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Comprehensive microbial analysis of combined mesophilic anaerobicethermophilic aerobic process treating high-strength food wastewater Hyun Min Jang a, Jeong Hyub Ha a,b,*, Jong Moon Park a,b,c,**, Mi-Sun Kim d, Sven G. Sommer e a

School of Environmental Science and Engineering, Pohang University of Science and Technology, 77, Cheongam-Ro, Pohang 790-784, Republic of Korea b Department of Chemical Engineering, Pohang University of Science and Technology, 77, Cheongam-Ro, Pohang 790784, Republic of Korea c Division of Advanced Nuclear Engineering, Pohang University of Science and Technology, 77, Cheongam-Ro, Pohang 790-784, Republic of Korea d Biomass and Waste Energy Laboratory, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, Republic of Korea e Institute of Chemical Engineering, Biotechnology and Environmental Technology, Faculty of Engineering, University of Southern Denmark, Niels Bohrs All
e 1, DK-5230 Odense M, Denmark

article info

abstract

Article history:

A combined mesophilic anaerobicethermophilic aerobic process was used to treat high-

Received 1 October 2014

strength food wastewater in this study. During the experimental period, most of solid

Received in revised form

residue from the mesophilic anaerobic reactor (R1) was separated by centrifugation and

27 January 2015

introduced into the thermophilic aerobic reactor (R2) for further digestion. Then, ther-

Accepted 28 January 2015

mophilic aerobically-digested sludge was reintroduced into R1 to enhance reactor perfor-

Available online 7 February 2015

mance. The combined process was operated with two different Runs: Run I with hydraulic retention time (HRT) ¼ 40 d (corresponding OLR ¼ 3.5 kg COD/m3 d) and Run II with

Keywords:

HRT ¼ 20 d (corresponding OLR ¼ 7 kg COD/m3). For a comparison, a single-stage meso-

High-strength food wastewater

philic anaerobic reactor (R3) was operated concurrently with same OLRs and HRTs as the

Combined biological process

combined process. During the overall digestion, all reactors showed high stability without

Methane production

pH control. The combined process demonstrated significantly higher organic matter

Pyrosequencing

removal efficiencies (over 90%) of TS, VS and COD and methane production than did R3.

Quantitative real-time PCR (qPCR)

Quantitative real-time PCR (qPCR) results indicated that higher populations of both bacteria and archaea were maintained in R1 than in R3. Pyrosequencing analysis revealed relatively high abundance of phylum Actinobacteria in both R1 and R2, and a predominance of phyla Synergistetes and Firmicutes in R3 during Run II. Furthermore, R1 and R2 shared genera (Prevotella, Aminobacterium, Geobacillus and Unclassified Actinobacteria), which suggests synergy between mesophilic anaerobic digestion and thermophilic aerobic digestion. For

* Corresponding author. Department of Chemical Engineering, Pohang University of Science and Technology, 77, Cheongam-Ro, Pohang, 790-784, Republic of Korea. Tel.: þ82 54 279 8315; fax: þ82 54 279 8659. ** Corresponding author. Department of Chemical Engineering, Pohang University of Science and Technology, 77, Cheongam-Ro, Pohang, 790-784, Republic of Korea. Tel.: þ82 54 279 2275; fax: þ82 54 279 8659. E-mail addresses: [email protected] (J.H. Ha), [email protected] (J.M. Park). http://dx.doi.org/10.1016/j.watres.2015.01.038 0043-1354/© 2015 Elsevier Ltd. All rights reserved.

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archaea, in R1 methanogenic archaea shifted from genus Methanosaeta to Methanosarcina, whereas genera Methanosaeta, Methanobacterium and Methanoculleus were predominant in R3. The results demonstrated dynamics of key microbial populations that were highly consistent with an enhanced reactor performance of the combined process. © 2015 Elsevier Ltd. All rights reserved.

1.

Introduction

The amount of food waste (FW) significantly increases and it is estimated about 1.3 billion tons in 2011 (Gustavsson et al., 2011). In Korea alone, ~4 million tons of FW per year has been generated and it accounts for over 26% of total municipal solid waste (MSW) generation (MOE, 2012). Typically collected FW is recycled as feedstuff, fertilizer and soil amendment through washing and composting processes. Food wastewater (FWW) is a byproduct of the recycling of food waste (FW). Korea produces >3.5 million tons of FWW per year (MOE, 2012) that contains a high concentration of organic matter. Traditional methods to treat FWW have adverse environmental effects, such as contamination of drainage system and malodor production, so development of appropriate management methods has become an environmental and public concern (Shin et al., 2001). Effective degradation of the organic fraction in the FWW is of key importance to reduce its disagreeable environmental effects. Anaerobic digestion (AD) is considered as one of the promising practical technology that exploits microbial metabolic abilities to reduce the organic fraction in FWW by converting the organics to methane (Li et al., 2011); AD can also prevent fugitive emissions more than can other technologies (Levis et al., 2010). Meanwhile, to increase the efficiency of AD, two-stage or multi-stage AD processes (i.e., temperaturephased anaerobic digestion (TPAD) or separated acidogenicemethanogenic step) have been developed (Pervin et al., 2013; Shin et al., 2010). Also, a combined anaerobicaerobic process has been used due to the potential to remove organic matter and produce methane efficiently under two extremely different redox conditions (Novak et al., 2003; Tomei et al., 2011). Microbial degradation of FWW is a synergistic and complex process that involves a diverse assemblage of bacteria and methanogenic archaea. Thus, to achieve efficient degradation of FWW, the AD reactor should support a delicately-balanced composition of bacteria and archaea to ensure that they interact effectively for the process. Therefore, understanding of the microbial communities in the AD and their responses to environmental changes such as pH, temperature, substrate, organic loading rate (OLR) and hydraulic retention time (HRT) might provide valuable information that can be used to optimize operation of AD. Various molecular microbiology techniques have been applied to understand the microbial communities in various types of AD and target organic wastes. Shin et al. (2010) and Regueiro et al. (2012) used denaturing gradient gel electrophoresis (DGGE), quantitative real-time PCR (qPCR) and

fluorescence in situ hybridization (FISH) techniques to quantify and qualify microbial community structures in lab or fullscale anaerobic digesters. Hori et al. (2006) and Niu et al. (2013) applied single-strand conformation polymorphism (SSCP) and terminal restriction fragment length polymorphism (T-RFLP) to identify the characteristic microorganisms during thermophilic AD. However, the throughput and sensitivity of these molecular techniques are not sufficient to cover the overall taxonomic distribution; they are also time-consuming and require highly-skilled operation (Ercolini, 2004; Gao and Tao, 2012). Recently-developed sequencing technologies provide higher throughput of sequence data in complex biological samples without the limitations of existing cultureindependent molecular techniques. One such recent technique is 454-pyrosequencing, which is based on sequencing by synthesis and relies on detection of pyrophosphate released during nucleotide incorporation (Margulies et al., 2005). Pyrosequencing has been used to investigate microbial community structure in 21 (Sundberg et al., 2013) and 7 (Lee et al., 2012) full-scale AD treating various organic matters and at different temperature ranges. Pervin et al. (2013) also indicated the applicability of pyrosequencing in multi-stage AD of sewage sludge. In this study, we proposed a novel combined biological process which consists of mesophilic anaerobic digestion (MAD) combined with thermophilic aerobic digestion (TAD) and solid separation unit for treating high-strength FWW. To compare the organic matter removal efficiency and methane production, a lab-scale combined process and conventional single-stage AD were operated simultaneously with the same feedstock for a period of 204 d. We also used pyrosequencing and qPCR methods to investigate microbial community structure and population in the proposed process. To the best of our knowledge, this is the first report on the reactor performance and phylogenetic diversity of bacteria and archaea in combined MAD-TAD process during the treatment of highstrength FWW.

2.

Material and methods

2.1.

Preparation of feedstock

The feedstock used in this study was collected from a FWW storage tank in FW recycling facility in Pohang city, South Korea. This facility treats ~180 ton/d of FW generated from public and private sectors, and generates ~50 ton/d of FWW. Samples from a FWW storage tank were filtered (1.0-mm

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Table 1 e Characteristics of FWW used in this study. Parameters pH Total Solids (TS) (g/L) Volatile Solids (VS) (g/L) VS/TS (%) Total Chemical Oxygen Demand (TCOD) (g/L) Soluble COD (SCOD) (g/L) Total nitrogen (TN) (g/L) Soluble TN (STN) (g/L) NHþ 4 eN (g/L) Total phosphorus (TP) (g/L) Soluble TP (STP) (g/L) Lactic acid (g COD/L) Acetic acid (g COD/L) Propionic acid (g COD/L) Butyric acid (g COD/L) Succinic acid (g COD/L) Total VFA (g COD/L)a Total Organic acids (g COD/L)b a b

Values (average ± standard deviation) 4.31 ± 0.02 118.49 ± 3.54 106.52 ± 3.41 90.5 ± 0.69 139.58 ± 2.79 90.46 ± 1.53 1.94 ± 0.15 1.02 ± 0.35 0.57 ± 0.03 2.22 ± 0.23 1.58 ± 0.02 40.74 ± 1.04 14.35 ± 0.59 3.32 ± 0.31 9.94 ± 0.51 1.12 ± 0.04 27.61 ± 1.02 69.47 ± 1.46

Sum of acetic-, propionic- and butyric acid. Sum of all organic acids.

sieve) to remove inert materials (mainly eggshell, plastic and vinyl), then distributed in 3-L bottles and stored at 25  C until use. The characteristics of FWW were assayed (Table 1); the main characteristics were as follows: pH 4.31 ± 0.02, total solids (TS) 118.49 ± 3.54 g/L, volatile solids (VS) 106.52 ± 3.41 g/ L, total chemical oxygen demand (TCOD) 139.58 ± 2.79, soluble COD (SCOD) 90.46 ± 1.53 g/L, total organic acid (TOA) 64.47 ± 1.46 g COD/L and total volatile fatty acid (TVFA) 25.80 ± 0.94 g acetic acid/L.

2.2.

Reactor operation

The combined process (Fig. 1) for high-strength FWW treatment consists of a MAD process (R1) with a TAD process (R2).

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During the experiment, most of the solids from R1 were separated by centrifugation (8000 rpm, 20 min) and recycled into R2 for further digestion. Then, thermophilic aerobicallydigested organic matter from R2 was reintroduced into R1 to enhance methane production by exploiting the synergy between MAD and TAD. MAD and TAD were operated with working volumes of 11 and 1 L, respectively (Table S1). Additionally, conventional single-stage MAD (R3; control process) was operated with working volume of 6L under the same total HRTs (40- and 20-d) as the combined process for 204 days to compare the reactor performance and the microbial communities in the reactors. Although same total HRTs were applied, the HRTs in R1 (22- and 13.75-d) were shorter than those of R3, due to the recirculation from R2 to R1. More details about the operating conditions are presented in Table S1. The seed for anaerobic reactors was obtained from a successfully-operated full-scale AD plant (Daegu, South Korea); the seed for the thermophilic aerobic reactor was obtained from an autothermal thermophilic aerobic digestion (ATAD) pilot plant (Daejeon, South Korea).

2.3.

DNA extraction and pyrosequencing

Total genomic DNA used for pyrosequencing and qPCR was extracted from samples at steady state of each Run (102 and 204 days) as described previously (Jang et al., 2014). Hypervariable regions within bacterial and archaeal 16s rRNA genes were amplified by PCR using universal primers (Table S2); for bacteria: Bac27F (50 -adaptor A-Barcode-AC-GAG TTT GAT CMT GGC TCA G-30 )/Bac541R (50 -adaptor B-Barcode-AC-WTT ACC GCG GCT GCT GG-30 ); for archaea: Arc344F (5'-adaptor A-Barcode-GA-YGG GGY GCA SCA GGS G-30 )/Arc927R (50 -adaptor BBarcode-GA-CCC GCC AAT TCC TTT AAG TTT C-30 ) (Jung et al., 2013). PCR amplification was conducted using FastStart High Fidelity PCR system (Roche, Branford, CT). The protocol was composed of: (1) initial denaturation at 94  C for 4 min; (2) 35 cycles of 94  C for 15 s, 55  C for 45 s, and 72  C for 1 min; (3) a final extension at 72  C for 8 min. The samples were purified

Fig. 1 e Conceptual schematic diagram of (a) combined and (b) control process (pump flow rates Q1e7 are described in Supplementary material Table S1).

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using PCR purification kit (Solgent, Korea), then DNA concentrations were measured using a fluorometer with QuantiT™ PicoGreen® dsDNA Assay Kit (Invitrogen ™, California). Equal amounts of PCR amplicons from each sample were pooled, then pyrosequencing was performed with 454-GS-FLX Titanium (Roche, Branford) using the massively parallel pyrosequencing protocol by a sequencing provider (Macrogen, Korea).

2.4.

Analysis of pyrosequencing data

Pyrosequencing raw data were processed and analysed using the Ribosomal Database Project (RDP) Pyrosequencing Pipeline Initial Process (http://pyro.cme.msu.edu/) (Cole et al., 2009). The sequencing reads were assigned to specific samples based on their unique barcodes, and then the barcodes and primers were trimmed. Sequencing reads with lengths 95% of coverage (Table S3); therefore the diversity and richness in this study are sufficient to characterize the major phylotypes in each sample.

3.4. Composition and possible functions of major bacterial species Taxonomic classification of clean sequences obtained from pyrosequencing provides comprehensive insight into the microbial communities and their possible functions assuming that closely-affiliated taxa share similar metabolic pathways  re et al., 2009; Zhang et al., 2010). In this and capabilities (Rivie study, the relative composition of the unclassified sequences at all taxonomic levels was within typical ranges reported previously (Lee et al., 2008; Lu et al., 2012; Sundberg et al., 2013). According to taxonomic classification and calculated Shannon-Weaver Indices (Table S3), all bacterial samples showed higher diversity and taxonomic distribution than those of archaeal samples; this finding is in agreement with the results of earlier studies of microbial communities in the full-scale biogas digesters (Regueiro et al., 2012; Sundberg et al., 2013). The taxonomic distributions of each bacterial sample were determined at the phylum and class level (Fig. 3). Unlike Anaerobic seed, Bacteroidetes was most abundant phylum in both anaerobic reactors and was detected in all reactors during the steady-state condition in Runs I and II. In contrast, the relative abundances of phyla Chloroflexi (most abundant phylum in Anaerobic seed) and Proteobacteria (second major phylum) in both anaerobic reactors decreased significantly in Runs I and II. The decline of those two phyla in this study may be due to the difference in characteristics of the substrates, as recently reported by Sundberg et al. (2013). Also, several studies mentioned potential function of microorganisms assigned into the phylum in AD mainly related to hydrolysis of organic matter and production of acetic acid (Kragelund et al., re et al., 2009). Thus, the dominance of phylum 2008; Rivie

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Bacteroidetes in both anaerobic reactors indicates that bacteria of this phylum may have important functions in hydrolysis and acetogenesis. Interestingly, phyla distributions were markedly different between R1 and R3 at Run II. PCoA also demonstrated clear regional separation (Fig. S2a). In R1, phyla Synergistetes (main class Synergistia) decreased from 17.82% in Run I to 4.07% in Run II, Firmicutes (main classes Clostridia and Bacilli) decreased from 10.71% in Run I to 8.91% in Run II, whereas the phylum Actinobacteria (main class Actinobacteria) increased from 6.05% in Run I to 17.55% in Run II. The large proportion of members of the phylum Actinobacteria is indicative of their contribution to efficient degradation of complex organic materials and production of organic acids in R1 (Ventura et al., 2007). In R3, members of the phyla Synergistetes increased from 18.25% in Run I to 28.12% in Run II, and those of Firmicutes (main class Clostridia) increased from 11.29% in Run I to 14.24% in Run II; whereas the relative abundance of phylum Actinobacteria was ~3.3% in both Runs I and II. Members of the phyla Synergistetes and Firmicutes commonly appear in AD; they can degrade proteins, lipids and carbohydrate with hydrolytic enzymes (Guo et al., 2014; Sundberg et al., 2013). In addition, some of them can obtain energy by oxidizing organic acids (syntrophic reaction), especially by fermenting VFAs to H2 and CO2 (Ito re et al., 2009). Therefore, the predominance et al., 2011; Rivie of the phyla Synergistetes and Firmicutes strongly suggests that syntrophic metabolism is highly activated in R3, especially during Run II. It is noteworthy that R2 maintained higher bacterial diversity and taxonomic distribution than did both R1 and R3 throughout digestion (Table S3). Also, the bacterial community composition of thermophilic aerobic seed differed greatly from those of Runs I and II, probably due to the difference in characteristics of substrate and operating parameters (e.g., reactor configuration and aeration efficiency), as recently reported by Piterina et al. (2012). In Run I, abundance of the phylum Firmicutes (most abundant phylum in thermophilic aerobic seed) decreased to ~17%, then remained steady during rest of the operating period. It should additionally be noted that most species of class Clostridia (~5% in Runs I and II) are obligate anaerobes. The presence of a significant proportion of such anaerobic bacteria in TAD has been reported by several authors (Juteau et al., 2004b; LaPara et al., 2002) and is probably due to the low oxygen solubility under thermophilic conditions. The variation patterns of ORP in R2 (Table 2) are consistent with the existence of an anaerobic period caused by the step-feeding system, whether they have metabolic activities in R2 remains unclear. Unlike phylum Firmicutes, phylum Actinobacteria (mainly class Actinobacteria) was not a major phylum in Seed (3.33%), but significantly increased to 22.98% in Run I and 24.55% in Run II, and became the most abundant phylum in R2. Although this phylum is frequently detected in the ATAD process (never as the dominant phylum) (Hayes et al., 2011; Juteau et al., 2004b), this is the first observation that members of phylum Actinobacteria emerge as the dominant phylum in TAD. Their predominance in this study suggests that they were selectively enriched from the thermophilic aerobic seed and had an important function related to organic matter removal throughout digestion.

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Fig. 3 e Distribution and relative abundance of taxa in bacterial samples based on sequences derived from pyrosequencing. Inner circle: composition at phylum level; outer circle: composition at class level. Sequences showing with percentage of reads >1.0% in all samples were grouped into ‘Others’.

Genus-level classification was conducted to further determine the phylogenetic discrimination and possible functions of the bacterial members in the reactors (Fig. 5a). Throughout digestion, both R1 and R3 also showed discernible distinctions in distribution of sequences at the genus level within phylum Firmicutes (Fig. 5a). The members assigned into genus Clostridium (belong to class Clostridia), which are syntrophic bacteria (Schnu¨rer et al., 1996), were predominant (~8%) in R3, but relatively uncommon (~1%) in R1. Interestingly, the genus Geobacillus (belong to class Bacilli) was observed at ~6% of abundances in R1, and was predominant (~11%) in R2; the

presence of this genus in thermophilic aerobic condition (mainly produce lytic enzyme) has been reported previously, but it is uncommon in anaerobic conditions. Other genera such as Prevotella and Aminobacterium, which are mostly anaerobic bacteria, were also detected in both R1 and R2. In particular, R1 and R2 shared a significant proportion (4.09% and 14.24% in R1; 19.28% and 17.64% in R2 at Runs I and II, respectively) of one uncharacterized genus Unclassified Actinobacteria throughout digestion. This may in fact be significantly important in maintenances of bacterial population and reactor performances. This was also highly in agreement with

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Fig. 4 e Distribution and relative abundance of taxa in archaeal samples based on sequences derived from pyrosequencing. Inner circle: composition at phylum level; outer circle: composition at order level. Sequences showing with percentage of reads >1.0% in all samples were grouped into ‘Others’.

the higher concentration of 16S rRNA copies in both R1 and R2 than that of R3 during the overall digestion (Fig. 2). Thus, the results from this study confirmed the hypothesis that R1 and R2 worked synergistically, but further investigation and characterization of uncultured and unclassified bacteria will be required to clarify their metabolic functions and activities in the proposed combined process.

3.5.

Changes in methanogen species

During typical AD, complex organic matter can be degraded and fermented by bacteria and converted finally into methane, by methanogens as part of their energy metabolism (Garcia et al., 2000). Understanding the taxonomic distribution and properties of methanogens allows us to infer methanogenic pathway in the reactor. 16S rRNA gene sequences obtained from pyrosequencing of archaeal samples were assigned to phylum Euryarchaota, which includes methanogens (Fig. 4). Furthermore, >99.5% of sequences were assigned to classified methanogen orders. This means that most of archaea found in this study were involved in methanogenesis. All archaeal samples showed lower diversity and taxonomic distribution than those of bacterial samples; particularly, the Shannon-Weaver Index in R1 showed the lowest value (0.44) in Run II (Table S3). The taxonomic distributions of the archaeal communities at the order level differed distinctly between the anaerobic

reactors (Fig. 4). Similar to this finding, PCoA plot of archaeal samples also indicated distinct regional separation (Fig. S2b). In R1, the relative abundance of the aceticlastic methanogen of order Methanosarcinales increased from 79.29% (Anaerobic seed) to 92.43% in Run I and to 95.26% in Run II. Meanwhile, in R3, this order decreased to 62.07% in Run I and 55.51% in Run II, whereas the proportion of hydrogenotrophic methanogens increased from 20.57% (Anaerobic seed) to 37.65% (order Methanobacteriales, 17.98%; order Methanomicrobiales, 19.67%) in Run I and to 44.13% (order Methanobacteriales, 16.94%; order Methanomicrobiales, 27.19%) in Run II. This significant increase of the hydrogenotrophic methanogens strongly indicates that the hydrogenotrophic methanogenesis pathway might contribute to methane production in R3, but that is not a predominant pathway in R1. Additionally, the high abundance of hydrogenotrophic methanogens in R3 was supported by the large proportions of syntrophic bacteria (i.e., genera Anaerobaculum and Clostridium) (Fig. 5a), which can produce H2, an important electron source for hydrogenotrophic methanogenesis (Wirth et al., 2012). The archaea sequences were also classified at the genus level (Fig. 5b). In anaerobic seed, the sequences were subdivided into 19 genera, but six of these genera (Unclassified Methanobacteriales, Methanosphaera, Methanobrevibacter, Methanolinea, Unclassified Methanomicrobiales incertae sedis and Unclassified Archaea) were never detected in R1 or R3. During Run I, Methanosaeta (42.93%) and Methanosarcina (44.54%)

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Fig. 5 e Heat map (constructed using R software with gplots package) of (a) top 25 bacterial genera and (b) top 20 archaeal genera. The colour intensity of scale indicates relative abundance of each genus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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(belong to order Methanosarcinales) were the two main genera in R1. Interestingly, during Run II, the relative abundance of genus Methanosarcina reached up to 95.02%, whereas members of the genus Methanosaeta were completely inhibited or washed out in R1. In contrast, in R3, >50% of sequences was assigned to genus Methanosaeta, whereas members of the genus Methanosarcina were never detected. Also, Methanobacterium (belong to order Methanobacteriales; 17.58% in Run I and 16.64% in Run II) and Methanoculleus (belong to order Methanomicrobiales; 17.11% in Run I and 24.64% in Run II) were the two main genera in R3. These genera are related to hydrogenotrophic methanogenesis and are predominant during AD of food-related wastes (Kim et al., 2014). Typically, members of genus Methanosarcina can produce methane by both aceticlastic and hydrogenotrophic methanogenesis pathways (Garcia et al., 2000). Furthermore, in AD the predominant methanogenic genera change from Methanosaeta to Methanosarcina, as a result of changes in operating parameters, particularly by increase in VFAs (especially acetic acid) and decrease in HRT (De Vrieze et al., 2012; McMahon et al., 2004). The change occurs mainly because these genera have different growth characteristics. Members of genus Methanosarcina can achieve stable growth at higher concentration of VFAs and have higher specific growth rates than genus Methanosaeta (Conklin et al., 2006). Thus, the relatively high concentration of TVFA (3.07 ± 0.01 g acetic acid/L) (Table 2) and short HRT (13.75 day) (Table S1) during Run II probably contributed to the predominance of genus Methanosarcina in R1. Given this information, the predominance of Methanosarcina in the methanogenic community in R1 strongly indicates that members of the genus probably have important functions in methanogenesis and may lead to a better performance with stable conditions at low HRT, compared to R3.

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biology technologies (e.g., metagenomics, microarrays, single cell genomics).

Acknowledgements This work was conducted under the framework of Research and Development Program of the Korea Institute of Energy Research (KIER) (B4-2474-02) and supported by the Advanced Biomass R&D Center (ABC) of Korea Grant funded by the Ministry of Education, Science, and Technology (ABC2013059453). The research was partially supported by BK21 þ program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology, the Manpower Development Program for Marine Energy funded by Ministry of Land, Transportation and Maritime Affairs (MLTM) of Korean government. This research was also supported by POSCO and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea Government Ministry of Knowledge Economy (No. 2012K130y). This research was a part of the project titled ‘Technology Development of Marine Industrial Biomaterials’, funded by the Ministry of Oceans and Fisheries, Korea.

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.watres.2015.01.038.

references

4.

Conclusions

In this study, a combined MAD (R1)-TAD (R2) process was used to treat high-strength food wastewater. The specific conclusions can be written as follows:  The proposed combined MAD (R1)-TAD (R2) process showed efficient treatment of organic matter removal (i.e., COD, TS and VS), and higher methane production than did the control process (R3).  Higher populations (based on the concentration of 16s rRNA gene copy number) of both bacteria and archaea were maintained in R1 than in R3 during the entire digestion.  R1 and R2 shared a significant proportion of bacterial genera (Prevotella, Aminobacterium, Geobacillus and Unclassified Actinobacteria) and the predominant genus of methanogenic archaea shifted from Methanosaeta to Methanosarcina in R1.  The qPCR and high-throughput pyrosequencing results demonstrated that the synergisms between MAD and TAD improved reactor performance by establishing substantial microbial communities.  To clarify the mechanism of collaboration between MAD and TAD, further studies of the function of shared microorganisms should be conducted using other molecular

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