Bioresource Technology 101 (2010) 9461–9470

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Bioresource Technology

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Qualitative and quantitative assessment of microbial community in batchanaerobic digestion of secondary sludge

Seung Gu Shin a, Seungyong Lee a, Changsoo Lee b, Kwanghyun Hwang a, Seokhwan Hwang a,*

a School of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 790-784, South Koreab Division of Environmental and Water Resources Engineering, School of Civil and Environmental Engineering, Nanyang Technological University, 50 NanyangAvenue, Singapore 639798, Singapore

a r t i c l e i n f o a b s t r a c t

Article history:Received 12 May 2010Received in revised form 19 July 2010Accepted 19 July 2010Available online 24 July 2010

Keywords:Anaerobic digestionDenaturing gradient gel electrophoresisNon-metric multidimensional scalingReal-time PCRSecondary sludge

0960-8524/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.biortech.2010.07.081

* Corresponding author.E-mail address: shwang@postech.ac.kr (S. Hwang)

Microbial community shifts were determined by denaturing gradient gel electrophoresis (DGGE) andreal-time PCR for an anaerobic batch digester treating secondary sludge. The batch process was success-fully operated with an organic removal efficiency of 35% associated with a 91% decrease in the bacterial16S rRNA gene concentration. The microbial community structures showed continuous shifts within fourbacterial phyla and three archaeal orders. Several bacterial species, such as Fusibacter-related, Clostrid-ium-like, and Syntrophus-like organisms, appeared to be responsible for acidogenesis or syntrophic aciddegradation. Both hydrogenotrophic and aceticlastic methanogens appear to have been involved in themethanogenesis with the acidogenic products. The quantitative structure of the methanogenic popula-tions varied continuously, with the growth of Methanomicrobiales and Methanosarcinales in series, toresult in a Methanomicrobiales-dominant population. The ordination of microbial community structuresdemonstrated that the quantitative methanogenic structure converged to the seed inoculum while thebacterial and archaeal DGGE band patterns diverged. These results provide an insight into the microbialbehavior in the transitional phase (e.g., a start-up period) of anaerobic sludge digestion.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

A conventional municipal wastewater treatment process gener-ates a large volume of excess sludge. The processing and disposal ofwaste sludge, mainly secondary sludge, is a major issue in waste-water management because about half of the total operating costis dedicated to sludge treatment. Accordingly, application of acost-effective sludge process has been a subject of interest to engi-neers. Anaerobic digestion has long been used to treat sewagesludge because it has a low operational cost and produces biogas(Kobayashi et al., 2008). Anaerobic digestion of organic materialsinvolves a series of reactions: hydrolysis, acidogenesis, and metha-nogenesis. The first two reactions are mediated by bacterial popu-lations which produce hydrogen and organic acids, and the lastreaction is performed by the archaeal group that produces meth-ane using the acidogenic products. Complete mineralization of or-ganic materials is dependent on the concerted activity of theseinteracting microbial populations. Therefore, a comprehensiveunderstanding of the microbial behavior is a basic requirementfor fundamental improvement of anaerobic digestion process(Karakashev et al., 2005).

ll rights reserved.

.

Secondary sludge is the excess biomass generated through thebiological wastewater treatment process. Therefore, the sludgeparticles are in principle concentrated aerobic microbial cells. Inthe anaerobic digestion of decaying sludge cells, however, boththe ‘‘substrate” and the ‘‘performing” microorganisms (i.e., theanaerobic microbial consortia) are prokaryotic cells, making theanalysis of the active microbial community technically demanding.Recent development of molecular techniques has provided valu-able tools to interpret complicated microbial communities. Thisenabled researchers to link microbial community structure to pro-cess performance. Although a few studies have recently reportedon the steady-state microbial compositions in the anaerobic diges-tion of sludge (Chouari et al., 2005; Kobayashi et al., 2008; Rivièreet al., 2009), information is lacking in the literature on the behaviorof microbial communities in transitional states. This communitytransition, particularly during start-up, is notable because a suc-cessful start-up is crucial for long-term digester stability and effi-ciency (Leclerc et al., 2001; Lee et al., 2009b).

Therefore, this study aimed to investigate microbial communityshifts during a batch anaerobiosis of secondary sludge. A batch pro-cess is a well-established way to study the transitional state (Leeet al., 2008). The shifts of microbial populations were interpretedin relation to changes in physicochemical profiles to elucidatethe anaerobic sludge digestion. For our purpose, denaturing gradi-ent gel electrophoresis (DGGE) was conducted to identify major

9462 S.G. Shin et al. / Bioresource Technology 101 (2010) 9461–9470

bacterial and archaeal species throughout the batch process. Thequantitative dynamics of bacterial and methanogenic populationswere further analyzed with real-time PCR. Ordination of the micro-bial community structures was performed with non-metric multi-dimensional scaling (NMS) for more in-depth discussion.

2. Methods

2.1. Experimental set-up

An anaerobic complete-stirred tank reactor, with a working vol-ume of 6 L, was operated in the batch mode. Waste activatedsludge (WAS) and anaerobic sludge (AS) were collected from a lo-cal municipal wastewater treatment plant and used as substrateand seed inoculum (1%, v/v), respectively. The volatile solids (VS)concentrations of the substrate and the seed inoculum were 10.6and 17.4 g/L, respectively. The substrate had high salinity (8.2 gNaCl/L) due to intermittent seawater inputs into the local waste-water stream (Lee et al., 2009a). Temperature was maintained at35 �C and pH was adjusted to no less than 7.0 with 6 N NaOH.The surface of the bioreactor was sealed in order to protect it fromlight.

2.2. Extraction of DNA

DNA was extracted using an automated nucleic acid extractor(Magtration System 6GC, Precision System Science, Chiba, Japan).Possible PCR inhibitors and DNA from cell debris were minimizedby removing residual medium twice after centrifugation. The puri-fied DNA was eluted with Tris–HCl buffer (pH 8.0) and stored at�20 �C until use. All DNAs were extracted and analyzed induplicate.

2.3. PCR–DGGE and band affiliation

Bacterial and archaeal 16S rRNA genes were amplified by PCRwith the domain-level universal primers (Table 1) (Lee et al.,2008; Shin et al., 2008). The 50-ends of BAC338F and ARC787F wereadded with 40-bp GC-clamps, 50-CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGGGGG-30 and 50-CGCCCGCCGCGCCCCGCGCCCGTCCCGCCGCCCCCGCCCG-30, respectively, to stabilize the meltingbehavior of the PCR products (Muyzer et al., 1993). A touch-downPCR was conducted according to the following protocol: initial

Table 1Characteristics of the oligonucleotides used in this study.

Target group Namea Sequence

Bacteria F: BAC338F ACTCCTACGGGAGT: BAC516F TGCCAGCAGCCGCR: BAC805R GACTACCAGGGTA

Archaea F: ARC787F ATTAGATACCCSBGT: ARC915F AGGAATTGGCGGGR: ARC1059R GCCATGCACCWCC

Methanobacteriales F: MBT857F CGWAGGGAAGCTT: MBT929F AGCACCACAACGCR: MBT1196R TACCGTCGTCCACT

Methanococcales F: MCC495F TAAGGGCTGGGCAT: MCC686F TAGCGGTGRAATGR: MCC832R CACCTAGTYCGCA

Methanomicrobiales F: MMB282F ATCGRTACGGGTTT: MMB749F TYCGACAGTGAGGR: MMB832R CACCTAACGCRCAT

Methanosarcinales F: MSL812F GTAAACGATRYTCT: MSL860F AGGGAAGCCGTGAR: MSL1159R GGTCCCCACAGWG

a F, T and R indicate forward primer, TaqMan probe and reverse primer, respectively.b Culture collection numbers are in parentheses.

denaturation at 94 �C for 10 min; 20 cycles of denaturation at94 �C for 30 s, annealing at 65 to 55 �C (reducing the temperatureby 0.5 �C per cycle) for 30 s, and extension at 72 �C for 1 min; 15additional cycles of 94 �C for 30 s, 55 �C for 30 s, and 72 �C for1 min; final extension at 72 �C for 7 min. DGGE was performedwith a DCode system (Bio-Rad, Hercules, CA). The PCR productwas loaded onto an 8% (w/v) acrylamide gel containing a 40–60%denaturant gradient, where 100% was defined as 7 M urea with40% (v/v) formamide. Electrophoresis was run at 150 V for 7 h in1� TAE buffer. After staining with ethidium bromide, visible bandswere excised and eluted with distilled water. The eluted solutionwas further amplified with PCR using the corresponding primerswithout the GC-clamp. The PCR products were purified from a 1%agarose gel and cloned into the pGEM-T Easy vector (Promega,Madison, WI). The cloned 16S rRNA gene fragments were se-quenced and the results were compared with the reference se-quences in the GenBank database using the BLAST program.Sequence alignment and phylogenetic analysis were performedusing the DNAMAN software (version 5.2.2, Lynnon Biosoft, Que-bec, Canada). The phylogenetic trees were constructed using theneighbor-joining method. The nucleotide sequences reported inthis study have been deposited under GenBank Accession Nos.GQ233042–GQ233104.

2.4. Real-time PCR

The six primer and probe sets targeting the domains Bacteriaand Archaea, and the methanogenic orders Methanobacteriales,Methanococcales, Methanomicrobiales, and Methanosarcinaleswere used in this study (Table 1) (Yu et al., 2005b). The four ordersets should cover most methanogens in anaerobic digesters (Yuet al., 2005b). Real-time PCR was performed using a LightCycler1.2 instrument (Roche Diagnostics, Mannheim, Germany). The20 lL real-time PCR mixture was prepared using the LightCyclerFastStart DNA Master Hybridization Probes kit (Roche Diagnos-tics): 9.6 lL of PCR-grade water, 2.4 lL of MgCl2 stock solution (fi-nal concentration 4 mM), 1 lL of each primer (final concentration500 nM), 2 lL of the TaqMan probe (final concentration 200 nM),2 lL of 10� reaction solution, and 2 lL of template DNA. Thetwo-step amplification protocol was as follows: initial denatur-ation for 10 min at 94 �C followed by 45 cycles of 10 s at 94 �Cand combined annealing and extension for 30 s at 60 �C (63 �Cfor the Methanomicrobiales-set). The standard curves for real-time

Representative strainsb

GCAG Escherichia coli K12 (DSM 1607)GGTAATACTCTAATCCTAGTCC Methanomicrobium mobile BP (DSM 1539)GGAGCACTCT Methanosarcina barkeri MS (DSM 800)GTTAAGT Methanobacterium formicicum M.o.H. (DSM 863)GTGGACCTTAGT Methanococcus voltae (DSM 1537)YGTTGATCC

RAGTTTAGTGGG M. mobile BP (DSM 1539)RACGAAAGCTGHGTTTAC

GC TAGGT M. barkeri MS (DSM 800)AGCGARCCTACC

VS, C

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1.4TVFAAcetatePropionateiso-Valerate

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A

Fig. 1. Changes in chemical profiles along with methane production. COD, chemicaloxygen demand; VS, volatile solids; TVFA, total volatile fatty acid.

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PCR analysis were constructed as previously described (Lee et al.,2009b) with the representative strains listed in Table 1.

2.5. Ordination

Ordination allows analysis of high-dimensional spaces by plot-ting the strongest structure into reduced dimensions (Falk et al.,2009). NMS is one of the most generally effective ordination meth-ods for ecological community data because it avoids distributionalassumptions commonly associated with other ordination tech-niques (McCune and Grace, 2002). In this study, NMS ordinationwas performed based on the Sorensen distance measure in thePC-ORD software (MjM Software Design, Gleneden Beach, OR).The presence or absence of each DGGE band was scored 1 or 0,respectively, to generate distance matrices for bacterial and archa-eal community structures. Another matrix employing methano-genic group abundance was generated for ordination ofmethanogenic communities in a quantitative aspect. Each mainmatrix was processed for ordination such that the stress (<10)and the instability (<10�4) criteria were met.

2.6. Analytical methods

The VS and chemical oxygen demand (COD) were measuredaccording to the procedures in Standard Methods (APHA, 2005).Protein concentration was determined by the Kjeldahl method(APHA, 2005). A gas chromatograph (6890 Plus, Agilent, Palo Alto,CA), equipped with an Innowax capillary column (Agilent) and aflame ionization detector, was used to quantify C2–C6 volatile fattyacids (VFAs) and ethanol. Another identical gas chromatograph,equipped with an HP-5 capillary column (Agilent) and a thermalconductivity detector, was used to analyze the composition ofthe biogas.

3. Results

3.1. Reactor performance

The batch reactor was operated for 25.0 days. Fig. 1 shows theoverall process performance of the anaerobic bioreactor. Methaneproduction started at day 1.2 without a distinct lag period(Fig. 1A). At day 8.9, the methane production rate increased sharplyfrom 46 to 106 mL CH4/L days. The cumulative methane produc-tion reached 1.15 L/L at day 19.9 when biogas production ceased.The corresponding methane yield coefficient was 0.30 L CH4 pro-duced/g VS removed or 0.22 L CH4 produced/g COD removed.

The VS and COD concentrations gradually decreased until 35.3%and 34.8% of the initial amounts were removed, respectively(Fig. 1A). The VS removal rate was the highest during the earlystage of the batch reaction; 40% of the total VS reduction (i.e.,14% of the initial VS concentration) was achieved by day 2.4. How-ever, the COD removal in this period was notably lower (i.e., 25% ofthe total COD reduction) than the VS removal, although the overallremoval efficiencies of VS and COD were similar. The proteinreduction efficiency was 48.7% throughout the reaction (Fig. 1A).The decrease of protein concentration was 59% of that of VS con-centration, suggesting that protein decomposition was a significantcatabolic pathway in this process.

The total VFA (TVFA) concentration rapidly increased up to1.2 g/L, which then decreased continuously until day 19.9 withthe production of methane (Fig. 1B). Acetate, propionate, and iso-valerate were the most abundant acidogenic products in this pro-cess. The sum of these three acids was more than 82% of the TVFA.Acetate was accumulated up to 662 mg/L at day 4.8, which corre-sponds to 25% equivalence of the overall methane production. Pro-

pionate and iso-valerate concentrations peaked at 314 mg/L at day11.9 and 208 mg/L at day 9.9, respectively. Due to the thermody-namic disadvantage of propionate degradation, propionate is oftenthe last organic acid that persists in an anaerobic digester(Batstone, 2002; Lee et al., 2008). Propionate was virtually theonly VFA remaining after day 15.9; propionate was almost com-pletely degraded (>98%) at day 19.9 when methane productionceased.

3.2. Bacterial and archaeal community shifts

PCR–DGGE targeting on bacterial and archaeal 16S rRNA geneswas performed to investigate the microbial community shifts inthis process. The DGGE profiles showed continuous changes in bac-terial and archaeal community structures during the incubationtime (Fig. 2). The partial 16S rRNA gene fragments obtained from36 bacterial (B1–36) and 27 archaeal (A1–27) bands were se-quenced and the affiliations were determined by comparison withthe GenBank database (Table 2).

B26 was one of the dominant bands during the initial incuba-tion period when the VS and protein concentrations rapidly de-creased with the elevation of the TVFA level (Fig. 1). B26 and B27were most closely related to a clone isolated from an oil reservoir,which has putative affiliation within the genus Fusibacter (Table 2).The members of the genus Fusibacter were reported to be thiosul-fate-reducers which utilize carbohydrates to produce acetate,butyrate, CO2, and H2 (Basso et al., 2009). B30 was not clearly ob-served at day 0 but visualized after 2.4 days of incubation (Fig. 2A).B30 showed 97% sequence similarity with Dethiosulfatibacter ami-novorans. D. aminovorans is also a thiosulfate reducing bacteriumwhich is reported to ferment various organic compounds into

Fig. 2. DGGE profiles of (A) bacterial and (B) archaeal 16S rRNA gene fragments. Numbers at the top designate the incubation time in days of each lane. WAS, waste activatedsludge; AS, anaerobic sludge.

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acetate, propionate, CO2, and H2 (Takii et al., 2007). The band inten-sity of B23 increased during the early batch period and remaineddistinct until the end of reaction (Fig. 2A). B23 was closely relatedto Clostridium aminobutyricum with 97% sequence similarity(Table 2). C. aminobutyricum is a spore-forming anaerobe whichferments amino acids to form acetate, butyrate, and ammonia(Balows et al., 1992). B33 was also closely (99%) associated witha Clostridium species, C. sticklandii (Table 2). C. sticklandii anaerobi-cally utilizes pairs of amino acids by Stickland reaction andproduces acetate, butyrate, and ammonia (Balows et al., 1992).

Band B36, observed throughout the reaction (Fig. 2A), had 100%sequence similarity with Lactobacillus delbrueckii subsp. bulgaricus(Table 2). Although most Lactobacillus species can grow on variouscarbohydrates, L. delbrueckii subsp. bulgaricus is reported to utilizemainly lactose to form lactate (Balows et al., 1992). The closestneighbor of B24 was an environmental clone which was affiliatedwithin the genus Syntrophus (Table 2). Members of the genus Syn-trophus are anaerobes which utilize butyrate, benzoate, or otheracids through syntrophism with H2 and/or formate utilizers suchas methanogens (Jackson et al., 1999). Putative roles of the other29 bacterial bands were relatively unclear. Bands B28–29 andB34–35 were not detected at day 0 and evolved thereafter(Fig. 2A). By contrast, band intensities of B1–9, B12–20, B25, andB31–32 gradually decreased throughout the reaction. It is note-worthy that some of these bands were closely related to bacteriaor clones with marine origin due to the high salinity (8.2 g NaCl/L) and the putative seawater input into the local wastewater treat-ment plant (Lee et al., 2009a).

The archael DGGE profiles showed continuous shifts in archaealcommunity structure during the batch period (Fig. 2B). Overall, thearchaeal band patterns were less complicated than the bacterial re-sults due to the relatively low diversity of the domain Archaea inmost microbial complexes (Curtis and Sloan, 2004). A26 was thedominant band after the onset of methane production (Fig. 1A).Eight bands including A26 were closely related to Methanoplanuspetrolearius (Table 2). Eleven other bands (A3–4, A6, A8–10, A14,A18, A20, A22, and A24) were also closely related to four hydro-genotrophic methanogens (Table 2). Among these bands, A18,A20, A22, and A24 were observed during the incubation period(Fig. 3B).

The band intensity of A27 increased considerably during theincubation period (Fig. 2B). A27 was closely related to Methanosar-cina mazei with 98% sequence similarity (Table 2). Bands A1–2 andA15–17 also corresponded to an aceticlastic methanogen, Methan-osaeta concilii (Table 2). A12 and A13 were observed during the ini-tial phase but disappeared after day 2.4 (Fig. 2B). These bands weremostly closely related to a clone which was isolated from a meth-anogenic consortium (Table 2). They were branched within the or-der Thermoplasmatales, a group of non-methanogenicthermophilic acidophiles, with their metabolic roles in this systemremaining unclear.

Neighbor-joining trees were constructed to characterize theaffiliation of the bacterial and archaeal band sequences to the data-base sequences (Fig. 3). The bacterial sequences were branchedwithin four phyla, Proteobacteria, Bacteroidetes, Firmicutes, andActinobacteria (Fig. 3A). In the archaeal neighbor-joining tree, 25

Table 2Phylogenetic affiliation of the 16S rRNA gene sequences from DGGE bands.

Band(s) Nearest sequence Accession No. % Similarity

B1 Yeosuana aromativorans AY682382 98B2, 4, 15–16 Denitromonas aromaticus AB049763 96B3 Aequorivita antarctica AY771732 99B5, 12–13 Uncultured Deltaproteobacterium Sylt 5 AM040101 91–92B6 Flavobacterium sp. 90d AF228796 88B7, 22 Uncultured bacterium 128O51 FJ416130 97B8–9 Uncultured Deltaproteobacterium RS25 AJ289747 98–99B10 Hyphomonas adhaerens AF082790 99B11 Uncultured Gammaproteobacterium pKB3B-20 AB247876 97B14 Uncultured bacterium JH10_C65 AY568817 95B17 Dechloromonas denitrificans AJ318917 97B18 Uncultured bacterium UA01 AB456223 93B19 Uncultured bacterium FS396_454_1000bp_1036B DQ909703 90B20 Uncultured bacterium boneC15B1 AY548995 98B21 Uncultured bacterium 43_BS5_8 FJ825571 97B23 Clostridium aminobutyricum X76161 97B24 Syntrophus sp. B3 AJ133796 97B25 Pelobacter masseliensis AY187308 98B26–27 Fusibacter sp. enrichment culture clone 22-7A EU517558 99B28 Uncultured bacterium BSA1B-15 AB175369 95B29 Uncultured Bacteriodetes bacterium PG-5-1-3-L EU626571 93B30 Dethiosulfatibacter aminovorans AB218661 97B31–32 Uncultured bacterium A-L-2 AB154497 95B33 Clostridium sticklandii M26494 99B34 Uncultured bacterium clone CLA-7 DQ068728 97B35 Uncultured Actinobacteria bacterium QEDN11DD05 FJ661111 95B36 Lactobacillus delbrueckii subsp. bulgaricus FJ915705 100A1–2, 15–17 Methanosaeta concilii X16932 98–99A3, 22 Methanocalculus pumilus AB008853 99–100A4 Methanoculleus sp. ZC-2 DQ787476 99A5, 7, 11, 19, 21, 23, 25–26 Methanoplanus petrolearius AY196681 97–98A6, 8, 10, 20, 24 Methanogenium marinum DQ177344 97A9, 14, 18 Methanocorpusculum bavaricum AF042197 97–98A12–13 Uncultured Euryarchaeote SMS-sludge-6 AB479397 99–100A27 Methanosarcina mazei AY196685 98

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out of 27 bands (92%) were assigned to methanogenic orders; 19bands (70%) within Methanomicrobiales and six bands (22%) with-in Methanosarcinales (Fig. 3B). No member of Methanobacterialesor Methanococcales was identified.

3.3. Quantitative population dynamics

Quantitative changes in the 16S rRNA gene concentrations werealso determined by real-time PCR (Fig. 4). The concentration pro-files showed significant temporal variations in bacterial, archaeal,and the order-level methanogenic populations with respect to per-formance data. The bacterial 16S rRNA gene concentration initiallyincreased from 7.0 � 109 to 9.8 � 109 copies/mL (39% increase) be-tween days 0 and 2.4, then it decreased continuously down to6.6 � 108 copies/mL (93% decrease from day 2.4 or 91% decreasefrom day 0) until the end of reaction. The archaeal 16S rRNA genelevel increased sharply in the early phase: from 3.5 � 106 to3.4 � 107 copies/mL (15-fold increase) on day 7.9. After day 7.9,the archaeal gene concentration did not change significantly,remaining above 3 � 107 copies/mL. Accordingly, the relativeabundance of archaea to bacteria increased continuously from0.1% to 9.9% throughout the reaction period. The bacteria to ar-chaea ratios of the WAS and the AS were 0.1% and 2.9%,respectively.

The three hydrogen-utilizing methanogenic groups, Methano-microbiales, Methanobacteriales, and Methanococcales, behaveddifferently in terms of their abundance (Fig. 4). The 16S rRNA geneconcentration of Methanomicrobiales increased sharply during theearly period. This trend coincides with the elevation of VFA con-centrations. The concentration increased up to 7.4 � 107 copies/mL (25-fold increase) on day 7.9. Similarly to the archaeal result,the Methanomicrobiales gene concentration did not change signif-

icantly after day 7.9, remaining above 6 � 107 copies/mL. By con-trast, no distinct growth of Methanobacteriales was observed.Methanococcales was not detected in any DNA samples in thisstudy. The 16S rRNA gene level of Methanosarcinales, the only ace-ticlastic order among methanogens, increased from 2.2 � 106 to1.0 � 107 copies/mL (4.6-fold increase) between days 4.8 and15.9. During this period, the acetate concentration gradually de-creased to zero (Fig. 1B). The Methanosarcinales level increasedsteeply between days 9.9 and 11.9, corresponding to the periodwhen the methane production rate increased sharply (Fig. 1A).The sum of the three methanogenic orders accounted for 64% to135% of archaea; it was less than 100% when non-methanogenicarchaeal DGGE bands (A12–13) were present.

The relative abundance of the methanogenic populations wasvisualized in Fig. 5. The substrate and the seed contained9.3 � 106 and 1.7 � 108 copies/mL of methanogenic 16S rRNA genewith Methanosarcinales and Methanomicrobiales as the majorpopulation, respectively. The aceticlastic Methanosarcinales wasthe most abundant population at day 0 (mixture of the substrate,99% v/v, and the seed) but supplanted by hydrogenotrophic meth-anogens with the rapid growth of Methanomicrobiales (Figs. 4 and5). The 16S rRNA gene ratio of Methanomicrobiales revealed itsmaximum of 94.3% at day 9.9 and remained greater than 88%thereafter (Fig. 5). On the other hand, Methanosarcinales main-tained a relatively small population (4–11%) after day 4.8.

3.4. Ordination

Changes in microbial community structures were also visual-ized by NMS ordination (Fig. 6). Fig. 6A and B shows the continuousshifts of bacterial and archaeal DGGE band patterns, respectively,while Fig. 6C shows the ordination of the methanogenic group

Fig. 3. Neighbor-joining tree illustrating the phylogenetic identities of the 16S rRNA gene sequences from (A) bacterial and (B) archaeal DGGE bands. a, b, c, and d designateclasses within the phylum Proteobacteria.

9466 S.G. Shin et al. / Bioresource Technology 101 (2010) 9461–9470

abundance quantitatively assessed by real-time PCR. The resultsmet the general criteria for good NMS performance, with the stressvalue below 10 and the instability lower than 10�4 (McCune andGrace, 2002).

The bacterial DGGE band patterns were assigned as a one-dimensional solution by the NMS analysis with the sole axis 1Aexplaining 94% of the variability in band structures (Fig. 6A). Onthe NMS map, the bacterial community structure was placed near

Fig. 3 (continued)

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the substrate at day 0 and shifted continuously towards, in general,the opposite direction of the seed. As a result, the end-point bacte-rial community at day 25.0 evolved to a point distinct from boththe substrate and the seed. The archaeal DGGE band patterns areillustrated on a two-dimensional NMS plot (Fig. 6B). The archaealpattern at day 0 was between, although not close to, the substrateand the seed points. The archaeal band patterns gradually migratedto the cluster including points at days 7.9–25.0, which was alsodistinct from the archaeal community of the substrate or the seed.Shifts in the quantitative methanogenic population profiles areshown in Fig. 6C. The methanogenic real-time PCR profile at day0 was placed near the substrate and continuously migrated intoa cluster (days 7.9–25.0) near the seed profile.

4. Discussion

Secondary sludge is the excess biomass generated from the acti-vated sludge process. The organic materials in the sludge consistmainly of prokaryotic cells, mostly aerobic bacteria. In this batchprocess, changes in the substrate were monitored primarily by

measuring crude organic materials such as VS and COD. The VSreduction efficiency in this study (35.3%) was comparable to previ-ous reports with mesophilic continuous operation, where 14–36%of VS was reduced with hydraulic retention times of 8–45 days(Bolzonella et al., 2005; Park et al., 2004). Alternatively, the bacte-rial 16S rRNA gene level determined by real-time PCR presumablyrepresents the quantity of the intact bacterial cells in the digester.The change in the bacterial rRNA gene concentration results fromthe combination of the decay of aerobic cells (‘‘substrate”) andthe growth and decay of anaerobic bacteria (‘‘acidogens”). There-fore, the 91% decrease in the bacterial 16S rRNA gene concentrationduring the batch process implies that more than 90% of the initialbacterial cells were decomposed. The decrease in bacterial DNAconcentration corresponds to the decrease of band intensities ofB1–9, B12–20, B25, and B31–32, implying that the bacteria relatedto these bands decayed. The relatively low organic removal effi-ciency (35%), compared to the decrease in the bacterial DNA con-centration (91%), is likely to be due to the low biodegradabilityof sludge solids by conventional anaerobic processes (Park et al.,2004; Siegrist et al., 2002).

Time (days)0 5 10 15 20 25

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Fig. 4. Quantitative changes in bacterial, archaeal, and methanogenic 16S rRNAgene concentrations determined by real-time PCR.

Fig. 5. Relative abundance of the three methanogenic orders based on the 16S rRNAgene concentrations.

Axis 1C

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A

C

Fig. 6. Non-metric multidimensional scaling ordination plot for (A) bacterial and(B) archaeal DGGE band patterns and (C) quantitative methanogenic profilesdetermined by real-time PCR. Each community profile on the plot is labeled witheither a number indicating the incubation time (days) or its identity. WAS, wasteactivated sludge; AS, anaerobic sludge.

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Protein is one of the major constituents (i.e., approximately halfof the dry weight) of a prokaryotic cell (Madigan and Martinko,2006). In anaerobic digesters, a specialized group of microorgan-isms, e.g. proteolytic clostridia, is responsible for protein catabo-lism. The maintenance of well-functioning proteolytic consortia,therefore, is important for the efficient operation of anaerobicsludge digestion. As revealed by DGGE analysis, two Clostridium-like bacteria (B23, B33) may have been mainly responsible forthe protein catabolism in this system. During this batch reaction,the protein removal efficiency (48.7%) was higher than the proteindegradation ratio of 39% in a previous study (Bougrier et al., 2007).The higher protein reduction efficiency could be attributed to abetter availability of the particulate proteins to the enzymatic ma-trix (Angelidaki and Sanders, 2004) and/or the existence of moreversatile proteolytic anaerobes in this digester.

The batch anaerobic digestion in this study can be roughly di-vided into an early hydrolytic/acidogenic phase and a later metha-nogenic phase; macromolecules were degraded into simpler acids,e.g. acetate, in the former while the acidogenic products were re-duced to biogas in the latter. Among the bacterial species detected,the Fusibacter-related (B26–27), the D. aminovorans-related (B30),the Clostridium-like (B23, B30), and the Lactobacillus-like (B36)organisms appear to have been involved in the production of VFAs.The VS removal by day 2.4 (40% of the total) was remarkably high-er than the COD removal (25% of the total), although both param-eters commonly describe the overall organic concentration ofwaste or wastewater (Angelidaki and Sanders, 2004). The relativelylow boiling point of intermediate acids (e.g., 118.1 �C for acetate),which encourages evaporation of these molecules at 105 �C in sol-

ids measuring procedures (APHA, 2005), could be responsible forthe gap between the VS and COD removal efficiencies in the earlierphase.

Acetate is often regarded as the major (70%) methanogenic pre-cursor (Speece, 1996) and can be utilized directly by methanogens.Accordingly, the acetate-utilizing Methanosarcinales has been re-ported to be the dominant methanogenic group in previous studies(Kobayashi et al., 2008; Yu et al., 2005a). Methanosarcinales wasthe most abundant methanogenic group, however, only at day 0in this system (Figs. 4 and 5). At this point, M. conilii-like archaeon(A15–17) was the only aceticlastic methanogen visualized in theDGGE profile. M. concilii can only utilize acetate for growth and iswidely distributed in nature due to its high affinity to acetate(Smith and Ingram-Smith, 2007). The 16S rRNA gene concentrationof Methanosarcinales increased after day 4.8 when the highest ace-tate concentration was observed (Fig. 1B). The Methanosarcinaleslevel increased steeply between days 9.9 and 11.9, coincident withthe appearance of M. mazei-like band (B27; Fig. 2B). M. mazei is anacetate-utilizing methanogen which can also convert other organ-ics to methane (Garrity, 2005). Methanosarcina spp. have highergrowth rate than Methanosaeta spp. (Lee et al., 2009b); therefore,

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the rapid increase of Methanosarcinales may have mainly resultedfrom the rapid growth of M. mazei-like methanogen in this system.The community shift from M. concilii to M. mazei, associated withthe rapid consumption of acetate, was important for the overallreaction efficiency because the accumulation of intermediate acidsduring a start-up period often leads to system failure (Batstone,2002). It is notable that the seed sludge contained no visible bandcorresponding to M. mazei, implying that the M. concilii-like archa-eon was the dominant aceticlastic methanogen due to the low ace-tate environment of the stable full-scale anaerobic digester(Demirel and Scherer, 2008; Yu et al., 2005a).

Hydrogenotrophic methanogenesis is one of the major metha-nogenic pathways in anaerobic digesters (Ahring, 2003). Due tothe thermodynamic limitations of the hydrogen-mediated metab-olism, low hydrogen partial pressure must be maintained for syn-trophic consortia to utilize various intermediates (Batstone et al.,2002). Therefore, efficient removal of hydrogen by hydrogen-utiliz-ing microorganisms is required for acidogenesis and/or acetogene-sis to occur. In this study, hydrogen-utilizing Methanomicrobialesincreased steeply during the early reaction period (Fig. 4); thischange coincided with the increase in band intensity of A26, theM. petrolearius-like band (Fig. 2B). M. petrolearius is a mesophilicmethanogen utilizing H2 and CO2, formate, and 2-propanol in thepresence of acetate (Garrity, 2005). The strong band intensity ofA26 and the acetate-containing environment suggested that theM. petrolearius-like species was in part responsible for themethanogenesis through Syntrophism with hydrogen- or formate-producing bacteria (e.g., the Syntrophus-like bacterium corre-sponding to B24). M. petrolearius was reported to favor halophilicconditions (up to 5% NaCl) (Ollivier et al., 1997), which could havebeen a selective pressure in favor of this species due to the highsalinity (8.2 g NaCl/L) of the substrate. Among other archaealbands, A18, A20, A22, and A24 were observed during the incuba-tion period (Fig. 2B), suggesting that the Methanocorpusculumbavaricum-like, the Methanogenium marinum-like, and the Methan-ocalculus pumilus-like organisms are also involved in methane pro-duction through hydrogen (Garrity, 2005).

The methanogenic populations in this study shifted from aMethanosarcinales-dominant one to a Methanomicrobiales-domi-nant one (Fig. 5). Similar changes have been attributed to shiftsin VFA-utilizing pathway associated with the growth of differentmethanogenic populations (Lee et al., 2009b). It is noteworthy thatsome previous studies have reported the dominance of aceticlasticmethanogens in the anaerobic digestion of sludge (Karakashevet al., 2005; Kobayashi et al., 2008; Yu et al., 2005a). However,many other reports have demonstrated the prevalence of thehydrogenotrophic pathway in anaerobic processes with differentsubstrates (Demirel and Scherer, 2008; Karakashev et al., 2005;Song et al., 2010); the key factor determining the major pathwaycould be the existence of high levels of inhibitory ions, which ingeneral have more severe effects on aceticlastic methanogens.The rapid growth of the A26-corresponding archaeon (Fig. 2B), re-lated to halophilic M. petrolearius, was likely to be responsible forthe outgrowth of Methanomicrobiales in this system.

The microbial community structures, as determined by DGGE,showed continuous shifts within four bacterial phyla and twoarchaeal orders. No archaeal DGGE band related to Methanobacte-riales was identified by DGGE, although this group was success-fully quantified with real-time PCR. The relatively low abundanceof this group (<13% of the sum of methanogenic orders) withinthe domain archaea could be responsible for the lack of visiblebands in the domain-level analysis (Shin et al., 2008). Interestingly,a comprehensive investigation on the microbial diversity has re-vealed Chloroflexi as the most abundant (32 ± 9%) bacterial phy-lum in anaerobic digestion of sludge (Rivière et al., 2009). Thephylum Chloroflexi is divided into two orders: Chloroflexales, the

obligate or facultative phototrophs, and Herpetosiphonales, theaerobic heterotrophs (Garrity, 2005). However, no DGGE band re-lated to Chloroflexi was identified in this study, probably due tothe anaerobic environment and the protection from light.

In this study, NMS was applied to visualize the patterns ofmicrobial community shifts in the batch anaerobic digestion ofsludge. The pattern of migration of bacterial community inFig. 6A is different from a previous study with the seed inoculumfrom the same anaerobic digester but with soluble whey permeateas substrate (Lee et al., 2008). In the previous study, the end-pointbacterial community returned close to the bacterial structure ofthe seed. The main cause for this discrepancy must be the differentmicrobial compositions in the substrate. The bacterial communityin this sludge digester was populated with many decaying species;this is supported by the decrease of bacterial 16S rRNA gene con-centration (Fig. 4) and the disappearing bands (Fig. 2A), whichacted as ‘‘noise” bands in the light of function in anaerobic miner-alization. On the other hand, the initial bacterial community in theprevious study was merely a dilution of the anaerobic seed inocu-lum and the shifts in bacterial populations were within the rangeof the diversity of the seed, which consisted of anaerobic micro-flora (Lee et al., 2008).

In Fig. 6C, the migration of ordination points into a cluster (days7.9–25.0) near the seed profile was observed. The low residual ace-tate concentration and high methanogenic activity during this per-iod was similar to the operational conditions of the full-scaleanaerobic digester which provided the seed (Lee et al., 2008). Be-cause the end-of-batch methanogenic community had a similarstructure to the stable sludge digester, the operation of a batchreaction could provide an efficient means for the enrichment ofmethanogens as a start-up process (Leclerc et al., 2004; Lee et al.,2009b). However, the NMS ordination profiles in Fig. 6B and C be-haved differently in terms of the proximity between the seed andthe end point (day 25.0). These results imply that, from a quantita-tive point of view, the methanogenic community evolved similarlyto the seed inoculum in correlation with the production of meth-ane (Fig. 1A), while the qualitative (noted by presence or absenceof DGGE bands) structure was ‘‘noised” by weak (less abundant)and/or redundant (e.g., A26 and its neighbors in Table 2) bands.Therefore, a community analysis based on the quantitative micro-bial profiles could be beneficial for ordination studies when thecommunity structure must be interpreted in relation to the majoroperational performance (Falk et al., 2009; Lee et al., 2009b).

5. Conclusions

The performance and microbial community shifts were charac-terized in anaerobic digestion of secondary sludge. The batch pro-cess was successfully operated with an organic removal efficiencyof 35% associated with a 91% decrease in bacterial 16S rRNA geneconcentration. The microbial community structures showed con-tinuous shifts within four bacterial phyla and three archaeal orders.The quantitative structure of methanogens varied continuously,with the growth of Methanomicrobiales and Methanosarcinales inseries, to result in a Methanomicrobiales-dominant population.The ordination demonstrated that the quantitative methanogenicstructure converged to the seed inoculum while the bacterial andarchaeal DGGE band patterns diverged.

Acknowledgements

This research was supported by Korea Ministry of Environmentas ‘‘Human Resource Development Project for Energy from Waste& Recycling” and Korea Ministry of Knowledge and Economythrough the ‘‘Manpower Development Program for Energy & Re-

9470 S.G. Shin et al. / Bioresource Technology 101 (2010) 9461–9470

sources”. This work was also supported by the Korea ResearchFoundation Grant funded by the Korean Government (MOEHRD).The authors are grateful to Dr. Byeongchul Park and Dr. Jaai Kimfor their contribution to this work and we also thank Bi Wen Zhoufor valuable discussions.

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