Rivière et al., 2009). Thus, the contribution of this classin this study is probably related to organic matter removal and toacetic acid production for syntrophic bacteria, aceticlastic metha-nogens, or both.

The bacterial composition is an important factor in formingmethanogen communities. Classes Clostridia (phylum Firmicutes)

Fig. 4. Relative abundance of bacteria 16S rRNA gene sequences showing the bacterial sgenus level. The sequences showing a percentage of reads below 1.0% in all samples we


and Synergistia (phylum Synergistetes) were very abundant duringPhases I and II. Both of these classes include numerous bacteriathat can efficiently degrade complex organic matters and fermentlactic or acetic acid to H2 and CO2 (Ito et al., 2011; Nelson et al.,2011; Wirth et al., 2012). The predominance of these classes inthe reactor indicates that a syntrophic relationship with hydro-genotrophic methanogens also increased during overall digestion.These results were consistent with the marked increase in the pop-ulation of hydrogenotrophic methanogens during Phases I and II(Table 4). Class Actinobacteria, also increased to 2.04% and 4.95%during the Phases I and II, respectively. Some bacteria in Actinobac-teria produce propionic acid (Nelson et al., 2011). Therefore, mem-bers of Actinobacteria may be responsible for the propionic acidaccumulation during Phase II (Fig. 2d).

At the genus level, most of bacteria sequences could not beassigned, but microbial communities at the genus level can providefurther valuable information about the functions of the communi-ties in the reactor. The relative abundances of two genera:Aminobacterium (phylum Synergistetes) and unclassified Porphy-romonadaceae (phylum Bacteroidetes) were uncommon in Seedand during Phase I, but increased to 26.66 (Aminobacterium) and

uccession of samples (Seed, Phases I and II): (a) phylum level, (b) class level and (c)re grouped into ‘Others’.



Fig. 5. Comparison of the bacterial communities in each sample based on the phylogenetic information using the unweighted UniFrac analysis: (a) hierarchical clustering (piecharts next to the sample name represent their respective community structures at the phylum level for visual comparison; scale bars represent unweighted UniFracdistance) and (b) principal coordinated analysis (PCoA).

Fig. 6. Canonical correspondence analysis (CCA) triplot to investigate the relation-ship between relative abundance of microorganism (bacteria (phylum level) andarchaea (order level)) and reactor performance data. The percentages on each axisindicate the variation in the samples. Straight arrows indicate the direction ofincrease of each variable and lengths are proportional to their strength on themicrobial communities. Directions of curved arrows indicate the routes of sampleson the CCA triplot during overall digestion.


24.48% (unclassified Porphyromonadaceae) during Phase II. This sig-nificant population increase implies that members of these generamost likely have an important function in AD during Phase II. Thisfinding is supported by previous studies, which reported the pres-ence of these two genera during the AD (Jabari et al., 2012; Kimet al., 2013). Overall, the bacterial community structure in eachsample was strongly consistent with reactor performance andmethanogenic communities.

3.2.4. Statistical comparisons of microbial communitiesTo compare the bacterial communities in each sample, phylog-

eny-based UniFrac analyses were performed using representativesequences derived from all detected 16S rRNA gene sequences. Inthe hierarchical clustering analysis (Fig. 5a), samples from PhasesI and II were grouped together relatively well, but the sample fromSeed showed low degree of similarity to Phases I and II. For a clearcomparison, PCoA was performed to cluster communities with themaximum variation (56.31% (PC1) and 43.69% (PC2)) (Fig. 5b).


Unweighted PCoA also demonstrated clear regional reparation,and samples from Phases I and II tended to cluster together,whereas Seed was clearly different from them. These clusteringresults suggest that Phases I and II shared similar bacterialcommunities, which are clearly different from those in Seed.

The relationship between relative abundance of microorgan-isms (bacteria (phylum level) and archaea (order level)) and reac-tor performance data was investigated using a multivariatestatistical CCA (Fig. 6). The first and second canonical axes repre-sented 46.1% and 23.1% of variance, respectively. The microbialcommunities were separated by period of digestion. Phase I wasdistinguished by the second canonical axis from other samples,and Phase II was separated from the Seed by the first axis; thesedistinctions suggest that significant microbial community shiftoccurred during overall digestion. These results agree with thetaxonomic distribution of microbial communities (Fig. 4; Table 4).

Triplot analysis showed that the phyla of Bacteroidetes,Firmicutes and Synergistetes were clearly related to the acetic acidconcentration in the reactor, and that Actinobacteria was stronglyrelated to propionic acid concentration. Based on these results,the phyla of Bacteroidetes, Firmicutes, Synergistetes and Actinobacteriamay be responsible for organic acid production and consumptionduring AD of high-strength FWW. The triplot analysis alsorepresented a clear relationship between hydrogenotrophicmethanogens and MPR.

4. Conclusion

This study focused on the feasibility of single-stage AD of high-strength FWW, and the response of microbial communities duringAD. Single-stage AD achieved efficient organic removal and highlystable methane production with the OLRs of 3.5 and 7 kg COD/m3 d. Barcoded-pyrosequencing data showed significant shift ofbacterial communities and that the phyla Bacteroidetes, Firmicutes,Synergistetes and Actinobacteria constituted the majority of the bac-terial community during overall digestion. Quantitative analysis ofthe archaeal communities indicated that methanogenic communi-ties shifted from aceticlastic to hydrogenotrophic methanogens,which were highly consistent with the bacterial communities.

Acknowledgements

This research was supported by a grant from Marine Biotech-nology Program Funded by Ministry of Land, Transport and Mari-time Affairs of Korean Government and the Advanced BiomassR&D Center (ABC) of Korea Grant funded by the Ministry of Educa-tion, Science, and Technology (ABC-2013059453). The researchwas partially supported by the Manpower Development Program




for Marine Energy funded by Ministry of Land, Transportation andMaritime Affairs (MLTM) of Korean government and the projecttitled ‘Technology Development of Marine Industrial Biomaterials’,funded by the Ministry of Oceans and Fisheries, Korea. This re-search was also supported by POSCO and a Korea Institute of En-ergy Technology Evaluation and Planning (KETEP) grant fundedby the Korea Government Ministry of Knowledge Economy (No.2012A095).

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