Acetoclastic methanogenesis led by Methanosarcina in anaerobic co-digestion of fats, oil and grease for enhanced production of methane

Accepted Manuscript Acetoclastic methanogenesis led by Methanosarcina in anaerobic co-digestion of fats, oil and grease for enhanced production of methane Mayur B. Kurade, Shouvik Saha, El-Sayed Salama, Swapnil M. Patil, Sanjay P. Govindwar, Byong-Hun Jeon PII: DOI: Reference:

S0960-8524(18)31478-0 https://doi.org/10.1016/j.biortech.2018.10.047 BITE 20611

To appear in:

Bioresource Technology

Received Date: Revised Date: Accepted Date:

19 September 2018 17 October 2018 19 October 2018

Please cite this article as: Kurade, M.B., Saha, S., Salama, E-S., Patil, S.M., Govindwar, S.P., Jeon, B-H., Acetoclastic methanogenesis led by Methanosarcina in anaerobic co-digestion of fats, oil and grease for enhanced production of methane, Bioresource Technology (2018), doi: https://doi.org/10.1016/j.biortech.2018.10.047

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Acetoclastic methanogenesis led by Methanosarcina in anaerobic co-digestion of fats, oil and grease for enhanced production of methane Mayur B. Kuradea, Shouvik Sahaa, El-Sayed Salamaa,b, Swapnil M. Patila, Sanjay P. Govindwara, Byong-Hun Jeona* aDepartment

of Earth Resources and Environmental Engineering, Hanyang University, Seoul,

04763, Republic of Korea. bDepartment

of Occupational and Environmental Health, School of Public Health, Lanzhou

University, Lanzhou 730000, Gansu Province, P.R. China. *Corresponding author: Prof. Byong-Hun Jeon, Department of Earth Resources and Environmental Engineering, Hanyang University, Seoul 133-791, Republic of Korea. Tel.: +82 02 2220 2242; Fax: +82 02 2281 7769 E-mail address: [email protected] Abstract Fats, oil and grease (FOG) are energy-dense wastes that substantially increase biomethane recovery. Shifts in the microbial community during anaerobic co-digestion of FOG was assessed to understand relationships between substrate digestion and microbial adaptations. Excessive addition of FOG inhibited the methanogenic activity during initial phase; however, it enhanced the ultimate methane production by 217% compared to the control. The dominance of Proteobacteria was decreased with a simultaneous increase in Firmicutes, Bacteriodetes, Synergistetes and Euryarchaeota during the co-digestion. A significant increase in Syntrophomonas (0.18-11%), Sporanaerobacter (0.14-6%) and Propionispira (0.02-19%) was

observed during co-digestion, which substantiated their importance in acetogenesis. Among methanogenic Archaea, the dominance of Methanosaeta (94%) at the beginning of co-digestion was gradually replaced by Methanosarcina (0.52-95%). The absence/relatively low abundance of syntrophic acetate oxidizers and hydrogenotrophic methanogens, and dominance of acetoclastic methanogens suggested that methane generation during co-digestion of FOG was predominantly conducted through acetoclastic pathway led by Methanosarcina. Keywords: Anaerobic co-digestion; Fats, oil and grease (FOG); High-throughput sequencing; Long-chain fatty acids; Methanosarcina; Methanosaeta. 1. Introduction Anaerobic digestion (AD) is undoubtedly one of the most promising and favorable technologies among environmental engineers due to its properties such as substantial reduction in biosolids and their pollution burden, relatively low energy consumption, and biogas production (Kurade et al., 2016; Xu et al., 2018; Ziels et al., 2018). Recently, more efforts have been devoted to effectively utilize high-strength organic substrates, such as fats, oil and grease (FOG), along with wastewater sludges in an anaerobic co-digestion (ACoD) approach as it supports significantly higher production of biogas (Kabouris et al., 2008; Li et al., 2013; Salama et al., 2019). Despite its high productive output, utilization of FOG as a co-substrate involves major issues, including inhibition of acetogens and methanogens at high doses of FOG, damage of cell membranes, reduced mass transport, and cell permeability due to the accumulation of long-chain fatty acids (LCFAs) (Angelidaki et al., 1992; Palatsi et al., 2009; Silva et al., 2016). Although the history of AD dates back to the beginning of the 10th century BCE with research interest emerging in the 17th century (Auer et al., 2017), ‘AD’ is still of interest among the scientific community due to its unresolved mysteries.

Thorough research investigations have revealed that several parameters, such as pH, temperature, pretreatment conditions, organic loading rate and hydraulic retention time, have a direct influence on AD (Li et al., 2013; Ferguson et al., 2018; Xu et al., 2018). However, microbial ecology, which is ‘the black box’ of AD, is partially undiscovered. Many efforts have been devoted in the last decade to understand the microbial structures and their functional networks (Ferry et al., 2011; Kim et al., 2014; Amha et al., 2018). It is now known that different stages of AD, i.e., hydrolysis, acidogenesis, acetogenesis, and methanogenesis, are regulated by numerous microbial communities that work in a symbiotic relationship; these communities significantly fluctuate with changes in the abovementioned parameters, and most importantly the substrates used for AD (Li et al., 2011; Yi et al., 2014; Ziels et al., 2016). It has been reported that the phyla Actinobacteria, Firmicutes, Bacteroidetes, Chloroflexi, Proteobacteria and Euryarchaeota are significantly influenced after FOG loading, whereas Firmicutes, Bacteroidetes, and Euryarchaeota dominate during ACoD (Yang et al. 2016; Amha et al., 2017; Ferguson et al., 2018; Wang et al., 2018). Ziels et al. (2016) found that LCFA-degrading Syntrophomonas bacteria were highly enriched in the digesters fed with LCFAs. Acetoclastic methanogenesis is considered a major pathway through which methane is produced in ACoD of FOG. At present, only two methanogenic genera (Methanosaeta and Methanosarcina) are known to perform acetoclastic methanogenesis. Methanosaeta is an obligatory acetate consumer that dominates at low acetate concentration, whereas Methanosarcina is the most metabolically and physiologically versatile methanogen that can convert different substrates, such as acetate, hydrogen, and methyl containing groups, to methane (Conklin et al., 2006; Blume et al., 2010; De Vrieze 2012). A metabolic shift from a Methanosaeta to a Methanosarcina-dominated acetoclastic methanogenesis may occur if

Methanosaeta is unable to perform methanogenesis due to changes such as increased organic loading rates or high levels of salts or ammonia nitrogen (Blume et al., 2010; De Vrieze 2012). Acetoclastic methanogenesis led by Methanosaeta can also be replaced by obligate hydrogenotrophic methanogens belonging to the orders of Methanobacteriales and/or Methanomicrobiales (Angenent et al., 2002; Amha et al., 2017). However, more scientific efforts are needed to investigate their interactions and actual methane generation pathways with respect to the substrate used for ACoD. There are contrasting opinions, especially related to the dominance of either Methanosaeta or Methanosarcina, which are engaged in methanogenesis during the utilization of FOG/LCFAs through ACoD (Yang et al., 2016; Amha et al., 2017; Ziels et al., 2016,2017). The microbial populations and its interactive roles have not been fully discovered till date, as the microbial networking is very complicated. A better understanding of the key ecological niches in anaerobic digestion and the metabolic characteristics of microbial populations inhabiting these niches could help to facilitate new process designs and operational strategies that can enhance the maximize biomethane recovery from organic waste. Thus, the present study aimed to understand the fundamental mechanism that regulates the methanogenic activities under the inhibition conditions caused by FOG, which are followed by enhanced production of methane. The dynamics of bacterial and archaeal communities during the operation were monitored using high-throughput Illumina MiSeq sequencing of 16S rRNA amplicons. Bioinformatics tools (Circos, principal coordinates analysis (PCoA), non-metric multidimensional scaling (NMDS), and Pearson correlation) were implemented to gain insight into responsible microbial communities and their functional complexity. The outcome of this study will provide an in-depth understanding of the possible mechanism of methanogenesis

owing to analysis insights into microbial responses to the inhibitions induced during the ACoD of FOG.

2. Materials and methods 2.1. Collection of sludge and fats, oil and grease All types of sludge, including primary sludge (PS), waste activated sludge (WAS), and anaerobically digested sludge (ADS), were collected from the Jungnang Sewage Treatment Facility, Seoul, Korea in 20 L polyethylene cans. The FOG waste was provided by the Resource Recycling Center, Dangjin, Republic of Korea. All samples were quickly transferred to the laboratory after collection and stored at 4 °C until further use. 2.2. Batch anaerobic co-digestion The methanogenic batch ACoD was conducted in 1 L airtight anaerobic digesters composed of glass material. Briefly, 400 mL of predigested ADS were transferred to bottles, which were used as seed inoculum (biogas production by the seed control was insignificant). Two substrates which include FOG and thickened sludge (a mixture of PS and WAS at a ratio of 70:30, v/v) were used as co-substrates. Briefly, 100 mL of mixed co-substrates containing thickened sludge (75 mL) and FOG (25 mL) was added to the digester containing 400 mL of ADS. The VS of FOG added to the digester was 72% of total volatile solids available in the anaerobic co-digester including the VS of all substrates and ADS. Sodium bicarbonate (5 g L⁻1) and L-cysteine HCl (0.5 g L⁻1) were added to the serum bottles as a pH buffer and reducing agent, respectively. The bottles were flushed with ultrapure N2 gas (99.99%) and then sealed with butyl rubber septa to ensure strictly anaerobic conditions. The preparation of control bottles

followed the same procedures, except for the addition of FOG. All treatments were run in triplicate and were kept in a shaking incubator (150 rpm) at 37 °C throughout the experiment. 2.3. Analytical methods 2.3.1. Characterization of fats, oil and grease and different sludges Basic characterization of sludge and FOG was performed to determine their proximate and ultimate compositions, as well as their physical properties (Saha et al. 2016). The total carbohydrate of the sludge and substrates was determined using the colorimetric phenol-sulfuric acid method. Nitrogen (wt.%) was used as a function to estimate the total protein of all the samples. The total lipid was estimated using the chloroform-methanol extraction method. 2.3.2. Headspace gas analysis Biogas was collected from the headspace into gas bags at regular time intervals, and the gas composition was measured using a gas chromatography (GC, 7890B Agilent Technologies, Palo Alto, CA, USA). Briefly, the GC was equipped with an HP-PLOT/Q column (30 m × 0.32 mm × 20 µm) and a thermal conductivity detector (TCD). The inlet, column and TCD temperature was fixed at 120, 45 and 150 °C, respectively, while maintaining a constant pressure of 10.232 psi. Argon was used as a carrier gas. A 100 µL gas sample from the gas bag was injected into the GC using a gas-tight sample lock syringe (Hamilton, PA, USA). A standard calibration was conducted using a gas mixture that contained a combination of CH4 (39.72%, mol/mol), H2 (25.06%, mol/mol), and CO2 (24.78%, mol/mol) balanced in N2 gas with a purity of 99.99% for each gas (Greengas, Seoul). The standard calibration curve obtained had an R2 value of 0.998. The volume of the gas collected in the gas bag was measured using a 60 mL Luer syringe, and the head space of the serum bottle was also considered to calculate the total volume of gas produced. A modified Gompertz equation was used to describe the methane production curves in

the batch kinetic assays (Saha et al., 2018):

{

M = Mmax × exp ‒ exp

(

Rm × e Mmax

)} (Eq. 1)

(λ ‒ t) + 1

M (mL) is the cumulative methane yield, Mmax (mL) is the total amount of methane produced at time t, Rm (mL d−1) is the maximum methane production rate, λ (d) is the lag phase, t is the incubation period (d) and e is 2.718. 2.3.3. Assessment of microbial community dynamics 2.3.3.1. DNA isolation and high-throughput sequencing of 16S rRNA The composition and population density of the microbial communities were assessed through metagenomic analysis of 16S rRNA amplicons. Samples were collected after each batch of fermentation and DNA was extracted using a QIAamp DNA Stool Kit (Qiagen, Valencia, CA, USA). Approximately 50-100 ng of purified DNA were obtained from each sample and stored at -20 °C. For screening of bacterial biodiversity, the V3-V4 variable region of the 16S rRNA gene was targeted. For the bacterial 16S PCR amplification, 519F (5′-CCTACGGGNGGCWGCAG3′) and 806R (5′-GACTACHVGGGTATCTAATCC-3′) primers were used (Wuchter et al., 2013), whereas for the archaeal sequence library, the primers Arch-349F (5′GYGCASCAGKCGMGAAW-3′) and Arch-915R (5′-GTGCTCCCCCGCCAATTCCT-3′) were utilized (Ziels et al., 2016). Library quantification was performed by real-time PCR using a CFX96 real-time system (BioRad, Hercules, CA, USA). After 16S amplification, the multiplexing step was performed using the Nextera XT Index Kit (Illumina). One microliter of the PCR product was analyzed on a Bioanalyzer DNA 1000 chip in order to verify the size. The libraries were sequenced after size verification using a 2X300-bp paired-end run (MiSeq Reagent Kit v3) on an Illumina MiSeq platform.

2.3.3.2. High-throughput sequencing data analysis using bioinformatics tools The MiSeq paired-end reads were joined using PEAR software. Demultiplexed amplicon read pairs were quality trimmed using Trimmomatic v0.35 (Bolger et al., 2014), and quality control of raw data was conducted to filter out reads of quality scores <30 using FastQC v0.11.4. Each set of paired-end reads was merged using an in-house pipeline to avail longer sequences. After processing, the clean reads were analyzed using the open-source QIIME software package (Caporaso et al., 2010) according to the default parameters for the samples by grouping them into surface layer, middle layer, and bottom layer according to specific objectives of the analyses. Operational taxonomic units (OTUs) corresponding to each read were chosen at 97% similarity against the Greengenes 16S rRNA database and matched with known bacterial genomes to identify members of the hypoxial community. Relative abundance was determined by OTUs. In addition, to determine the relative abundance between each group (temporal), a comparative analysis was performed using the MetaCoMET web platform. 2.4. Metagenome sequence and statistical analysis Operational taxonomic unit (OTU) numbers, Shannon, Chao 1, and Simpson diversity indexes, along with phylogenetic distance, were assessed as indicators of α-diversity. Principal coordinates analysis (PCoA) of unweighted UniFrac distances using the Bray-Curtis similarity index in PAST v3 was implemented to compare the β-diversity of microbial communities within samples recovered at different time intervals of FOG ACoD. Non-metric multidimensional scaling (NMDS) analysis using the Bray-Curtis similarity dissimilarity matrix in PAST v3 was implemented for the visualization of divergence between microbial community structures of studied samples by optimizing the object locations for a two-dimensional scatter plot. Correspondence analysis (CA) was performed to determine the correlation between microbial

communities and different phases of the reactor and its performance. The data presented in this study are the mean and standard deviation of triplicate experiments. Statistical analysis was conducted using the IBM SPSS software version 21.0 for Windows. One-way analysis of variance using the Tukey-Kramer multiple comparison procedure was used to compare tests between the control and treatments, and the correlations (Pearson’s correlation test) among the different parameters were evaluated. The variations and correlations were considered statistically significant at a confidence interval of p<0.05.

3. Results and discussion 3.1. Characteristics of the sludge and the substrates The physicochemical properties of different sludge and substrates collected from the wastewater treatment plant (WWTP) and used in this study are presented in Table 1. The pH of the PS, TWAS, and ADS was near to neutral, which is a typical characteristic of municipal wastewater streams. The PS and TWAS had comparatively higher total solids than the ADS due to the sludge thickening processes at the WWTP. The ADS was characterized by a relatively lower proportion of organic matter (63.3%) than the PS and TWAS. The major contributors of organic fractions in the sludge were carbohydrates and proteins. Lower contents of organic matter, carbohydrates, and lipids in the ADS compared to the municipal sludge feed indicated its digestibility. The low C/N ratios in all the sludge types were the result of extensive amounts of proteins in the sludge. The low C/N ratio avoids rapid acidification of reactors during the hydrolysis of fatty acids in FOG, and maintains an ideal pH for acidogenic fermentation. The FOG had an acidic pH due to the presence of fatty acids. It contained high organic matter with a predominant proportion of lipids (94.4%). The excessive amounts of triglycerides

in the FOG contributed to high contents of carbon (77.5%) and hydrogen (16.3%), thereby making it a carbonaceous substrate with a very high C/N ratio. In the process of fermentation, ratios of co-substrates are critical, especially the C/N ratio. A C/N ratio of 20-30 is considered an optimal range for AD, and the establishment and maintenance of an optimal C/N ratio is one of the major factors in the performance of successful AD (Saha et al., 2016). Considering the very low C/N ratio of municipal sludge, the incorporation of complementary co-substrates having high C/N ratios, such as FOG, would be a better strategy to obtain an optimum C/N ratio (Xu et al., 2018). 3.2. Enhanced production of methane through co-digestion of fats, oil and grease The addition of excessive amounts of FOG (70% VS) enhanced the ultimate methane production by 217% compared to that of the control (without FOG) after 150 d of ACoD (Fig. 1). Daily methane production was highly affected in the FOG reactor until day 20, and had a slower rate of methane production compared to the control (Fig. 1a). The deterioration of daily methane production was also reflected in the cumulative gas production, where the cumulative gas collection was lower than the control until day 60 of the ACoD (Fig. 1b). A regression analysis of the experimental sets showed that the cumulative methane production was well fitted with the modified Gompertz model (Eq. 1), as it showed an R2 value between 0.96-0.99 (Table 2). The Tukey's multiple comparisons test confirmed the significant (p<0.001) improvement in the production of methane due to FOG supplementation. Co-digestion of lipidic wastes is associated with several issues, such as lipid flotation, accumulation of LCFAs, lower degradation rates and decrease in pH attributed to the accumulation of volatile organic acids, such as propionate and acetate (Wan et al., 2011; Li et al., 2013). The initial pH was maintained at 7.0 in the present study, which was decreased to 6.14 after 14 d compared to the pH of 7.6 in the

control. Nevertheless, the pH was gradually increased (6.8 to 7.6) in later phases due to the high buffering capacity of sludge and the additive buffer agent. The FOG is instantaneously hydrolyzed into glycerol and LCFAs in anaerobic conditions. The further conversion of LCFAs is carried out by an obligatory syntrophic partnership between acetogenic β-oxidizing bacteria and acetoclastic and/or hydrogenotrophic methanogenic archaea. However, this conversion process is considered as a rate-limiting process (Cirne et al., 2007). The accumulation of LCFAs can also limit the kinetics of syntrophic β-oxidizing bacteria which ultimately impedes with degradation of LCFAs (Ziels et al., 2017). The relative slower biodegradation of LCFAs than lipid hydrolysis can lead to its accumulation. The accumulation of LCFAs is inhibitory to the methanogenic process even at low concentrations as it adsorbs on the surface of methanogenic bacteria (Silva et al., 2016; Ziels et al., 2017, 2018). Such accumulation impedes the mass transfer effect and prohibits substrate utilization, thereby inhibiting methanogenesis which leads to process instability (Xu et al., 2018). The LCFAs, such as oleic acid, palmitic acid and stearic acid possess IC50 values over 75 mg L−1, 1100 mg L−1 and 1500 mg L−1, respectively (Palatsi et al., 2009). The FOG utilized in the present study contained oleic acid (33%) and palmitic acid (18%) as the dominant LCFAs (data not shown). The presence of excessive amounts of oleic and palmitic acid in the FOG was also one of the major inhibitory factors of ACoD in the earlier phase. Although the excessive addition of FOG inhibited the methanogenic activity during the initial phase (lag phase) of the experiment, the methanogens recovered from the earlier inhibition and showed highly enhanced gas production thereafter (after 20 d) (Fig. 1). Other than the transient inhibition, the delay in methane production in the FOG reactor could also be explained by the fact that the microbial communities require buffer time for their acclimation to the excessive loading of carbonaceous substrate or to the LCFAs

produced during lipid hydrolysis (Tandukar and Pavlostathis, 2015). The delay or inhibition of methane generation leading to failures in AD due to the limited substrate and product transport, reduced activity of microflora, and damaged microbial cells has been observed in the past (Chen et al., 2008; Kabouris et al., 2008). During the ACoD of sewage sludge and FOG, Wan et al. (2011) observed a high level of inhibition in methanogenesis at 75% of VS loading, which led to failure in AD due to intensive acidification of the digester. 3.3. Diversions in microbial community structure during co-digestion of fats, oil and grease The changes in microbial diversity during ACoD were discovered using high-throughput sequencing of 16S rRNA amplicons. The distribution patterns of OTUs plotted in a Venn diagram suggested that 23% of OTUs were shared among the samples retrieved at 0 and 40 d (Fig. 2). As the digestion time increased, the number of common OTUs decreased, thereby showing very high diversity between the samples recovered at 0 and 120 d (only 7% common OTUs). The highest amount of common OTUs were shared among 80 and 120 d samples (28%). Overall, the microbial diversity was relatively higher at 0 d, which showed 5505 OTUs, compared to the later phases of ACoD where the OTU counts gradually decreased. The analysis of α-diversity measured by the Shannon and Chao indexes exhibited the richness of microbial diversity and their relative abundance in the sludge samples. The evaluation of α-diversity revealed a significant difference in the reactor at different time intervals. The highest Shannon and Chao diversity was observed at 0 d; it then gradually decreased by 16% over time and through phases of ACoD. These results were in agreement with the observations reported earlier, where the increase in organic loading or change in substrate significantly influenced and decreased the α-diversity (Yang et al., 2016; Xu et al., 2018). The Simpson index, which explains the evenness of community structure, showed a decreasing trend as co-digestion

progressed. Community evenness signposts a fair microbial distribution between the various functional groups in AD; thus, the improved digester performance was related to higher community evenness (Werner et al., 2011; Ferguson et al., 2018). Multivariate ordination PCoA of community β-diversity clearly indicated gradual temporal alteration in the microbial communities. The PCoA1 (54.20%) and PCoA2 (26.87%) described a total of 81.07% variation in microbial community composition. The microbiome clusters were separately spotted on the PCoA plot without grouping; the microbiomes of 0 and 40 d were distinctly separated across the two axes compared to the microbiomes of 80 and 120 d, which were relatively close to each other, thereby indicating significant discrepancies in the variance. Moreover, the NMDS plot calculated using the Bray-Curtis index also indicated a substantial divergence within the microbiomes of different time intervals, which was in agreement with the PCoA. As shown in the UPGMA clustering tree, samples were visually clustered in the pairwise distances showing the relative closeness of the microbiomes of 0 d and 40 d and a significant discrepancy with the microbiomes of 80 d and 120 d. There were a total of 26 phyla found in the amplicons with varying abundance in different phases of ACoD. Among these, 10 phyla showed >1% relative abundance, and were considered to be major/functional groups responsible for conducting several processes in ACoD. The phylum level analysis showed that Proteobacteria (43%), Firmicutes (21%), and Bacteroidetes (12%) were the dominant phyla at the beginning of ACoD (0 d), and cumulatively comprised 77% of the total population. Pseudomonadales, which belongs to the phylum Proteobacteria, was the dominant order at 0 d, whereas the orders Bacteroidales, Bacillales, Synergistales, Clostridiales and Methanosarcinales were highly enriched during co-digestion of FOG. The dominance of Proteobacteria decreased with a simultaneous increase in Firmicutes,

Bacteriodetes, Synergistetes and Euryarchaeota during the ACoD of FOG (Fig. 3). Bacteroidetes was gradually increased to 32% at the end of 120 d, whereas there was a significant increase (p<0.001) in Firmicutes density, comprising 73% at 80 d. Firmicutes and Bacteroidetes are the two most dominant phyla found in most anaerobic digesters treating different biomass, including lipidic waste (Amha et al., 2017; Ferguson et al., 2018), food waste (Kim et al., 2014; Xu et al., 2018), and molasses (Meng et al., 2017). Firmicutes includes several different syntrophic bacteria that can degrade various substrates and produce volatile fatty acids (VFAs), including acetic acid, which is a prime substrate for acetoclastic methanogens to produce methane (Yi et al., 2014). Some of the genera from Firmicutes are also reported to utilize butyrate (Yang et al., 2014). The microbial communities belonging to the phylum Bacteroidetes are known to synthesize various lytic enzymes, such as hydrolases, lyases, ligases and lipases, which degrade complex organic matter and produce acetic acid as their end products (Chen et al., 2007). In contrast to other major phyla, Proteobacteria significantly (p<0.001) decreased from 43% to 1.3% at the end of ACoD in this study. Pseudomonadaceae, which belongs to the phylum Proteobacteria, was the primary family (32%) at the beginning of ACoD. Some of the families related to the phylum Proteobacteria, especially Betaproteobacteria and Gammaproteobacteria, can degrade organic matter and utilize glucose and several VFAs, such as propionate, butyrate and acetate (Yang et al., 2014). Nevertheless, Proteobacteria is a trivial phylum observed in AD (Meng et al., 2017). Synergistetes was highly enriched by >5 fold at 40 d from its initial density of only 5%. Synergistaceae, which was the only family belonging to Synergistetes phylum found during ACoD, showed a 514% increase in its relative abundance at 40 d. The bacteria in the family Synergistaceae can ferment glucose and organic acids to produce acetate, hydrogen, and carbon

dioxide, provided that they are co-cultured with hydrogenotrophic methanogens in order to avoid product inhibition (Si et al., 2016). Similarly, the relative abundance of the phylum Euryarchaeota was significantly increased (p<0.001) by 515% at 120 d compared to 0 d. The families Methanosaetaceae and Methanosarcinaceae were dominant in the earlier and later phase, respectively. It has been reported that acetoclastic methanogen families are usually predominant in anaerobic digesters (Yang et al., 2014). The relationships of some of the predominant bacterial/archaeal phyla and their distinctive temporal distribution were analyzed through multivariate correspondence analysis (CA). The analysis suggested that Proteobacteria were closely grouped around 0 d, whereas Synergistetes, Firmicutes, and Euryarchaeota were grouped at 40, 80 and 120 d, respectively. The phyla, which dispersed away from the central axis origin, were significantly affected due to the exposure to FOG and the ACoD process itself. The genus level analysis of the 40 dominant genera in co-digestion of FOG is presented in Fig. 4. It showed that Pseudomonas was dominant (32%) at 0 d; however, its growth was significantly inhibited during the ACoD due to anaerobic activity, which decreased its population to <0.01% at 120 d. The PS and TWAS would be the most possible source of Pseudomonas. The densities of Sporanaerobacter and Propionispira (belonging to the phylum Firmicutes) were significantly increased from 0.14% to 6% and 0.02% to 19%, respectively, during 40 d, in which the population of Sporanaerobacter remained constant until 80 d; moreover, there was a significant enrichment in Proteiniphilum (belonging to the phylum Bacteroidetes) (12 fold) at 80 d. These acetogenic genera can produce several VFAs, including acetate, isobutyrate, isovalerate, propionic acid, and carbon dioxide, as a result of fermentation of organic matter (HernandezEugenio et al., 2002; Ueki et al., 2014; Wang et al., 2018). Aminivibrio and Aminobacterium, which are members of the family Synergistaceae, are known to ferment a variety of amino acids

and produce a range of VFAs and ammonia, especially in co-culture with Methanobacterium as a hydrogen scavenger (Hamdi et al., 2015; Ferguson et al., 2018); they were significantly increased by 13 and 550-fold, respectively, at 40 d. The presence of these genera helps maintain a neutral pH during AD due to the production of ammonia. Clostridium, which is involved in the hydrolysis and acidogenesis processes, was enriched by 138% during the initial phase (40 d) (Wang et al., 2018). Syntrophomonas was increased to 11% at 80 d. Syntrophomonas comprises the majority of syntrophic fatty acid oxidizers that can metabolize LCFAs (Ziels et al., 2017). During anaerobic degradation, FOG is rapidly hydrolyzed into glycerol and long-chain fatty acids (LCFAs). The LCFAs are converted into methane through an obligatory syntrophic partnership between acetogenic β-oxidizing bacteria and methanogenic archaea (Sousa et al., 2009). So far, only seven isolated bacterial species are capable of β-oxidizing LCFAs in syntrophy with methanogens, and all belong to the families of Syntrophomonadaceae and Syntrophaceae (Zhang et al., 2012; Ziels et al., 2018). Thus, the presence and dominance of Syntrophomonas during anaerobic codigestion of FOG is vital for its rapid conversion to acetate which is further catalyzed to methane through acetoclastic methanogens. The significant increase in Syntrophomonas density at the exponential phase of ACoD indicated that Syntrophomonas was active in the catalysis of LCFAs and prevented its inhibitory accumulation. The increased abundance of Syntrophomonas was positively correlated with higher LCFAs degradation (Sousa et al., 2009; Ziels et al., 2017). In an earlier study, Syntrophomonas communities in a reactor fed with FOG was increased to 14% compared to 3% in control (Ziels et al., 2016). The syntrophic populations in anaerobic digesters can have different adaptive capacities, and that selection for divergent populations may be achieved by adjusting reactor operating conditions to maximize

biomethane recovery, or the FOG loading capacity can be determined using the existing populations of syntrophic bacteria in order to avoid/minimize the process inhibition caused by LCFAs. An enhanced LCFAs degradation rate was attained at higher abundance of Syntrophomonas, and therefore the FOG-loading capacity of a digester can be determined by monitoring the richness of Syntrophomonas community in the digester (Ziels et al., 2016; Amha et al., 2017). The methanogenic genera, namely Methanosaeta and Methanosarcina, which belong to the phylum Euryarchaeota, were significantly influenced due to FOG loading, where the Methanosaeta population was significantly decreased (from 1.27% to 0.1%) with a concomitant increase in Methanosarcina (0.01% to 8% of the total general population) during the 120 d period. The syntrophic fatty acid oxidizing bacteria and methanogenic communities live in a symbiotic relationship, as the syntrophic fatty acid oxidizing bacteria produce acetate and H2, and the latter utilize and convert it to methane, which avoids the inhibition of syntrophs by acetate (Smith et al., 2015; Ziels et al., 2016). Thus, there is always a positive correlation between the abundance of these communities (Ziels et al., 2016; Junicke et al., 2016). The rate of fatty acid oxidation by syntrophic bacteria was shown to limit fatty acid conversion when the methanogenic partner maintained low concentrations of acetogenesis products. These community shifts related to methanogenic genera are thoroughly discussed in the next section (3.3.1) in order to better understand the ecophysiology of these microorganisms. 3.3.1. Acetoclastic partnership between Methanosaeta and Methanosarcina The archaeal structure related to the methanogenic community at the order and genera level in the reactor is depicted in Fig. 5, which shows the relative abundance of the archaeal 16S rRNA gene at the genus level. It was observed that the order Methanosarcinales dominated

during the entire ACoD process (Fig 5a). At the beginning of the co-digestion process (0 d), the sludge was mainly dominated by Methanosaeta belonging to the order Methanosarcinales, which contributed to 94% of the population among the methanogenic communities (Fig 5b). Common anaerobic digesters are significantly dominated by Methanobacterium, Methanobrevibacter, and Methanoculleus; however, their population density was very low (<2%) at 0 d in the present study. An overlooked population of Methanosarcina at 0 d (0.52%) was significantly increased to 95% after 120 d, and overtook the population of Methanosaeta. The population of Methanosaeta showed a strong negative correlation with Methanosarcina as indicated by the Pearson correlation matrix. The breakdown of FOG/LCFAs in ACoD is a rate-limiting process (Angelidaki and Ahring, 1992; Ziels et al., 2016), and the accumulation of LCFAs in the early lab phase of ACoD is normally experienced. It has been observed that Methanosaeta possesses a higher tolerance to LCFAs in comparison with Methanosarcina (Silva et al., 2016), which explains the high abundance of Methanosaeta during the initial 40 d of FOG co-digestion. A community shift from Methanosaeta to Methanosarcina-dominated acetoclastic methanogenesis has been observed (Conklin et al., 2006; Lins et al., 2014). This can be explained by the physiognomy of Methanosarcina, which is characterized by a three times higher μmax and on average 10 times higher Ks value than Methanosaeta (Conklin et al., 2006; De Vrieze 2012). Moreover, Methanosarcina dominates at higher acetate concentrations (250 to 500 mg COD L−1) compared with Methanosaeta, which is generally dominant at acetate concentrations lower than 100 to 150 mg COD L−1, although it has a higher affinity for acetate (Conklin et al., 2006; Yu et al., 2006; Blume et al., 2010). The beginning of the ACoD process was dominated by hydrolytic processes led by Firmicutes (Fig. 3), which caused the breakdown of FOG/LCFAs into simpler volatile acids.

This was a rate-limiting process in which the production of acetate was slower, which supported the dominance of Methanosaeta. The concentration of acetate increased as the ACoD progressed, thereby creating a suitable environment for Methanosarcina to surpass Methanosaeta. Earlier reports also suggested that Methanosarcina is the dominant genus over Methanosaeta at high concentrations of VFAs and total ammonia nitrogen (Lerm et al., 2011; Wang et al., 2018). At present, two methanogenic pathways from acetate have been identified (De Vrieze 2012). The first is the acetoclastic methanogenesis pathway conducted by Methanosarcinaceae or Methanosaetaceae through conversion of methyl and carboxyl groups of acetate to CH4 and CO2, respectively (Eq. 2). The second is a non-acetoclastic pathway that is co-metabolically regulated by syntrophic acetate oxidizing bacteria and hydrogenotrophic methanogens (Eq. 3). In this pathway, acetate is first oxidized to CO2, which is further reduced to CH4. Acetoclastic cleavage: CH3COO ‒ + H2O →CH4 + HCO

‒ 3

Non-acetoclastic oxidation: CH3COO ‒ + 4H2O = 2HCO

‒ 3

HCO

‒ 3

(Eq. 2) + 4H2 + H +

+ 4H2 + H + → CH4 + 3H2O (Eq. 3)

In order to predict which major methanogenic pathway followed during co-digestion of FOG, understanding the correlation between the relative abundance of syntrophic acetate oxidizers (SAOs) and acetoclastic methanogens is helpful (Muller et al., 2013; Westerholm et al., 2018). At the genus level, acetoclastic methanogens, including the obligatory acetate utilizer Methanosaeta and facultative Methanosarcina, which both belong to Methanosarcinales, were exclusively dominant throughout the ACoD period. The cumulative population density of both these acetoclastic methanogens was nearly constant around 96% during the entire ACoD process (Table 3). Conversely, the cumulative population of all the hydrogenotrophic methanogens among the methanogenic communities was 5.53% at 0 d, which gradually decreased to 3.47%

after 120 d of co-digestion (Table 3). This was also supported by the low partial hydrogen pressure (data not shown); thus, there would be less possibility that acetate might have oxidized to CO2 and produced H2 by SAOs. The relative abundance of well-known SAOs, such as Thermacetogeniumphaeum, Syntrophaceticusschinkii, Tepidanaerobacteracetatoxydans, Clostridium ultunense, and Pseudothermotogalettingae (Muller et al., 2013; Westerholm et al., 2018), which can regulate non-acetoclastic pathways, was extremely low or absent during the FOG co-digestion. In an earlier study, acetoclastic methanogens were predominant (87%) at a neutral pH, whereas the hydrogenotrophic methanogen population increased to 65% with a converse decrease in acetoclastic methanogens when the pH decreased to 4.8 (Li et al., 2018). The researchers concluded that acetoclastic methanogens favor neutral pH and hydrogenotrophic methanogens prefer acidic pH. This was in line with the present observation of the predominance of acetoclastic methanogens throughout the ACoD process, where the pH was buffered to near neutral. In summary, the relatively low abundance of SAOs and hydrogenotrophic methanogens and high abundance of acetoclastic methanogens suggested that methane generation via the ACoD of FOG was predominantly directed through acetoclastic pathways. However, there is still an unanswered question that, whether Methanosarcina acted as a hydrogen scavenger or not during co-digestion of FOG considering the fact that Methanosarcina can also follow hydrogenotrophic methanogenesis. Further in-depth tracer studies are needed to provide additional evidence to prove that Methanosarcina followed the acetoclastic pathway for methane production.

Conclusion

Extensive dosing of FOG initially deteriorated ACoD performance; however, it gradually enhanced ultimate methane production, suggesting importance of microbial adaptation. FOG addition led to a significant increase in syntrophic fatty acid oxidizers which were linked with increases in methane production. The significant increase in Methanosarcina with a simultaneous decrease in Methanosaeta indicated a negative correlation. A trivial presence of SAOs and hydrogenotrophic methanogens, and dominance of Methanosarcina suggested that methane generation during ACoD of FOG was predominantly conducted through acetoclastic pathways. These findings provide a better understanding of microbial community functions in a complex AD process of high-strength organic feedstock.

Acknowledgments The authors thankfully acknowledge the financial support from the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20163010092250; No. 20173010092470).

Appendix A. Supplementary data E-supplementary data associated with this article can be found in the e-version of this paper online.

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[47]. Zhang, F., Liu, X.L. and Dong, X.Z., 2012. Thermosyntropha tengcongensis sp nov., a thermophilic bacterium that degrades long-chain fatty acids syntrophically. Int. J. Syst. Evol. Microbiol. 62, 759-763. [48]. Ziels, R.M., Beck, D.A.C. and Stensel, H.D., 2017. Long-chain fatty acid feeding frequency in anaerobic codigestion impacts syntrophic community structure and biokinetics. Water Res. 117, 218-229. [49]. Ziels, R.M., Karlsson, A., Beck, D.A.C., Ejlertsson, J., Yekta, S.S., Bjorn, A., Stensel, H.D. and Svensson, B.H., 2016. Microbial community adaptation influences long-chain fatty acid conversion during anaerobic codigestion of fats, oils, and grease with municipal sludge. Water Res. 103, 372-382. [50]. Ziels, R.M., Sousa, D.Z., Stensel, H.D. and Beck, D.A.C., 2018. DNA-SIP based genomecentric metagenomics identifies key long-chain fatty acid-degrading populations in anaerobic digesters with different feeding frequencies. ISME J. 12(1), 112-123. Figure captions Figure 1. Cumulative methane production under mesophilic conditions. Fats, oil and grease (FOG) comprised 70% (volatile solids (VS)) of the total substrate loaded in the digester. The error bars represent the standard deviation of the mean (n=4). Figure 2. Venn diagram showing the number of unique and shared operational taxonomic units (OTUs) in the digester at different time intervals (plotted in MetaCoMET). The number in the overlapping region designates the common OTUs shared between different groups, and the number in the non-overlapping region designates the exclusive number of OTUs in that specific time. For interpretation of the references to high-resolution color in this figure, the readers are referred to the web version of this article. Figure 3. A chord diagram showing the relative distribution pattern and networks of the top 10 phyla within the samples retrieved at regular time intervals during ACoD of fats, oil

and grease. The width of the ribbons from each phylum indicates its relative abundance in the samples. This chord diagram was visualized using Circos software. For interpretation of the references to high-resolution color in this figure, the readers are referred to the web version of this article. Figure 4. Hierarchical cluster analysis of the microbial communities at different time intervals (0 to 120 d). The Y-axis in the heat map shows the clustering of the top 40 abundant genera. The color intensity of the scale indicates the relative abundance of each genus. For interpretation of the references to high-resolution color in this figure, the readers are referred to the web version of this article. Figure 5. Community structure and dynamics of methanogenic Archaea presented by order level (a) and genus level (b) within the fats, oil and grease co-digester at different time intervals (0 to 120 d). For interpretation of the references to high-resolution color in this figure, the readers are referred to the web version of this article.

Daily methane production (mL g-1 VSadded)

30 25 20 15 10 5 0 0

Cumulative methane production (mL g-1 VSadded)

a)

Control FOG

20

40

60

80

100 120 140 160

Time (d)

b)

Control FOG Simulated

400 300 200 100 0 0

20

40

60

80

Time (d)

Figure 1

100 120 140 160

Figure 2

Figure 3

Acetobacterium Acholeplasma Aminivibrio Aminobacterium Aminomonas Anaerosinus Armatimonas Bellilinea Carnobacterium Cloacibacillus Clostridium Desulfotomaculum Erysipelothrix Haloplasma Intestinimonas Levilinea Lutaonella Lysinibacillus Mariniphaga Methanosaeta Methanosarcina Mycobacterium Pedobacter Petrimonas Propionispira Proteiniclasticum Proteiniphilum Proteinivorax Pseudomonas Romboutsia Sporanaerobacter Sporosarcina Sunxiuqinia Synergistes Syntrophomonas Tangfeifania Thermoactinomyces Thermomonas Thermovirga Tissierella Unasigned Others

40.60

32.48

24.36

16.24

8.120

0.000

0

40

80

Time (d) Figure 4

120

Relative abundance (%)

a)

100 80

Methanosarcinales Methanomicrobiales Methanomassiliicoccales Methanobacteriales

60 40 20

Relative abundance (%)

0

b)

100

Methanospirillum Methanosphaerula Methanosphaera Methanosarcina Methanosaeta Methanoregula Methanomethylovorans Methanomassiliicoccus Methanolinea Methanofollis Methanoculleus Methanobrevibacter Methanobacterium

80 60 40 20 0

0

40

80

120

Time (d) Figure 5 Table 1. Characteristics of the Fats, oil and grease (FOG) and different sludges collected from the food wastes leachate and the WWTP in Daegu, South Korea. Values given are the mean and standard deviation (SD) of triplicate analyses.

PS

TWAS

ADS

FOG

pH

6.15 ± 0.02

6.54 ± 0.00

6.86 ± 0.01

4.65 ±

TS (g L−1)

49.3 ± 0.17

39.7 ± 0.64

26.2 ± 0.12

954 ±

VS (g L−1)

35.1 ± 0.02

28.0 ± 0.01

16.6 ± 0.37

923 ±

VS/TS (wt.%)

71.1 ± 0.04

70.5 ± 0.04

63.3 ± 1.41

96.8 ±

Ash (wt.%)

0.60 ± 0.02

0.49 ± 0.01

1.02 ± 0.03

3.05 ±

Fixed carbon (wt.%)

28.3 ± 0.07

29.0 ± 0.27

35.7 ± 1.39

0.15 ±

Total carbohydrate (wt.%)

15.2 ± 0.08

13.9 ± 0.43

5.32 ± 0.31

ND

Total protein (wt.%)

31.6 ± 0.23

34.9 ± 0.32

36.2 ± 0.36

7.15 ±

Total lipid (wt.%)

4.51 ± 0.12

2.12 ± 0.02

ND

94.39

Total carbon (wt.%)

38.8 ± 0.41

36.2 ± 0.02

32.4 ± 0.26

77.51

Total nitrogen (wt.%)

5.42 ± 0.06

5.83 ± 0.11

4.89 ± 0.03

1.14 ±

Total hydrogen (wt.%)

5.12 ± 0.02

4.93 ± 0.08

5.18 ± 0.04

16.32

Total sulfur (wt.%)

0.81 ± 0.18

0.68 ± 0.06

0.76 ± 0.21

1.48 ±

Total oxygen (wt.%)

49.85 ± 0.36

51.93 ± 0.56

56.77 ± 0.24

3.55 ±

C/N ratio

7.16

6.21

6.63

68.29

Properties Physical properties

Proximate analysis

Ultimate analysis

PS: Primary sludge TWAS: Thickened waste activated sludge ADS: Anaerobically digested sludge ND: not detected TS: Total solids VS: Volatile solids.

Table 2. Regression analysis of the cumulative methane production data between the experimental values and the modeling values obtained using the Gompertz equation. Control

FOG

R2

0.96

0.99

Standard error

7.21

5.67

F-value

1177

28341

Significance F

1.97 × 10−35

3.42 × 10−68

FOG:

p-value

<0.01

0.17

Fats, oil and

grease

Table 3. Variations in the relative abundance of methanogenic Archaea (with respect to the relative methanogenic population and total general population) at different time intervals. The methanogenic genera have been classified according to their order level and their type of methanogenesis pathway [47].

Relative abund Genera

Order

Methanogenesis pathway

general popula 0d

40

Methanobacterium

Methanobacteriales

Hydrogenotrophic

1.65/0.02

1.

Methanobrevibacter

Methanobacteriales

Hydrogenotrophic

1.55/0.02

0.

Methanoculleus

Methanomicrobiales

Hydrogenotrophic

0.05/0

0.

Methanofollis

Methanomicrobiales

Hydrogenotrophic

0/0*

0/

Methanolinea

Methanomicrobiales

Hydrogenotrophic

1.40/0.02

0.

Methanomassiliicoccus Methanomassiliicoccales Hydrogeno/methylotrophic

0.16/0*

0.

Methanomethylovorans Methanosarcinales

Methylotrophic

0.05/0*

0/

Methanoregula

Methanomicrobiales

Hydrogenotrophic

0.05/0*

0.

Methanosaeta

Methanosarcinales

Acetoclastic

94/1.27

26

Methanosarcina

Methanosarcinales

Acetoclastic/Hydrogeno/Methylotrophic

0.52/0.01

71

Methanosphaera

Methanobacteriales

Hydrogeno/methylotrophic

0.31/0*

0.

Methanosphaerula

Methanomicrobiales

Hydrogenotrophic

0.10/0*

0/

Methanospirillum

Methanomicrobiales

Hydrogenotrophic

0.21/0*

0.

Total hydrogenotrophic population (%)

5.53/0.07

2.

Total acetoclastic population (%)

94.5/1.23

97

0*: The abundance is extremely low and is nearly zero