Qualitative and quantitative assessment of microbial community in batch anaerobic digestion of secondary sludge

Bioresource Technology 101 (2010) 9461–9470

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Qualitative and quantitative assessment of microbial community in batch anaerobic 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 Korea Division of Environmental and Water Resources Engineering, School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore

b

a r t i c l e

i n f o

Article history: Received 12 May 2010 Received in revised form 19 July 2010 Accepted 19 July 2010 Available online 24 July 2010 Keywords: Anaerobic digestion Denaturing gradient gel electrophoresis Non-metric multidimensional scaling Real-time PCR Secondary sludge

a b s t r a c t Microbial community shifts were determined by denaturing gradient gel electrophoresis (DGGE) and real-time PCR for an anaerobic batch digester treating secondary sludge. The batch process was successfully operated with an organic removal efficiency of 35% associated with a 91% decrease in the bacterial 16S rRNA gene concentration. The microbial community structures showed continuous shifts within four bacterial phyla and three archaeal orders. Several bacterial species, such as Fusibacter-related, Clostridium-like, and Syntrophus-like organisms, appeared to be responsible for acidogenesis or syntrophic acid degradation. Both hydrogenotrophic and aceticlastic methanogens appear to have been involved in the methanogenesis with the acidogenic products. The quantitative structure of the methanogenic populations varied continuously, with the growth of Methanomicrobiales and Methanosarcinales in series, to result in a Methanomicrobiales-dominant population. The ordination of microbial community structures demonstrated that the quantitative methanogenic structure converged to the seed inoculum while the bacterial and archaeal DGGE band patterns diverged. These results provide an insight into the microbial behavior in the transitional phase (e.g., a start-up period) of anaerobic sludge digestion. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction A conventional municipal wastewater treatment process generates a large volume of excess sludge. The processing and disposal of waste sludge, mainly secondary sludge, is a major issue in wastewater management because about half of the total operating cost is dedicated to sludge treatment. Accordingly, application of a cost-effective sludge process has been a subject of interest to engineers. Anaerobic digestion has long been used to treat sewage sludge because it has a low operational cost and produces biogas (Kobayashi et al., 2008). Anaerobic digestion of organic materials involves a series of reactions: hydrolysis, acidogenesis, and methanogenesis. The first two reactions are mediated by bacterial populations which produce hydrogen and organic acids, and the last reaction is performed by the archaeal group that produces methane using the acidogenic products. Complete mineralization of organic materials is dependent on the concerted activity of these interacting microbial populations. Therefore, a comprehensive understanding of the microbial behavior is a basic requirement for fundamental improvement of anaerobic digestion process (Karakashev et al., 2005).

* Corresponding author. E-mail address: [email protected] (S. Hwang). 0960-8524/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2010.07.081

Secondary sludge is the excess biomass generated through the biological wastewater treatment process. Therefore, the sludge particles are in principle concentrated aerobic microbial cells. In the anaerobic digestion of decaying sludge cells, however, both the ‘‘substrate” and the ‘‘performing” microorganisms (i.e., the anaerobic microbial consortia) are prokaryotic cells, making the analysis of the active microbial community technically demanding. Recent development of molecular techniques has provided valuable tools to interpret complicated microbial communities. This enabled researchers to link microbial community structure to process performance. Although a few studies have recently reported on the steady-state microbial compositions in the anaerobic digestion of sludge (Chouari et al., 2005; Kobayashi et al., 2008; Rivière et al., 2009), information is lacking in the literature on the behavior of microbial communities in transitional states. This community transition, particularly during start-up, is notable because a successful start-up is crucial for long-term digester stability and efficiency (Leclerc et al., 2001; Lee et al., 2009b). Therefore, this study aimed to investigate microbial community shifts during a batch anaerobiosis of secondary sludge. A batch process is a well-established way to study the transitional state (Lee et al., 2008). The shifts of microbial populations were interpreted in relation to changes in physicochemical profiles to elucidate the anaerobic sludge digestion. For our purpose, denaturing gradient gel electrophoresis (DGGE) was conducted to identify major

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bacterial and archaeal species throughout the batch process. The quantitative dynamics of bacterial and methanogenic populations were further analyzed with real-time PCR. Ordination of the microbial community structures was performed with non-metric multidimensional scaling (NMS) for more in-depth discussion. 2. Methods 2.1. Experimental set-up An anaerobic complete-stirred tank reactor, with a working volume of 6 L, was operated in the batch mode. Waste activated sludge (WAS) and anaerobic sludge (AS) were collected from a local municipal wastewater treatment plant and used as substrate and seed inoculum (1%, v/v), respectively. The volatile solids (VS) concentrations of the substrate and the seed inoculum were 10.6 and 17.4 g/L, respectively. The substrate had high salinity (8.2 g NaCl/L) due to intermittent seawater inputs into the local wastewater stream (Lee et al., 2009a). Temperature was maintained at 35 °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 from light.

denaturation at 94 °C for 10 min; 20 cycles of denaturation at 94 °C for 30 s, annealing at 65 to 55 °C (reducing the temperature by 0.5 °C per cycle) for 30 s, and extension at 72 °C for 1 min; 15 additional cycles of 94 °C for 30 s, 55 °C for 30 s, and 72 °C for 1 min; final extension at 72 °C for 7 min. DGGE was performed with a DCode system (Bio-Rad, Hercules, CA). The PCR product was loaded onto an 8% (w/v) acrylamide gel containing a 40–60% denaturant gradient, where 100% was defined as 7 M urea with 40% (v/v) formamide. Electrophoresis was run at 150 V for 7 h in 1 TAE buffer. After staining with ethidium bromide, visible bands were excised and eluted with distilled water. The eluted solution was further amplified with PCR using the corresponding primers without 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 sequenced and the results were compared with the reference sequences in the GenBank database using the BLAST program. Sequence alignment and phylogenetic analysis were performed using the DNAMAN software (version 5.2.2, Lynnon Biosoft, Quebec, Canada). The phylogenetic trees were constructed using the neighbor-joining method. The nucleotide sequences reported in this study have been deposited under GenBank Accession Nos. GQ233042–GQ233104.

2.2. Extraction of DNA 2.4. Real-time PCR 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 minimized by removing residual medium twice after centrifugation. The purified DNA was eluted with Tris–HCl buffer (pH 8.0) and stored at 20 °C until use. All DNAs were extracted and analyzed in duplicate. 2.3. PCR–DGGE and band affiliation Bacterial and archaeal 16S rRNA genes were amplified by PCR with the domain-level universal primers (Table 1) (Lee et al., 2008; Shin et al., 2008). The 50 -ends of BAC338F and ARC787F were added with 40-bp GC-clamps, 50 -CGCCCGCCGCGCGCGGCGGGCGG GGCGGGGGCACGGGGGG-30 and 50 -CGCCCGCCGCGCCCCGCGCCCG TCCCGCCGCCCCCGCCCG-30 , respectively, to stabilize the melting behavior of the PCR products (Muyzer et al., 1993). A touch-down PCR was conducted according to the following protocol: initial

The six primer and probe sets targeting the domains Bacteria and Archaea, and the methanogenic orders Methanobacteriales, Methanococcales, Methanomicrobiales, and Methanosarcinales were used in this study (Table 1) (Yu et al., 2005b). The four order sets should cover most methanogens in anaerobic digesters (Yu et al., 2005b). Real-time PCR was performed using a LightCycler 1.2 instrument (Roche Diagnostics, Mannheim, Germany). The 20 lL real-time PCR mixture was prepared using the LightCycler FastStart DNA Master Hybridization Probes kit (Roche Diagnostics): 9.6 lL of PCR-grade water, 2.4 lL of MgCl2 stock solution (final concentration 4 mM), 1 lL of each primer (final concentration 500 nM), 2 lL of the TaqMan probe (final concentration 200 nM), 2 lL of 10 reaction solution, and 2 lL of template DNA. The two-step amplification protocol was as follows: initial denaturation for 10 min at 94 °C followed by 45 cycles of 10 s at 94 °C and combined annealing and extension for 30 s at 60 °C (63 °C for the Methanomicrobiales-set). The standard curves for real-time

Table 1 Characteristics of the oligonucleotides used in this study. Target group

Namea

Sequence

Representative strainsb

Bacteria

F: BAC338F T: BAC516F R: BAC805R F: ARC787F T: ARC915F R: ARC1059R F: MBT857F T: MBT929F R: MBT1196R F: MCC495F T: MCC686F R: MCC832R F: MMB282F T: MMB749F R: MMB832R F: MSL812F T: MSL860F R: MSL1159R

ACTCCTACGGGAGGCAG TGCCAGCAGCCGCGGTAATAC GACTACCAGGGTATCTAATCC ATTAGATACCCSBGTAGTCC AGGAATTGGCGGGGGAGCAC GCCATGCACCWCCTCT CGWAGGGAAGCTGTTAAGT AGCACCACAACGCGTGGA TACCGTCGTCCACTCCTT TAAGGGCTGGGCAAGT TAGCGGTGRAATGYGTTGATCC CACCTAGTYCGCARAGTTTA ATCGRTACGGGTTGTGGG TYCGACAGTGAGGRACGAAAGCTG CACCTAACGCRCATHGTTTAC GTAAACGATRYTCGC TAGGT AGGGAAGCCGTGAAGCGARCC GGTCCCCACAGWGTACC

Escherichia coli K12 (DSM 1607)

Archaea

Methanobacteriales

Methanococcales

Methanomicrobiales

Methanosarcinales

a b

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

Methanomicrobium mobile BP (DSM 1539) Methanosarcina barkeri MS (DSM 800) Methanobacterium formicicum M.o.H. (DSM 863)

Methanococcus voltae (DSM 1537)

M. mobile BP (DSM 1539)

M. barkeri MS (DSM 800)

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18

2.5. Ordination

14

2.6. Analytical methods The VS and chemical oxygen demand (COD) were measured according 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 a flame ionization detector, was used to quantify C2–C6 volatile fatty acids (VFAs) and ethanol. Another identical gas chromatograph, equipped with an HP-5 capillary column (Agilent) and a thermal conductivity detector, was used to analyze the composition of the biogas. 3. Results 3.1. Reactor performance The batch reactor was operated for 25.0 days. Fig. 1 shows the overall process performance of the anaerobic bioreactor. Methane production started at day 1.2 without a distinct lag period (Fig. 1A). At day 8.9, the methane production rate increased sharply from 46 to 106 mL CH4/L days. The cumulative methane production reached 1.15 L/L at day 19.9 when biogas production ceased. The corresponding methane yield coefficient was 0.30 L CH4 produced/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 early stage of the batch reaction; 40% of the total VS reduction (i.e., 14% of the initial VS concentration) was achieved by day 2.4. However, the COD removal in this period was notably lower (i.e., 25% of the total COD reduction) than the VS removal, although the overall removal efficiencies of VS and COD were similar. The protein reduction efficiency was 48.7% throughout the reaction (Fig. 1A). The decrease of protein concentration was 59% of that of VS concentration, suggesting that protein decomposition was a significant catabolic pathway in this process. The total VFA (TVFA) concentration rapidly increased up to 1.2 g/L, which then decreased continuously until day 19.9 with the production of methane (Fig. 1B). Acetate, propionate, and isovalerate were the most abundant acidogenic products in this process. 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 corresponds to 25% equivalence of the overall methane production. Pro-

VS, COD, Protein (g/L)

16

COD 1.0 VS Protein 0.8 CH 4

12 10

0.6 8 0.4

6 4

0.2 2 0.0

0 1.4

B

1.2

Volatile fatty acids (g/L)

Ordination allows analysis of high-dimensional spaces by plotting the strongest structure into reduced dimensions (Falk et al., 2009). NMS is one of the most generally effective ordination methods for ecological community data because it avoids distributional assumptions commonly associated with other ordination techniques (McCune and Grace, 2002). In this study, NMS ordination was performed based on the Sorensen distance measure in the PC-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 archaeal community structures. Another matrix employing methanogenic group abundance was generated for ordination of methanogenic communities in a quantitative aspect. Each main matrix was processed for ordination such that the stress (<10) and the instability (<104) criteria were met.

1.2

A

Cumulative CH production (L/L) 4

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

TVFA Acetate Propionate iso-Valerate

1.0 0.8 0.6 0.4 0.2 0.0 0

5

10

15 Time (days)

20

25

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

pionate and iso-valerate concentrations peaked at 314 mg/L at day 11.9 and 208 mg/L at day 9.9, respectively. Due to the thermodynamic disadvantage of propionate degradation, propionate is often the last organic acid that persists in an anaerobic digester (Batstone, 2002; Lee et al., 2008). Propionate was virtually the only VFA remaining after day 15.9; propionate was almost completely degraded (>98%) at day 19.9 when methane production ceased. 3.2. Bacterial and archaeal community shifts PCR–DGGE targeting on bacterial and archaeal 16S rRNA genes was performed to investigate the microbial community shifts in this process. The DGGE profiles showed continuous changes in bacterial and archaeal community structures during the incubation time (Fig. 2). The partial 16S rRNA gene fragments obtained from 36 bacterial (B1–36) and 27 archaeal (A1–27) bands were sequenced and the affiliations were determined by comparison with the GenBank database (Table 2). B26 was one of the dominant bands during the initial incubation period when the VS and protein concentrations rapidly decreased with the elevation of the TVFA level (Fig. 1). B26 and B27 were 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 thiosulfate-reducers which utilize carbohydrates to produce acetate, butyrate, CO2, and H2 (Basso et al., 2009). B30 was not clearly observed at day 0 but visualized after 2.4 days of incubation (Fig. 2A). B30 showed 97% sequence similarity with Dethiosulfatibacter aminovorans. D. aminovorans is also a thiosulfate reducing bacterium which is reported to ferment various organic compounds into

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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 activated sludge; AS, anaerobic sludge.

acetate, propionate, CO2, and H2 (Takii et al., 2007). The band intensity of B23 increased during the early batch period and remained distinct until the end of reaction (Fig. 2A). B23 was closely related to Clostridium aminobutyricum with 97% sequence similarity (Table 2). C. aminobutyricum is a spore-forming anaerobe which ferments amino acids to form acetate, butyrate, and ammonia (Balows et al., 1992). B33 was also closely (99%) associated with a Clostridium species, C. sticklandii (Table 2). C. sticklandii anaerobically utilizes pairs of amino acids by Stickland reaction and produces 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 various carbohydrates, L. delbrueckii subsp. bulgaricus is reported to utilize mainly lactose to form lactate (Balows et al., 1992). The closest neighbor of B24 was an environmental clone which was affiliated within the genus Syntrophus (Table 2). Members of the genus Syntrophus are anaerobes which utilize butyrate, benzoate, or other acids through syntrophism with H2 and/or formate utilizers such as methanogens (Jackson et al., 1999). Putative roles of the other 29 bacterial bands were relatively unclear. Bands B28–29 and B34–35 were not detected at day 0 and evolved thereafter (Fig. 2A). By contrast, band intensities of B1–9, B12–20, B25, and B31–32 gradually decreased throughout the reaction. It is noteworthy that some of these bands were closely related to bacteria or clones with marine origin due to the high salinity (8.2 g NaCl/ L) and the putative seawater input into the local wastewater treatment plant (Lee et al., 2009a).

The archael DGGE profiles showed continuous shifts in archaeal community structure during the batch period (Fig. 2B). Overall, the archaeal band patterns were less complicated than the bacterial results due to the relatively low diversity of the domain Archaea in most microbial complexes (Curtis and Sloan, 2004). A26 was the dominant band after the onset of methane production (Fig. 1A). Eight bands including A26 were closely related to Methanoplanus petrolearius (Table 2). Eleven other bands (A3–4, A6, A8–10, A14, A18, A20, A22, and A24) were also closely related to four hydrogenotrophic 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 the incubation period (Fig. 2B). A27 was closely related to Methanosarcina mazei with 98% sequence similarity (Table 2). Bands A1–2 and A15–17 also corresponded to an aceticlastic methanogen, Methanosaeta concilii (Table 2). A12 and A13 were observed during the initial phase but disappeared after day 2.4 (Fig. 2B). These bands were mostly closely related to a clone which was isolated from a methanogenic consortium (Table 2). They were branched within the order Thermoplasmatales, a group of non-methanogenic thermophilic acidophiles, with their metabolic roles in this system remaining unclear. Neighbor-joining trees were constructed to characterize the affiliation of the bacterial and archaeal band sequences to the database sequences (Fig. 3). The bacterial sequences were branched within four phyla, Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria (Fig. 3A). In the archaeal neighbor-joining tree, 25

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S.G. Shin et al. / Bioresource Technology 101 (2010) 9461–9470 Table 2 Phylogenetic affiliation of the 16S rRNA gene sequences from DGGE bands. Band(s)

Nearest sequence

Accession No.

% Similarity

B1 B2, 4, 15–16 B3 B5, 12–13 B6 B7, 22 B8–9 B10 B11 B14 B17 B18 B19 B20 B21 B23 B24 B25 B26–27 B28 B29 B30 B31–32 B33 B34 B35 B36 A1–2, 15–17 A3, 22 A4 A5, 7, 11, 19, 21, 23, 25–26 A6, 8, 10, 20, 24 A9, 14, 18 A12–13 A27

Yeosuana aromativorans Denitromonas aromaticus Aequorivita antarctica Uncultured Deltaproteobacterium Sylt 5 Flavobacterium sp. 90d Uncultured bacterium 128O51 Uncultured Deltaproteobacterium RS25 Hyphomonas adhaerens Uncultured Gammaproteobacterium pKB3B-20 Uncultured bacterium JH10_C65 Dechloromonas denitrificans Uncultured bacterium UA01 Uncultured bacterium FS396_454_1000bp_1036B Uncultured bacterium boneC15B1 Uncultured bacterium 43_BS5_8 Clostridium aminobutyricum Syntrophus sp. B3 Pelobacter masseliensis Fusibacter sp. enrichment culture clone 22-7A Uncultured bacterium BSA1B-15 Uncultured Bacteriodetes bacterium PG-5-1-3-L Dethiosulfatibacter aminovorans Uncultured bacterium A-L-2 Clostridium sticklandii Uncultured bacterium clone CLA-7 Uncultured Actinobacteria bacterium QEDN11DD05 Lactobacillus delbrueckii subsp. bulgaricus Methanosaeta concilii Methanocalculus pumilus Methanoculleus sp. ZC-2 Methanoplanus petrolearius Methanogenium marinum Methanocorpusculum bavaricum Uncultured Euryarchaeote SMS-sludge-6 Methanosarcina mazei

AY682382 AB049763 AY771732 AM040101 AF228796 FJ416130 AJ289747 AF082790 AB247876 AY568817 AJ318917 AB456223 DQ909703 AY548995 FJ825571 X76161 AJ133796 AY187308 EU517558 AB175369 EU626571 AB218661 AB154497 M26494 DQ068728 FJ661111 FJ915705 X16932 AB008853 DQ787476 AY196681 DQ177344 AF042197 AB479397 AY196685

98 96 99 91–92 88 97 98–99 99 97 95 97 93 90 98 97 97 97 98 99 95 93 97 95 99 97 95 100 98–99 99–100 99 97–98 97 97–98 99–100 98

out of 27 bands (92%) were assigned to methanogenic orders; 19 bands (70%) within Methanomicrobiales and six bands (22%) within Methanosarcinales (Fig. 3B). No member of Methanobacteriales or Methanococcales was identified. 3.3. Quantitative population dynamics Quantitative changes in the 16S rRNA gene concentrations were also determined by real-time PCR (Fig. 4). The concentration profiles showed significant temporal variations in bacterial, archaeal, and the order-level methanogenic populations with respect to performance data. The bacterial 16S rRNA gene concentration initially increased from 7.0  109 to 9.8  109 copies/mL (39% increase) between days 0 and 2.4, then it decreased continuously down to 6.6  108 copies/mL (93% decrease from day 2.4 or 91% decrease from day 0) until the end of reaction. The archaeal 16S rRNA gene level increased sharply in the early phase: from 3.5  106 to 3.4  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 relative abundance of archaea to bacteria increased continuously from 0.1% to 9.9% throughout the reaction period. The bacteria to archaea ratios of the WAS and the AS were 0.1% and 2.9%, respectively. The three hydrogen-utilizing methanogenic groups, Methanomicrobiales, Methanobacteriales, and Methanococcales, behaved differently in terms of their abundance (Fig. 4). The 16S rRNA gene concentration of Methanomicrobiales increased sharply during the early period. This trend coincides with the elevation of VFA concentrations. 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 contrast, no distinct growth of Methanobacteriales was observed. Methanococcales was not detected in any DNA samples in this study. The 16S rRNA gene level of Methanosarcinales, the only aceticlastic order among methanogens, increased from 2.2  106 to 1.0  107 copies/mL (4.6-fold increase) between days 4.8 and 15.9. During this period, the acetate concentration gradually decreased to zero (Fig. 1B). The Methanosarcinales level increased steeply between days 9.9 and 11.9, corresponding to the period when the methane production rate increased sharply (Fig. 1A). The sum of the three methanogenic orders accounted for 64% to 135% of archaea; it was less than 100% when non-methanogenic archaeal DGGE bands (A12–13) were present. The relative abundance of the methanogenic populations was visualized in Fig. 5. The substrate and the seed contained 9.3  106 and 1.7  108 copies/mL of methanogenic 16S rRNA gene with Methanosarcinales and Methanomicrobiales as the major population, respectively. The aceticlastic Methanosarcinales was the most abundant population at day 0 (mixture of the substrate, 99% v/v, and the seed) but supplanted by hydrogenotrophic methanogens with the rapid growth of Methanomicrobiales (Figs. 4 and 5). The 16S rRNA gene ratio of Methanomicrobiales revealed its maximum of 94.3% at day 9.9 and remained greater than 88% thereafter (Fig. 5). On the other hand, Methanosarcinales maintained a relatively small population (4–11%) after day 4.8. 3.4. Ordination Changes in microbial community structures were also visualized by NMS ordination (Fig. 6). Fig. 6A and B shows the continuous shifts of bacterial and archaeal DGGE band patterns, respectively, while Fig. 6C shows the ordination of the methanogenic group

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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 designate classes within the phylum Proteobacteria.

abundance quantitatively assessed by real-time PCR. The results met the general criteria for good NMS performance, with the stress value below 10 and the instability lower than 104 (McCune and Grace, 2002).

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

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Fig. 3 (continued)

the substrate at day 0 and shifted continuously towards, in general, the opposite direction of the seed. As a result, the end-point bacterial community at day 25.0 evolved to a point distinct from both the substrate and the seed. The archaeal DGGE band patterns are illustrated on a two-dimensional NMS plot (Fig. 6B). The archaeal pattern at day 0 was between, although not close to, the substrate and the seed points. The archaeal band patterns gradually migrated to the cluster including points at days 7.9–25.0, which was also distinct from the archaeal community of the substrate or the seed. Shifts in the quantitative methanogenic population profiles are shown in Fig. 6C. The methanogenic real-time PCR profile at day 0 was placed near the substrate and continuously migrated into a cluster (days 7.9–25.0) near the seed profile. 4. Discussion Secondary sludge is the excess biomass generated from the activated sludge process. The organic materials in the sludge consist mainly of prokaryotic cells, mostly aerobic bacteria. In this batch process, changes in the substrate were monitored primarily by

measuring crude organic materials such as VS and COD. The VS reduction efficiency in this study (35.3%) was comparable to previous 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 bacterial 16S rRNA gene level determined by real-time PCR presumably represents the quantity of the intact bacterial cells in the digester. The change in the bacterial rRNA gene concentration results from the combination of the decay of aerobic cells (‘‘substrate”) and the growth and decay of anaerobic bacteria (‘‘acidogens”). Therefore, the 91% decrease in the bacterial 16S rRNA gene concentration during the batch process implies that more than 90% of the initial bacterial cells were decomposed. The decrease in bacterial DNA concentration corresponds to the decrease of band intensities of B1–9, B12–20, B25, and B31–32, implying that the bacteria related to these bands decayed. The relatively low organic removal efficiency (35%), compared to the decrease in the bacterial DNA concentration (91%), is likely to be due to the low biodegradability of sludge solids by conventional anaerobic processes (Park et al., 2004; Siegrist et al., 2002).

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A

Bacteria Archaea Methanomicrobiales Methanosarcinales Methanobacteriales

1e+10

7.9

9.9 11.9 13.9

15.9 19.9 25.0

4.8

Axis 1A

16S rRNA gene (copies/mL)

1e+11

1e+9 1e+8

2.4 1.0 WAS

1e+7

0

0.4

AS

1e+6

B

1e+5 0

5

10

15

20

25

9.9

AS

7.9

Time (days)

15.9 25.0 13.9 19.9

4.8 2.4

Axis 2B

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

11.9

0 0.4 1.0

WAS

Axis 1B

C

1.0

2.4

0

Axis 2C

4.8

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

Protein is one of the major constituents (i.e., approximately half of the dry weight) of a prokaryotic cell (Madigan and Martinko, 2006). In anaerobic digesters, a specialized group of microorganisms, e.g. proteolytic clostridia, is responsible for protein catabolism. The maintenance of well-functioning proteolytic consortia, therefore, is important for the efficient operation of anaerobic sludge digestion. As revealed by DGGE analysis, two Clostridiumlike bacteria (B23, B33) may have been mainly responsible for the protein catabolism in this system. During this batch reaction, the protein removal efficiency (48.7%) was higher than the protein degradation ratio of 39% in a previous study (Bougrier et al., 2007). The higher protein reduction efficiency could be attributed to a better availability of the particulate proteins to the enzymatic matrix (Angelidaki and Sanders, 2004) and/or the existence of more versatile proteolytic anaerobes in this digester. The batch anaerobic digestion in this study can be roughly divided into an early hydrolytic/acidogenic phase and a later methanogenic phase; macromolecules were degraded into simpler acids, e.g. acetate, in the former while the acidogenic products were reduced 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 higher than the COD removal (25% of the total), although both parameters commonly describe the overall organic concentration of waste or wastewater (Angelidaki and Sanders, 2004). The relatively low boiling point of intermediate acids (e.g., 118.1 °C for acetate), which encourages evaporation of these molecules at 105 °C in sol-

9.9 7.9 19.9 15.9

0.4

WAS 11.9 13.9 25.0

AS

Axis 1C Fig. 6. Non-metric multidimensional scaling ordination plot for (A) bacterial and (B) archaeal DGGE band patterns and (C) quantitative methanogenic profiles determined by real-time PCR. Each community profile on the plot is labeled with either a number indicating the incubation time (days) or its identity. WAS, waste activated sludge; AS, anaerobic sludge.

ids measuring procedures (APHA, 2005), could be responsible for the gap between the VS and COD removal efficiencies in the earlier phase. Acetate is often regarded as the major (70%) methanogenic precursor (Speece, 1996) and can be utilized directly by methanogens. Accordingly, the acetate-utilizing Methanosarcinales has been reported to be the dominant methanogenic group in previous studies (Kobayashi et al., 2008; Yu et al., 2005a). Methanosarcinales was the most abundant methanogenic group, however, only at day 0 in this system (Figs. 4 and 5). At this point, M. conilii-like archaeon (A15–17) was the only aceticlastic methanogen visualized in the DGGE profile. M. concilii can only utilize acetate for growth and is widely distributed in nature due to its high affinity to acetate (Smith and Ingram-Smith, 2007). The 16S rRNA gene concentration of Methanosarcinales increased after day 4.8 when the highest acetate concentration was observed (Fig. 1B). The Methanosarcinales level increased steeply between days 9.9 and 11.9, coincident with the appearance of M. mazei-like band (B27; Fig. 2B). M. mazei is an acetate-utilizing methanogen which can also convert other organics to methane (Garrity, 2005). Methanosarcina spp. have higher growth rate than Methanosaeta spp. (Lee et al., 2009b); therefore,

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the rapid increase of Methanosarcinales may have mainly resulted from the rapid growth of M. mazei-like methanogen in this system. The community shift from M. concilii to M. mazei, associated with the rapid consumption of acetate, was important for the overall reaction efficiency because the accumulation of intermediate acids during a start-up period often leads to system failure (Batstone, 2002). It is notable that the seed sludge contained no visible band corresponding to M. mazei, implying that the M. concilii-like archaeon was the dominant aceticlastic methanogen due to the low acetate environment of the stable full-scale anaerobic digester (Demirel and Scherer, 2008; Yu et al., 2005a). Hydrogenotrophic methanogenesis is one of the major methanogenic pathways in anaerobic digesters (Ahring, 2003). Due to the thermodynamic limitations of the hydrogen-mediated metabolism, low hydrogen partial pressure must be maintained for syntrophic consortia to utilize various intermediates (Batstone et al., 2002). Therefore, efficient removal of hydrogen by hydrogen-utilizing microorganisms is required for acidogenesis and/or acetogenesis to occur. In this study, hydrogen-utilizing Methanomicrobiales increased steeply during the early reaction period (Fig. 4); this change coincided with the increase in band intensity of A26, the M. petrolearius-like band (Fig. 2B). M. petrolearius is a mesophilic methanogen utilizing H2 and CO2, formate, and 2-propanol in the presence of acetate (Garrity, 2005). The strong band intensity of A26 and the acetate-containing environment suggested that the M. petrolearius-like species was in part responsible for the methanogenesis through Syntrophism with hydrogen- or formateproducing bacteria (e.g., the Syntrophus-like bacterium corresponding to B24). M. petrolearius was reported to favor halophilic conditions (up to 5% NaCl) (Ollivier et al., 1997), which could have been a selective pressure in favor of this species due to the high salinity (8.2 g NaCl/L) of the substrate. Among other archaeal bands, A18, A20, A22, and A24 were observed during the incubation period (Fig. 2B), suggesting that the Methanocorpusculum bavaricum-like, the Methanogenium marinum-like, and the Methanocalculus pumilus-like organisms are also involved in methane production through hydrogen (Garrity, 2005). The methanogenic populations in this study shifted from a Methanosarcinales-dominant one to a Methanomicrobiales-dominant one (Fig. 5). Similar changes have been attributed to shifts in VFA-utilizing pathway associated with the growth of different methanogenic populations (Lee et al., 2009b). It is noteworthy that some previous studies have reported the dominance of aceticlastic methanogens in the anaerobic digestion of sludge (Karakashev et al., 2005; Kobayashi et al., 2008; Yu et al., 2005a). However, many other reports have demonstrated the prevalence of the hydrogenotrophic pathway in anaerobic processes with different substrates (Demirel and Scherer, 2008; Karakashev et al., 2005; Song et al., 2010); the key factor determining the major pathway could be the existence of high levels of inhibitory ions, which in general have more severe effects on aceticlastic methanogens. The rapid growth of the A26-corresponding archaeon (Fig. 2B), related to halophilic M. petrolearius, was likely to be responsible for the outgrowth of Methanomicrobiales in this system. The microbial community structures, as determined by DGGE, showed continuous shifts within four bacterial phyla and two archaeal orders. No archaeal DGGE band related to Methanobacteriales was identified by DGGE, although this group was successfully quantified with real-time PCR. The relatively low abundance of this group (<13% of the sum of methanogenic orders) within the domain archaea could be responsible for the lack of visible bands in the domain-level analysis (Shin et al., 2008). Interestingly, a comprehensive investigation on the microbial diversity has revealed Chloroflexi as the most abundant (32 ± 9%) bacterial phylum in anaerobic digestion of sludge (Rivière et al., 2009). The phylum Chloroflexi is divided into two orders: Chloroflexales, the

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obligate or facultative phototrophs, and Herpetosiphonales, the aerobic heterotrophs (Garrity, 2005). However, no DGGE band related to Chloroflexi was identified in this study, probably due to the anaerobic environment and the protection from light. In this study, NMS was applied to visualize the patterns of microbial community shifts in the batch anaerobic digestion of sludge. The pattern of migration of bacterial community in Fig. 6A is different from a previous study with the seed inoculum from the same anaerobic digester but with soluble whey permeate as substrate (Lee et al., 2008). In the previous study, the end-point bacterial community returned close to the bacterial structure of the seed. The main cause for this discrepancy must be the different microbial compositions in the substrate. The bacterial community in this sludge digester was populated with many decaying species; this is supported by the decrease of bacterial 16S rRNA gene concentration (Fig. 4) and the disappearing bands (Fig. 2A), which acted as ‘‘noise” bands in the light of function in anaerobic mineralization. On the other hand, the initial bacterial community in the previous study was merely a dilution of the anaerobic seed inoculum and the shifts in bacterial populations were within the range of the diversity of the seed, which consisted of anaerobic microflora (Lee et al., 2008). In Fig. 6C, the migration of ordination points into a cluster (days 7.9–25.0) near the seed profile was observed. The low residual acetate concentration and high methanogenic activity during this period was similar to the operational conditions of the full-scale anaerobic digester which provided the seed (Lee et al., 2008). Because the end-of-batch methanogenic community had a similar structure to the stable sludge digester, the operation of a batch reaction could provide an efficient means for the enrichment of methanogens as a start-up process (Leclerc et al., 2004; Lee et al., 2009b). However, the NMS ordination profiles in Fig. 6B and C behaved differently in terms of the proximity between the seed and the end point (day 25.0). These results imply that, from a quantitative point of view, the methanogenic community evolved similarly to the seed inoculum in correlation with the production of methane (Fig. 1A), while the qualitative (noted by presence or absence of 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 microbial profiles could be beneficial for ordination studies when the community structure must be interpreted in relation to the major operational performance (Falk et al., 2009; Lee et al., 2009b). 5. Conclusions The performance and microbial community shifts were characterized in anaerobic digestion of secondary sludge. The batch process was successfully operated with an organic removal efficiency of 35% associated with a 91% decrease in bacterial 16S rRNA gene concentration. The microbial community structures showed continuous shifts within four bacterial phyla and three archaeal orders. The quantitative structure of methanogens varied continuously, with the growth of Methanomicrobiales and Methanosarcinales in series, to result in a Methanomicrobiales-dominant population. The ordination demonstrated that the quantitative methanogenic structure converged to the seed inoculum while the bacterial and archaeal DGGE band patterns diverged. Acknowledgements This research was supported by Korea Ministry of Environment as ‘‘Human Resource Development Project for Energy from Waste & Recycling” and Korea Ministry of Knowledge and Economy through the ‘‘Manpower Development Program for Energy & Re-

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sources”. This work was also supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD). The authors are grateful to Dr. Byeongchul Park and Dr. Jaai Kim for their contribution to this work and we also thank Bi Wen Zhou for valuable discussions. References Ahring, B.K., 2003. Biomethanation. Springer, New York, NY. Angelidaki, I., Sanders, W., 2004. Assessment of the anaerobic biodegradability of macropollutants. Rev. Environ. Sci. Biotechnol. 3, 117–129. APHA, 2005. Standard methods for the examination of water and wastewater. American Public Health Association, Washington, DC. Balows, A., Trüper, H.G., Dworkin, M., Harder, W., Schleifer, K.H., 1992. The Prokaryotes: A Handbook on the Biology of Bacteria: Ecophysiology, Isolation, Identification, Applications. Springer, New York, NY. Basso, O., Lascourreges, J.-F.i., Le Borgne, F.i., Le Goff, C., Magot, M., 2009. Characterization by culture and molecular analysis of the microbial diversity of a deep subsurface gas storage aquifer. Res. Microbiol. 160, 107–116. Batstone, D.J., 2002. Anaerobic Digestion Model No. 1. IWA Publishing, London, UK. Batstone, D.J., Keller, J., Angelidaki, I., Kalyuzhnyi, S.V., Pavlostathis, S.G., Rozzi, A., Sanders, W.T.M., Siegrist, H., Vavilin, V.A., 2002. The IWA anaerobic digestion model No 1(ADM 1). Water Sci. Technol. 45, 65–73. Bolzonella, D., Pavan, P., Battistoni, P., Cecchi, F., 2005. Mesophilic anaerobic digestion of waste activated sludge: influence of the solid retention time in the wastewater treatment process. Process Biochem. 40, 1453–1460. Bougrier, C., Delgen, J.P., Carrére, H., 2007. Impacts of thermal pre-treatments on the semi-continuous anaerobic digestion of waste activated sludge. Biochem. Eng. J. 34, 20–27. Chouari, R., Le Paslier, D., Daegelen, P., Ginestet, P., Weissenbach, J., Sghir, A., 2005. Novel predominant archaeal and bacterial groups revealed by molecular analysis of an anaerobic sludge digester. Environ. Microbiol. 7, 1104–1115. Curtis, T.P., Sloan, W.T., 2004. Prokaryotic diversity and its limits: microbial community structure in nature and implications for microbial ecology. Curr. Opin. Microbiol. 7, 221–226. Demirel, B., Scherer, P., 2008. The roles of acetotrophic and hydrogenotrophic methanogens during anaerobic conversion of biomass to methane: a review. Rev. Environ. Sci. Biotechnol. 7, 173–190. Falk, M.W., Song, K.-G., Matiasek, M.G., Wuertz, S., 2009. Microbial community dynamics in replicate membrane bioreactors – natural reproducible fluctuations. Water Res. 43, 842–852. Garrity, G.M., 2005. Bergey’s Manual of Systematic Bacteriology. Springer, New York, NY. Jackson, B.E., Bhupathiraju, V.K., Tanner, R.S., Woese, C.R., McInerney, M.J., 1999. Syntrophus aciditrophicus sp nov., a new anaerobic bacterium that degrades fatty acids and benzoate in syntrophic association with hydrogen-using microorganisms. Arch. Microbiol. 171, 107–114. Karakashev, D., Batstone, D.J., Angelidaki, I., 2005. Influence of environmental conditions on methanogenic compositions in anaerobic biogas reactors. Appl. Environ. Microbiol. 71, 331–338. Kobayashi, T., Li, Y.Y., Harada, H., 2008. Analysis of microbial community structure and diversity in the thermophilic anaerobic digestion of waste activated sludge. Water Sci. Technol. 57, 1199–1205.

Leclerc, M., Delbes, C., Moletta, R., Godon, J.J., 2001. Single strand conformation polymorphism monitoring of 16 S rDNA Archaea during start-up of an anaerobic digester. FEMS Microbiol. Ecol. 34, 213–220. Leclerc, M., Delgenes, J.-P., Godon, J.-J., 2004. Diversity of the archaeal community in 44 anaerobic digesters as determined by single strand conformation polymorphism analysis and 16S rDNA sequencing. Environ. Microbiol. 6, 809– 819. Lee, C., Kim, J., Chinalia, F., Shin, S., Hwang, S., 2009a. Unusual bacterial populations observed in a full-scale municipal sludge digester affected by intermittent seawater inputs. J. Ind. Microbiol. Biotechnol. 36, 769–773. Lee, C., Kim, J., Hwang, K., O’Flaherty, V., Hwang, S., 2009b. Quantitative analysis of methanogenic community dynamics in three anaerobic batch digesters treating different wastewaters. Water Res. 43, 157–165. Lee, C., Kim, J., Shin, S.G., Hwang, S., 2008. Monitoring bacterial and archaeal community shifts in a mesophilic anaerobic batch reactor treating a highstrength organic wastewater. FEMS Microbiol. Ecol. 65, 544–554. Madigan, M.T., Martinko, J.M., 2006. Brock Biology of Microorganisms, 11 ed. Pearson Prentice Hall, Upper Saddle River, NJ. McCune, B., Grace, J.B., 2002. Analysis of Ecological Communities. MjM Software Design, Gleneden Beach, OR. Muyzer, G., de Waal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700. Ollivier, B., Cayol, J.L., Patel, B.K.C., Magot, M., Fardeau, M.L., Garcia, J.L., 1997. Methanoplanus petrolearius sp nov., a novel methanogenic bacterium from an oil-producing well. FEMS Microbiol. Lett. 147, 51–56. Park, B., Ahn, J.H., Kim, J., Hwang, S., 2004. Use of microwave pretreatment for enhanced anaerobiosis of secondary sludge. Water Sci. Technol. 50, 17–23. Rivière, D., Desvignes, V., Pelletier, E., Chaussonnerie, S., Guermazi, S., Weissenbach, J., Li, T., Camacho, P., Sghir, A., 2009. Towards the definition of a core of microorganisms involved in anaerobic digestion of sludge. ISME J. 3, 700–714. Shin, S.G., Lee, C., Hwang, K., Ahn, J.-H., Hwang, S., 2008. Use of order-specific primers to investigate the methanogenic diversity in acetate enrichment system. J. Ind. Microbiol. Biotechnol. 35, 1345–1352. Siegrist, H., Vogt, D., Garcia-Heras, J.L., Gujer, W., 2002. Mathematical model for meso- and thermophilic anaerobic sewage sludge digestion. Environ. Sci. Technol. 36, 1113–1123. Smith, K.S., Ingram-Smith, C., 2007. Methanosaeta, the forgotten methanogen? Trends Microbiol. 15, 150–155. Song, M., Shin, S.G., Hwang, S., 2010. Methanogenic population dynamics assessed by real-time quantitative PCR in sludge granule in upflow anaerobic sludge blanket treating swine wastewater. Bioresour. Technol. 101, S23–S28. Speece, R.E., 1996. Anaerobic Biotechnology for Industrial Wastewaters. Archae Press, Nashville, TN. Takii, S., Hanada, S., Tamaki, H., Ueno, Y., Sekiguchi, Y., Ibe, A., Matsuura, K., 2007. Dethiosulfatibacter aminovorans gen nov., sp. nov., a novel thiosulfate-reducing bacterium isolated from coastal marine sediment via sulfate-reducing enrichment with Casamino acids. Int. J. Syst. Evol. Microbiol. 57, 2320–2326. Yu, Y., Lee, C., Hwang, S., 2005a. Analysis of community structures in anaerobic processes using a quantitative real-time PCR method. Water Sci. Technol. 52, 85–91. Yu, Y., Lee, C., Kim, J., Hwang, S., 2005b. Group-specific primer and probe sets to detect methanogenic communities using quantitative real-time polymerase chain reaction. Biotechnol. Bioeng. 89, 670–679.