Comparative analyses of microbial structures and gene copy numbers in the anaerobic digestion of various types of sewage sludge

Accepted Manuscript Comparative analyses of microbial structures and gene copy numbers in the anaerobic digestion of various types of sewage sludge Taira Hidaka, Ikuo Tsushima, Jun Tsumori PII: DOI: Reference:

S0960-8524(17)32242-3 https://doi.org/10.1016/j.biortech.2017.12.097 BITE 19352

To appear in:

Bioresource Technology

Received Date: Revised Date: Accepted Date:

2 November 2017 25 December 2017 27 December 2017

Please cite this article as: Hidaka, T., Tsushima, I., Tsumori, J., Comparative analyses of microbial structures and gene copy numbers in the anaerobic digestion of various types of sewage sludge, Bioresource Technology (2017), doi: https://doi.org/10.1016/j.biortech.2017.12.097

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Comparative analyses of microbial structures and gene copy numbers in the anaerobic digestion of various types of sewage sludge

Taira Hidaka *a1, Ikuo Tsushima b, Jun Tsumori a2

a

Materials and Resources Research Group, Public Works Research Institute, 1-6,

Minamihara, Tsukuba, Ibaraki 305-8516, JAPAN b

Water Environment Research Group, Public Works Research Institute, 1-6, Minamihara,

Tsukuba, Ibaraki 305-8516, JAPAN

*Corresponding author Tel: +81-29-879-6765; Fax: +81-29-879-6797 E-mail: [email protected] (T. Hidaka)

1 Present address: Department of Environmental Engineering, Kyoto University, C1, KyotoDaigaku-Katsura, Nishikyo-ku, Kyoto, 6158540, JAPAN 2 Present address: Ministry of Land, Infrastructure, Transport and Tourism, JAPAN

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ABSTRACT Anaerobic co-digestion of various sewage sludges is a promising approach for greater recovery of energy, but the process is more complicated than mono-digestion of sewage sludge. The applicability of microbial structure analyses and gene quantification to understand microbial conditions was evaluated. The results show that information from gene analyses is useful in managing anaerobic co-digestion and damaged microbes in addition to conventional parameters like total solids, pH and biogas production. Total bacterial 16S rRNA gene copy numbers are the most useful tools for evaluating unstable anaerobic digestion of sewage sludge, rather than mcrA and total archaeal 16S rRNA gene copy numbers, and high-throughput sequencing. First order decay rates of gene copy numbers during pH failure were higher than typical decay rates of microbes in stable operation. The sequencing analyses, including multidimensional scaling, showed very different microbial structure shifts, but the results were not consistent.

Keywords: anaerobic digestion; microbial community; high-throughput sequencing; PCR quantification; sewage sludge

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1. Introduction Anaerobic co-digestion is a promising approach to wastewater treatment, even in small-scale wastewater treatment plants (WWTPs), for greater recovery of energy from sewage sludge and other organic wastes with a high moisture content, such as kitchen garbage. Co-digestion includes sewage sludge collection from scattered small-scale WWTPs with sewage sludge of varying composition. In small-scale WWTPs, oxidation ditch processes (OD) are widely applied (WEF, 2010; JSWA, 2014). The concept of OD was to minimize sludge production by enhancing self-degradation and retaining solids for a longer time. Although sewage sludge from OD generally has a low biogas production rate and is not suitable for anaerobic digestion, anaerobic co-digestion with sewage sludge from ODs has recently gained attention for use in small facilities in Japan. For example, in Ishikawa Prefecture, Japan, there is one WWTP with anaerobic co-digestion using sewage sludge from OD and other biomass such as kitchen garbage in 2007 (LeBlanc et al., 2008) and another WWTP adopted in 2017 with a new anaerobic co-digester that is fed with dewatered sewage sludge of 11 surrounding WWTPs, among which five WWTPs use OD processes. If this practice will be successful, this idea is expected to spread all over Japan. Design and operational parameters have been optimized for conventional anaerobic monodigestion of sewage sludge (WEF, 2010; JSWA, 2014). However, sewage sludge from different WWTPs varies greatly. There is the potential for operation with high solid substrates

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because of the small reactor size and less heating energy consumed (Liu et al., 2016), as well as the low transportation cost of dewatered sludge (Hidaka et al., 2013; Wang et al., 2014). The typical operational total solids (TS) concentration in Japan is less than 5% (JSWA, 2016); there is little information in digestion of sludge at higher solids content. Various preor post-treatment technologies, such as ultrasonic (Yang et al., 2015) and microwave (Zhang et al., 2016) treatments, are under development, and characteristics of substrates used in these processes vary. Co-digestion is more complicated than mono-digestion of sewage sludge, and accumulated knowledge and guidelines cannot be applied to co-digestion with variable substrates. Furthermore, the number of experienced operators is decreasing. More elaborate strategies to understand reactor conditions and to decide how to respond to unstable conditions are required. Microbial structure analysis is a promising approach to understand biological treatment processes. Microbial structures in biological treatments, including anaerobic digestion, can be analyzed easily using biomolecular techniques, and data are accumulating. Effects of pre-treatments (Yang et al., 2015, Zhang et al., 2016), high solid conditions (Liu et al., 2016; Li et al., 2017), and co-digestion with food waste (Xu et al., 2017) on microbial communities have been reported. Correlations between bacterial populations and process parameters have been compared in full-scale sewage sludge digesters (Shin et al., 2016). This technology to understand operational conditions, without the requirement of experienced

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operators, is promising. However, most studies do not focus on design and operational parameters for biological treatment. Multidimensional scaling analyses provide information on microbial structure shifts during long-term continuous operations (Shin et al., 2016), but each component depends on the data set used for the analyses, and these data are not universal. Therefore, it is difficult to use the previously reported results of microbial structure analyses to establish general guidelines. Real-time PCR quantification can be more related to treatment performance than diversity and evenness indices (Gagliano et al., 2015). Mathematical modelling, combined with real-time PCR quantification, has been proposed (Hidaka et al., 2010), but it has been limited to thermophilic L-lactate fermentation by Bacillus coagulans. This technique uses a targeted approach. It must be decided beforehand which taxa to quantify, while most community members have not been identified, isolated or studied in anaerobic digestion of sewage sludge. Thus adequate primers for the majority of the specific community members are not available. In one study, real-time PCR quantification using universal primers was compared with performance parameters in four full-scale digesters (Koo et al., 2017), but the results were not comprehensive. The applicability of real-time PCR quantification has not been verified for anaerobic digestion of various types of sewage sludge. Co-digestion of sewage sludge and other organic wastes such as kitchen garbage is too complicated, although this is the practical target. Co-digestion also means that collecting

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sewage sludge from several plants. The present study focuses on various types of sewage sludge. The objectives of this study were to compare microbial community analyses and treatment performances of anaerobic digestion of sewage sludge under various conditions, including high solid conditions, and to verify the applicability of microbial community analyses to the diagnosis and management of anaerobic co-digestion. To determine unfavorable operating conditions, one reactor was operated with high-solid sewage sludge and acid failure caused by excessing loading for more than 3 years. Treatment parameters such as pH, solid concentrations, volatile fatty acid (VFA) accumulation, and biogas production were obtained, microbial structure analyses by high-throughput sequencing and real-time PCR quantification were performed, and the relationships between them were comparatively evaluated.

2. Materials and methods 2.1 Sludge collection Characteristics of sewage sludge used in the continuous anaerobic digestion experiments are summarized in Table 1. Sludge was collected at eight WWTPs (a–h). All of the WWTPs treated municipal domestic wastewater in Japan. The types of biological wastewater treatment consisted of conventional activated sludge (CAS) and CAS with biological nitrogen removal (recycled nitrification/denitrification or step-feed biological nitrogen removal) for large- or middle-sized facilities and OD for small facilities. Sludge 6

types consisted of mixed sludge (MS), a mixture of primary and secondary sludge, and waste activated sludge (secondary sludge only). Concentrated or dewatered sludge was collected. Continuous anaerobic digestion experiments with them were performed, and treatment performances were reported (Hidaka et al., 2015a; 2015b; 2017). In the present study, microbial communities in these reactors were comparatively evaluated.

2.2 Continuous anaerobic digestion experiments 2.2.1 Excessive loading condition (MCa) An experiment of continuous anaerobic digestion was performed under mesophilic (35°C) conditions, with MS obtained at WWTP-a for 1,120 days (Hidaka et al., 2015a). A laboratory-scale reactor with a working volume of 3 L was maintained in an anaerobic state under complete mixing conditions. After Day 426, the organic loading rate (OLR) was adjusted to 2.0 (OLR1), 2.7 (OLR2), 3.2 (OLR3), 3.9 (OLR4), 4.2 (OLR5), and 1.0 (OLR6) g volatile solids (VS)/(L∙d) (Table 2). Because the TS of the raw MS was 2–3%, and it was lower than the planned value, MS was concentrated by centrifugation at 3,000 g for 15 min, and the amount of TS was adjusted to 10%. VS/TS and chemical oxygen demand (COD)/VS ratios of the substrate were approximately 0.9 and 1.4–1.5, respectively. At the end of OLR4, the performance of anaerobic digestion performance decreased, with reduced pH and gas production, and VFAs accumulated. The OLR was temporary increased as OLR5 for 10 days to operate the reactor under acidic failure conditions, and then the OLR was deceased as

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OLR6.

2.2.2 Co-digestion of sewage sludge and microalgae Two continuous thermophilic anaerobic digestion reactors fed with sewage sludge and cultivated algae, and with sewage sludge only (T_MAa and T_MCa, respectively), were operated for as long as 10 months (Hidaka et al., 2017). The working volumes of T_MAa and T_MCa were 10 L and 2 L, respectively. The temperature was controlled at 55°C using a water bath. T_MCa was a control reactor, fed only with MS obtained at WWTP-a every weekday (five times a week). The hydraulic retention time (HRT), based on the amount of the mixed sludge, was 28 d. T_MAa was fed with the MS every weekday at the same loading rate as T_MCa and the cultivated microalgae using a recycle flow sample from T_MAa once a week. Cultivation reactors for microalgae were operated with an HRT of 7.8 d. The withdrawn microalgae were concentrated 36-fold by centrifugation at 3 000 g, and then the concentrated microalgae was fed into T_MAa once a week. The volume ratio of cultivated microalgae added to the mixed sludge was 4−15%.

2.2.3 Comparison of various types of sludge Continuous anaerobic digestion reactors, excluding MCa, T_MAa, and T_MCa, were operated under different substrate conditions (Table 1) (Hidaka et al., 2015b). OLR was between 0.5 and 2.6 kgVS/(m3•d), with an HRT of 20–70 d. Because substrate TS

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concentrations varied, HRT varied a lot, but they were within typically proposed practical values (WEF, 2010; JSWA, 2014). Operation and biogas production were stable, except for the case showing a substrate TS of over 13%.

2.3 DNA extraction and 16S rRNA gene sequencing Microbial communities of the anaerobically digested sludge were analyzed using a new generation sequencer (MiSeq, Illumina Inc., San Diego, CA, USA). DNA was extracted with an Extrap Soil DNA Kit Plus ver. 2 (Nippon Steel and SUMIKIN Eco-Tech, Tokyo, Japan). The extracted DNA was used as a template for PCR amplification using primers) with overhang adapter sequences (F: 5′-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG-3′, R: 5′-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA G-3′) targeting the eubacterial16S rRNA gene region (Bac0341F and Bac785R (Table 3) (Herlemann et al, 2011). The following PCR protocol was used: initial denaturation was conducted at 95°C for 3 min, followed by 25 cycles at 95°C/30 s, 55°C/30 s, and 72°C/30 s. The PCR amplification products were purified with an AMPure XP kit (Beckman Coulter Genomics Inc., Brea, CA, USA). DNA sequencing was conducted using a MiSeq Reagent Kit v3 (600 cycle, Illumina Inc., San Diego, CA, USA) for analysis. A chimera check of the base sequences of each read obtained from the analysis was performed in USEARCH v6.1 (Edgar, 2010). Reads with more than 97% sequence similarity were grouped into the same operational taxonomic unit (OTU), and OTU picking and a cluster analysis were performed in QIIME 1.8 (Caporaso et 9

al., 2010). OTUs were identified using the Greengenes database (ver. 13_8) (McDonald et al., 2012) as a reference. A multidimensional scaling analysis was performed using BellCurve for Excel (Social Survey Research Information Co., Ltd., Japan).

2.4 Real-time PCR quantification DNA was extracted as described in section 2.3. The DNA concentration of purified extracts was measured using a PicoGreen dsDNA Assay Kit (Invitrogen, CA, USA). Total bacterial 16S rRNA gene copy numbers were quantified by quenching probe PCR (QProbe PCR) using a Rotor-Gene Q (Qiagen, Hilden, Germany) (Tani et al., 2009). The primer pair Bac1055YF and Bac1392R and a QProbe of modified Bac1115Probe were used (Ritalahti et al., 2006). Amplification of the target gene was performed under the following conditions: an initial 2-min incubation at 93°C and 50 cycles of 93°C/15 s, 61°C/20 s, and 72°C/25 s. The standard curve was constructed with the presentative strain of Escherichia coli K12 (ATCC 10798). Total 16S rRNA gene copy numbers of archaea were quantified by QProbe PCR using a LightCycler 1.0 (Roche, IND, USA). The primer pair ARC787F and ARC1059R and a QProbe of modified ARC915F-3G-QP were used (Yu et al., 2005). The target gene was amplified under the following conditions: an initial 2-min incubation at 95°C and 60 cycles of 95°C/10 s and 60°C/30 s. The standard curve was constructed with the presentative strain 10

of Methanobacterium bryantii M.o.H. (ATCC 33272). The mcrA 16S rRNA gene copy numbers were determined by SYBR Green qPCR using a LightCycler 1.0 (Roche). The mcrA gene was employed to quantify methanogens. The primer pair ME1f and ME2r was used (Hales et al., 1996). The target gene was amplified under the following conditions: an initial 0.5-min incubation at 95°C and 45 cycles of 95°C/15 s, 52°C/20 s, and 72°C/25 s. The standard curve was constructed with the presentative strain of Methanobacterium bryantii M.o.H. (ATCC 33272).

3. Results and discussion 3.1 Microbial structure difference under different sludge conditions Figure 1 summarizes microbial structure shifts based on phyla and the ratios detected in all of the continuously operated reactors. To simplify the figure, only the eight most commonly detected phyla, which accounted for almost 80% of the detected OTUs are shown. Under different sludge conditions, the contents were completely different, and with the same sludge type, the contents were similar. For example, ODg and ODh, both of which were operated with dewatered sewage sludge from OD, had similar microbial structures, although each reactor was operated independently under different concentrations of substrate sludges from different WWTPs (WWTP-g and WWTP-h, respectively). Under thermophilic anaerobic conditions (T_MAa and T_MCa), results obtained were different from those obtained in mesophilic reactors. With MCa and excess loading condition, an obvious 11

microbial structure shift was detected with the drop in pH and gas production described below. Except for MCa, microbial structure shifts were not obvious during continuous experiments in any reactor. Family level analyses showed similar results. In mesophilic operations, OTUs closely related to the order SHA-98 (phylum Firmicutes, class Clostridia) were dominant. SHA-98 was previously detected at a high ammonia concentration (Muller et al., 2016; Sun et al., 2016). This dominance may be because sewage sludge, particularly waste activated sludge, contains protein that are released in the form of ammonia during anaerobic digestion. Ammonia concentrations were less than inhibitory level of 4,000 mgN/L (Yenigun and Demirel, 2013) under stable operation. With MCa failure under the high OLR, VFAs accumulated, and the percentage of OTUs closely related to SHA-98 dramatically decreased. This agrees with results reported by Goux et al. (2015), who found that the ratio of SHA-98 decreased when the OLR increased. In some cases, the SHA-98 ratio might be an index that can be used to understand reactor stability, but SHA-98 was not detected and the operation was stable in MCd on Day 147. Therefore, this cannot be an absolute indicator to monitor reactors. Another example is that OTUs closed related to Actinobacteria were gradually displaced from several of the reactors, such as ODh5, MDe, MDb15, while became more prominent in MCa. Relationship between increase or decrease in Actinobacteria and sludge characteristics is uncertain. There were some more potential indicator taxa. However, the detected ratios were much lower, and the relationship

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was applied to the particular reactors only. Therefore, they cannot be universal indicators. Figure 2 summarizes microbial structure shifts based on phyla and multidimensional scaling. Similar to the results of the ratio of OTUs detected, under thermophilic anaerobic operations (T_MAa and T_MCa), results were different from those obtained in mesophilic reactors, even though mesophilic reactors other than MCd were mapped closely. The variation within each reactor was larger than the variation among different reactors, including with MCa and excess loading condition. In the present study, only sewage sludge was added, except for microalgae cultivated using digested liquor (T_MAa), and the target of anaerobic digestion is general sewage sludge rather than other biomass such as kitchen garbage and specific chemicals. Shin et al. (2016) reported non-metric multidimensional scaling of bacterial structures in four digesters. The four digesters were clearly separated in the scaling, but the treatment performance of the digesters differed. These results imply that microbial structure differences determined by high-throughput sequencing might not be suitable for monitoring the operational stability of anaerobic digestion fed with various types of sewage sludge.

3.2 Effect of acid failure on 16S rRNA gene copy numbers Performances before and after acid failure with MCa are summarized in Figure 3. The detailed performance with MCa, excluding gene analyses, are described by Hidaka et al. (2015a). During OLR5, TS and VS of the digested sludge increased, whereas the pH, biogas 13

production, and 16S rRNA gene copy numbers decreased. At the end of OLR6, performances slightly improved with slight gas production, but were not restored to the previous level. Relationships among pH, gas production, and 16S rRNA gene copy numbers are summarized in Figure 4. Generally, the copy numbers of bacterial genes and the mcrA gene showed a decrease corresponding to the decrease in pH and gas production. Decreases in 16S rRNA gene copy numbers and pH were observed simultaneously; however, the decrease in 16S rRNA gene copy numbers was slightly delayed compared to gas production. A delay was also observed in the decrease in pH compared to gas production. After reducing the OLR, pH recovered first. Gas production also began to recover, but an increase in 16S rRNA gene copy numbers after acid failure was not observed. These results show that pH is the most sensitive item to monitor reactor condition. 16S rRNA gene copy numbers also followed treatment performances even after pH failure, when biogas production was still low, though pH recovered. It takes a long time to recover from ammonia inhibition (Hidaka et al., 2013) and acid failure (Van Ngo et al., 2016). Mathematical modelling during acid failure is a new approach, because the well-known IWA ADM1 model (Batstone et al., 2002) can express inhibition effects only in inhibitory material concentrations (Van Ngo et al., 2016). First order rate constants for decrease in concentrations of total bacterial genes and the mcrA gene are 45 and 64 (1/d), respectively, during this pH failure. These values are much higher than that of first order decay rates for

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microbes in anaerobic digestion (Batstone et al., 2002). To evaluate inhibition mechanisms, only pH, ammonia or other inhibitory material concentrations are not sufficient, but damaged microbial conditions need to be considered. Gene analyses could be helpful to evaluate microbial conditions, including acid failure conditions, in addition to pH monitoring.

3.3 Applicability of real-time PCR quantification to different substrate conditions Figure 5 shows the relationships among total bacterial 16S rRNA gene copy numbers, mcrA gene copy numbers, total archaeal gene copy numbers, and the TS of digested sludge in mesophilic stable operations. MCa with excessive loading conditions is excluded, because it is described in detail in Figures 3 and 4. The relationship between mcrA gene copy numbers and total archaeal gene copy numbers was quite clear. This is reasonable, because typical methanogens are archaea (Koo et al., 2017). This result indicates that either mcrA or archaeal gene quantification is sufficient for monitoring the anaerobic methane fermentation step in digestion of general sewage sludge. Therefore, mcrA gene analyses were used in the following discussion. On the other hand, the relationship between mcrA and total bacterial 16S rRNA gene copy numbers was not so clear. Each microbial group has a different role in anaerobic digestion, and their ratios may be affected by substrates and operational conditions. Both gene copy numbers were higher with lower TS concentrations, but this relationship is not clear, particularly with much lower TS concentrations. In continuous operations, OLR was set at low values for stable operation. This might have caused lower gene copy numbers 15

but sufficient microbes for the applied OLR, regardless of the high TS concentrations. Although TS could be a conventional indicator for microbial concentrations like mixed liquor suspended solids (MLSS) in activated sludge processes, 16S rRNA gene copy numbers can be more precise to understand microbial concentrations, particularly under different substrate concentrations. Figure 6 shows the relationships among total bacterial 16S rRNA gene copy numbers, mcrA gene copy numbers, and biogas production in mesophilic operations, excluding MCa with excessive loading conditions. In each reactor, a clear relationship between total bacterial 16S rRNA gene copy numbers and biogas production was observed, whereas the relationship between mcrA gene copy numbers and biogas production was not so clear. The biogas production reaction consists of two steps: acid fermentation and methane fermentation. The reactor operations were stable and acid failure was not observed, suggesting that no VFAs accumulated. The acetate produced was immediately converted into methane and carbon dioxide, and the methane fermentation step was not rate limiting. This may have resulted in the uncertain relationship between mcrA gene copy numbers and biogas production. Koo et al. (2017) also reported that the relationship between methanogen population size and COD removal efficiency was unclear. Co-digestion operations are more complicated than those of monodigestion. TS and VS are typical indicators of microbial concentrations in anaerobic digestion, although they

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are not directly related to microbial activity. The results show that 16S rRNA gene copy number analyses can support the diagnosis and management of unstable co-digestion, in addition to conventional parameters such as TS, VS, pH, and biogas production. Quantification of total bacterial 16S rRNA gene copy numbers is more useful than that of mcrA gene copy numbers.

4. Conclusions The present study showed that among compared microbial analyses, total bacterial 16S rRNA gene copy numbers are the most useful measures to understand anaerobic digestion of sewage sludge, rather than mcrA and total archaeal gene copy numbers, and sequencing. The sequencing analyses showed very different microbial structures, but the results were not consistent. First order gene copy number decay rates during pH failure were higher than decay rates of microbes in stable operation. Gene analyses, especially real-time PCR quantification, are useful in developing diagnostic tools for the operation of complicated anaerobic co-digestion and damaged microbes, in addition to conventional parameters.

ACKNOWLEDGEMENTS The authors sincerely thank the staff of the local government and the wastewater treatment plant for their kind support.

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REFERENCES 1. Batstone, D.J., Keller, J., Angelidaki, I., Kalyuzhnyi, S.V., Pavlostathis, S.G., Rozzi, A., Sanders, W.T., Siegrist, H., Vavilin, V.A., 2002. Anaerobic Digestion Model No. 1 2.

3. 4.

5.

6.

7.

8.

9.

(ADM1). IWA publishing, Longon, UK. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of highthroughput community sequencing data, Nat Meth. 7, 335-336. Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST, Bioinformatics. 26, 2460-2461. Gagliano, M.C., Braguglia, C.M., Gallipoli, A., Gianico, A., Rossetti, S., 2015. Microbial diversity in innovative mesophilic/thermophilic temperature-phased anaerobic digestion of sludge, Environmental Science and Pollution Research. 22, 7339-7348. Hales, B.A., Edwards, C., Ritchie, D.A., Hall, G., Pickup, R.W., Saunders, J.R., 1996. Isolation and identification of methanogen-specific DNA from blanket bog peat by PCR amplification and sequence analysis, Appl. Environ. Microbiol. 62, 668-675. Herlemann, D.P., Labrenz, M., Jurgens, K., Bertilsson, S., Waniek, J.J., Andersson, A.F., 2011. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea, ISME J. 5, 1571-1579. Hidaka, T., Horie, T., Akao, S., Tsuno, H., 2010. Kinetic model of thermophilic l-lactate fermentation by Bacillus coagulans combined with real-time PCR quantification, Water Res. 44, 2554-2562. Hidaka, T., Wang, F., Togari, T., Uchida, T., Suzuki, Y., 2013. Comparative performance of mesophilic and thermophilic anaerobic digestion for high-solid sewage sludge, Bioresour. Technol. 149, 177-183. Hidaka, T., Sawahara, H., Togari, T., Tsumori, J., 2015a. Anaerobic digestion of sewage sludge with high solid content and its use as fertilizer, Journal of Water and Environment Technology. 13, 349-358.

10. Hidaka, T., Tsushima, I., Tsumori, J., Minamiyama, M., 2015b. Effects of Dewatered Sewage Sludge Characteristics on Mesophilic Anaerobic Digestion, Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research). 71, III_27-III_37 (in Japanese). 11. Hidaka, T., Takabe, Y., Tsumori, J., Minamiyama, M., 2017. Characterization of microalgae cultivated in continuous operation combined with anaerobic co-digestion of sewage sludge and microalgae, Biomass Bioenergy. 99, 139-146.

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12. Japan Sewage Works Association (JSWA): Operation of Municipal Wastewater Treatment Plants, Tokyo, Japan, 2014. (in Japanese) 13. Japan Sewage Works Association (JSWA): Sewage works statistics fiscal year 2014, Tokyo, Japan, 2016. (in Japanese) 14. Koo, T., Shin, S.G., Lee, J., Han, G., Kim, W., Cho, K., Hwang, S., 2017. Identifying methanogen community structures and their correlations with performance parameters in four full-scale anaerobic sludge digesters, Bioresource Technology. 228, 368-373. 15. LeBlanc, R.J., Matthews, P., Richard, R.P., United Nations Human Settlements Programme. Global atlas of excreta, wastewater sludge, and biosolids management : moving forward the sustainable and welcome uses of a global resource, United Nations Human Settlements Programme, Nairobi, Kenya, 2009. 16. Li, N., He, J., Yan, H., Chen, S., Dai, X., 2017. Pathways in bacterial and archaeal communities dictated by ammonium stress in a high solid anaerobic digester with dewatered sludge, Bioresource Technology. 241, 95-102. 17. Liu, C., Li, H., Zhang, Y., Si, D., Chen, Q., 2016. Evolution of microbial community along with increasing solid concentration during high-solids anaerobic digestion of sewage sludge, Bioresource Technology. 216, 87-94. 18. McDonald, D., Price, M.N., Goodrich, J., Nawrocki, E.P., DeSantis, T.Z., Probst, A., Andersen, G.L., Knight, R., Hugenholtz, P., 2011. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea, The ISME Journal. 6, 610-618. 19. Ritalahti, K.M., Amos, B.K., Sung, Y., Wu, Q., Koenigsberg, S.S., Loffler, F.E., 2006. Quantitative PCR targeting 16S rRNA and reductive dehalogenase genes simultaneously monitors multiple Dehalococcoides strains, Appl. Environ. Microbiol. 72, 2765-2774. 20. Shin, S.G., Koo, T., Lee, J., Han, G., Cho, K., Kim, W., Hwang, S., 2016. Correlations between bacterial populations and process parameters in four full-scale anaerobic digesters treating sewage sludge, Bioresource Technology. 214, 711-721. 21. Tani, H., Miyata, R., Ichikawa, K., Morishita, S., Kurata, S., Nakamura, K., Tsuneda, S., Sekiguchi, Y., Noda, N., 2009. Universal quenching probe system: flexible, specific, and cost-effective real-time polymerase chain reaction method, Anal. Chem. 81, 5678-5685. 22. Van Ngo, A., Nguyen, H.T., Van Le, C., Goel, R., Terashima, M., Yasui, H., 2016. A dynamic simulation of methane fermentation process receiving heterogeneous food wastes and modelling acidic failure, Journal of Material Cycles and Waste Management. 18, 239-247. 23. Wang, F., Hidaka, T., Uchida, T., Tsumori, J., 2014. Thermophilic anaerobic digestion of sewage sludge with high solids content, Water Sci. Technol. 69, 1949-1955.

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24. Water Environment Federation (WEF): Design of Municipal Wastewater Treatment Plants: WEF Manual of Practice No. 8, ASCE Manuals and Reports on Engineering Practice No. 76, Fifth Edition, WEF Press, Virginia, USA, 2010. 25. Xu, R., Yang, Z., Zheng, Y., Zhang, H., Liu, J., Xiong, W., Zhang, Y., Ahmad, K., 2017. Depth-resolved microbial community analyses in the anaerobic co-digester of dewatered sewage sludge with food waste, Bioresource Technology. 26. Yang, C., Zhou, A., He, Z., Jiang, L., Guo, Z., Wang, A., Liu, W., 2015. Effects of ultrasonic-assisted thermophilic bacteria pretreatment on hydrolysis, acidification, and microbial communities in waste-activated sludge fermentation process, Environmental Science and Pollution Research. 22, 9100-9109. 27. Yenigün, O., Demirel, B., 2013. Ammonia inhibition in anaerobic digestion: A review, Process Biochemistry. 48, 901-911. 28. Yu, Y., Lee, C., Kim, J., Hwang, S., 2005. Group-specific primer and probe sets to detect methanogenic communities using quantitative real-time polymerase chain reaction, Biotechnol. Bioeng. 89, 670-679. 29. Zhang, J., Lv, C., Tong, J., Liu, J., Liu, J., Yu, D., Wang, Y., Chen, M., Wei, Y., 2016. Optimization and microbial community analysis of anaerobic co-digestion of food waste and sewage sludge based on microwave pretreatment, Bioresource Technology. 200, 253-261.

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Actinobacteria

Bacteroidetes

Firmicutes

OP9

Proteobacteria

Thermotogae

WS6

WWE1

ODh5(232) ODh5(204) ODh5(176) ODh5(134) ODh16(99) ODh10(232) ODh10(204) ODh10(176) ODh10(134) ODh10(99) ODg5(232) ODg5(204) ODg5(176) ODg13(134) ODg13(99) ODg10(232) ODg10(204) ODg10(176) ODg10(134) ODg10(99) WDf(92) WDf(64) WDf(36) WCf(92) WCf(64) WCf(36) MDe(133) MDe(105) MDe(77) MDe(42) MDe(7) MCe(133) MCe(105) MCe(77) MCe(42) MCe(7) MDd(147) MDd(119) MDd(91) MDd(56) MDd(21) MCd(147) MCd(119) MCd(91) MCd(56) MCd(21) MDc(147) MDc(119) MDc(91) MDc(56) MDc(21) MCc(147) MCc(119) MCc(91) MCc(56) MCc(21) MDb15(232) MDb15(204) MDb15(176) MDb15(134) MDb15(99) MDb10(232) MDb10(204) MDb10(176) MDb10(134) MDb10(99) MCa(1105) MCa(1077) MCa(1049) MCa(1029) MCa(1007) MCa(972) MCa(922) T_MCa(350) T_MCa(322) T_MCa(294) T_MCa(259) T_MAa(350) T_MAa(322) T_MAa(294) T_MAa(259) 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

detected ratio (-)

Figure 1. Microbial structure shifts based on phyla and the ratios detected. ( ) indicates sampling day.

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0.1

0.0

T_MAa MCa MDb15 MDc T_MAa MCa MDd MDe MDb15 MDc WDf MDd ODg13, 5 ODh16,5

0.4

0.3

T_MCa MDb10 MCc MCd T_MCa MCe MDb10 MCc WCf MCdODg10 MCeODh10 final

-0.1

0.2

0.0

0.1

0.2

0.3

0.1

0.0

-0.1

-0.2

-0.3

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

Figure 2. Microbial structure shifts based on phyla and multidimensional scaling. Open square indicates the final sampling of each continuous operation.

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OLR4

TS, VS(%)

a

OLR5

OLR6

10 5

TSin TSout

VSin VSout

0

920 940 960 980 1000102010401060108011001120

b pH(-)

9 8 7

6 920 940 960 980 1000102010401060108011001120

c Gas production (LN/(L·d))

4 3

2 1 0

920 940 960 980 1000102010401060108011001120

Gene copy numbers (copies/mL)

d

1.E+10 1.E+09 1.E+08 1.E+07 1.E+06 1.E+05

bacteria

mcrA gene

920 940 960 980 1000102010401060108011001120 Time (d)

Figure 3. Anaerobic digestion performers in MCa (a) TS and VS, (b) pH, (c) Biogas production, and (d) 16S rRNA Gene copy numbers. (a) (b) (c) are after Hidaka et al. (2015a).

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Gene copy numbers (copies/mL)

a

1.E+10 1.E+09 1.E+08

1.E+07 bacteria

1.E+06

mcrA gene 1.E+05 6.5

7.0

7.5

8.0

pH(-)

Gene copy numbers (copies/mL)

b

1.E+10 1.E+09 1.E+08

1.E+07 bacteria

1.E+06

mcrA gene 1.E+05 0.0

1.0

2.0

3.0

Gas production (LN/(L･d)) 8.0

c

pH(-)

7.5

7.0

6.5 0.0

1.0

2.0

3.0

Gas production (LN/(L d))

Figure 4. Relationships among pH, biogas production and 16S rRNA gene copy numbers in MCa. (a) gene copy numbers and pH (b) gene copy numbers and gas production (c) pH and gas production. The sampling days for each data set are on 922, 972, 1,007, 1,029, 1,049, 1,077 and 1,105.

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MCd MCe ODh5, 10, 16

a 1.E+09

b1.E+09 mcrA gene(copies/mL)

MDd MDe ODg5, 10, 13

Archaeal gene(copies/mL)

MDb10, 15 1.E+09 MCc 1.E+06 WCf

1.E+08

1.E+07

1.E+06 1.E+06

1.E+07

1.E+08

1.E+08

1.E+07

1.E+06 1.E+08

1.E+09

MDc WDf

mcrA gene(copies/mL)

1.E+09

1.E+10

1.E+11

c 1.E+10

d 1.E+09

Bacterial gene(copies/mL)

mcrA gene(copies/mL)

Bacterial gene(copies/mL)

1.E+09

1.E+08

1.E+08

1.E+07

1.E+06

0

2

4

6

8

0

TS of digested sludge(%)

2

4

6

8

TS of digested sludge(%)

Figure 5. Relationships among total bacterial 16S rRNA gene copy numbers, mcrA gene copy numbers, total archaeal gene copy numbers and TS of the digested sludge in mesophilic operations, excluding MCa. (a) mcrA gene copy numbers and total bacterial gene copy numbers (b) total archaeal gene copy numbers and mcrA gene copy numbers. (c) total bacterial gene copy numbers and TS of the digested sludge (d) mcrA gene copy numbers and TS of the digested sludge.

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Bacterial gene(copies/mL)

a 1.E+10 MDb10, 15 MDd MCd MDc MCc MDe MCe WDf WCf ODg5, 10, 13 ODh5, 10, 16

1.E+09

1.E+08 0

0.5

1

1.5

Biogas production(LN/(L・d))

mcrA gene(copies/mL)

b 1.E+09 MDb10, 15 MDd MCd MDc MCc MDe MCe WDf WCf ODg5, 10, 13 ODh5, 10, 16

1.E+08

1.E+07

1.E+06 0

0.5

1

1.5

Biogas production(LN/(L・d))

Figure 6. Relationships among total bacterial 16S rRNA gene copy numbers, mcrA gene concentrations and biogas production in mesophilic operations, excluding MCa. (a) total bacterial gene copy numbers and biogas production (b) mcrA gene copy numbers and biogas production. Error bars indicate the standard deviations.

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Table 1. Conditions of sewage sludge used in the continuous anaerobic digestion experiments Reactor MCa T_MAa T_MCa MDb10 MDb15 MCc MDc MCd MDd MCe MDe WCf WDf ODg10 ODg13, 5 ODh10 ODh16,5

WWTP a a a b b c c d d e e f f g g h h

AD temperature mesophilic thermophilic thermophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic mesophilic

sludge type mixed sludge, concentrated mixed sludge and microalgae, concentrated mixed sludge, concentrated mixed sludge, dewatered mixed sludge, dewatered mixed sludge, concentrated mixed sludge, dewatered mixed sludge, concentrated mixed sludge, dewatered mixed sludge, concentrated mixed sludge, dewatered waste activated sludge, concentrated waste activated sludge, dewatered waste activated sludge, dewatered waste activated sludge, dewatered waste activated sludge, dewatered waste activated sludge, dewatered

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wastewater treatment type CAS CAS recycled nitrification/ denitrification CAS CAS step-feed biological nitrogen removal CAS OD

1 OD

1

Table 2. Operational OLR condition in MCa (after Hidaka et al. (2015a))

day OLR(gVS/(L･d))

OLR1

OLR2

OLR3

426–599 2

600–832 2.7

833–922 3.2

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OLR4 923– 1,020 3.9

OLR5 1,021– 1,030 4.2

OLR6 1,031– 1,120 1

Table 3. A list of 16S rRNA or mcrA gene targeted-oligonucleotide primers and probes used in this study Bac341F Bac805R Bac1055YF Bac1392R Bac1115Probe ARC787F ARC1059R ARC915F ME1f ME2r

Specificity Bacteria Bacteria Bacteria Bacteria Bacteria Archaea Archaea Archaea Most methanogens Most methanogens

Sequence (5’ -3’) CCTACGGGNGGCWGCAG GACTACHVGGGTATCTAATCC ATGGYTGTCGTCAGCT ACGGGCGGTGTGTAC CAACGAGCGCAACCC ATTAGATACCCSBGTAGTCC GCCATGCACCWCCTCT AGGAATTGGCGGGGGAGCAC

Target site 341-357 785-805 1055-1070 1392-1406 1100-1114 787-806 1044-1059 915-934

References Herlemann et al., 2011 Herlemann et al., 2011

Ritalahti et al., 2006 Ritalahti et al., 2006 Ritalahti et al., 2006 Yu et al., 2005 Yu et al., 2005 Yu et al., 2005

GCMATGCARATHGGWATGTC mcrA gene

Hales et al., 1996

TCATKGCRTAGTTDGGRTAGT

Hales et al., 1996

29

mcrA gene

Highlights

pH and biogas production are useful to monitor anaerobic digestion, but insufficient. Applicability of microbial structure analyses and gene quantification are evaluated. Seventeen digestion experiments of sewage sludge from eight plants are compared. Microbial structure was analyzed by using high-throughput sequencing. Total bacterial 16S rRNA gene copy numbers are the most useful. 30.

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