Batch-Mode Analysis of Thermophilic Methanogenic Microbial Community Changes in the Overacidification Stage in Beverage Waste Tr

International Journal of Environmental Research and Public Health

Article Batch-Mode Analysis of Thermophilic Methanogenic Microbial Community Changes in the Overacidiﬁcation Stage in Beverage Waste Treatment

Shuhei Matsuda 1, Takahiro Yamato 1, Yoshiyuki Mochizuki 2, Yoshinori Sekiguchi 2 and Takashi Ohtsuki 1,* 1 Graduate School of Medicine, Engineering and Agricultural Sciences, University of Yamanashi, Kofu, Yamanashi 400-8510, Japan; [email protected] (S.M.); [email protected] (T.Y.) 2 Iwata Chemical Co. Ltd., Iwata, Shizuoka 438-0078, Japan; [email protected] (Y.M.); [email protected] (Y.S.) * Correspondence: [email protected]; Tel.: +81-055-220-8646

Received: 21 September 2020; Accepted: 12 October 2020; Published: 15 October 2020

Abstract: Biogasification by methane fermentation is an important and effective way to utilize beverage wastes. Beverage wastes are good feedstocks for methane fermentation because of their richness in sugars and proteins, although overacidification and inhibition of methane production caused by high substrate loading often become problematic. This study investigated changes in microbial communities in the overacidification state of the thermophilic methane fermentation process with beverage waste by establishing a simulated batch culture. We assessed 20 mL-scale batch cultures using a simulant beverage waste mixture (SBWM) with different amounts of addition; high cumulative methane production was achieved by adding 5 mL of SBWM (11358 mg—chemical oxygen demand—COD/L of organic loading), and overacidification was observed by adding 10 mL of SBWM (22715 mg—COD/L of organic loading). The results of 16S rRNA amplicon sequence analysis using nanopore sequencer suggested that Coprothermobacter proteolyticus, Defluviitoga tunisiensis, Acetomicrobium mobile, and Thermosediminibacter oceani were predominantly involved in hydrolysis/acidogenesis/acetogenesis processes, whereas Methanothrix soehngenii was the major acetotrophic methane producer. A comparison of microbial population between the methane-producing cultures and overacidification cultures revealed characteristic population changes especially in some minor species under 0.2% of population. We concluded that careful monitoring of population changes of the minor species is a potential indicator for prediction of overacidification.

Keywords: batch culture; beverage waste; methanogenic microbial community; overacidiﬁcation

1. Introduction Methane fermentation is a conventional biological process that produces methane from various organic wastes. Methane fermentation has recently attracted attention as an eﬃcient bioenergy production method to tackle global environmental problems associated with the use of fossil fuels [1]. Many studies of methanogenic microbial community dynamics in methane fermentation have been conducted using continuous or semi-continuous culture. Of course, methane fermentation is usually continuous process and its study of microbial community dynamics is important. However, stepwise changes of experimental conditions in a continuous culture preclude the comparison of initial response to each condition among microbial community at a ‘same state’ as represented by population of members. In small-scale batch culture experiment, by starting multiple cultures from one inoculum culture, it is possible to make the initial states of microbial communities constant, and to compare the response of the communities to diﬀerent conditions. In our knowledge, there is no report

Int. J. Environ. Res. Public Health 2020, 17, 7514; doi:10.3390/ijerph17207514 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 7514 2 of 13 to investigate dynamics of methanogenic microbial community in batch culture, though studies to assess correlation between methane production and experimental conditions have been conducted with batch culture [2–5]. In food waste, the amount of beverage waste is enormous; for example, the United Kingdom was estimated to generate 1,300,000 tons of beverage waste in 2012 [6]. In Japan, 2,232,000 tons (this may include solid waste) were estimated as wastes from beverage-related business in 2017 [7]. To our knowledge, there is only a countable number of studies for stable production of methane from beverage waste, although the importance of treatment has been recognized [8,9]. Additionally, the existing reports described mesophilic methanogenic processes, and thus the study of thermophilic process has been desired. The methanogenic microbial community comprises countless microorganisms, and the process of methane production is highly complicated. In general, under anaerobic conditions, eubacteria perform substrate hydrolysis, acid production (acidogenesis), hydrogen and carbon dioxide production, and methanogens produce methane from acetate, formate, hydrogen, and carbon dioxide (methanogenesis) [10]. Therefore, acetate produced during the acid production process (acetogenesis) is a particularly important metabolic intermediate. In the acidogenesis process, volatile fatty acids (VFA), such as acetate, butyrate, and propionate, are produced, but methanogens cannot directly use VFA other than acetate and formate. The production of acetate by the oxidation of propionate or butyrate is an endergonic reaction, and if not maintained at an appropriate concentration, the function of the fermenter will be reduced [11]. In methane fermentation, challenges to increase the amount of substrate input per unit time have affected efficient production of methane. However, if the substrate load is high, VFA accumulate and methane production is inhibited [12]. This phenomenon is known as overacidification status. For stable methane production, considerable researches investigating the relationship between methanogenic microbial community dynamics and VFA accumulation have been conducted with food wastes and manures as feedstocks [13–17]. In the previous reports, no indication generally observed in changes of microbial communities has been found in the phylum or family level, while some parameters such as changes of propionate/acetate ratio have been proposed as signs of instability of methanogenic process [18]. To obtain the detailed information of microbial community dynamics, a trial to compare the methanogenic community members between stable and instable states in the ‘species’ level has been needed. This study aimed to investigate the difference of initial response of thermophilic microbial community to different organic loading by small-scale batch culture with the stably maintained sludge as a ‘same state’ inoculum. In this study, we compared the behavior of microorganisms in the methane-producing state and in the overacidification state under different substrate loading conditions. Furthermore, possible indicator microorganisms in the species level for the prediction of overacidification occurrence are suggested.

2. Materials and Methods

2.1. Maintenance of Anaerobic Sludge Anaerobic sludge was collected from a 1000-kL digester of a waste beverage treatment plant located in Shizuoka, Japan. The 3-L volume sludge was initially purged with nitrogen gas and anaerobically maintained in a fermenter (Model BMS-05PI, Biott, Tokyo, Japan) at 55 ◦C with agitation at 100 rpm. Simulant beverage waste mixture (SBWM) was continuously fed at a rate of 50 mL/day (757 mg-chemical oxygen demand—COD/L day of organic loading rate) and simultaneously, the wastewater was drained at the same rate (HRT 60). The SBWM used for this study included (v/v): 40% 1/2 MRS broth (Oxoid, Hampshire, UK), 20% coﬀee (UCC Holdings, Hyogo, Japan), 10% orange juice (Ehime Beverage, Ehime, Japan), 10% isotonic drink (Coca-Cola Customer Marketing Company, Tokyo, Japan), 5% green tea (Lifedrink Company, Osaka, Japan), 5% black tea (Kirin Beverage Company, Tokyo, Japan), 5% soymilk Int. J. Environ. Res. Public Health 2020, 17, 7514 3 of 13

(Kikkoman, Chiba, Japan), and 5% yogurt (Morinaga Milk Industry, Tokyo, Japan). The ratio of SBWM was determined based on the feedstock being treated at the plant where the inoculation source was provided. The pH was adjusted to 7.0 with sodium hydroxide, and the mixture was autoclaved. Twelve month-cultured sludge with stable production of 0.6 L methane/L day was used as an inoculum.

2.2. Methane Production at High Substrate Loading in Batch Culture Experiments There is a thermophilic methane fermentation plant in Shizuoka, Japan, which currently use beverage waste as the main feedstock. In the plant, high substrate loading often lowers pH and decreases methane production. We succeeded in optimizing a condition where pH was decreased by high substrate loading in batch culture at 55 ◦C using the inocula, which acclimated for SBWM. The inocula, sterilized water, and SBWM were mixed in 30-mL glass vials. The amount of SBWM added was changed to prepare the variation of substrate loading (Table1). Each vial was sealed with a butyl rubber stopper and plastic cap. The headspace of each glass vial was purged with nitrogen gas for 1 min and statically cultured at 55 ◦C for 15 days.

Table 1. Composition of batch cultures examined.

Run SBWM (mL) Sterilizaed Water (mL) Inoculum (mL) A 0 10.0 10.0 B 0.5 9.5 10.0 C 2.0 8.0 10.0 D 5.0 5.0 10.0 E 10.0 0 10.0 SBWM—simulant beverage waste mixture. 2.3. Analytical Methods The COD of SWBM was determined using Spectroquant COD Cell Test Kit (MilliporeSigma, Burlington, MA, US). Every five days of batch culture experiment, the normalized volume of positive pressure gases in the vials were measured and collected using needles and syringes. Then, the caps of the vials were opened, and 1.5 mL of culture suspensions were sampled. After sampling, the vials were sealed, re-purged with nitrogen, and continued the culture. Methane contents in the collected gas samples were determined by gas chromatography (Model GC2014, Shimadzu, Kyoto, Japan) equipped with a Molecular Sieve 5A 60–80 column (3 mm ID 3 m, GL science, Tokyo, Japan) × and a thermal conductivity detector. The column temperature was maintained at 40 ◦C for 15 min, and thereafter, the temperature was increased by 20 ◦C/min and finally maintained at 200 ◦C for 20 min. The temperature of the injector and detector was 200 ◦C. Nitrogen was used as the carrier gas (30 mL/min). The sampled culture suspensions were centrifuged at 11,000 g for 7 min, and the pellets × were stored at 20 C for DNA analysis. The pH, electrical conductivity (EC), and nitrate concentration − ◦ of the supernatants were measured using pocket meters (LAQUAtwin Models B-212, EC-33, and B-742, Horiba, Kyoto, respectively). Contents of VFA in supernatants were determined by gas chromatography (Model GC-2014, Shimadzu, Kyoto), which was equipped with an HP-Innowax column (0.53 mm ID 15 m 1.0 µm thick, Agilent Technologies, Santa Clara, CA, US) and a flame ionized detector. × The column temperature was maintained at 100 ◦C for 34 min, and thereafter, the temperature was increased by 10 ◦C/min and finally maintained at 220 ◦C for 25 min. The temperatures of the injector and detector were 250 ◦C and 300 ◦C, respectively. Helium was used as a carrier gas (3.59 mL/min, 10:1 of split ratio).

2.4. Amplicon Sequence Analysis of 16S rRNA Genes Using a Nanopore Sequencer Genomic DNA was extracted from the pellets harvested from the culture as per previously described methods [19]. Ten nanograms of extracted DNA was ampliﬁed by PCR with the Tks Gﬂex DNA polymerase (Takara Bio, Shiga, Japan). The primer sets Pro341F (50-CCTACGGGNBGCASCAG-30) Int. J. Environ. Res. Public Health 2020, 17, 7514 4 of 13

and Pro805R (50-GACTACNVGGGTATCTAATCC-30) were used [20]. PCR amplification was performed by an initial denaturation at 98 ◦C for 2 min, followed by 11 cycles of denaturation at 98 ◦C for 10 s, annealing at 65 ◦C for 15 s (the temperature was decreased by 1 ◦C every cycle to reach the temperature at 55 ◦C), and extension at 68 ◦C for 30 s; this was followed by an additional 24 cycles of denaturation at 98 ◦C for 10 s, annealing at 55 ◦C for 15 s, and extension at 68 ◦C for 30 s. Purification and barcoding of PCR products were performed using the PCR Barcoding Kit (SQK-PBK004, Oxford Nanopore Technologies, Oxford, UK). The AxyPrep MAG Clean-Up kit (Corning, NY, USA) was used for all purification steps of the PCR products. The concentration of genomic DNA and PCR products was checked using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA). Twelve barcoded PCR products were mixed at 8 femtomoles each and loaded on a flow cell (FLO-MIN106 R9, Oxford Nanopore Technologies, Oxford, UK); sequencing was performed using the MinION Mk1B apparatus (Oxford Nanopore Technologies, Oxford, UK).

2.5. Data Analysis Sequenced data collecting, base calling, and quality checking were performed with the MinION software (Release 18.12.6, Oxford Nanopore Technologies, Oxford, UK) to obtain fastq files. FASTQ BARCODING (v.3.10.2, Oxford Nanopore Technologies, Oxford, UK) in the EPI2ME Desktop Agent (v.2.59.1896509, Oxford Nanopore Technologies, Oxford, UK) was used to sort and to remove barcode sequences. Statistical analysis of the microbial community structure in the family or genus level was performed using Metagenome@KIN software (v.2.0.0, World Fusion, Tokyo, Japan) which based on R package (v.3.1.0, The R Foundation, Vienna, Austria). Sequences with 97% similarity were assigned to the same ≥ operational taxonomic units (OTU). Representative sequence for each OTU was used for taxonomic annotation with BLAST (National Center for Biotechnology Information, Bethesda, MD, USA). The 16S rRNA database release 120.0 of DNA Data Bank of Japan (released on May 29, 2020) was used for reference. Numbers of OTUs were log-transformed when necessary to normalize the distribution and to achieve homogeneity of variance. To illustrate the analysis results, ClustVis web tool [21] was also used. In comparison of microbial population changes among the batch cultures in the species level, we illustrated ‘growth curve-like’ figure for comprehensive presentation. FASTQ WIMP (v.3.2.1, Oxford Nanopore Technologies, Oxford, UK) was used for direct assignment of fastq sequence reads (not dependent on OTU strapping) for species level monitoring. The WIMP workflow employs a novel microbial classification engine ‘Centrifuge’ that enables rapid and accurate assignment of reads and quantification of species [22].

3. Results and Discussion

3.1. Methane Production in Batch Cultures Using SBWM We conducted batch cultures with diﬀerent substrate loading. Addition of 0.5, 2, 5 and 10 mL SBWM corresponded with 1136, 4543, 11,358, and 22,715 mg-COD/L of organic loading, respectively. Cumulative methane production and changes in pH and VFA contents in batch cultures with diﬀerent amounts of SBWM added are shown in Figure1. In blank cultures without the addition of SBWM, negligible levels of methane production and pH change were observed (run A), thereby indicating that the feeding of SBWM in maintenance culture was minimal and that changes in the batch experiment could be attributed to the addition of SBWM. The highest cumulative methane production (1387.5 mL/L) was obtained with 5 mL of SBWM addition (run D), whereas production drastically decreased (493.5 mL/L) with 10 mL of SBWM addition (run E). The pH decreased at day 5 in all SBWM-added cultures. In run E, the pH decreased to 6.1 and did not return above pH 7 until day 15, although pH in the other cultures increased at days 10 and 15. The decrease in pH was considered to be mainly due to the accumulation of VFA. According to Steinbusch et al. [23], methane production Int. J. Environ. Res. Public Health 2020, 17, 7514 5 of 13

Int. J. Environ. Res. Public Health 2020, 17, x 5 of 14 decreases rapidly and butyrate production increases at pH 6. The correspondence between the drastic decreasebetween inthe methane drastic decrease production in methane and pH decrease production with and 10 pH mL decrease of SBWM with addition 10 mL clearly of SBWM demonstrated addition acid-inducingclearly demonstrated inhibition acid of-inducing methane inhibition production. of methane production.

FigureFigure 1.1. CumulativeCumulative methane production and and pH pH changes changes (upper (upper row), row), and and concentration concentration of of VFA VFA (lower(lower row)row) inin thethe batchbatch culturescultures withwith simulantsimulant beverage waste waste mixture mixture ( (SBWM).SBWM). The The amounts amounts of of SBWMSBWM addedadded werewere 0,0, 0.5,0.5, 2,2, 5,5, and 10 mL for runs A, B, C, D D,, a andnd E, E, r respectively.espectively. Experiments Experiments were were performedperformed inin triplicate,triplicate, andand errorerror barsbars representrepresent standard deviations.

TheThe pHpH decreaseddecreased dependingdepending on the amount of of SBWM SBWM added, added, and and therefore therefore we we checked checked the the VFAVFA contents contents inin thethe cultureculture supernatants.supernatants. V Volatileolatile fatty acids acids mainly mainly comprised comprised acetate, acetate, propionate, propionate, andand butyrate. butyrate. PropionatePropionate and and butyrate butyrate have have been been known known to to be be used used by by methanogens methanogens after after conversion conversion to acetateto acetate by eubacteriaby eubacteria [24 [].24 Thus,]. Thus, the the change change in in VFA VFA concentration concentration is is one one of of the the important important indicators indicators in methanein methane fermentation. fermentation. In runs In runsA, B, A,C, and B, C, D, and the total D, the VFA total content VFA was content around was 500 around mg/L throughout 500 mg/L thethroughout culture period. the culture In run period. D, which In run had D, the which highest had methane the highest production, methane theproduction, propionate the concentration propionate wasconcentration lower than was in lower other than runs, in and other no runs, other and VFA no accumulated.other VFA accumulated. However, However, the total the VFA tota amountsl VFA inamounts runs A, in B, runs C, and A, B, D C were, and highlyD were similar, highly similar, and there and was there no was correlation no correlation with the with decrease the decrease in pH. Inin contrast,pH. In incontrast run E,, acetate,in run propionate, E, acetate, propionate, and butyrate and accumulated butyrate accumulated with the highest with concentrations the highest ofconcentrations 725, 324, and of 1080 725, mg 324/L,, and respectively. 1080 mg/L, The respectively. amount of T acetatehe amount increased of acetate with increased the passage with of the the culturepassage time, of the whereas culture time, the amount whereas of the butyrate amount reached of butyrate a maximum reached a on maximum the fifth on day the of fifth the day culture. of Highthe culture. concentrations High concentrations of butyrate of are butyrate not considered are not considered to affect methane to affect production methane production [25]. However, [25]. RedzwanHowever, and Redzwan Banks an [26d] Banks reported [26] thatreported methane that methane production production could be could inhibited be inhibited by an excess by an excess of total VFAof total over VFA 1500 over mg /1500L. In mg/L. run E, In it run was E, suggested it was suggested that overproduction that overproduction of total VFAof total would VFA resultwould in theresult suppression in the suppression of methanogenic of methanogenic archaea. archaea. Accumulation Accumulation of VFA andof VFA decrease and decrease of pH in of run pH E in clearly run indicateE clearly that indicate the microbial that the microbial community community got into the got overacidification into the overacidification status. status. TheThe electricalelectrical conductivity conductivity (EC) (EC) of of the the culture culture supernatant supernatant in in all all batch batch cultures cultures increased increased with with the amountthe amount of SBWM of SBWM added added (Figure (Figure2). However, 2). However, the concentration the concentr of nitrateation inof thenitrate culture in supernatantthe culture didsupernatant not behave did the not same behave way the (Figure same2 way). This (Figure increased 2). This in aincreased time-dependent in a time manner-dependent to reach manner a peak to valuereach ata peak day 15value in runsat day C 15 and in D,runs whereas C and D, the whereas concentration the concentration in run E peaked in run E at peaked day 10 at and day then 10 decreasedand then decreased at day 15 (Figureat day 215). (Figure Considering 2). Considering that total VFA that amountstotal VFA were amounts approximately were approximately equivalent inequivalent runs A–D, in exceptruns A in–D, run except E, additional in run E, additional ionic substances ionic substances other than other VFA than and VFA nitrate and may nitrate affect may EC. Therefore,affect EC. methaneTherefore, production methane inproduction the microbial in the community microbial usedcommunity in this studyused in was this not study suppressed was not by 100–200suppressed mg/ Lby levels 100–200 of nitrate mg/L levels concentration of nitrate experienced concentration in runsexperienced C and D in (Figures runs C1 and and D2). (Figures Methane 1 productionand 2). Methane by methanogenic production by microbial methanogenic communities microbial cultured communities in rich media,cultured such in rich as rumen media, and such feces, as rumen and feces, can be typically inhibited by the presence of 300–600 mg/L of nitrate [27–29]. In contrast, methane production was inhibited under oligotrophic conditions, such as in soil, at

Int. J. Environ. Res. Public Health 2020, 17, 7514 6 of 13 canInt. J. beEnviron. typically Res. Public inhibited Health 20 by20, the17, x presence of 300–600 mg/L of nitrate [27–29]. In contrast, methane6 of 14 production was inhibited under oligotrophic conditions, such as in soil, at approximately 60 mg/L approximately 60 mg/L of nitrate [30]. In this study, although SBWM was apparently rich in organic of nitrate [30]. In this study, although SBWM was apparently rich in organic matter, the microbial matter, the microbial communities maintained methane production and were not suppressed by the communities maintained methane production and were not suppressed by the 100–200 mg/L level of 100–200 mg/L level of nitrate concentration. nitrate concentration.

Electrical conductivity Nitrate concentration 12 250 A B C D E A B C D E 10 200 8 150

/ 6 g

S m 100 m 4

2 50

0 0 5 10 15 5 10 15 Culture period (day) Culture period (day)

Figure 2. Electrical conductivities (left panel) and concentrations of nitrate (right panel) in runs A, B, C, D,Figure and E.2. Electrical Experiments conductivities were performed (left panel) in triplicate, and concentrations and error bars of represent nitrate (right standard panel) deviations. in runs A, B, C, D, and E. Experiments were performed in triplicate, and error bars represent standard deviations. 3.2. Changes in the Microbial Population in Batch Cultures with SBWM 3.2. ChangesThe amplicon in the M sequenceicrobial Population analysis in of Batch 16S C rRNAultures genes with SBWM resulted in between 212638 to 826885 quality-checkedThe amplicon reads sequence per sample analysis obtained of 16S from rRNA batch genescultures resulted with SBWM. in between Populations 212638 of to the 826885 phyla morequality than-checked 1% consisted reads per of Firmicutes sample obtained (38.2–60.7%), from batch Thermotogae cultures (16.5–48.5%),with SBWM. SynergistetesPopulations of (1.8–10.9%), the phyla Euryarchaeotamore than 1% consisted (0.3–8.0%), of Firmicutes Proteobacteria (38.2– (0.7–2.7%),60.7%), Thermotogae Actinobacteria (16.5–48.5%), (1.0–2.3%), Synergistetes Bacteroidetes (1.8– (0.02–2.1%)10.9%), Euryarchaeota and Chloroflexi (0.3–8.0%), (1.0–1.9%). Proteobacteria Tyagi et al. (0.7 [12–]2.7%), have reportedActinobacteria that Euryarchaeota (1.0–2.3%), Bacteroidetes (7.6–44.4%), Proteobacteria(0.02–2.1%) and (4.7–38.0%),Chloroflexi (1.0 Bacteroidetes–1.9%). Tyagi (9.7–27.1%), etF ial.g. [12]2 M haveat Firmicutessu dreporteda et al. that (8.4–22.7%) Euryarchaeota and Synergistetes (7.6–44.4%), (0.7–13.2%)Proteobacteria were (4.7 the–38.0%), dominant Bacteroidetes phyla in the(9.7 mesophilic–27.1%), Firmicutes anaerobic (8.4 pulsed–22.7%) bed and filter Synergistetes system treating (0.7– beverage13.2%) were wastewater. the dominant In contrast, phyla in the the microbial mesophilic community anaerobic in pulsed our study bed filter showed system the treating greater abundancebeverage wastewater. of Firmicutes In and contrast, Thermotogae. the microbial These community phyla have in often our been study found showed in thermophilic the greater methaneabundance production of Firmicutes process and T[31hermotogae.], indicating These that phyla there have were often so many been membersfound in thermophilic involved in hydrolysismethane production/acidogenesis process/acetogenesis [31], indicating processes. that there were so many members involved in hydrolysis/acidogenesis/acetogenesisThe Simpson and Shannon indices processes. of microbial communities in the batch cultures were shown in TableThe2 .Simpson These indices and Shannon reflect community indices of microbial diversity; communities the Simpson in index the batch is sensitive cultures to were community shown evenness,in Table 2. while These the indices Shannon reflect index community is sensitive diversity; to community the Simpson richness. index The is microbialsensitive to community community in runevenness, D with while the highest the Shannon methane index production is sensitive had to the community largest Simpson richness index. The (0.708–0.725) microbial community and Shannon in indexrun D (2.248–2.369),with the highest indicating methane that production the species had richness the largest of the Simpson community index increased(0.708–0.725) but someand Shannon specific speciesindex (2.248 became–2.369), more indicating dominantly. that However,the species both richness the Simpson of the community and Shannon increased indices but of some the microbial specific communityspecies became in runmore E dominantly. were apparently However, smaller both than the those Simpson in run and D, Shannon meaning indices that the of th richnesse microbial and evennesscommunity were in decreased.run E were apparently smaller than those in run D, meaning that the richness and evenness were decreased.

Table 2. The Simpson and Shannon indices of microbial communities in the family level.

Simpson index Shannon index Run Day 5 Day 10 Day 15 Day 5 Day 10 Day 15 A 0.630 0.629 0.556 1.900 1.872 1.517 B 0.587 0.640 0.663 1.833 1.988 1.983 C 0.627 0.636 0.713 1.929 1.925 2.181 D 0.721 0.708 0.725 2.293 2.248 2.369 E 0.670 0.667 0.652 2.023 1.959 1.834 Simpson and Shannon indices of microbial community on day 0 were 0.757 and 2.545, respectively.

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Table 2. The Simpson and Shannon indices of microbial communities in the family level.

Simpson index Shannon index Run Day 5 Day 10 Day 15 Day 5 Day 10 Day 15 A 0.630 0.629 0.556 1.900 1.872 1.517 B 0.587 0.640 0.663 1.833 1.988 1.983 C 0.627 0.636 0.713 1.929 1.925 2.181 D 0.721 0.708 0.725 2.293 2.248 2.369 E 0.670 0.667 0.652 2.023 1.959 1.834 Int. J. Environ. Res. Public Health 2020, 17, x 7 of 14 Simpson and Shannon indices of microbial community on day 0 were 0.757 and 2.545, respectively.

To visualizevisualize the didifferencesfferences ofof communitycommunity structure,structure, principalprincipal componentcomponent analysis (PCA) and clustering ofof thethe taxonomic taxonomic compositions compositions in thein the family family level level were were performed, performed, and the and results the res areults shown are inshown Figure in3 Figure. The plots 3. The of theplots communities of the communities in same runsin same tended runs to tended gather to together, gather together, and the result and the of clusteringresult of clustering demonstrated demonstrated that the communitiesthat the communities in run A in and run B A formed and B aformed different a different cluster from cluster those from in otherthose runs.in other Interestingly, runs. Interestingly, the communities the communiti at dayes 15 at in day run C15 and in run day C 5 inand run day E formed5 in run completely E formed independentcompletely independent clusters each clusters (Figure 3each). There (Figure was 3). a possibilityThere was thata possibility a depletion that at a day depletion 15 in run at Cday and 15 an in initialrun C excessand an feedinginitial excess at day feeding 5 in run at E day of SBWM 5 in run nutrients E of SBWM in the nutrients cultures in would the cultures affect the would diversities affect ofthe community. diversities of community.

Figure 3. PrincipalPrincipal component component analysis analysis of of the the taxonomic taxonomic compositions compositions among among the the batch batch cultures. cultures. Annotated numbers of operationaloperational taxonomic taxonomic units units (OTUs) (OTUs) werewere log-transformed. log-transformed. Unit variance scaling was applied to rows; singular valuevalue decomposition with imputation was used to calculate principal components. Samples Samples were were also also clustered using correlation distance and average linkage, and the plots belonging to same clusters are indicated byby samesame colors.colors. Figure4 shows the detailed changes in population of the species more than 0.2%. The predominant Figure 4 shows the detailed changes in population of the species more than 0.2%. The species, Coprothermobacter proteolyticus and Deﬂuviitoga tunisiensis, were identiﬁed in all runs. predominant species, Coprothermobacter proteolyticus and Defluviitoga tunisiensis, were identified in all C. proteolyticus is frequently found in the anaerobic digestion process [32,33] and is presumed to runs. C. proteolyticus is frequently found in the anaerobic digestion process [32,33] and is presumed participate in the degradation of proteins mainly supplied with soymilk in SBWM. D. tunisiensis is to participate in the degradation of proteins mainly supplied with soymilk in SBWM. D. tunisiensis is known to assimilate carbohydrates during the anaerobic digestion process [34]. These predominant known to assimilate carbohydrates during the anaerobic digestion process [34]. These predominant species were considered to play major roles in the production of hydrogen, carbon dioxide, and VFA, species were considered to play major roles in the production of hydrogen, carbon dioxide, and VFA, such as acetate, in batch cultures. Thermosediminibacter oceani constituted between 1% and 3% of the such as acetate, in batch cultures. Thermosediminibacter oceani constituted between 1% and 3% of the population, and acetate production by this bacterium has also been reported previously [35]. T. oceani exhibited stable population throughout all runs. In runs D and E, significant changes in the population of Acetomicrobium mobile, Clostridium chauvoei, and Methanothrix soehngenii were observed (Figure 4D and 4E). An increase in the population of A. mobile was observed in a dose-dependent manner with SBWM addition, especially in 10% of the population on days 10 and 15 in run E. The population of C. chauvoei also increased by >1% on day 5 in runs D and E, although this species is generally conceived as a minor member in the anaerobic digestion process. A. mobile and C. chauvoei have been reported to produce acetate, butyrate, propionate, hydrogen, and carbon dioxide [36,37]. There was a possibility that the increase in population of these species, in addition to the predominant species C. proteolyticus and D. tunisiensis, was key to the accumulation of VFA in run E. M. soehngenii is an acetotrophic methanogen [38], and its population decreased on day 5 in all runs. In runs B, C, and D, population of M. soehngenii recovered on days 10 and 15, but not in run E

Int. J. Environ. Res. Public Health 2020, 17, x 8 of 14

(Figure 4). The optimal pH for growth of M. soehngenii ranged from 7.4 to 7.8 [38], suggesting that a failure of population-recovery in run E could be correlated with the decrease in pH. The total population of M. soehngenii decreased from 0.94% to 0.73% in run D, and 0.072% in run E (Figure 4). However,Int. J. Environ. the Res. total Public population Health 2020, 17of, 7514M. soehngenii in run C increased to 2.1% was the highest, although 8 of 13 cumulative methane production was lower than that in run D (Figure 1). It is already known that population,balanced reaction and acetate rates of production hydrolysis, by acidogenesis, this bacterium acetogenesis, has also been and reported methanogenesis previously are [35 ].necessaryT. oceani forexhibited stable stable and population efficient production throughout of all methane runs. [39,40]. In this study, the highest cumulative productionIn runs of D andmethane E, signiﬁcant was achieved changes in inrun the D; population however, oftheAcetomicrobium balance between mobile eubacteria, Clostridium involved chauvoei in, hydrolysis/acidogenesis/acetogenesisand Methanothrix soehngenii were observed and (Figure methanogenic4D,E). An archaea increase involved in the population in methanogenesis of A. mobile wasseemed observed to be prone in a dose-dependent to breakdown. manner with SBWM addition, especially in 10% of the population on daysThe 10 microbial and 15 in community run E. The populationin run E quickly of C. chauvoeidevelopedalso into increased a rich diverse by >1% population, on day 5 in runswhich D was and crowdedE, although with this species species that is generallyeach constituted conceived lessas than a minor 1% of memberthe overall in thepopulation, anaerobic and digestion the population process. A.of methanogens, mobile and C. chauvoeiincludinghave M. s beenoehngenii, reported drastically to produce decreased acetate, to levels butyrate, lower propionate, than 0.2% hydrogen,(Figure 4 runand E). carbon Interestingly, dioxide [36 the,37 population]. There was of a Thermoanaerobacter possibility that the brockii increase increased in population on days of 10 these and species, 15. T. brockiiin addition has beento the reported predominant to yield species methaneC. proteolyticus from branchedand D.- tunisiensischain amino, was acids key to[41 the]; therefore, accumulation this bacteriumof VFA in runmay E. contribute to a slight accumulation of methane in run E.

Figure 4. A B C D E Figure 4. Changes in population of the species in runs A, B, C, D, and E. TheThe speciesspecies thatthat amountedamounted more than 0.2% population are shown. more than 0.2% population are shown. M. soehngenii is an acetotrophic methanogen [38], and its population decreased on day 5 in From another viewpoint, we identified species with an increase or decrease in population all runs. In runs B, C, and D, population of M. soehngenii recovered on days 10 and 15, but not changes more than 10-fold in all species assigned (Table 3). In methanogens, the population of in run E (Figure4). The optimal pH for growth of M. soehngenii ranged from 7.4 to 7.8 [38], Methanosaeta harundinacea decreased in run E only, together with the population of M. soehngenii. M. suggesting that a failure of population-recovery in run E could be correlated with the decrease in harundinacea, and M. soehngenii are methane producers via acetate assimilation [38,42], and the pH. The total population of M. soehngenii decreased from 0.94% to 0.73% in run D, and 0.072% in population decreases in these species could be related to the accumulation of acetate and decrease in run E (Figure4). However, the total population of M. soehngenii in run C increased to 2.1% was the methane production in run E. The population of C. chuvoei changed with 10-fold or more differences highest, although cumulative methane production was lower than that in run D (Figure1). It is already in all SBWM-added cultures (runs B, C, D, and E), whereas that of Clostridium formicaceticum known that balanced reaction rates of hydrolysis, acidogenesis, acetogenesis, and methanogenesis are experienced drastic changes only in run E (Table 3). C. formicaceticum is known to be a highly efficient necessary for stable and efficient production of methane [39,40]. In this study, the highest cumulative acetate producer [43]; thus, this species also contributes to the accumulation of acetate in run E. Three production of methane was achieved in run D; however, the balance between eubacteria involved in species, Petrimonas mucosa, Paludibacter propionicigenes, and Candidatus Solibacter usitatus, have been hydrolysis/acidogenesis/acetogenesis and methanogenic archaea involved in methanogenesis seemed found in methanogenic microbial communities as members involved in hydrolysis and acidogenesis to be prone to breakdown. [44–46]. It should be noted that Neorickettsia helminthoeca also experienced drastic changes in the The microbial community in run E quickly developed into a rich diverse population, which was population in runs D and E. N. helminthoeca and Candidatus Solibacter sp. have been reported to be crowded with species that each constituted less than 1% of the overall population, and the population of methanogens, including M. soehngenii, drastically decreased to levels lower than 0.2% (Figure4 run E). Interestingly, the population of Thermoanaerobacter brockii increased on days 10 and 15. T. brockii has Int. J. Environ. Res. Public Health 2020, 17, 7514 9 of 13 been reported to yield methane from branched-chain amino acids [41]; therefore, this bacterium may contribute to a slight accumulation of methane in run E. From another viewpoint, we identified species with an increase or decrease in population changes more than 10-fold in all species assigned (Table3). In methanogens, the population of Methanosaeta harundinacea decreased in run E only, together with the population of M. soehngenii. M. harundinacea, and M. soehngenii are methane producers via acetate assimilation [38,42], and the population decreases in these species could be related to the accumulation of acetate and decrease in methane production in run E. The population of C. chuvoei changed with 10-fold or more differences in all SBWM-added cultures (runs B, C, D, and E), whereas that of Clostridium formicaceticum experienced drastic changes only in run E (Table3). C. formicaceticum is known to be a highly efficient acetate producer [43]; thus, this species also contributes to the accumulation of acetate in run E. Three species, Petrimonas mucosa, Paludibacter propionicigenes, and Candidatus Solibacter usitatus, have been found in methanogenic microbial communities as members involved in hydrolysis and acidogenesis [44–46]. It should be noted that Neorickettsia helminthoeca also experienced drastic changes in the population in runs D and E. N. helminthoeca and Candidatus Solibacter sp. have been reported to be able to solubilize inorganic phosphorus in grass-field soil [47], although the roles of these species in methanogenic microbial communities are unknown. These results were in accordance with the decrease of richness and evenness of the microbial community in run E. Even though the population levels were low except for C. chauvoei, there is a possibility that changes in population of species shown in Table3 may become indices prior to overacidification in the methane production process. In order to promote the use of waste beverages by methane fermentation in society, it is most important to establish a technology for stable production of methane. Once methane production stops due to overacidification, it is hard to recover the methane production again, leading to economic losses. Therefore, the breakdown of the fermenter due to overacidification must be prevented in advance. We proposed microorganisms to be monitored in order to gain insight into the development of methods to prevent overacidification. On the other hand, a comprehensive analysis of metabolites other than VFA should be performed to prevent overacidification and to further investigate the relationship with microbial population changes proposed in this study. The discovery of the candidate bacterial species for prediction of overacidification will help a development of testing approach, such as the daily quantification of specific species based on the isothermal amplification method (SmartAmp) with Exiton Primer [48,49]. Int. J. Environ. Res. Public Health 2020, 17, 7514 10 of 13

Int. J. Environ.Int. J. Environ.Int. Res. J. Environ.PublicInt. Res. J. PublicHealthEnviron.Int. Res. J. Environ. HealthPublic2020 Res., 17 Health2020Public ,Res. x , 17 Public2020Health, x , 17Health 2020, x , 172020, x , 17, x Table 3. Representation of the species revealed drastic changes in population in10 runsof 1410 A, of B,1410 C, of D, 1410 and of 14 E.10 of 14

Run A Run B Run C Run D Run E Culture Period (Day) TableTable 3. RepresentationTable 3. Representation Table3. RepresentationTable 3.of Representation the 3. of species Representation the ofspecies therevealed ofspecies revealedthe ofdrastic species0 therevealed drasticspecies changes 5revealed drastic changesrevealed 10 in drastic populationchanges in 15drastic population changes in populationchangesin 0 runs in inpopulation 5A, runsin B, populationin C,A, runs 10 D,B, in C,andA, runs D, 15B, inE. andC, A,runs D, B, E. 0 andA,C, B,D, E. C,and 5 D, E. and 10 E. 15 0 5 10 15 0 5 10 15

CultureCulture PeriodCulture Period Culture Period Culture PeriodRun Period ARun ARun A Run ARun ASpecies Run B Run B Run B Run B Run B Run C Run C Run C Run C Run C Run D Run D Run D Run D Run D Run E Run E Run E Run E Run E (Day) (Day) (Day)0 (Day)0 (Day) 5 0 5 10 0 5 1015 0 510 15 05 1015 0 10 5 150 5 1510 0 5 1015 0 510 15 50 1015 0 10 5 150 5 1510 0 5 1015 0 510 15 50 1015 0 10 5 150 5 1510 0 5 1015 0 510 15 50 1015 0 10 5 150 5 1510 0 5 1015 0 510 15 5 1015 10 15 15 Candidatus Solibacter SpeciesSpecies Species Species Species 0.007 0.004 0.002 0.003 0.007 0.005 0.004 0.004 0.007 0.019 0.005 0.004 0.007 0.066 0.008 0.005 0.007 0.007 0.287 0.017 usitatus CandidatusCandidatus SolibacterCandidatus SolibacterCandidatus SolibacterCandidatus Solibacter Solibacter 0.007 0.0070.004 0.007 0.0040.002 0.0070.004 0.002 0.0030.007 0.004 0.002 0.003 0.0070.004 0.002 0.003 0.007 0.0020.005 0.003 0.007 0.005 0.0030.004 0.007 0.005 0.004 0.0070.004 0.005 0.004 0.004 0.0050.007 0.004 0.004 0.007 0.0040.019 0.004 0.007 0.019 0.0040.005 0.007 0.019 0.005 0.0070.004 0.019 0.005 0.004 0.0190.007 0.005 0.004 0.007 0.0050.066 0.004 0.007 0.066 0.0040.008 0.007 0.066 0.008 0.0070.005 0.066 0.008 0.005 0.0660.007 0.008 0.005 0.007 0.0080.007 0.005 0.007 0.007 0.0050.287 0.007 0.007 0.287 0.0070.017 0.007 0.287 0.017 0.007 0.287 0.017 0.287 0.017 0.017 usitatususitatus usitatus usitatus usitatus Clostridium chauvoei VL VL VL 0.002 VL 0.263 0.185 0.073 VL 0.300 0.327 0.076 VL 1.982 0.802 0.634 VL 3.696 0.874 0.882 ClostridiumClostridium chauvoeiClostridium chauvoei Clostridium VLchauvoeiClostridium VL chauvoei VL VLchauvoeiVL VL VL VL VL 0.002VL VL VL0.002 VL VL 0.002 VL0.263Clostridium VL 0.002 VL0.263 0.0020.185 VL0.263 0.185 0.073 VL0.263 0.185 0.073 0.263 VL0.185 0.073 VL 0.1850.300 0.073 VL0.300 0.0730.327 VL0.300 0.327 0.076 VL0.300 0.327 0.076 0.300 VL0.327 0.076 VL 0.3271.982 0.076 VL1.982 0.0760.802 VL1.982 0.802 0.634 VL1.982 0.802 0.634 1.982 VL0.802 0.634 VL 0.8023.696 0.634 VL3.696 0.6340.874 VL3.696 0.874 0.882 VL3.696 0.874 0.882 3.696 0.874 0.882 0.874 0.882 0.882 ClostridiumClostridium Clostridium Clostridium Clostridium 0.004 0.002 0.001 VL 0.004 VL VL 0.001 0.004 0.004 0.001 0.001 0.004 0.017 0.003 0.006 0.004 VL 0.101 0.005 0.004 0.0040.002 0.004 0.0020.001 0.0040.002 0.001 0.004 VL0.002 0.001 VL 0.0040.002 0.001 VL 0.004formicaceticum 0.001 VL VL 0.004 VL VL 0.004 VL VL 0.0040.001 VL VL 0.001 0.004 VL VL 0.001 0.004 0.004 VL 0.001 0.004 0.004 0.001 0.004 0.004 0.001 0.0040.001 0.004 0.001 0.001 0.004 0.001 0.001 0.004 0.0010.017 0.001 0.004 0.017 0.0010.003 0.004 0.017 0.003 0.0040.006 0.017 0.003 0.006 0.0170.004 0.003 0.006 0.004 0.003 VL0.006 0.004 VL 0.0060.101 0.004 VL0.101 0.0040.005 VL0.101 0.005 VL0.101 0.005 0.101 0.005 0.005 formicaceticumformicaceticumformicaceticum formicaceticum formicaceticum MethanosaetaMethanosaeta Methanosaeta Methanosaeta Methanosaeta Methanosaeta 0.256 0.2560.186 0.256 0.1860.168 0.2560.186 0.168 0.1220.256 0.186 0.168 0.122 0.2560.186 0.168 0.122 0.256 0.1680.183 0.122 0.256 0.183 0.1220.338 0.256 0.183 0.338 0.2560.434 0.183 0.2560.338 0.434 0.1830.256 0.338 0.1860.434 0.256 0.3380.153 0.434 0.1680.256 0.153 0.4340.269 0.256 0.1220.153 0.269 0.2560.586 0.153 0.2560.269 0.586 0.1530.256 0.269 0.1830.586 0.256 0.2690.119 0.3380.586 0.256 0.119 0.5860.145 0.4340.256 0.119 0.145 0.2560.202 0.2560.119 0.145 0.202 0.1190.256 0.1530.145 0.202 0.256 0.1450.050 0.2690.202 0.256 0.050 0.2020.037 0.5860.256 0.050 0.037 0.2560.020 0.2560.050 0.037 0.020 0.050 0.1190.037 0.020 0.037 0.1450.020 0.020 0.202 0.256 0.050 0.037 0.020 harundinaceaharundinacea harundinacea harundinacea harundinacea harundinacea MethanothrixMethanothrix Methanothrix Methanothrix Methanothrix Methanothrix 0.945 0.9450.635 0.945 0.6350.422 0.9450.635 0.422 0.0010.945 0.635 0.422 0.001 0.9450.635 0.422 0.001 0.945 0.4220.566 0.001 0.945 0.566 0.0011.164 0.945 0.566 1.164 0.9451.358 0.566 0.9451.164 1.358 0.5660.945 1.164 0.6351.358 0.945 1.1640.517 1.358 0.4220.945 0.517 1.3580.947 0.945 0.0010.517 0.947 0.9452.126 0.517 0.9450.947 2.126 0.5170.945 0.947 0.5662.126 0.945 0.9470.364 1.1642.126 0.945 0.364 2.1260.442 1.3580.945 0.364 0.442 0.9450.734 0.9450.364 0.442 0.734 0.3640.945 0.5170.442 0.734 0.945 0.4420.147 0.9470.734 0.945 0.147 0.7340.110 2.1260.945 0.147 0.110 0.9450.072 0.9450.147 0.110 0.072 0.147 0.3640.110 0.072 0.110 0.4420.072 0.072 0.734 0.945 0.147 0.110 0.072 soehngeniisoehngenii soehngenii soehngenii soehngenii soehngenii NeorickettsiaNeorickettsia Neorickettsia Neorickettsia Neorickettsia Neorickettsia 0.027 0.0270.031 0.027 0.0310.019 0.0270.031 0.019 0.0280.027 0.031 0.019 0.028 0.0270.031 0.019 0.028 0.027 0.0190.061 0.028 0.027 0.061 0.0280.052 0.027 0.061 0.052 0.0270.080 0.061 0.0270.052 0.080 0.0610.027 0.052 0.0310.080 0.027 0.0520.137 0.080 0.0190.027 0.137 0.0800.157 0.027 0.0280.137 0.157 0.0270.143 0.137 0.0270.157 0.143 0.1370.027 0.157 0.0610.143 0.027 0.1570.002 0.0520.143 0.027 0.002 0.1430.376 0.0800.027 0.002 0.376 0.0270.291 0.0270.002 0.376 0.291 0.0020.027 0.1370.376 0.291 0.027 0.3760.002 0.1570.291 0.027 0.002 0.2910.001 0.1430.027 0.002 0.001 0.027 VL0.0270.002 0.001 VL 0.002 0.002 0.001 VL 0.0010.376 VL 0.291VL 0.027 0.002 0.001 VL helminthoecahelminthoeca helminthoeca helminthoeca helminthoeca helminthoeca PaludibacterPaludibacter Paludibacter Paludibacter Paludibacter ND ND0.001 ND0.001 ND ND 0.001ND 0.002 ND 0.001ND 0.002 0.001 ND ND0.002 NDPaludibacter0.005ND 0.002 ND 0.005 0.0020.006 ND0.005 0.006 0.008ND 0.005 0.006 0.008 0.005ND 0.006 0.008 ND 0.0060.027 0.008ND 0.027 0.0080.032 ND0.027 0.032 0.038ND 0.027 0.032 0.038 0.027ND 0.032 0.038 ND 0.0320.018 0.038ND 0.018 0.0380.110 ND0.018 0.110 0.108ND 0.018 0.110 0.108 0.018ND 0.110 0.108 ND 0.110ND 0.108 ND ND 0.108 ND ND ND ND0.109 ND ND ND 0.109 ND ND0.109 ND 0.109 0.109 propionicigenespropionicigenespropionicigenes propionicigenes propionicigenes ND 0.001 ND 0.002 ND 0.005 0.006 0.008 ND 0.027 0.032 0.038 ND 0.018 0.110 0.108 ND ND ND 0.109 PetrimonasPetrimonas mucosaPetrimonas mucosa Petrimonas0.004 mucosa Petrimonas 0.004 0.002 mucosa 0.004 mucosa0.002 0.002 0.0040.002 0.002 0.2410.004 0.002 0.002 0.241 0.0040.002 0.002 0.241 0.004propionicigenes 0.0020.012 0.241 0.004 0.012 0.2410.021 0.004 0.012 0.021 0.0040.015 0.012 0.021 0.015 0.0120.004 0.021 0.015 0.004 0.0210.071 0.015 0.004 0.071 0.0150.100 0.004 0.071 0.100 0.0040.099 0.071 0.100 0.099 0.0710.004 0.100 0.099 0.004 0.1000.051 0.099 0.004 0.051 0.0990.265 0.004 0.051 0.265 0.0040.304 0.051 0.265 0.304 0.0510.004 0.265 0.304 0.004 0.265ND 0.304 0.004 ND 0.304 VL 0.004 ND VL 0.0040.262 ND VL0.262 ND VL0.262 VL0.262 0.262 Petrimonas mucosa 0.004 0.002 0.002 0.241 0.004 0.012 0.021 0.015 0.004 0.071 0.100 0.099 0.004 0.051 0.265 0.304 0.004 ND VL 0.262 In all Inspecies all Inspecies identifiedall speciesIn identified all In speciesin identifiedall the species in amplicon identified the in ampliconidentified the sequence inamplicon the sequence in amplicon the analysis, sequence amplicon analysis, sequence th eanalysis, sequencespecies th eanalysis, species thshowing eanalysis, species showingthe increasesspecies thshowinge increasesspecies showing or increases decreases showing or increasesdecreases or byincreases decreases more or by decreases morethan or by decreases 10-foldmorethan by 10-fold thanmore ar bye 10-foldlisted morethan are listed10-foldandthan are colored 10-foldlistedand ar ecolored andlisted toar erepresent colored listed and to represent colored and to the represent colored to the represent to the represent the the In all species identiﬁed in the amplicon sequence analysis, the species showing increases or decreases by more than 10-fold are listed and colored to represent the population range. Colors populationpopulation populationrange. range.population Colors populationrange. Colors of population range. Colors of population range. Colors of populationrange Colors of range population(%): of population range(%): of population range(%): < 0.001; range (%): < range 0.001; (%): (%):< 0.001; 0.001–0.01; < 0.001; < 0.001; <0.001–0.01; 0.001; 0.001–0.01; 0.001–0.01; 0.001–0.01; 0.01–0.1; 0.001–0.01; 0.01–0.1; 0.01–0.1; 0.01–0.1; 0.01–0.1;0.1–1.0; 0.1–1.0; 0.01–0.1; 0.1–1.0; 0.1–1.0; > 1.0. >1.0.0.1–1.0; The The0.1–1.0;>1.0. blank bl Theank>1.0. (white) bl (white) The ank>1.0. panels bl(white) Theank>1.0. panels indicate bl(white) The ankpanels ‘notbl (white)ank panels changed (white) panels by panels more than 10-fold’ in the runs. ND and VL indicate ‘not detectable’ and ‘very low level less than 0.001%’, respectively. indicateindicate ‘notindicate changed ‘notindicate changed ‘not byindicate changed more ‘not by changed morethan‘not by changed 10-fold’morethan by 10-fold’ thanmore inby the 10-fold’ morethan in runs. the 10-fold’than inruns.ND the10-fold’ and inNDruns. theVL and in ND runs.indicate the VL and runs. NDindicate VL ‘not and ND indicate detectable’ VL‘not and indicate detectable’VL ‘not indicate anddetectable’ ‘not ‘very anddetectable’ ‘not ‘verylow detectable’and level low‘very and lesslevel low‘very and than lesslevel ‘verylow 0.00than less level low1%’, 0.00 than levelless respectively.1%’, 0.00 than less respectively.1%’, 0.00than respectively. 1%’, 0.00 respectively.1%’, respectively.

Int. J. Environ. Res. Public Health 2020, 17, 7514 11 of 13

4. Conclusions In this study, we successfully performed overacidification and inhibition of methane production by a high substrate loading, in a batch culture of methanogenic microbial community with beverage waste. From the result of 16S rRNA amplicon sequencing analysis, it was considered that the predominant species C. proteolyticus, D. tunisiensis, A. mobile, and T. oceani, were involved in hydrolysis/acidogenesis/acetogenesis processes, and M. soehngenii was the major species conducting methanogenesis. Furthermore, monitoring the population changes of minor species revealed less than 0.2% of the total population represents the possibility of developing a method for prediction of overacidification occurrence. However, a research on the detailed relationship between the population change of the candidate indicator species and the amounts of various metabolites has been still remained. After the elucidation of the relationship, an adoption of low-cost, rapid, and easy-operating technique, such as SmartAmp, will enable us to predict overacidification occurrence on-site in methane fermentation plants.

Author Contributions: Conceptualization, S.M., Y.S. and T.O.; methodology, S.M. and T.O.; formal analysis, S.M.; investigation, S.M and T.Y.; resources, Y.M. and Y.S.; data curation, S.M.; writing—original draft preparation, S.M.; writing—review and editing, T.O.; visualization, S.M.; supervision, T.O.; project administration, T.O. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: We would like to thank Editage (www.editage.com) for English language editing. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Meegoda, J.N.; Li, B.; Patel, K.; Wang, L.B. A review of the processes, parameters, and optimization of anaerobic digestion. Int. J. Environ. Res. Public Health 2018, 15, 2224. [CrossRef][PubMed] 2. Abouelenien, F.; Nakashimada, Y.; Nishio, N. Dry mesophilic fermentation of chicken manure for production of methane by repeated batch culture. J. Biosci. Bioeng. 2008, 107, 293–295. [CrossRef][PubMed] 3. Chen, T.-H.; Hashimoto, A.G. Effects of pH and substrate: Inoculum ratio on batch methane fermentation. Bioresour. Technol. 1996, 56, 179–186. [CrossRef] 4. Tekippe, J.A.; Hristov, A.N.; Heyler, K.S.; Zheljazkov, V.D.; Ferreira, J.F.S.; Cantrell, C.L.; Varga, G.A. Effects of plants and essential oils on ruminal in vitro batch culture methane production and fermentation. Can. J. Anim. Sci. 2012, 92, 395–408. [CrossRef] 5. Yáñez-Ruiz, D.R.; Bannik, A.; Dijkstra, J.; Kebreab, E.; Morgavi, D.P.; O’Kiely, P.; Reynolds, C.K.; Schwarm, A.; Shingfield, K.J.; Yu, Z.; et al. Design, implementation and interpretation of in vitro batch culture experiments to assess enteric methane mitigation in ruminants—A review. Anim. Feed Sci. Technol. 2016, 216, 1–18. [CrossRef] 6. Quested, T.; Ingle, R.; Parry, A. Household Food and Drink Waste in the United Kingdom 2012; Waste & Resources Action Programme: Banbury, UK, 2013; p. 40. 7. Report of Survey on Recycling Status in Food Industries FY2017. Available online: https://www.maff.go.jp/j/ shokusan/recycle/syoku_loss/attach/pdf/161227_8-56.pdf (accessed on 9 October 2020). (In Japanese). 8. Tyagi, V.K.; Liu, J.; Poh, L.S.; Ng, W.J. Anaerobic-aerobic system for beverage effluent treatment: Performance evaluation and microbial community dynamics. Bioresour. Technol. Rep. 2019, 7, 100309. [CrossRef] 9. Wickham, R.; Xie, S.; Galway, B.; Bustamante, H.; Nghiem, L.D. Anaerobic digestion of soft drink beverage waste and sewage sludge. Bioresour. Technol. 2018, 262, 141–147. [CrossRef] 10. Tang, Y.-Q.; Shigematsu, T.; Morimura, S.; Kida, K. Dynamics of the microbial community during continuous methane fermentation in continuously stirred tank reactors. J. Biosci. Bioeng. 2015, 119, 375–383. [CrossRef] 11. Leng, L.; Yang, P.; Singh, S.; Zhuang, H.; Xu, L.; Chen, W.-H.; Dolfing, J.; Li, D.; Zhang, Y.; Zeng, H.; et al. A review on the bioenergetics of anaerobic microbial metabolism close to the thermodynamic limits and its implications for digestion applications. Bioresour. Technol. 2018, 247, 1095–1106. [CrossRef] Int. J. Environ. Res. Public Health 2020, 17, 7514 12 of 13

12. Wang, L.; Hossen, E.H.; Aziz, T.N.; Ducoste, J.J.; de los Reyes, F.L., III. Increased loading stress leads to convergence of microbial communities and high methane yields in adapted anaerobic co-digesters. Water Res. 2020, 169, 115155. [CrossRef] 13. Blume, F.; Bergman, I.; Nettmann, E.; Schelle, H.; Rehde, G.; Mundt, K.; Klocke, M. Methanogenic population dynamics during semi-continuous biogas fermentation and acidification by overloading. J. Appl. Microbiol. 2010, 109, 441–450. [CrossRef][PubMed] 14. Kleyböcker, A.; Liebrich, M.; Verstraete, W.; Kraume, M.; Würdemann, H. Early warning indicators for process failure due to organic loading by rapeseed oil in one-stage continuously stirred tank reactor, sewage sludge and waste digesters. Bioresour. Technol. 2012, 123, 534–541. [CrossRef][PubMed] 15. Li, L.; He, Q.; Ma, Y.; Wang, X.; Peng, X. Dynamics of microbial community in a mesophilic anaerobic digester treating food waste: Relationship between community structure and process stability. Bioresour. Technol. 2015, 189, 113–120. [CrossRef][PubMed] 16. Tale, V.P.; Maki, J.S.; Struble, C.A.; Zitomer, D.H. Methanogen community structure-activity relationship and bioaugmentation of overloaded anaerobic digesters. Water Res. 2011, 45, 5249–5256. [CrossRef] 17. Wu, S.; Xu, S.; Chen, X.; Sun, H.; Hu, M.; Bai, Z.; Zhuang, G.; Zhuang, X. Bacterial communities changes during food waste spoilage. Sci. Rep. 2018, 8, 8220. [CrossRef] 18. Marchaim, U.; Krause, C. Propionic to acetic acid ratios in overloaded anaerobic digestion. Bioresour. Technol. 1993, 43, 195–203. [CrossRef] 19. Zhu, H.; Qu, F.; Zhu, L.-H. Isolation of genomic DNAs from plants, fungi and bacteria using benzyl chloride. Nucleic Acids Res. 1993, 21, 5279–5280. [CrossRef] 20. Takahashi, S.; Tomita, J.; Nishioka, K.; Hisada, T.; Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of Bacteria and Archaea using next-generation sequencing. PLoS ONE 2014, 9, e105592. [CrossRef] 21. ClustVis: A Web Tool for Visualizing Clustering of Multivariate Data (BETA). Available online: https: //biit.cs.ut.ee/clustvis/ (accessed on 18 September 2020). 22. Kin, D.; Song, L.; Breitwieser, F.P.; Salzberg, S.L. Centrifuge: Rapid and sensitive classification of metagenomic sequences. Genome Res. 2016, 26, 1721–1729. 23. Steinbusch, K.J.J.; Arvaniti, E.; Hamelers, H.V.M.; Buisman, C.J.N. Selective inhibition of methanogenesis to enhance ethanol and n-butyrate production through acetate reduction in mixed culture fermentation. Bioresour. Technol. 2009, 100, 3261–3267. [CrossRef] 24. Kaur, G.; Johnravindar, D.; Wong, J.W.C. Enhanced volatile fatty acid degradation and methane production efficiency by biochar addition in food waste-sludge co-digestion: A step towards increased organic loading efficiency in co-digestion. Bioresour. Technol. 2020, 308, 123250. [CrossRef][PubMed] 25. Xu, S.; Han, R.; Zhang, Y.; He, C.; Liu, H. Differentiated stimulating effects of activated carbon on methanogenic degradation of acetate, propionate and butyrate. Waste Manag. 2018, 76, 394–403. [CrossRef][PubMed] 26. Redzwan, G.; Banks, C.J. An Evaluation of Soft-drink Wastewater Treatment by Anaerobic Digestion Process. Malays. J. Sci. 2007, 26, 35–42. 27. Allison, C.; Macfarlane, G.T. Effect of nitrate on methane production and fermentation by slurries of human faecal bacteria. J. Gen. Microbiol. 1988, 134, 1397–1405. [CrossRef][PubMed] 28. Sakthivel, P.C.; Kamra, N.; Agarwal, N.; Chaudhary, L.C. Effect of sodium nitrate and nitrate reducing bacteria on in vitro methane production and fermentation with buffalo rumen liquor. Asian Aust. J. Anim. Sci. 2012, 25, 812–817. [CrossRef] 29. Zhou, Z.; Yu, Z.; Meng, Q. Effects on nitrate on methane production, fermentation, and microbial populations in in vitro ruminal cultures. Bioresour. Technol. 2012, 103, 173–179. [CrossRef][PubMed] 30. Roy, R.; Conrad, R. Effect of methanogenic precursors (acetate, hydrogen, propionate) on the suppression of methane production by nitrate in anoxic rice field soil. FEMS Microbiol. Ecol. 1999, 28, 49–61. [CrossRef] 31. Niu, Q.; Qiao, W.; Qiang, H.; Li, Y.-Y. Microbial community shifts and biogas conversion computation during steady, inhibited and recovered stages of thermophilic methan fermentation on chicken manure with a wide variation of ammonia. Bioresour. Technol. 2013, 146, 223–233. [CrossRef] 32. Gagliano, M.C.; Braguglia, C.M.; Rossetti, S. In situ identification of the synthrophic protein fermentative Coprothermobacter spp. involved in the themophilic anaerobic digestion process. FEMS Microbiol. Lett. 2014, 358, 55–63. [CrossRef] Int. J. Environ. Res. Public Health 2020, 17, 7514 13 of 13

33. Gagliano, M.C.; Braguglia, C.M.; Petruccioli, M.; Rossetti, S. Ecology and biotechnological potential of the thermophilic fermentative Coprothermobacter spp. FEMS Microbiol. Ecol. 2015, 91, fiv018. [CrossRef] 34. Hania, W.B.; Godbane, R.; Postec, A.; Hamdi, M.; Ollivier, B.; Fardeau, M.-L. Defluviitoga tunisiensis gen. nov., sp. nov., a thermophilic bacterium isolated from a mesothermic and anaerobic whey digester. Int. J. Syst. Evol. Microbiol. 2012, 62, 1377–1382. [CrossRef][PubMed] 35. Lee, Y.-J.; Wagner, I.D.; Brice, M.E.; Kevbrin, V.V.; Mills, G.L.; Romanck, C.S.; Wiegel, J. Thermosediminibacter oceani gen. nov., sp. nov. and Thermosediminibacter lithoriperuensis sp. nov., new anaerobic thermophilic bacteria isolated from Peru Margin. Extremophiles 2005, 9, 375–383. [CrossRef][PubMed] 36. Fröschle, B.; Messelhäusser, U.; Höller, C.; Lebuhn, M. Fate of Clostridium botulinum and incidence of pathogenic clostridia in biogas processes. J. Appl. Microbiol. 2015, 119, 936–947. [CrossRef][PubMed] 37. Wainaina, S.; Awasthi, L.M.K.; Taherzadeh, M.J. Bioengineering of anaerobic digestion for volatile fatty acids, hydrogen or methane production: A critical review. Bioengineered 2019, 10, 437–458. [CrossRef] 38. Huser, B.A.; Wuhrmann, K.; Zehnder, A.J.B. Methanothrix soehngenii gen. nov. sp. nov., a new acetotrophic non-hydrogen-oxidizing methane bacterium. Arch. Microbiol. 1982, 132, 1–9. [CrossRef] 39. Ennouri, H.; Miladi, B.; Diaz, S.Z.; Güelfo, L.A.F.; Solera, R.; Hamdi, M.; Bouallagui, H. Effect of thermal pretreatment on the biogas production and microbial communities balance during anaerobic digestion of urban and industrial waste activated sludge. Bioresour. Technol. 2016, 214, 184–191. [CrossRef] 40. Enzmann, F.; Mayer, F.; Rother, M.; Holtmann, D. Methanogens: Biochemical background and biotechnological applications. AMB Expr. 2018, 8, 1. [CrossRef] 41. Scully, S.M.; Orlygsson, J. Brabched-chain alcohol formation from branched-chain amino acids by Thermoanaerobacter brockii and Themoanaerobacter yonseiensis. Anaerobe 2014, 30, 82–84. [CrossRef] 42. Ma, K.; Liu, X.; Dong, X. Methanosaeta harundinacea sp. nov., a novel acetate-scavenging methanogen isolated from a UASB reactor. Int. J. Syst. Evol. Microbiol. 2006, 56, 127–131. [CrossRef] 43. Huang, Y.L.; Mann, K.; Novak, J.M.; Yang, S.-T. Acetic acid production from fructose by Clostridium formicoaceticum immobilized in a fibrous-bed bioreactor. Biotechnol. Prog. 1998, 14, 800–806. [CrossRef] 44. Hahnke, S.; Langer, T.; Koeck, D.E.; Klocke, M. Description of Proteiniphilum saccharofermentans sp. nov., Petrimonas mucosa gen. nov., sp. nov., isolated from mesophilic laboratory-scale biogas reactors, and emended description of the genus Proteiniphilum. Int. J. Syst. Evol. Microbiol. 2016, 66, 1466–1475. [CrossRef][PubMed] 45. Hunger, S.; Gößner, A.S.; Drake, H.L. Anaerobic trophic interactions of contrasting methane-emitting mire soils: Processes versus taxa. FEMS Microbiol. Ecol. 2015, 91, fiv045. [CrossRef] 46. Ueki, A.; Akasaka, H.; Suzuki, D.; Ueki, K. Paludibacter propionicigenes gen. nov., sp. nov., a novel strictly anaerobic, Gram-negative, propionate-producing bacterium isolated from plant residue in irrigated rice-field soil in Japan. Int. J. Syst. Evol. Microbiol. 2006, 56, 39–44. [CrossRef] 47. Zhou, Y.; Zhu, H.; Yao, Q. Contrasting P acquisition strategies of the bacterial communities associated with legume and grass in subtropical orchard soil. Environ. Microbiol. Rep. 2018, 10, 310–319. [CrossRef] 48. Mitani, Y.; Lezhava, A.; Kawai, Y.; Kikuchi, T.; Oguchi-Katayama, A.; Kogo, Y.; Itoh, M.; Miyagi, T.; Takakura, H.; Hoshi, K.; et al. Rapid SNP diagnostics using asymmetric isothermal amplification and a new mismatch-suppression technology. Nat. Method. 2007, 4, 257–262. [CrossRef][PubMed] 49. Lezhava, A.; Ishidao, T.; Ishizu, Y.; Naito, K.; Hanami, T.; Katayama, A.; Kogo, Y.; Soma, T.; Ikeda, S.; Murakami, K.; et al. Exciton primer-mediated SNP detection in SmartAmp2 reactions. Hum. Mutat. 2010, 31, 208–217. [CrossRef]

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