Microbiome dysbiosis regulates the level of energy production under anaerobic condition

The microbiome of the anaerobic digester (AD) regulates the level of energy production. To assess the microbiome dysbiosis in different stages of anaerobic digestion, we analyzed 16 samples dividing into four groups (Group-I = 2; Group-II = 5; Group-III = 5 and Group-IV = 4) through whole metagenome sequencing (WMS). The physicochemical analysis revealed that highest CH4 production (74.1%, on Day 35 of digestion) was associated with decreased amount of non-metal (phosphorus and sulfur) and heavy metals (chromium, lead and nickel). The WMS generated 380.04 million reads mapped to ~ 2800 distinct bacterial, archaeal and viral genomes through PathoScope (PS) and MG-RAST (MR) analyses. The PS analysis detected 768, 1421, 1819 and 1774 bacterial strains in Group-I, Group-II, Group-III and Group-IV, respectively which were represented by Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes and Fibrobacteres (> 93.0% of the total abundances). The archaeal fraction of the AD microbiomes was represented by 343 strains, of which 95.90% strains shared across these metagenomes. The indicator species analysis showed that Methanosarcina vacuolate, Dehalococcoides mccartyi, Methanosarcina sp. Kolksee and Methanosarcina barkeri were the highly specific for energy production in Group-III and Group-IV. However, most of the indicator phylotypes displayed reduced abundance in the initial stage of biogas production (Group-I and Group-II) compared to their increased relative abundances in Group-IV (Day 35). The correlation network analysis showed that different strains of Euryarcheota and Firmicutes phyla were associated with highest level (74.1%) of energy production (Group-IV). In addition to taxonomic dysbiosis, top CH4 producing microbiomes showed increased genomic functional activities related to one carbon and biotin metabolism, oxidative stress, proteolytic pathways, MT1-MMP pericellular network, acetyl-CoA production, motility and chemotaxis. This study reveals distinct changes in composition and diversity of the AD microbiomes including different indicator species, and their genomic features that are highly specific for energy production.

inherent limitations including the polymerase chain reaction (PCR) bias, inability to detect viruses, 118 lower taxonomic resolution (up to genus level only), and limiting information on gene abundance 119 and functional profiling have made this technique questionable [25,26]. Conversely, the whole 120 metagenome sequencing (WMS) or shotgun approach which can identify the total microbial 121 components of a sample (including viruses, bacteria, archaea, fungi, and protists) is being used 122 prudently to decipher the phylogenetic composition, microbiome structure and diversity including 123 profiling of their functional characteristics and interconnections [12,28]. 124 To address the dynamic shifts in microbiome diversity and composition to be associated 125 with different level of renewable energy production in the controlled AD, we present a 126 comprehensive deep metagenomic (WMS) analysis of sixteen (n=16) samples collected from the 127 same AD under different pH, CO 2 , O 2 and H 2 S levels and temperature. Using a homogeneous 128 mapping and annotation workflow associated with a de-replication strategy, our analyses identified 129~ 2800 distinct bacterial and archaeal species along with their co-presence networking, 130 antimicrobial resistance and metabolic functional profiling. This study therefore provides an 131 opportunity to in-depth study the genetic potential and performance of microbial taxa represented 132 by WMS, and to relate their activities to generate renewable energy under changing environmental 133 conditions and process parameters.

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Digester setup and experiment design 136 The experiment was conducted using an anaerobic digester (AD) plant prepared with 137 provisions to measure temperature, slurry and gas sample collection and substrate charging. The 138 biogas plant consisted of a digester, inlet-chamber, three slurry outlet pipes, gas outlet pipe and 139 thermometer ( Fig S1). The AD that contains the substrate (organic wastes) and converts it into  (Table S1). Initially, the digester 146 was started with charge of 375 kg feedstock where the ratio of raw cow dung and active sludge 147 was 1:1. The raw semi-solid cow dung (CD) was mixed with seed sludge from previous biogas 148 plant (slurry) before charged into the AD. The AD was portable, light in weight, low cost and 149 retains more heat inside. In addition, data on physicochemical parameters were recorded up to Day 45 (Table 1)

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Physicochemical properties of substrate and digesta 245 The physicochemical properties of the digester feedstock before and after the anaerobic 246 digestion of cow dung are shown in Figure 1 and  Table S1). The overall environmental temperature, AD  Table S2).  (Table 2). However, the amount of zinc and copper did not vary significantly throughout the 267 digestion period (Table 2). (2.77%) (Data S1).

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The alpha diversity (i.e., within-sample diversity) of the AD microbiomes was computed 280 using the Shannon and Simpson estimated indices (i.e., a diversity index accounting both evenness and richness) at the strain level. In this study, both Shannon and Simpson indices estimated 282 diversity significantly varied across the four sample groups (p = 0.03541, Kruskal-Wallis test).

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The pair-wise comparison of the within sample diversity revealed that the microbiomes of the 284 Group-II significantly differed with those of Group-III and Group-IV (p= 0.048, Wilcoxon rank 285 sum test for each) compared to Group-I (p= 0.91, Wilcoxon rank sum test) (Fig 2A, B). The

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In this study, on an average 0.43% WMS reads (assigned for r RNA genes) mapped to 28, 295 110 and 552 bacterial phyla, orders and genera respectively, and relative abundance of the 296 microbiome differed significantly (p = 0.034, Kruskal-Wallis test) across the metagenome groups 297 (Data S1). We observed significant shifts/dysbiosis in the microbiome composition at strain level.

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The PS analysis detected 2,513 bacterial strains across the four metagenomes, of which 768, 1421, 299 1819 and 1774 strains were found in Group-I, Group-II, Group-III and Group-IV metagenomes, 300 respectively. Only, 18.34% detected strains were found to be shared across the four energy 301 producing metagenomes (Fig 3, Data S1). The archaeal fraction of the AD microbiomes was   metagenome. The rest of the genera remained much lower (< 1.0%) in relative abundances but 352 varied significantly across the four metagenomes ( Fig S4, Data S1).

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Higher indicator values (IVs) suggested better performances in the microbial signature of the  (Fig 5A; Data S1).

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Considering the combined group effects of the indicator species associated with energy production, with the microbial taxa of other two groups (Fig 6). These findings therefore suggest that different metagenomes, being more prevalent in the Group-III microbiomes (Fig 7). In addition to these 450 ARGs, the highest CH 4 producing microbiomes were enriched with the higher relative abundance  . Conversely, C/N ratio remained lowest in this peak stage of 503 CH 4 production. Organic carbon is essential for bacterial growth, and determining of the C/N ratio 504 is essential for optimal biogas production [45]. Moreover, the total content of phosphorus, Sulphur 505 and heavy metals (chromium, lead and nickel) also remained lowest at this highest stage of biogas 506 production (Day 35). Of note, the highest CH 4 producing microbiomes were enriched with the 507 higher relative abundance of genes coding for heavy metals (cobalt-zinc-cadmium, chromium 508 compounds, arsenic, zinc, and cadmium) compared to the microbes of other three metagenomes.

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The lowest chromium, lead and nickel concentration during highest CH 4 producing stage might be 510 associated with their higher abundances of heavy metal resistance genes (Fig S6, Data S2), and small concentrations of these metals found in the process are essential for microbial maintenance

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The within (alpha) and between (beta) sample diversity of the AD microbiomes showed 517 that that microbial dysbiosis in the AD is closely linked to different levels of biogas production.