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Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis
Journal Pre-proofs Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis Pooja Ghosh, Madan Kumar, Rimika Kapoor, Smita S Kumar, Lakhveer Singh, Vandit Vijay, Virendra Kumar Vijay, Vivek Kumar, Indu Shekhar Thakur PII: DOI: Reference:
S0960-8524(19)31505-6 https://doi.org/10.1016/j.biortech.2019.122275 BITE 122275
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Bioresource Technology
Received Date: Revised Date: Accepted Date:
18 September 2019 9 October 2019 15 October 2019
Please cite this article as: Ghosh, P., Kumar, M., Kapoor, R., Kumar, S.S., Singh, L., Vijay, V., Kumar Vijay, V., Kumar, V., Shekhar Thakur, I., Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis, Bioresource Technology (2019), doi: https://doi.org/10.1016/ j.biortech.2019.122275
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Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis Pooja Ghosha1*, Madan Kumara1, Rimika Kapoora, Smita S Kumara, Lakhveer Singhb, Vandit Vijaya, Virendra Kumar Vijaya, Vivek Kumara , Indu Shekhar Thakurc aCentre
for Rural Development and Technology, Indian Institute of Technology, New
Delhi-110016, India bFaculty
of Civil and Environmental Engineering, University Malaysia Pahang, Kuantan-
26300, Malaysia cSchool 1
of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India
Pooja Ghosh and Madan Kumar have equal contribution
* Corresponding author
Corresponding Author details: Dr. Pooja Ghosh DST-INSPIRE Faculty Centre for Rural Development and Technology Indian Institute of Technology, Delhi India Email id: [email protected]
Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis 1
Abstract The present study intends to evaluate the potential of co-digestion for utilizing Organic fraction of Municipal Solid Waste (OFMSW) and sewage sludge (SS) for enhanced biogas production. Metagenomic analysis was performed to identify the dominant bacteria, archaea and fungi, changes in their communities with time and their functional roles during the course of anaerobic digestion (AD). The cumulative biogas yield of 586.2 mL biogas/gVS with the highest methane concentration of 69.5% was observed under an optimum ratio of OFMSW:SS (40:60 w/w). Bacteria and fungi were found to be majorly involved in hydrolysis and initial stages of AD. Probably, the most common archaea Methanosarsina sp. primarily followed the acetoclastic pathway. The hydrogenotrophic pathway was less followed as indicated by the reduction in abundance of syntrophic acetate oxidizers. An adequate understanding of microbial communities is important to manipulate and inoculate the specific microbial consortia to maximize CH4 production through AD. Keywords: Municipal solid waste; Anaerobic digestion; Metagenomics; Bacteria; Archaea; Fungi 1. Introduction Municipal solid waste (MSW) management and energy insecurity are among the foremost challenges faced by India nowadays. About 1.5 lakh tonnes of MSW is generated per day in Urban India, of which only 70–75% gets collected and 20–25% is treated (Negi et al., 2018). Also, the collected unsegregated waste is majorly dumped in the unengineered landfills leading to anthropogenic methane (CH4) emissions and groundwater contamination (Ghosh et al., 2015). Most of the developing countries are presently not in a 2
situation to meet the Sustainable Development Goal of Affordable and Clean Energy (SDG 7) (Sahota et al., 2018).Waste derived bioenergy will not only help in providing a cleaner renewable alternate energy but can also help in tapping the huge potential of waste along with solving the problem of waste management (Singh and Kumar, 2019). Biogas is a promising renewable energy produced by anaerobic digestion (AD) of feedstocks. It majorly comprises of CH4 and CO2 ranging from 40 to 60% and 35 to 55%, respectively along with traces of hydrogen sulfide (H2S), moisture, and other contaminants. AD consists of four stages-hydrolysis, acidogenesis, acetogenesis and methanogenesis (Kumar et al., 2019a). Methanogenesis step which is carried out by a specialized microbial group of archaea is highly sensitive to variations in temperature, pH, C/N ratio and maintaining the optimum conditions for the AD process is a key for enhancing biogas yield (Mao et al., 2015). A common problem usually encountered in biogas production is low biogas yield due to the use of single feedstock which may be either recalcitrant to digestion or have a low or high C/N ratio. Moreover, most of the studies involving single-stage AD have reported low CH4 content in biogas. These limitations can be resolved by co-digesting diverse feedstocks and optimizing the C/N ratio, total solids content (TS) and volatile solids (VS) content. This also helps in reducing ammonia production during AD, thus reducing the chances of inhibition caused by ammonia. This also increases the digestibility of the feedstock, thereby enhancing the production of biogas (Mata-Alvarez et al., 2014). For optimizing the C/N ratio and to seed anaerobic microbes for AD, co-digesting MSW along with sewage sludge (SS) from wastewater treatment plant containing mixed microbial consortia (MMC) is a promising solution. Utilizing sewage sludge from
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wastewater treatment plant for AD also helps in reducing the environmental burden associated with sludge disposal (Kumar et al., 2018). For maximizing the biogas yield in AD, it is imperative to understand the microbial consortia of archaea, bacteria, and fungi in terms of taxonomy, diversity and metabolic pathways. With the advancements in sequencing methods, studies on metagenomics of micro-organisms involved in AD have gained momentum. Several reports on metagenomics are available which study the dominant microbes; monitor their community shift and methanogenesis pathway. These are important for understanding the synergistic relation between micro-organisms and digester performance. Nevertheless, in developing countries like India, the microbial identities and dynamics in AD are frequently ignored and the process is considered as a ‘black-box’. However, to improve process economic viability of AD and to maximize CH4 yield, it is important to analyze the metabolic pathway so as to engineer the environmental parameters to augment the development and action of key genera. Besides bacteria, eukaryotic microorganisms are also important participants in the AD pathway. However, very less research has been carried out to understand the role of fungi in production of biogas. Considering the presence of only limited and diffused knowledge on microbial diversity particularly that of fungi in mediating production of biogas from MSW and SS, present work deals with using a high-throughput DNA sequencing technique for taxonomic and functional characterization of microbial consortia present in the SS involved in production of biogas with a high percentage of CH4 from MSW (majorly unutilized organic wastes produced in large quantities in India). This study will help in gaining a fundamental
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knowledge regarding the SS microbial community composition and the mechanisms followed by them for production of biogas with high CH4 concentration. 2. Materials and methods 2.1 Sampling and sampling sites Approximately 12,000 tonnes of MSW is daily generated in Delhi, majority of which is dumped in an unsegregated manner in three non-engineered landfill sites namely Okhla, Bhalswa, Ghazipur and an engineered landfill site at Narela-Bawana (Ghosh et al., 2019; Kumar et al., 2019b). Freshly dumped MSW was collected from 5 sites within the non-engineered dump sites - Ghazipur, India in October 2018. The waste was later segregated manually in the lab into organic and inorganic fractions. The organic fraction of MSW (OFMSW) was homogenized by grinding to smaller sized particles (<80 mm). The sludge samples were also collected in October 2018 from Sewage Treatment Plant located at Vasant Kunj, New Delhi, India. The collected sludge samples were concentrated by gravity settling overnight and stored at 4°C. 2.2 Characterization of MSW, sludge and co-digested substrates Proximate analysis (moisture, total solids (TS), volatile solids (VS)) of the OFMSW and sewage sludge (SS) was done according to American Public Health Association method 2540 G (APHA, 1998). pH was measured with the help of a pH meter (D-74 Horiba Scientific). For ultimate analysis, C, H and N contents of OFMSW, sludge and the co-digested substrates was carried out with the help of CHN analyzer (Elemental Vario el Cube analyzer, Germany). All the experiments were conducted in triplicates. 5
2.3 Experimental design and measurement of biogas production and composition The experiments were conducted using the organic fraction of MSW after proper segregation from the mixed MSW. For studying the optimum concentration of OFMSW and SS for maximal biogas generation, 6 batch experiments in triplicates (100% OFMSW, 100% SS and OFMSW: SS in different ratios of 20:80, 40:60, 60:40 and 80:20 w/w) were carried out in 3 L anaerobic digesters. The digesters are named as AD1, AD2, AD3, AD4, AD5 and AD6 for 100% OFMSW, 100% SS, 20:80, 40:60, 60:40 and 80:20 w/w OFMSW: SS respectively. The pH of the MSW was slightly acidic (~6.5) and SS was alkaline (~8). The pH in all the digesters was set to 7.2 for appropriate AD. The biogas generation was measured by water displacement method and analysis of the gaseous composition using the Geotech BIOGAS 5000 Analyser. 2.4 Metagenomic analysis 2.4.1 Genomic DNA extraction For the metagenomic studies, 2 mL samples were collected from the anaerobic digester showing maximum biogas production potential at different intervals of time (0, 15 and 30 days). The samples were named as MSWM1, MSWM2 and MSWM3 corresponding to 0 day, 15 days and 30 days collected samples respectively. Genomic DNA from the sample was extracted with the help of DNA Isolation Kit (Power Soil MO BIO Laboratories). The DNA concentration was measured with the help of NanoDrop ND-1000 spectrophotometer by determining A260/A280. DNA purity was tested by agarose gel electrophoresis (Kumar et al., 2017).
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2.4.2 DNA sequencing and bioinformatics The QC passed samples were processed for the first generation of amplicon followed by NGS library preparation using Index Kit (Nextera XT Illumina Inc.). Sequencing of libraries was carried out on MiSeq using 2x300 bp chemistry. For each of the sample sequence, around 80 Mb of data for bacteria, ~116-175 Mb for archaea and ~5568 Mb for fungi was generated. This corresponded to ~1,70,000 reads, 2,29,000 -3,47,000 reads and 1,08,000-1,24,000 reads for bacteria, archaea and fungi respectively. To process the data of three bacterial 16S samples, three archaeal 16S samples, and three 18S fungal samples, QIIME software was used and the following steps were performed: (1) The raw reads were processed through Trimmomatic v0.38 for removal of ambiguous reads, adapter, and low-quality sequences to obtain high quality reads. (2) The Paired-End data was stitched into single-end reads. (3) The Operational Taxonomic Units (OTUs) were chosen on the basis of similarity of sequences within the reads. Sequences having a similarity of ≥97% were allocated to the same OTUs. (4) OTU was assigned to a taxonomic identity using reference databases. The representative sequence for 16S from each OTU against the Greengenes database (version 13_8) and for 18S against the UNITE database (version 7.2) was selected. (5) Diversity metrics for each sample was calculated and compared the types of communities, using taxonomic assignments. Sequences of bacteria, archaea, and fungi were submitted to the NCBI database. 3. Results and discussion 3.1 Anaerobic digestion of OFMSW and sludge
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The MSW sampled from the landfill contained around 65% of organic waste (food waste, vegetable market waste, yard clippings); ~25 % of paper waste, plastic, metal, textile and 10% inert materials. After manual segregation of waste, the organic fractions of MSW and sewage sludge were characterized (Table 1). As evident from Table 1, the OFMSW has high C/N ratio of 32.5, whereas, the SS has low C/N ratio of 17.1 and both the feedstock individually are not suitable for AD, since an optimal C/N ratio of 20 to 30 is essential. Thus, OFMSW and SS were co-digested in different ratios to optimize the suitable mixing ratio for efficient digestion and enhanced biogas and CH4 yields. Fig. 1 illustrates the variations in the cumulative yields of biogas (mL biogas/gVS) and methane (mL CH4/g VS) with time for OFMSW, SS and their co-digestion (20:80, 40:60, 60:40, 80:20 w/w of OFMSW:SS). The mono-digested sample of OFMSW (AD1) showed the least cumulative biogas (220.5 mL biogas/gVS) and CH4 yield (24.03 mL CH4/gVS). In AD4, the highest cumulative biogas and methane yield of 586.2mL biogas/gVS and 377 mL CH4/gVS respectively were observed. There was 2.66 and 15 fold increase in biogas and methane yield respectively with respect to mono-digested OFMSW. On 22nd day of AD, a high concentration of CH4 (~69.5%) was also observed in AD4. It has been reported in literature that under optimal conditions, OFMSW can yield around 300 to 500 mL of CH4/g VS, average yield being 367 mL CH4/gVS (Rubia et al., 2018). The result obtained in the present study is in accordance with the reported literature. The performance of AD is dependent on TS and VS content of the feedstock. Dhar et al. (2016) stated that the optimum TS concentration of the feedstock should be in the range of 4-10% , and any deviation in the TS content beyond this range leads to a decrease
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in gas production (Dhar et al., 2016). This is also evident from the results obtained in the present study. It can be observed in Table 1 that at 40:60 ratio (9.5% TS), maximum gas yield was observed and beyond this, in 60:40 (10.9% TS) and 80:20 (12.1% TS) codigested samples, there was a drastic reduction in gas yield (Fig. 1). A common problem faced in case of single feedstock is the presence of inhibitory compounds and an inappropriate C/N ratio which is not optimum for the methanogens. In case of both OFMSW and SS, the C/N ratio was not in the optimal range (Table 1). Several researchers have reported that co-digestion synergistically improves the digestibility and consequentially biogas production and also reduces the production of ammonia which acts as an inhibitor for biogas generation (Mata-Alvarez et al., 2014). Pagés Díaz et al. (2011) co-digested diverse agro-industrial and slaughterhouse wastes and investigated their effects on the generation of biogas (Pagés Díaz et al., 2011). Synergetic phenomena were observed by the authors due to co-digestion, leading to a 43% increase in CH4 yield in comparison to mono-digestion. Callaghan et al. (2002) also reported an increased CH4 yield after codigesting fruit and vegetable wastes (FVW) with cattle manure in the ratio 1:1 (Callaghan et al., 2002). In fact, co-digestion has the potential to augment biogas generation from 25% to 400% as compared to the feedstocks digested individually (Cavinato et al., 2010; Shah et al., 2015). These reports clearly emphasize that co-digestion improves biomethane yield, both in terms of quantity and quality. 3.2 Diversity and abundance of bacteria, archaea, and fungi
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The complete sequenced dataset (bacteria, archaea, and fungi) was registered at the NCBI database under the SRA accession number PRJNA548424. Results revealed that, with ≥97% similarity levels, the sequence dataset had thoroughly sampled the microbial diversity. The rarefaction curve has been plotted to estimate the species richness of each sample and to detect the adequacy of sequencing (Fig. 2). As depicted, a sharp increase in the OTUs denotes that the most widespread species were sampled initially. Thereafter, the curve plateau indicates that only the rarest species is left to be sampled. The steep slope signifies that a larger fraction of the species diversity still remains to be discovered. Nevertheless, the number of OTUs were still increasing, suggesting more undetected diversity. For understanding the diversity of bacteria, archaea, and fungi during the AD process, OTUs-based alpha diversity analysis in terms of Shannon index and Simpson index was performed (Table 2). The diversity indices clearly revealed the variations in community composition of bacterial, archaeal and fungal groups during the course of anaerobic digestion. The Shannon and Simpson diversity indices point towards a comparative increase in the bacterial and fungal diversity during the course of AD. However, an initial decrease followed by increase in archaeal diversity was observed as the archaea are predominantly involved in the final stage of AD i.e., methanogenesis. 3.3 Taxonomic composition analysis 3.3.1 Bacteria
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The most abundant phylum in MSWM1 were Proteobacteria (46.36%) followed by Bacteroidetes (27.85%) and Firmicutes (11.14%). However, there was a shift in abundance with time and Bacteroidetes became the most abundant phylum in both MSWM2 (38.25%) as well as MSWM3 (36.15%). The most common bacterial genera in MSWM1 was Arcobacter (35.72%), Bacteroides (14.93%) and Fibrobacter (5.42%) (Fig. 3). However, relative abundance of Arcobacter was observed to decrease (from 35.72 in MSWM1 to 3% in MSWM3) temporally. Whereas, the relative abundance of an uncharacterized genus from the order Bacteroidales was observed to increase (from 4% in MSWM1 to 16.3% in MSWM3). Bacteroidetes, Firmicutes, and Proteobacteria are reported to be amongst the most common phyla accounting for greater than 70% abundance in AD of MSW and are mainly involved in fermentation of complex polysaccharides (Bareither et al., 2013; Cardinali-Rezende et al., 2009). 3.3.2
Archaea Phylum Euryarchaeota (94-99%) and class Methanomicrobia (63-80%) were found
to be the dominant groups in archaeal domain for all the samples. In MSWM1,the most abundant genera included Methanoculleus (30.4%), Candidatus Methanoplasma (27.29%) and Methanosarcina (9.9%) (Fig. 4). However, with time, in MSWM2, a shift in abundance was observed with Methanosarcina (62.7%) being the most abundant genera followed by Candidatus Methanoplasma (18.4%) and Methanoculleus (9.6%) (Fig. 4). In MSWM3, Methanosarcina (42.8%) was still the most abundant genera but Methanoculleus (20.3%) dominated over Candidatus Methanoplasma (16.9%) compared to MSWM2 (Fig. 3). The selective enrichment of various archaea groups were observed in the different stages during
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the course of AD. The increased abundance of Methanosarcina sp. with time can be explained by the fact that compared to other methanogens, it is quite robust and is able to tolerate high acetate concentrations as well as pH shock (De Vrieze et al., 2012). 3.3.3 Fungi The most dominant phylum was Ascomycota in case of MSWM1 (98.4%) and MSWM2 (95.92%) which was replaced by an unidentified phylum (69.89%) in case of MSWM3. Fungi are less characterized and the majority of them remain unidentified with no information still available in the database. During the early stages of anaerobic digestion i.e., MSWM1, genus Hanseniaspora (55.1%) and Pichia (17.1%) of order Saccharomycetales were dominant. However, a shift in abundance was observed with Aspergillus sp. (26.6%) and an unidentified genus (69.9%) being the most abundant in MSWM2 and MSWM3 respectively. Fisgativa et al. (2017) also reported Ascomycota to be the most abundant phylum playing a major role in AD of food waste (Fisgativa et al., 2017). 3.4 Metabolic pathway analysis based on OTUs The microbial metabolic pathway during the anaerobic digestion was constructed based on OTUs. 3.4.1 Bacteria There were around 1,207 to 1,325 bacterial OTUs observed in the samples during AD. Amongst these, 15 most abundant OTUs were functionally classified for their role in
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different stages of AD. The most abundant bacteria during the early stages of AD were BOTU1, 4 and 6. B-OTU1 showed 97.14% similarity to Arcobacter butzleri and its abundance was found to gradually decrease from 35.7% to 3% during the course of AD (Fig. 3). This can be explained by the fact that it predominantly participated in the initial stages of AD namely acidogenesis and acetogenesis (Supaphol et al., 2011). During the later stages, it was mainly involved in acetate oxidation leading to the formation of hydrogen and carbon-di-oxide (homoacetogenesis) thereby promoting the function of hydrogenotrophic methanogenesis (Rabii et al., 2019). B-OTU4 (94.75% similar to Bacteroides thetaiotaomicron) was the second most abundant OTU during the initial stages of AD (14.9%) with a gradual decrease (2.3%) in abundance during the later stages. This can be attributed to its role in hydrolysis by secreting different hydrolyzing enzymes (Wang et al., 2017). B-OTU6 showing 92.95% similarity to Fibrobacter succinogenes was functionally involved in the hydrolysis of polysaccharides particularly cellulose. Although this strain was reported for hydrolyzing different types of polysaccharides but preferably utilizes hydrolytic by-products of cellulose only as evident from earlier genomic studies (Suen et al., 2011). F. succinogenes was also involved in acido- and aceto-genesis producing succinate, formate, and acetate as major fermentative end products. The abundance of B-OTU2, 14 and 15 increased during the course of AD. BOTU14 (98.49% similar to uncultured Porphyromonadaceae bacterium), was found to increase in abundance from 4% to 16.3%. The bacterial strains belonging to Porphyromonadaceae family have been linked with high biogas production due to their involvement in degradation of carbohydrates, proteins, and peptides, and in the production
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of Volatile Fatty Acids by acidogenesis (Granada et al., 2018; Hahnke et al., 2015). It has also been reported that their inhibition results in the accumulation of ammonia and longchain fatty acids in the reactors (Regueiro et al., 2015). The abundance of B-OTU15 (87.54% similar to Sphaerochaeta globosa) increased from 1.1 to 8.7 % and probably played a role in syntrophic acetate-oxidation (Lee et al., 2013). Microbial syntrophy exists between bacteria and methanogens. This leads to the oxidation of acetate to H2 and CO2 by the syntrophic acetate-oxidizing (SAO) bacteria. These products are subsequently utilized through the hydrogenotrophic methanogenesis by the hydrogen-scavenging methanogens (Hattori, 2008). Acinetobacter- and Spirochaetales-related bacteria are also reported to be involved in anaerobic iron respiration and help in enhanced biomethanation (Baek et al., 2014). The abundance of B-OTU2, which showed 92.33 % similarity to Clostridium stercorarium, increased from 0.7 to 2%. This acidogenic bacterium converts glucose, fructose, cellobiose, xylose, xylan into formate, lactate, acetate, H2 and CO2 (Tian et al., 2017). In the later stages of AD, it was also found to be involved in acetate oxidation (Karakashev et al., 2006). The OTUs that showed almost constant abundance throughout AD were B-OTU5 and 7. B-OTU5 showed 94.65% similarity to Acinetobacter schindleri, a homoacetogen catabolizing acetogen to produce hydrogen and CO2 and promoting the hydrogenotrophic methanogenesis (Sigala et al., 2017). B-OTU 7 (88.18% similar to Paludibacter propionicigenes), was majorly involved in acidogenesis leading to the generation of propionic acid (Ziganshin et al., 2011). 3.4.2 Archaea
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The number of archaeal OTUs detected were between 114 and 125 in the samples. 15 of the most abundant OTUs were further functionally classified to understand which methanogenesis pathway is the most prevalent. The most abundant OTUs (30.4%) at the beginning of AD were obligate hydrogenotrophic methanogens namely A-OTU8 (95.6% similar to Methanoculleus marisnigri) and A-OTU9 (99.9 % similar to Methanoculleus bourgensis). This was followed by A-OTU2 (94.33% similar to Candidatus Methanoplasmatermitum), also an obligate hydrogenotrophic bacterium showing 27.3% abundance initially. However, during the course of AD, their abundance was found to decrease, which was in line with the decrease in the abundance of SAO bacteria, such as Arcobacter sp., Clostridium sp., Acinetobacter sp.and Sphaerochaeta sp. (from 39.1% to 13.9%). Interestingly, the abundance of Methanosarcina mazei (A-OTU1), Methanosarcina flavescens (A-OTU3), Methanosarcina lacustris (A-OTU6) and Methanosarcina siciliae (A-OTU7) increased to 62.7%. Though methanogens are known to be the most vulnerable of the different microbial consortia involved in AD, however, Methanosarcina sp. in comparison to other methanogens are often more tolerant against various stresses and are characterized by high growth rates compared to other methanogens (De Vrieze et al., 2012; Shin et al., 2011). Also, an added advantage is Methanosarcina can make use of all the three methanogenesis pathways and this makes them more tolerant to specific inhibitors of each pathway as compared to other methanogens (Thauer et al., 2008). Acetoclastic methanogens included the obligatory acetate utilizers Methanosaeta concilii (A-OTU12), Methanothrix soehngenii (A-OTU13) and facultative Methanosarcina
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sp. (A-OTU 1, 3, 6, 7) belonging to Methanosarcinales, a microbial group dominant throughout the AD process. The abundance of these acetoclastic microbes was initially low around 13.4% MSWM1 but increased to 63.2% in MSWM2 and further reduced to 43.2% in MSWM3 (Fig. 4). On the contrary, the cumulative population density of all the obligatory, as well as facultative hydrogenotrophic methanogens, was 48.8% in MSWM1, which gradually increased to 84.7% in MSWM2 and 75.7% in MSWM3 (Fig. 4). However, the abundance of SAO bacteria reduced during AD, probably because the facultative Methanosarcina sp. predominantly followed the acetoclastic pathway. However, this can be further validated through tracer studies. 3.4.3 Fungi The number of fungal OTUs observed was between 53 and 70 in the samples. During the initial stages in MSWM1, the most abundant fungal OTUs were F-OTU4 (99.73% similar to Hanseniaspora meyeri) and F-OTU-2, 14 (100% similar to Pichia sp.) representing 55.1% and 17.5% of the fungal population. With the progress of AD (MSWM2), the abundance of Aspergillus sp. (F-OTU1, 6, 9, 11, 13) and that of Candida sp. (F-OTU7, 8, 12) was found to increase to 26.6 % and 14.6% respectively. F-OTU3 (100 % similar to Debaryomyces hansenii) was found to be abundant initially, primarily due to its involvement in fermentation of lactose and metabolism of lactic acid, thereby limiting the acidification produced by lactic acid bacteria. The metabolism of acids is important to prevent strong acidification conditions during the course AD (Fisgativa et al., 2017). The genus Mucor (F-OTU15) found during the initial stages of AD played an important role in digestion of protein component of waste due to its high protease activity (Alves et al.,
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2005). Most of the detected fungi played a role in hydrolysis and were mainly responsible for speeding up the substrate decomposition and with time their abundance decreased and that of an unidentified fungus increased to 69.9% (Bengelsdorf et al., 2013). The dominance of unidentified fungus in the last stage of AD process implies their role in interspecies hydrogen transfer to methanogens for enhanced CH4 production (Dollhofer et al., 2015). This syntrophic relationship between the anaerobic fungi and methanogens maintain low hydrogen partial pressure essential for high biogas production (Kazda et al., 2014). Hydrogen transfer also affects the fungal catabolic pathways by shifting the production from oxidized end products (lactate, ethanol) to reduced ones (acetate, formate) which are preferred growth substrates for the methanogens (Cheng et al., 2009). 4. Conclusions The microbial diversity of sludge was found to be promising in terms of bacteria, archaea, and fungi providing an economical approach for increasing anaerobic digestion efficiency of MSW. However, this study also revealed that most of the fungi detected were unknown/ insufficiently characterized thereby indicating the necessity for further microand molecular biological research on the role of anaerobic fungi in biogas digesters. It is imperative to improve our understanding about interactions of fungi with the bacteria and archaea as well as with operating parameters to harness their potential in enhancing biogas production. Acknowledgments
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Department of Science and Technology (DST), Govt. of India is highly acknowledged for providing INSPIRE Faculty fellowship [DST/INSPIRE/04/2016/000362] to Ghosh P. Conflict of interest The authors declare no conflicts of interest. Supplementary data E-supplementary data of this work can be found in online version of the paper. References 1. Alves, M.H., De Campos-Takaki, G.M., Okada, K., Ferreira Pessoa, I.H., Milanez, A.I., 2005. Detection of extracellular protease in Mucor species. Rev. Iberoam. Micol. 22, 114–117. https://doi.org/10.1016/S1130-1406(05)70020-6 2. Baek, G., Kim, J., Lee, C., 2014. Influence of ferric oxyhydroxide addition on biomethanation of waste activated sludge in a continuous reactor. Bioresour. Technol. 166, 596–601. https://doi.org/10.1016/j.biortech.2014.05.052 3. Bareither, C.A., Wolfe, G.L., McMahon, K.D., Benson, C.H., 2013. Microbial diversity and dynamics during CH4 production from municipal solid waste. Waste Manag. 33, 1982–1992. https://doi.org/10.1016/j.wasman.2012.12.013 4. Bengelsdorf, F.R., Gerischer, U., Langer, S., Zak, M., Kazda, M., 2013. Stability of a biogas-producing bacterial, archaeal and fungal community degrading food residues. FEMS Microbiol. Ecol. 84, 201–212. https://doi.org/10.1111/15746941.12055
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Figures Fig. 1 (a) Cumulative biogas yield (mL/gVS) and (b) Cumulative CH4 yield (mL/gVS) for all substrates and co-digestion in batch biogas reactors at 35 °C. Data plotted is average of triplicate experiments. Fig. 2 Rarefaction curves to estimate richness of (a) Bacteria, (b) Archaea and (c) Fungi. Fig. 3 Comparative Analysis of the 3 samples at the Genus level for Bacteria. Fig. 4 Comparative Analysis of the 3 samples at the Genus level for Archaea. Fig. 5 Comparative Analysis of the 3 samples at the Genus level for Fungi. Fig. 6 Metabolic pathway constructed using the 15 most abundant bacterial, fungal and archaeal species. B represents Bacteria, A represents Archaea and F represents Fungi.
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Table 1 Substrates for Experimental BioCH4 Potential Substrates OFMSW Sludge Co-digestion 1 Co-digestion 2 Co-digestion 3 Co-digestion 4
OFMSW/Sludge (%wt) 20/80 40/60 60/40 80/20
Digester name AD1 AD2 AD3 AD4 AD5 AD6
Moisture (%) 85.92±0.2 93.2±0.3 89.6±0.1 90.4±0.2 87.8±0.3 85.2±0.1
TS (%) 14.08±0.14 6.8±0.04 8.3±0.3 9.5±0.4 10.9±0.11 12.1±0.23
VS (% TS) 73.4±0.25 84.11±0.35 82.56±0.15 79.82±0.41 77.68±0.07 75.54±0.22
27
C/N 32.5 17.1 21.6 23.5 27.1 29.4
Table 2 Observed OTUs, Shannon and Simpson diversity index results for each sample Samples Bacteria MSWM1 MSWM2 MSWM3 Archaea MSWM1 MSWM2 MSWM3 Fungi MSWM1 MSWM2 MSWM3
Shannon
Simpson
1,207 1,325 1,316
5.751 7.153 7.332
0.902 0.981 0.983
125 118 114
4.271 2.62 3.001
0.914 0.651 0.785
53 70 69
2.524 3.771 3.99
0.668 0.850 0.895
700 600 500 400 300 200 100 0
Cumulative Biogas Yield (mL/g VS)
(a)
Observed OTUs
0
5
10
15 20 Time (d)
25
AD1 (OFMSW)
AD2 (Sludge)
AD3 (20:80)
AD4 (40:60)
AD5 (60:40)
AD6 (80:20)
30
35
28
(b)
500 Cumulative Methane Yield (mL/g VS)
400 300 200 100 0 0
5
10
15 20 Time (d)
25
AD1 (OFMSW)
AD2 (Sludge)
AD3 (20:80)
AD4 (40:60)
AD5 (60:40)
AD6 (80:20)
30
35
Fig. 1
29
(a)
(b)
(c)
Fig. 2
30
Legend
Genus
MSWM116S (%)
MSWM216S (%)
MSWM316S (%)
Arcobacter
35.7
6.6
3
Bacteroides
14.9
9.7
2.3
Fibrobacter
5.4
0.1
0.6
Coprococcus
4.5
0.2
0.3
Unidentified (Family Porphyromonadaceae)
4
16.2
16.3
Massiliprevotella
1.9
2.4
2.1
Paludibacter
1.6
2.2
2.8
Acinetobacter
1.6
3.3
0.2
Treponema
1.2
0.3
1.2
Unidentified (Order Clostridiales)
1.1
3.2
3.1
Sphaerochaeta
1.1
5.1
8.7
Pseudomonas
1
0
0.4
Unidentified (Order Clostridiales)
0.8
1.8
0.6
Unidentified (Class Lentisphaeria)
0.8
0.6
2.1
Unidentified
0.8
2.5
2.7
Unidentified (Order Bacteroidales)
0.7
0.6
0.7
Clostridium
0.7
1.2
2
Unidentified (Family Pseudomonadaceae)
0.7
2.4
1
Unidentified (Family Bacteroidaceae)
0.6
0.5
0.7
Unidentified (Class Clostridia)
0.6
0.6
0.7
Unidentified (Family Acholeplasmataceae)
0.6
0.8
1.8
Unidentified (Family Anaerolinaceae)
0.5
1.6
2.1
Lactococcus
0.5
0
0
Unidentified (Family Enterobacteriaceae)
0.5
0
0
Unidentified (Family Ruminococcaceae)
0.3
2.3
2
Unidentified (Phylum Tenericutes)
0.3
0.6
3.6
Unidentified (Order Pedosphaerales)
0.3
0.1
0.8
Unidentified
0.3
0.7
1.9
Acholeplasma
0.2
0.7
0.2
31
Legend
Genus
MSWM116S (%)
MSWM216S (%)
MSWM316S (%)
Unidentified (Family Rikenellaceae)
0.1
1.7
2.7
Proteiniclasticum
0.1
1.4
0.9
Oscillibacter
0.1
1
0.6
Unidentified (Family Anaerobrancaceae)
0.1
0.2
0.8
Unidentified (Family Erysipelotrichaceae)
0.1
0.3
0.6
Unidentified (Family Sphingomonadaceae)
0.1
0.5
0.4
Unidentified (Family Helicobacteraceae)
0.1
0.3
1.1
Unidentified (Phylum Spirochaetes)
0.1
2.8
1.9
Unidentified (Phylum Verrucomicrobia)
0.1
0.9
0.7
Unidentified (Family Cloacamonaceae)
0.1
0
0.8
Unidentified (Order Bacteroidales)
0
0
0.7
Unidentified (Phylum Chlorobi)
0
1.5
1.6
Enterococcus
0
3.4
1.4
Fusobacterium
0
0.5
0
Unidentified (Family Alcaligenaceae)
0
1.5
0.4
Fig. 3
32
Legend
Genus Methanoculleus CandidatusMethanoplasma Methanosarcina Unidentified (Class Methanomicrobia) Methanocorpusculum Unidentified (Phylum Crenarchaeota) Methanosaeta Methanospirillum Unidentified (Order Methanomicrobiales) Unidentified (Family Methanomassiliicoccaceae) Methanobacterium Methanobrevibacter
MSWM1Arch (%) 30.4 27.3 9.9 7.2 6.5 6 3.5 2.4 2 1.8 0.9 0.9
MSWM2Arch (%) 9.6 18.4 62.7 1.7 1.1 1.8 0.5 1.2 1.1 0.2 0.7 0.3
MSWM3Arch (%) 20.3 16.9 42.8 0.2 14 1.4 0.4 0.7 0.5 0.3 0.9 0.2
Fig. 4
33
Legend
Genus Unidentified (Family Aspergillaceae) Aspergillus Candida Hanseniaspora Pichia Unidentified Unidentified (Order Saccharomycetales) Unidentified (Order Saccharomycetales) Unidentified (Phylum Ascomycota) Debaryomyces Torulaspora Mucor
MSWM1ITS (%) 13 2.9 5.8 55.1 17.5 0.5 2.4 0.1 0 0.8 0.5 0.8
MSWM2ITS (%) 34 26.6 14.6 12.1 3.6 3.1 1.6 1 0.5 0.3 0 0
MSWM3ITS (%) 11.1 11.9 2.2 2.1 0.7 69.9 0 0.1 0.1 0.2 0 0
Fig. 5
34
Proteins
Carbohydrates
Sugars Acidogenesesis
B-OTU 4 F-OTU 2, 3, 4, 5, 7, 8, 10, 12, 14
B-OTU 4, 7, 10, 14 F-OTU 2, 3, 4, 5, 7, 8, 10, 12, 14, 15
B-OTU 3, 4, 6, 7, 11, 12, 13,14 F-OTU 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13, 14
Hydrolysis
Lipids
Amino acids
B-OTU 1, 2, 3, 6, 7, 8, 9, 11, 12, 13, 14, 15 F-OTU 2, 3, 4, 5, 7, 8, 10, 12, 14
Long chain fatty acids (LCFAs)
B-OTU 10, 14
Volatile Fatty Acids (VFAs) other than acetic acid, Organic acids, Ethanol
Acetogenesis
B-OTU 1, 2, 6, 7, 11, 15
B-OTU 2, 4, 15
β-oxidation A-OTU 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15
B-OTU 2
Homoacetogenesis
Methanol, Methylamines, Methylsulphides Methylotrophic methanogenesis A-OTU 1, 3, 6, 7
Acetic acid Acetoclastic methanogenesis
B-OTU 1, 2, 5, 15 A-OTU 1, 3, 6, 7, 12, 13
CH4 + CO2
H2 + CO2 Hydrogenotrophic methanogenesis
A-OTU 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15
Fig. 6
35
Organic MSW + Sewage sludge
Anaerobic co-digestion
Metagenomics for studying the Anaerobic Microbiome and metabolic pathway analysis
36
Declaration of interests
√ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
None
37
Highlights
Co-digestion of organic fraction of MSW with sewage sludge was studied.
Enhanced biogas production with high methane content was obtained.
Temporal dynamics of microbial communities were studied by metagenomics.
OTU based elucidation of metabolic pathway involved in anaerobic digestion.
38
S0960-8524(19)31505-6 https://doi.org/10.1016/j.biortech.2019.122275 BITE 122275
To appear in:
Bioresource Technology
Received Date: Revised Date: Accepted Date:
18 September 2019 9 October 2019 15 October 2019
Please cite this article as: Ghosh, P., Kumar, M., Kapoor, R., Kumar, S.S., Singh, L., Vijay, V., Kumar Vijay, V., Kumar, V., Shekhar Thakur, I., Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis, Bioresource Technology (2019), doi: https://doi.org/10.1016/ j.biortech.2019.122275
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Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis Pooja Ghosha1*, Madan Kumara1, Rimika Kapoora, Smita S Kumara, Lakhveer Singhb, Vandit Vijaya, Virendra Kumar Vijaya, Vivek Kumara , Indu Shekhar Thakurc aCentre
for Rural Development and Technology, Indian Institute of Technology, New
Delhi-110016, India bFaculty
of Civil and Environmental Engineering, University Malaysia Pahang, Kuantan-
26300, Malaysia cSchool 1
of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India
Pooja Ghosh and Madan Kumar have equal contribution
* Corresponding author
Corresponding Author details: Dr. Pooja Ghosh DST-INSPIRE Faculty Centre for Rural Development and Technology Indian Institute of Technology, Delhi India Email id: [email protected]
Enhanced biogas production from municipal solid waste via co-digestion with sewage sludge and metabolic pathway analysis 1
Abstract The present study intends to evaluate the potential of co-digestion for utilizing Organic fraction of Municipal Solid Waste (OFMSW) and sewage sludge (SS) for enhanced biogas production. Metagenomic analysis was performed to identify the dominant bacteria, archaea and fungi, changes in their communities with time and their functional roles during the course of anaerobic digestion (AD). The cumulative biogas yield of 586.2 mL biogas/gVS with the highest methane concentration of 69.5% was observed under an optimum ratio of OFMSW:SS (40:60 w/w). Bacteria and fungi were found to be majorly involved in hydrolysis and initial stages of AD. Probably, the most common archaea Methanosarsina sp. primarily followed the acetoclastic pathway. The hydrogenotrophic pathway was less followed as indicated by the reduction in abundance of syntrophic acetate oxidizers. An adequate understanding of microbial communities is important to manipulate and inoculate the specific microbial consortia to maximize CH4 production through AD. Keywords: Municipal solid waste; Anaerobic digestion; Metagenomics; Bacteria; Archaea; Fungi 1. Introduction Municipal solid waste (MSW) management and energy insecurity are among the foremost challenges faced by India nowadays. About 1.5 lakh tonnes of MSW is generated per day in Urban India, of which only 70–75% gets collected and 20–25% is treated (Negi et al., 2018). Also, the collected unsegregated waste is majorly dumped in the unengineered landfills leading to anthropogenic methane (CH4) emissions and groundwater contamination (Ghosh et al., 2015). Most of the developing countries are presently not in a 2
situation to meet the Sustainable Development Goal of Affordable and Clean Energy (SDG 7) (Sahota et al., 2018).Waste derived bioenergy will not only help in providing a cleaner renewable alternate energy but can also help in tapping the huge potential of waste along with solving the problem of waste management (Singh and Kumar, 2019). Biogas is a promising renewable energy produced by anaerobic digestion (AD) of feedstocks. It majorly comprises of CH4 and CO2 ranging from 40 to 60% and 35 to 55%, respectively along with traces of hydrogen sulfide (H2S), moisture, and other contaminants. AD consists of four stages-hydrolysis, acidogenesis, acetogenesis and methanogenesis (Kumar et al., 2019a). Methanogenesis step which is carried out by a specialized microbial group of archaea is highly sensitive to variations in temperature, pH, C/N ratio and maintaining the optimum conditions for the AD process is a key for enhancing biogas yield (Mao et al., 2015). A common problem usually encountered in biogas production is low biogas yield due to the use of single feedstock which may be either recalcitrant to digestion or have a low or high C/N ratio. Moreover, most of the studies involving single-stage AD have reported low CH4 content in biogas. These limitations can be resolved by co-digesting diverse feedstocks and optimizing the C/N ratio, total solids content (TS) and volatile solids (VS) content. This also helps in reducing ammonia production during AD, thus reducing the chances of inhibition caused by ammonia. This also increases the digestibility of the feedstock, thereby enhancing the production of biogas (Mata-Alvarez et al., 2014). For optimizing the C/N ratio and to seed anaerobic microbes for AD, co-digesting MSW along with sewage sludge (SS) from wastewater treatment plant containing mixed microbial consortia (MMC) is a promising solution. Utilizing sewage sludge from
3
wastewater treatment plant for AD also helps in reducing the environmental burden associated with sludge disposal (Kumar et al., 2018). For maximizing the biogas yield in AD, it is imperative to understand the microbial consortia of archaea, bacteria, and fungi in terms of taxonomy, diversity and metabolic pathways. With the advancements in sequencing methods, studies on metagenomics of micro-organisms involved in AD have gained momentum. Several reports on metagenomics are available which study the dominant microbes; monitor their community shift and methanogenesis pathway. These are important for understanding the synergistic relation between micro-organisms and digester performance. Nevertheless, in developing countries like India, the microbial identities and dynamics in AD are frequently ignored and the process is considered as a ‘black-box’. However, to improve process economic viability of AD and to maximize CH4 yield, it is important to analyze the metabolic pathway so as to engineer the environmental parameters to augment the development and action of key genera. Besides bacteria, eukaryotic microorganisms are also important participants in the AD pathway. However, very less research has been carried out to understand the role of fungi in production of biogas. Considering the presence of only limited and diffused knowledge on microbial diversity particularly that of fungi in mediating production of biogas from MSW and SS, present work deals with using a high-throughput DNA sequencing technique for taxonomic and functional characterization of microbial consortia present in the SS involved in production of biogas with a high percentage of CH4 from MSW (majorly unutilized organic wastes produced in large quantities in India). This study will help in gaining a fundamental
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knowledge regarding the SS microbial community composition and the mechanisms followed by them for production of biogas with high CH4 concentration. 2. Materials and methods 2.1 Sampling and sampling sites Approximately 12,000 tonnes of MSW is daily generated in Delhi, majority of which is dumped in an unsegregated manner in three non-engineered landfill sites namely Okhla, Bhalswa, Ghazipur and an engineered landfill site at Narela-Bawana (Ghosh et al., 2019; Kumar et al., 2019b). Freshly dumped MSW was collected from 5 sites within the non-engineered dump sites - Ghazipur, India in October 2018. The waste was later segregated manually in the lab into organic and inorganic fractions. The organic fraction of MSW (OFMSW) was homogenized by grinding to smaller sized particles (<80 mm). The sludge samples were also collected in October 2018 from Sewage Treatment Plant located at Vasant Kunj, New Delhi, India. The collected sludge samples were concentrated by gravity settling overnight and stored at 4°C. 2.2 Characterization of MSW, sludge and co-digested substrates Proximate analysis (moisture, total solids (TS), volatile solids (VS)) of the OFMSW and sewage sludge (SS) was done according to American Public Health Association method 2540 G (APHA, 1998). pH was measured with the help of a pH meter (D-74 Horiba Scientific). For ultimate analysis, C, H and N contents of OFMSW, sludge and the co-digested substrates was carried out with the help of CHN analyzer (Elemental Vario el Cube analyzer, Germany). All the experiments were conducted in triplicates. 5
2.3 Experimental design and measurement of biogas production and composition The experiments were conducted using the organic fraction of MSW after proper segregation from the mixed MSW. For studying the optimum concentration of OFMSW and SS for maximal biogas generation, 6 batch experiments in triplicates (100% OFMSW, 100% SS and OFMSW: SS in different ratios of 20:80, 40:60, 60:40 and 80:20 w/w) were carried out in 3 L anaerobic digesters. The digesters are named as AD1, AD2, AD3, AD4, AD5 and AD6 for 100% OFMSW, 100% SS, 20:80, 40:60, 60:40 and 80:20 w/w OFMSW: SS respectively. The pH of the MSW was slightly acidic (~6.5) and SS was alkaline (~8). The pH in all the digesters was set to 7.2 for appropriate AD. The biogas generation was measured by water displacement method and analysis of the gaseous composition using the Geotech BIOGAS 5000 Analyser. 2.4 Metagenomic analysis 2.4.1 Genomic DNA extraction For the metagenomic studies, 2 mL samples were collected from the anaerobic digester showing maximum biogas production potential at different intervals of time (0, 15 and 30 days). The samples were named as MSWM1, MSWM2 and MSWM3 corresponding to 0 day, 15 days and 30 days collected samples respectively. Genomic DNA from the sample was extracted with the help of DNA Isolation Kit (Power Soil MO BIO Laboratories). The DNA concentration was measured with the help of NanoDrop ND-1000 spectrophotometer by determining A260/A280. DNA purity was tested by agarose gel electrophoresis (Kumar et al., 2017).
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2.4.2 DNA sequencing and bioinformatics The QC passed samples were processed for the first generation of amplicon followed by NGS library preparation using Index Kit (Nextera XT Illumina Inc.). Sequencing of libraries was carried out on MiSeq using 2x300 bp chemistry. For each of the sample sequence, around 80 Mb of data for bacteria, ~116-175 Mb for archaea and ~5568 Mb for fungi was generated. This corresponded to ~1,70,000 reads, 2,29,000 -3,47,000 reads and 1,08,000-1,24,000 reads for bacteria, archaea and fungi respectively. To process the data of three bacterial 16S samples, three archaeal 16S samples, and three 18S fungal samples, QIIME software was used and the following steps were performed: (1) The raw reads were processed through Trimmomatic v0.38 for removal of ambiguous reads, adapter, and low-quality sequences to obtain high quality reads. (2) The Paired-End data was stitched into single-end reads. (3) The Operational Taxonomic Units (OTUs) were chosen on the basis of similarity of sequences within the reads. Sequences having a similarity of ≥97% were allocated to the same OTUs. (4) OTU was assigned to a taxonomic identity using reference databases. The representative sequence for 16S from each OTU against the Greengenes database (version 13_8) and for 18S against the UNITE database (version 7.2) was selected. (5) Diversity metrics for each sample was calculated and compared the types of communities, using taxonomic assignments. Sequences of bacteria, archaea, and fungi were submitted to the NCBI database. 3. Results and discussion 3.1 Anaerobic digestion of OFMSW and sludge
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The MSW sampled from the landfill contained around 65% of organic waste (food waste, vegetable market waste, yard clippings); ~25 % of paper waste, plastic, metal, textile and 10% inert materials. After manual segregation of waste, the organic fractions of MSW and sewage sludge were characterized (Table 1). As evident from Table 1, the OFMSW has high C/N ratio of 32.5, whereas, the SS has low C/N ratio of 17.1 and both the feedstock individually are not suitable for AD, since an optimal C/N ratio of 20 to 30 is essential. Thus, OFMSW and SS were co-digested in different ratios to optimize the suitable mixing ratio for efficient digestion and enhanced biogas and CH4 yields. Fig. 1 illustrates the variations in the cumulative yields of biogas (mL biogas/gVS) and methane (mL CH4/g VS) with time for OFMSW, SS and their co-digestion (20:80, 40:60, 60:40, 80:20 w/w of OFMSW:SS). The mono-digested sample of OFMSW (AD1) showed the least cumulative biogas (220.5 mL biogas/gVS) and CH4 yield (24.03 mL CH4/gVS). In AD4, the highest cumulative biogas and methane yield of 586.2mL biogas/gVS and 377 mL CH4/gVS respectively were observed. There was 2.66 and 15 fold increase in biogas and methane yield respectively with respect to mono-digested OFMSW. On 22nd day of AD, a high concentration of CH4 (~69.5%) was also observed in AD4. It has been reported in literature that under optimal conditions, OFMSW can yield around 300 to 500 mL of CH4/g VS, average yield being 367 mL CH4/gVS (Rubia et al., 2018). The result obtained in the present study is in accordance with the reported literature. The performance of AD is dependent on TS and VS content of the feedstock. Dhar et al. (2016) stated that the optimum TS concentration of the feedstock should be in the range of 4-10% , and any deviation in the TS content beyond this range leads to a decrease
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in gas production (Dhar et al., 2016). This is also evident from the results obtained in the present study. It can be observed in Table 1 that at 40:60 ratio (9.5% TS), maximum gas yield was observed and beyond this, in 60:40 (10.9% TS) and 80:20 (12.1% TS) codigested samples, there was a drastic reduction in gas yield (Fig. 1). A common problem faced in case of single feedstock is the presence of inhibitory compounds and an inappropriate C/N ratio which is not optimum for the methanogens. In case of both OFMSW and SS, the C/N ratio was not in the optimal range (Table 1). Several researchers have reported that co-digestion synergistically improves the digestibility and consequentially biogas production and also reduces the production of ammonia which acts as an inhibitor for biogas generation (Mata-Alvarez et al., 2014). Pagés Díaz et al. (2011) co-digested diverse agro-industrial and slaughterhouse wastes and investigated their effects on the generation of biogas (Pagés Díaz et al., 2011). Synergetic phenomena were observed by the authors due to co-digestion, leading to a 43% increase in CH4 yield in comparison to mono-digestion. Callaghan et al. (2002) also reported an increased CH4 yield after codigesting fruit and vegetable wastes (FVW) with cattle manure in the ratio 1:1 (Callaghan et al., 2002). In fact, co-digestion has the potential to augment biogas generation from 25% to 400% as compared to the feedstocks digested individually (Cavinato et al., 2010; Shah et al., 2015). These reports clearly emphasize that co-digestion improves biomethane yield, both in terms of quantity and quality. 3.2 Diversity and abundance of bacteria, archaea, and fungi
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The complete sequenced dataset (bacteria, archaea, and fungi) was registered at the NCBI database under the SRA accession number PRJNA548424. Results revealed that, with ≥97% similarity levels, the sequence dataset had thoroughly sampled the microbial diversity. The rarefaction curve has been plotted to estimate the species richness of each sample and to detect the adequacy of sequencing (Fig. 2). As depicted, a sharp increase in the OTUs denotes that the most widespread species were sampled initially. Thereafter, the curve plateau indicates that only the rarest species is left to be sampled. The steep slope signifies that a larger fraction of the species diversity still remains to be discovered. Nevertheless, the number of OTUs were still increasing, suggesting more undetected diversity. For understanding the diversity of bacteria, archaea, and fungi during the AD process, OTUs-based alpha diversity analysis in terms of Shannon index and Simpson index was performed (Table 2). The diversity indices clearly revealed the variations in community composition of bacterial, archaeal and fungal groups during the course of anaerobic digestion. The Shannon and Simpson diversity indices point towards a comparative increase in the bacterial and fungal diversity during the course of AD. However, an initial decrease followed by increase in archaeal diversity was observed as the archaea are predominantly involved in the final stage of AD i.e., methanogenesis. 3.3 Taxonomic composition analysis 3.3.1 Bacteria
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The most abundant phylum in MSWM1 were Proteobacteria (46.36%) followed by Bacteroidetes (27.85%) and Firmicutes (11.14%). However, there was a shift in abundance with time and Bacteroidetes became the most abundant phylum in both MSWM2 (38.25%) as well as MSWM3 (36.15%). The most common bacterial genera in MSWM1 was Arcobacter (35.72%), Bacteroides (14.93%) and Fibrobacter (5.42%) (Fig. 3). However, relative abundance of Arcobacter was observed to decrease (from 35.72 in MSWM1 to 3% in MSWM3) temporally. Whereas, the relative abundance of an uncharacterized genus from the order Bacteroidales was observed to increase (from 4% in MSWM1 to 16.3% in MSWM3). Bacteroidetes, Firmicutes, and Proteobacteria are reported to be amongst the most common phyla accounting for greater than 70% abundance in AD of MSW and are mainly involved in fermentation of complex polysaccharides (Bareither et al., 2013; Cardinali-Rezende et al., 2009). 3.3.2
Archaea Phylum Euryarchaeota (94-99%) and class Methanomicrobia (63-80%) were found
to be the dominant groups in archaeal domain for all the samples. In MSWM1,the most abundant genera included Methanoculleus (30.4%), Candidatus Methanoplasma (27.29%) and Methanosarcina (9.9%) (Fig. 4). However, with time, in MSWM2, a shift in abundance was observed with Methanosarcina (62.7%) being the most abundant genera followed by Candidatus Methanoplasma (18.4%) and Methanoculleus (9.6%) (Fig. 4). In MSWM3, Methanosarcina (42.8%) was still the most abundant genera but Methanoculleus (20.3%) dominated over Candidatus Methanoplasma (16.9%) compared to MSWM2 (Fig. 3). The selective enrichment of various archaea groups were observed in the different stages during
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the course of AD. The increased abundance of Methanosarcina sp. with time can be explained by the fact that compared to other methanogens, it is quite robust and is able to tolerate high acetate concentrations as well as pH shock (De Vrieze et al., 2012). 3.3.3 Fungi The most dominant phylum was Ascomycota in case of MSWM1 (98.4%) and MSWM2 (95.92%) which was replaced by an unidentified phylum (69.89%) in case of MSWM3. Fungi are less characterized and the majority of them remain unidentified with no information still available in the database. During the early stages of anaerobic digestion i.e., MSWM1, genus Hanseniaspora (55.1%) and Pichia (17.1%) of order Saccharomycetales were dominant. However, a shift in abundance was observed with Aspergillus sp. (26.6%) and an unidentified genus (69.9%) being the most abundant in MSWM2 and MSWM3 respectively. Fisgativa et al. (2017) also reported Ascomycota to be the most abundant phylum playing a major role in AD of food waste (Fisgativa et al., 2017). 3.4 Metabolic pathway analysis based on OTUs The microbial metabolic pathway during the anaerobic digestion was constructed based on OTUs. 3.4.1 Bacteria There were around 1,207 to 1,325 bacterial OTUs observed in the samples during AD. Amongst these, 15 most abundant OTUs were functionally classified for their role in
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different stages of AD. The most abundant bacteria during the early stages of AD were BOTU1, 4 and 6. B-OTU1 showed 97.14% similarity to Arcobacter butzleri and its abundance was found to gradually decrease from 35.7% to 3% during the course of AD (Fig. 3). This can be explained by the fact that it predominantly participated in the initial stages of AD namely acidogenesis and acetogenesis (Supaphol et al., 2011). During the later stages, it was mainly involved in acetate oxidation leading to the formation of hydrogen and carbon-di-oxide (homoacetogenesis) thereby promoting the function of hydrogenotrophic methanogenesis (Rabii et al., 2019). B-OTU4 (94.75% similar to Bacteroides thetaiotaomicron) was the second most abundant OTU during the initial stages of AD (14.9%) with a gradual decrease (2.3%) in abundance during the later stages. This can be attributed to its role in hydrolysis by secreting different hydrolyzing enzymes (Wang et al., 2017). B-OTU6 showing 92.95% similarity to Fibrobacter succinogenes was functionally involved in the hydrolysis of polysaccharides particularly cellulose. Although this strain was reported for hydrolyzing different types of polysaccharides but preferably utilizes hydrolytic by-products of cellulose only as evident from earlier genomic studies (Suen et al., 2011). F. succinogenes was also involved in acido- and aceto-genesis producing succinate, formate, and acetate as major fermentative end products. The abundance of B-OTU2, 14 and 15 increased during the course of AD. BOTU14 (98.49% similar to uncultured Porphyromonadaceae bacterium), was found to increase in abundance from 4% to 16.3%. The bacterial strains belonging to Porphyromonadaceae family have been linked with high biogas production due to their involvement in degradation of carbohydrates, proteins, and peptides, and in the production
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of Volatile Fatty Acids by acidogenesis (Granada et al., 2018; Hahnke et al., 2015). It has also been reported that their inhibition results in the accumulation of ammonia and longchain fatty acids in the reactors (Regueiro et al., 2015). The abundance of B-OTU15 (87.54% similar to Sphaerochaeta globosa) increased from 1.1 to 8.7 % and probably played a role in syntrophic acetate-oxidation (Lee et al., 2013). Microbial syntrophy exists between bacteria and methanogens. This leads to the oxidation of acetate to H2 and CO2 by the syntrophic acetate-oxidizing (SAO) bacteria. These products are subsequently utilized through the hydrogenotrophic methanogenesis by the hydrogen-scavenging methanogens (Hattori, 2008). Acinetobacter- and Spirochaetales-related bacteria are also reported to be involved in anaerobic iron respiration and help in enhanced biomethanation (Baek et al., 2014). The abundance of B-OTU2, which showed 92.33 % similarity to Clostridium stercorarium, increased from 0.7 to 2%. This acidogenic bacterium converts glucose, fructose, cellobiose, xylose, xylan into formate, lactate, acetate, H2 and CO2 (Tian et al., 2017). In the later stages of AD, it was also found to be involved in acetate oxidation (Karakashev et al., 2006). The OTUs that showed almost constant abundance throughout AD were B-OTU5 and 7. B-OTU5 showed 94.65% similarity to Acinetobacter schindleri, a homoacetogen catabolizing acetogen to produce hydrogen and CO2 and promoting the hydrogenotrophic methanogenesis (Sigala et al., 2017). B-OTU 7 (88.18% similar to Paludibacter propionicigenes), was majorly involved in acidogenesis leading to the generation of propionic acid (Ziganshin et al., 2011). 3.4.2 Archaea
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The number of archaeal OTUs detected were between 114 and 125 in the samples. 15 of the most abundant OTUs were further functionally classified to understand which methanogenesis pathway is the most prevalent. The most abundant OTUs (30.4%) at the beginning of AD were obligate hydrogenotrophic methanogens namely A-OTU8 (95.6% similar to Methanoculleus marisnigri) and A-OTU9 (99.9 % similar to Methanoculleus bourgensis). This was followed by A-OTU2 (94.33% similar to Candidatus Methanoplasmatermitum), also an obligate hydrogenotrophic bacterium showing 27.3% abundance initially. However, during the course of AD, their abundance was found to decrease, which was in line with the decrease in the abundance of SAO bacteria, such as Arcobacter sp., Clostridium sp., Acinetobacter sp.and Sphaerochaeta sp. (from 39.1% to 13.9%). Interestingly, the abundance of Methanosarcina mazei (A-OTU1), Methanosarcina flavescens (A-OTU3), Methanosarcina lacustris (A-OTU6) and Methanosarcina siciliae (A-OTU7) increased to 62.7%. Though methanogens are known to be the most vulnerable of the different microbial consortia involved in AD, however, Methanosarcina sp. in comparison to other methanogens are often more tolerant against various stresses and are characterized by high growth rates compared to other methanogens (De Vrieze et al., 2012; Shin et al., 2011). Also, an added advantage is Methanosarcina can make use of all the three methanogenesis pathways and this makes them more tolerant to specific inhibitors of each pathway as compared to other methanogens (Thauer et al., 2008). Acetoclastic methanogens included the obligatory acetate utilizers Methanosaeta concilii (A-OTU12), Methanothrix soehngenii (A-OTU13) and facultative Methanosarcina
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sp. (A-OTU 1, 3, 6, 7) belonging to Methanosarcinales, a microbial group dominant throughout the AD process. The abundance of these acetoclastic microbes was initially low around 13.4% MSWM1 but increased to 63.2% in MSWM2 and further reduced to 43.2% in MSWM3 (Fig. 4). On the contrary, the cumulative population density of all the obligatory, as well as facultative hydrogenotrophic methanogens, was 48.8% in MSWM1, which gradually increased to 84.7% in MSWM2 and 75.7% in MSWM3 (Fig. 4). However, the abundance of SAO bacteria reduced during AD, probably because the facultative Methanosarcina sp. predominantly followed the acetoclastic pathway. However, this can be further validated through tracer studies. 3.4.3 Fungi The number of fungal OTUs observed was between 53 and 70 in the samples. During the initial stages in MSWM1, the most abundant fungal OTUs were F-OTU4 (99.73% similar to Hanseniaspora meyeri) and F-OTU-2, 14 (100% similar to Pichia sp.) representing 55.1% and 17.5% of the fungal population. With the progress of AD (MSWM2), the abundance of Aspergillus sp. (F-OTU1, 6, 9, 11, 13) and that of Candida sp. (F-OTU7, 8, 12) was found to increase to 26.6 % and 14.6% respectively. F-OTU3 (100 % similar to Debaryomyces hansenii) was found to be abundant initially, primarily due to its involvement in fermentation of lactose and metabolism of lactic acid, thereby limiting the acidification produced by lactic acid bacteria. The metabolism of acids is important to prevent strong acidification conditions during the course AD (Fisgativa et al., 2017). The genus Mucor (F-OTU15) found during the initial stages of AD played an important role in digestion of protein component of waste due to its high protease activity (Alves et al.,
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2005). Most of the detected fungi played a role in hydrolysis and were mainly responsible for speeding up the substrate decomposition and with time their abundance decreased and that of an unidentified fungus increased to 69.9% (Bengelsdorf et al., 2013). The dominance of unidentified fungus in the last stage of AD process implies their role in interspecies hydrogen transfer to methanogens for enhanced CH4 production (Dollhofer et al., 2015). This syntrophic relationship between the anaerobic fungi and methanogens maintain low hydrogen partial pressure essential for high biogas production (Kazda et al., 2014). Hydrogen transfer also affects the fungal catabolic pathways by shifting the production from oxidized end products (lactate, ethanol) to reduced ones (acetate, formate) which are preferred growth substrates for the methanogens (Cheng et al., 2009). 4. Conclusions The microbial diversity of sludge was found to be promising in terms of bacteria, archaea, and fungi providing an economical approach for increasing anaerobic digestion efficiency of MSW. However, this study also revealed that most of the fungi detected were unknown/ insufficiently characterized thereby indicating the necessity for further microand molecular biological research on the role of anaerobic fungi in biogas digesters. It is imperative to improve our understanding about interactions of fungi with the bacteria and archaea as well as with operating parameters to harness their potential in enhancing biogas production. Acknowledgments
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Department of Science and Technology (DST), Govt. of India is highly acknowledged for providing INSPIRE Faculty fellowship [DST/INSPIRE/04/2016/000362] to Ghosh P. Conflict of interest The authors declare no conflicts of interest. Supplementary data E-supplementary data of this work can be found in online version of the paper. References 1. Alves, M.H., De Campos-Takaki, G.M., Okada, K., Ferreira Pessoa, I.H., Milanez, A.I., 2005. Detection of extracellular protease in Mucor species. Rev. Iberoam. Micol. 22, 114–117. https://doi.org/10.1016/S1130-1406(05)70020-6 2. Baek, G., Kim, J., Lee, C., 2014. Influence of ferric oxyhydroxide addition on biomethanation of waste activated sludge in a continuous reactor. Bioresour. Technol. 166, 596–601. https://doi.org/10.1016/j.biortech.2014.05.052 3. Bareither, C.A., Wolfe, G.L., McMahon, K.D., Benson, C.H., 2013. Microbial diversity and dynamics during CH4 production from municipal solid waste. Waste Manag. 33, 1982–1992. https://doi.org/10.1016/j.wasman.2012.12.013 4. Bengelsdorf, F.R., Gerischer, U., Langer, S., Zak, M., Kazda, M., 2013. Stability of a biogas-producing bacterial, archaeal and fungal community degrading food residues. FEMS Microbiol. Ecol. 84, 201–212. https://doi.org/10.1111/15746941.12055
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Figures Fig. 1 (a) Cumulative biogas yield (mL/gVS) and (b) Cumulative CH4 yield (mL/gVS) for all substrates and co-digestion in batch biogas reactors at 35 °C. Data plotted is average of triplicate experiments. Fig. 2 Rarefaction curves to estimate richness of (a) Bacteria, (b) Archaea and (c) Fungi. Fig. 3 Comparative Analysis of the 3 samples at the Genus level for Bacteria. Fig. 4 Comparative Analysis of the 3 samples at the Genus level for Archaea. Fig. 5 Comparative Analysis of the 3 samples at the Genus level for Fungi. Fig. 6 Metabolic pathway constructed using the 15 most abundant bacterial, fungal and archaeal species. B represents Bacteria, A represents Archaea and F represents Fungi.
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Table 1 Substrates for Experimental BioCH4 Potential Substrates OFMSW Sludge Co-digestion 1 Co-digestion 2 Co-digestion 3 Co-digestion 4
OFMSW/Sludge (%wt) 20/80 40/60 60/40 80/20
Digester name AD1 AD2 AD3 AD4 AD5 AD6
Moisture (%) 85.92±0.2 93.2±0.3 89.6±0.1 90.4±0.2 87.8±0.3 85.2±0.1
TS (%) 14.08±0.14 6.8±0.04 8.3±0.3 9.5±0.4 10.9±0.11 12.1±0.23
VS (% TS) 73.4±0.25 84.11±0.35 82.56±0.15 79.82±0.41 77.68±0.07 75.54±0.22
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C/N 32.5 17.1 21.6 23.5 27.1 29.4
Table 2 Observed OTUs, Shannon and Simpson diversity index results for each sample Samples Bacteria MSWM1 MSWM2 MSWM3 Archaea MSWM1 MSWM2 MSWM3 Fungi MSWM1 MSWM2 MSWM3
Shannon
Simpson
1,207 1,325 1,316
5.751 7.153 7.332
0.902 0.981 0.983
125 118 114
4.271 2.62 3.001
0.914 0.651 0.785
53 70 69
2.524 3.771 3.99
0.668 0.850 0.895
700 600 500 400 300 200 100 0
Cumulative Biogas Yield (mL/g VS)
(a)
Observed OTUs
0
5
10
15 20 Time (d)
25
AD1 (OFMSW)
AD2 (Sludge)
AD3 (20:80)
AD4 (40:60)
AD5 (60:40)
AD6 (80:20)
30
35
28
(b)
500 Cumulative Methane Yield (mL/g VS)
400 300 200 100 0 0
5
10
15 20 Time (d)
25
AD1 (OFMSW)
AD2 (Sludge)
AD3 (20:80)
AD4 (40:60)
AD5 (60:40)
AD6 (80:20)
30
35
Fig. 1
29
(a)
(b)
(c)
Fig. 2
30
Legend
Genus
MSWM116S (%)
MSWM216S (%)
MSWM316S (%)
Arcobacter
35.7
6.6
3
Bacteroides
14.9
9.7
2.3
Fibrobacter
5.4
0.1
0.6
Coprococcus
4.5
0.2
0.3
Unidentified (Family Porphyromonadaceae)
4
16.2
16.3
Massiliprevotella
1.9
2.4
2.1
Paludibacter
1.6
2.2
2.8
Acinetobacter
1.6
3.3
0.2
Treponema
1.2
0.3
1.2
Unidentified (Order Clostridiales)
1.1
3.2
3.1
Sphaerochaeta
1.1
5.1
8.7
Pseudomonas
1
0
0.4
Unidentified (Order Clostridiales)
0.8
1.8
0.6
Unidentified (Class Lentisphaeria)
0.8
0.6
2.1
Unidentified
0.8
2.5
2.7
Unidentified (Order Bacteroidales)
0.7
0.6
0.7
Clostridium
0.7
1.2
2
Unidentified (Family Pseudomonadaceae)
0.7
2.4
1
Unidentified (Family Bacteroidaceae)
0.6
0.5
0.7
Unidentified (Class Clostridia)
0.6
0.6
0.7
Unidentified (Family Acholeplasmataceae)
0.6
0.8
1.8
Unidentified (Family Anaerolinaceae)
0.5
1.6
2.1
Lactococcus
0.5
0
0
Unidentified (Family Enterobacteriaceae)
0.5
0
0
Unidentified (Family Ruminococcaceae)
0.3
2.3
2
Unidentified (Phylum Tenericutes)
0.3
0.6
3.6
Unidentified (Order Pedosphaerales)
0.3
0.1
0.8
Unidentified
0.3
0.7
1.9
Acholeplasma
0.2
0.7
0.2
31
Legend
Genus
MSWM116S (%)
MSWM216S (%)
MSWM316S (%)
Unidentified (Family Rikenellaceae)
0.1
1.7
2.7
Proteiniclasticum
0.1
1.4
0.9
Oscillibacter
0.1
1
0.6
Unidentified (Family Anaerobrancaceae)
0.1
0.2
0.8
Unidentified (Family Erysipelotrichaceae)
0.1
0.3
0.6
Unidentified (Family Sphingomonadaceae)
0.1
0.5
0.4
Unidentified (Family Helicobacteraceae)
0.1
0.3
1.1
Unidentified (Phylum Spirochaetes)
0.1
2.8
1.9
Unidentified (Phylum Verrucomicrobia)
0.1
0.9
0.7
Unidentified (Family Cloacamonaceae)
0.1
0
0.8
Unidentified (Order Bacteroidales)
0
0
0.7
Unidentified (Phylum Chlorobi)
0
1.5
1.6
Enterococcus
0
3.4
1.4
Fusobacterium
0
0.5
0
Unidentified (Family Alcaligenaceae)
0
1.5
0.4
Fig. 3
32
Legend
Genus Methanoculleus CandidatusMethanoplasma Methanosarcina Unidentified (Class Methanomicrobia) Methanocorpusculum Unidentified (Phylum Crenarchaeota) Methanosaeta Methanospirillum Unidentified (Order Methanomicrobiales) Unidentified (Family Methanomassiliicoccaceae) Methanobacterium Methanobrevibacter
MSWM1Arch (%) 30.4 27.3 9.9 7.2 6.5 6 3.5 2.4 2 1.8 0.9 0.9
MSWM2Arch (%) 9.6 18.4 62.7 1.7 1.1 1.8 0.5 1.2 1.1 0.2 0.7 0.3
MSWM3Arch (%) 20.3 16.9 42.8 0.2 14 1.4 0.4 0.7 0.5 0.3 0.9 0.2
Fig. 4
33
Legend
Genus Unidentified (Family Aspergillaceae) Aspergillus Candida Hanseniaspora Pichia Unidentified Unidentified (Order Saccharomycetales) Unidentified (Order Saccharomycetales) Unidentified (Phylum Ascomycota) Debaryomyces Torulaspora Mucor
MSWM1ITS (%) 13 2.9 5.8 55.1 17.5 0.5 2.4 0.1 0 0.8 0.5 0.8
MSWM2ITS (%) 34 26.6 14.6 12.1 3.6 3.1 1.6 1 0.5 0.3 0 0
MSWM3ITS (%) 11.1 11.9 2.2 2.1 0.7 69.9 0 0.1 0.1 0.2 0 0
Fig. 5
34
Proteins
Carbohydrates
Sugars Acidogenesesis
B-OTU 4 F-OTU 2, 3, 4, 5, 7, 8, 10, 12, 14
B-OTU 4, 7, 10, 14 F-OTU 2, 3, 4, 5, 7, 8, 10, 12, 14, 15
B-OTU 3, 4, 6, 7, 11, 12, 13,14 F-OTU 1, 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13, 14
Hydrolysis
Lipids
Amino acids
B-OTU 1, 2, 3, 6, 7, 8, 9, 11, 12, 13, 14, 15 F-OTU 2, 3, 4, 5, 7, 8, 10, 12, 14
Long chain fatty acids (LCFAs)
B-OTU 10, 14
Volatile Fatty Acids (VFAs) other than acetic acid, Organic acids, Ethanol
Acetogenesis
B-OTU 1, 2, 6, 7, 11, 15
B-OTU 2, 4, 15
β-oxidation A-OTU 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15
B-OTU 2
Homoacetogenesis
Methanol, Methylamines, Methylsulphides Methylotrophic methanogenesis A-OTU 1, 3, 6, 7
Acetic acid Acetoclastic methanogenesis
B-OTU 1, 2, 5, 15 A-OTU 1, 3, 6, 7, 12, 13
CH4 + CO2
H2 + CO2 Hydrogenotrophic methanogenesis
A-OTU 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15
Fig. 6
35
Organic MSW + Sewage sludge
Anaerobic co-digestion
Metagenomics for studying the Anaerobic Microbiome and metabolic pathway analysis
36
Declaration of interests
√ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
None
37
Highlights
Co-digestion of organic fraction of MSW with sewage sludge was studied.
Enhanced biogas production with high methane content was obtained.
Temporal dynamics of microbial communities were studied by metagenomics.
OTU based elucidation of metabolic pathway involved in anaerobic digestion.
38