Microbiome signature and diversity regulates the level of energy production under anaerobic condition

Physicochemical properties of substrate and digesta

We observed distinct differences in the physicochemical properties of the digester feedstock throughout the experiment which, thus, could have affected the digester performance. The physicochemical properties of the digester feedstock before and after the anaerobic digestion of CD are shown in Fig. 1. The fermentation process of the AD was run for 45 days (Day 0 to Day 44) with a periodic loading of raw CD. The mean loading dose of the raw CD was 90.56 kg/load (highest input volume = 375 kg and lowest input volume = 35 kg) (Table S1). Periodic increments of the raw CD resulted in increased biogas production (Fig. 1A, Table S1). This trend was observed until Day 35, after which biogas production began to decline gradually, although the CD loading was continued. The log phase of methane (CH₄) production started around the second day of the experiment, and reached its maximum percentage (74.1%) at Day 35 of the digestion process. It should be pointed out that after Day 35, the CH₄ percentage started to decrease, reaching 59.2% on Day 44 (Table 1). Average concentration (%) CO₂ was observed 39.52 (minimum = 27.7, maximum = 56) throughout 44 days of the digestion process. Concentration of H₂S was maximum (938 ppm) at Day 3, and later on the concentration fluctuated based on feeding (Fig. 1A, Table S1). The overall environmental temperature, AD temperature, AD pressure and humidity were 34.75 °C (maximum = 38.8 °C, minimum = 32.0 °C), 34.46 °C (maximum = 51.0 °C, minimum = 0.0 °C), 22.52 mb (maximum = 56.41 mb, minimum = 0.0 mb), and 55.5% (maximum = 94.0%, minimum = 42.0%) (Fig. 1B, Table S2). On Day 35 of the digestion, when maximum methanogenesis was observed, the concentration of organic carbon (OC) and total nitrogen (TN) in the fermentation pulp were 15.48% and 1.22%, respectively, whereas the concentration of OC and TN in the slurry (CD + seed sludge; Day-0) were 34.39% and 1.96%, respectively (Table 2). The overall C/N ratio of the feedstock also gradually decreased with the advent of anaerobic digestion process, and found lowest (12.7:1) at Day 35. Similarly, the amount of non-metallic element (phosphorus and sulfur) and heavy metals (chromium, lead and nickel) content significantly decreased at the Day 35 of the digestion process (Table 2). However, the amount of zinc and copper did not vary significantly throughout the digestion period (Table 2). As shown in Fig. 2, number of operational taxonomic units (OTUs) were significantly positively correlated with AD bacterial α-diversity (Observed, Chao1 and Shannon indices; r were 1.00, 1.00, and 0.57, respectively, p < 0.05). Similarly, we found significant positive correlation between CH₄ concentration (%) and pH (r = 0.92, p < 0.001), CH₄ concentration (%) and O₂ level (%) (r = 0.54, p < 0.05), and CH₄ concentration (%) and environmental temperature (°C) (r = 0.57, p < 0.05) (Fig. 2). We also found positive correlation between the AD CO₂ (%) and H₂S (%), environmental temperature and digester temperature, environmental temperature and digester pressure, digester temperature and digester pressure (r = 0.64, p < 0.01; r = 0.71, p < 0.01; r = 0.72, p < 0.01; r = 0.89, p < 0.01, respectively). Interestingly, we also found a negative correlation between CO₂ and humidity, O₂ and humidity, environmental temperature and humidity, and digester temperature and humidity (Fig. 2).

Microbiome composition and diversity in the AD

The samples (n = 16) were categorized into four groups (Group-I, Group-II, Group-III and Group-IV) based on the level of energy production (CH₄%) (Table 1). Group-I (n = 2) belonged to initial time of energy production, Group-II (n = 5) and Group-III (n = 5) had 21–34% and 47–58% of CH₄ concentration, respectively. The samples having the highest 71–74% of CH₄ was categorized into Group-IV (n = 4). The WMS of 16 sample libraries resulted in 380.04 million reads passing 343.26 million reads quality filters, which corresponded to 90.32% total reads (individual reads per sample are shown in Data S1). The major microbial domain in all samples was Bacteria with an abundance of 81.80%, followed by Archaea (15.43%), and Viruses (2.77%) (Data S1). From these WMS reads, 380–1384 OTUs (average = 850 ± 22) were identified representing the distribution of microbial taxa cross four groups of the AD. Remarkably, the Group-IV metagenome always had higher number of OTUs compared to other groups (Table 1). The alpha diversity (i.e., within-sample diversity) of the AD microbiomes was computed 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 diversity significantly varied across the four sample groups (p = 0.03541, Kruskal–Wallis test). The pair-wise comparison of the within sample diversity revealed that the microbiomes of the Group-II significantly differed with those of Group-III and Group-IV (p = 0.048, Wilcoxon rank sum test for each) compared to Group-I (p = 0.91, Wilcoxon rank sum test) (Fig. 3A, B). The rarefaction analysis of the observed species showed a plateau after, on average, 21.45 million reads (Fig. S2, Data S1)-indicating that the coverage depth for most samples was sufficient to capture the entire microbial diversity. We also observed significant differences in the microbial community structure among the four metagenome groups (i.e., beta diversity analysis). Principal coordinate analysis (PCoA) at the strain level (Fig. 3C), showed a distinct separation of samples by the experimental groups. Besides, we found significant (p = 0.032, Kruskal Wallis test) differences in the abundance of ARGs and metabolic functional genes/pathways (DataS2) which could strongly modulate the level of energy production through microbiome dysbiosis in the AD.

In this study, on an average 0.43% WMS reads (assigned for r RNA genes) mapped to 28, 110 and 552 bacterial phyla, orders and genera respectively, and relative abundance of the microbiome differed significantly (p = 0.034, Kruskal–Wallis test) across the metagenome groups (Data S1). We observed significant shifts/dysbiosis in the microbiome composition at strain level. The PS analysis detected 2,513 bacterial strains across the four metagenomes, of which 768, 1421, 1819 and 1774 strains were found in Group-I, Group-II, Group-III and Group-IV metagenomes, respectively. Only, 18.34% bacterial strains were found to be shared across the four energy producing metagenomes (Fig. 3D, Data S1). The archaeal fraction of the AD microbiomes was represented by 5, 17, 61 and 343 archaeal phyla, orders, genera and strains, respectively, and the relative abundance of these microbial taxa also varied significantly among the four metagenome groups (Fig. 3E). Remarkably, 95.90% (329/343) of the detected archaeal strains shared across these metagenomes (Fig. 3E, Data S1). In addition, 472, 536, 535 and 536 strains of bacterial viruses (bacteriophages) were identified in Group-I, Group-II, Group-III and Group = IV metagenomes, respectively (Data S1).

Microbial community dynamically changed over time in the AD

Significant changes in the abundances of core microbial groups were observed under anaerobic condition of the AD. At phylum level, the AD metagenome was dominated by Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes and Fibrobacteres comprising > 93.0% of the total bacterial abundances. Among these phyla, Firmicutes was the most abundant phylum with a relative abundance of 41.94%, 37.99%, 40.40% and 38.96% in Group-1, Group-II, Group-III and Group-IV, respectively. The relative abundance of Bacteroidetes (from 37.87% in Group-I to 22.40% in Group-IV) and Actinobacteria (from 3.94% in Group-I to 3.30% in Group-IV) gradually decreased with the advance of AD digestion time. Conversely, relative abundance of Proteobacteria (from 8.08% in Group-I to 18.92% in Group-IV) and Spirochaetes (from 1.28% in Group-I to 3.70% in Group-IV) gradually increased with the increase of anaerobic digestion time in AD. The rest of phyla also differed significantly across these four groups keeping comparatively higher relative abundances during highest CH₄ producing stage (Group-IV) of the AD (Fig. 3F). The relative abundances of archaeal phylum Euryarchaeota, steadily increased with the increasing demand of energy (lowest relative abundance in Group-I and highest relative abundance in Group-IV) (Fig. 3F). Similarly, Clostridiales and Bacteroidales were identified as the top abundant order in Group-1, Group-II, Group-III and Group-IV with a relative abundance of 32.37%, 27.81%, 29.22% and 27.87%, and 32.49%, 27.42%, 27.53% and 14.94%, respectively (Data S1).

The structure and relative abundances of the bacteria at the genus level also showed significant differences (p = 0.031, Kruskal–Wallis test) across the study groups. In Group-I, Group-II and Group-III metagenomes, Bacteroides was the most abundant bacteria with a relative abundance of 18.10%, 14.90% and 15.16%, respectively, but remained lower (8.31%) in Group-IV samples. Clostridium was found as the second most predominant bacterial genus, and the relative abundance of this bacterium was 11.92%, 11.13%, 11.73% and 12.15% in Group-1, Group-II, Group-III and Group-IV, respectively. The relative abundance of Ruminococcus, Eubacterium, Parabacteroides, Fibrobacter, Paludibacter, Porphyromonas and Bifidobacterium gradually decreased with the increase of energy (CH₄) production rate, and remained lowest in Group-IV. Conversely, Candidatus, Bacillus, Treponema and Geobacter showed an increasing trend in their relative abundances gradually with the advance of digestion time and remained lowest in relative abundances in Group-IV. The rest of the bacterial genera had lower relative abundances in four metagenomes of the AD (Fig. S3, Data S1). In our present study, Methanosarcina was the most abundant archaeal genus, and the relative abundance of this genus remained two-fold higher in Group-III (35.84%) and Group-IV (36.53%) compared to Group-I (17.52%) and Group-II (18.32%). Notably, the relative abundance of Methanoculleus was found higher in Group-II (11.59%) and Group-IV (13.80%) and lowest in Group-I (3.46%). Likewise, Methanobrevibacter was predominantly abundant at the initial phage of digestion (highest in Group-I; 19.35%) and remained lowest in abundance in the top CH₄ producing metagenome (Group-IV; 5.01%). Besides these genera, Methanothermobacter (5.30%), Methanosaeta (5.16%), Methanococcus (4.74%), Thermococcus (2.96%), Methanocaldococcus (2.53%), Pyrococcus (2.35%), Methanosphaera (2.32%) Methanococcoides (2.10%) and Archaeoglobus (2.01%) were the predominantly abundant archaeal genera in Group-I samples and their relative abundances gradually decreased with the increase of energy production (Fig. S4, Data S1). On the other hand, Methanoregula (6.43%), Methanosphaerula (2.99%), Methanoplanus (2.37%) and Methanohalophilus (1.39%) were the most abundant archaeal genera in Group-IV metagenome. The rest of the genera remained much lower (< 1.0%) in relative abundances but varied significantly across the four metagenomes (Fig. S4, Data S1).

The strain-level composition, diversity and relative abundances of the microbiomes across four metagenomes revealed significant variations (p = 0.011, Kruskal–Wallis test) (Data S1). In this study, 2,513 bacterial and 343 archaeal strains were detected, of which 18.35% (461/2513) bacterial and 95.92% archaeal strains shared across the study metagenomes (Fig. 3, Data S1). Most of the bacterial strains detected were represented by the phylum Firmicutes followed by Bacteroidetes, Gammaproteobacteria and Betaproteobacteria (Fig. S5, Data S1). Of the detected strains, methanogenic archaeal strains were more prevalent (higher relative abundances) compared to bacterial strains, and this stain-level microbiome profiling was more evident in highest energy producing metagenome group (Group-IV). The most prevalent energy producing archaeal strains in Group-IV were Methanosarcina vacuolata Z-761 (17.31%), Methanosarcina sp. Kolksee (16.63%), Methanoculleus marisnigri JR1 (5.0%), Methanothrix soehngenii GP6 (4.61%), Methanobacterium formicicum DSM 1535 (3.60%), Methanoculleus sp. MAB1 (2.07%) and Methanoculleus bourgensis DSM 3045 (2.07%), and rest of strains had lower (< 2.0%) relative abundances (GData S1). Moreover, the relative abundances of these strains gradually increased with the increase of energy production (lowest relative abundance in Group-I and highest relative abundance in Group-IV) (Data S1). Conversely, the relative abundances of most of the bacterial strains identified gradually decreased with the advance of digestion time (increase of energy production), and mostly remained higher in relative abundances in Group-I (Data S1). Of the top abundant bacterial strains, Bifidobacterium pseudolongum subsp. globosum DSM 20,092 (12.0%), Phocaeicola dorei DSM 17,855 (6.61%), Fibrobacter succinogenes subsp. succinogenes S85 (4.57%), Faecalibacterium prausnitzii M21/2 (2.89%), Clostridiales bacterium CCNA10 (2.78%), and Flintibacter sp. KGMB00164 (2.07%) were found in Group-I, and their abundances gradually decreased with the increase of level of CH₄ production. In addition, Dysosmobacter welbionis J115 (5.48%) remained more prevalent in Group-II (Data S1). The rest of the bacterial strains were less abundant (< 2.0%) across the four metagenomes (Data S1). The viral fraction of the microbiomes mostly dominated by different strains of bacteriophages such as Gordonia phage Secretariat (16.12%), Streptomyces phage Bing (5.33%) and Arthrobacter phage Gordon (5.05%) in Group-I, Megavirus chiliensis (1.81%), Acanthamoeba polyphaga moumouvirus (1.60%) and Orpheovirus IHUMI-LCC2 (1.50%) in Group-II, Stenotrophomonas phage Mendera (4.88%), Choristoneura fumiferana granulovirus (3.0%) and Gordonia phage Secretariat (2.55%) in Group-III and Stenotrophomonas phage Mendera (2.58%), Choristoneura fumiferana granulovirus (2.37%) and Bacillus phage Mater (1.47%) in Group-IV (Data S1).

Dominant microbial consortia in the AD

In the present study, we detected 43 dominant species including 31 bacterial and 12 archaeal species belonged of 9 phyla (8 bacterial and 1 archaeal) (Fig. 3G, Data S1). In the digestate of AD, Firmicutes, Bacteroidetes, Fibrobacteres, Spirochaetes, Actinobacteria, Candidatus Cloacimonetes, Proteobacteria, and Thermotogae were the dominant bacterial phyla while Euryarchaeota was the only archaeal phylum dominating in the AD. Methanogenic archaeal species were dominant in Group-III and Group-VI (M. vacuolata, Methanosarcina sp. Kolksee, M. marisnigri, M. soehngenii, M. formicicum and Natrialbaceae archaeon XQ-INN 246) (Fig. 4A). Most of the dominant bacterial species had negative correlation with digester pH, CH₄, humidity and pressure. Of the detected dominant species, most of the dominating methanogenic archaeal species such as M. vacuolata, Methanosarcina sp. Kolksee, M. marisnigri, M. soehngenii, M. formicicum, Methanoculleus sp. MAB1, M. bourgensis, M. labreanum, M. barkeri, M. formicica and M. congolense showed strongest positive correlation with pH, CH₄ (%), digester pressure (%) and temperature (°C) (Spearman correlation; r > 0.6, p < 0.01) (Fig. 4B). On the other hand, bacterial species including Roseburia hominis, Flintibacter sp., P. dorei, Alistipes shahii, B. thetaiotaomicron, C. butyricum, Megasphaera elsdenii, R. intestinalis, C. bacterium CCNA10, B. pseudolongum, B. fragilis, L bacterium GAM79, F. prausnitzii and I. butyriciproducens, and archaea species N. archaeon XQ-INN 246 had significant negative correlation with digester pH, CH₄ concentration (%) and humidity (%) (Spearman correlation; r > 0.6, p < 0.01) (Fig. 4B).

Identification of potential indicator species and their co-occurrence

To identify microbial taxa (bacteria and archaea) that could discriminate across the four metagenome groups of the AD in terms of energy production (% CH₄), the indicator species analysis (ISA) was performed both in individual group and combination basis, as shown in (Fig. 5). Indicator species were those which were significantly more abundant and present in all samples belonging to one group, and also absent or low abundance in the other group (Fig. 5, Data S1). The core taxa were selected based on their relative frequency (≥ 75% occurrence in each of the four groups) (Data S1). Although, 26, 3 and 19 indicator species were found in Group-I, Group-II and Group-IV, respectively, and no indicator species were identified in Group-III (Fig. 5A, Data S1). Higher indicator values (IVs) suggested better performances in the microbial signature of the assigned taxa. Desulfosporosinus youngiae, Treponema caldarium, Pseudoclostridium thermosuccinogenes, Dehalobacterium formicoaceticum, Methanofollis liminatans, Methanoregula boonei, Syntrophomonas wolfei, Hungateiclostridium clariflavum, Candidatus Cloacimonas acidaminovorans and Methanocorpusculum labreanum were highly specific for energy production in Group-IV (highest CH₄ production rate; 74.1%), with IVs of 0.983, 0.978, 0.949, 0.907, 0.887, 0.885, 0.882, 0.851, 0.795 and 0.786, respectively (Fig. 5A; Data S1). Considering the combined group effects of the indicator species associated with energy production, our analysis revealed that Methanosarcina vacuolate, Dehalococcoides mccartyi, Methanosarcina sp. Kolksee and Methanosarcina barkeri in Group-III + Group-IV (top CH₄ producing groups) having IVs of 0.88, 0.887, 0.879 and 0.879, respectively were highly specific for energy production (Fig. 5B, Data S1). All of the indicator phylotypes displayed reduced abundance in the initial stage of biogas production (Group-I and Group-II, lower CH₄ production rate) compared to their increased relative abundance up to Day 35 of the experiment (in Group-III and Group-IV) (Data S1).

We then visualized networks within each metagenome group of the AD for both positive and negative co-occurrence relationships (Fig. 6, Data S1). The correlation networks analysis was performed based on the significantly altered species (n = 106) in different groups as revealed by indispecies analysis. This network analysis explored significant association (p = 0.021, Kruskal–Wallis test) in the co-occurrence patterns of the energy producing microbial taxa (species and/or strains) based on their relative abundances in four metagenome groups. In the correlation network of four metagenomes; Group-I to Group-IV), Firmicutes and Bacteroidetes exhibited strongest relation. The resultant network consists of 106 nodes (17 in Group-I, 58 in Group-II, 5 in Group-III and 26 in Group-IV) which were clearly separated into four modules/clusters (Fig. 6). Taxa in the same group may co-occur under the same AD conditions (temperature, O₂ and H₂S percentage, pressure and humidity). Across different metagenome groups of AD, Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria were the top abundant phyla in Group-I and Group-II with a cutoff of 1.0 while Bacteroidetes and Chlorhexi in Group-III, and Euryarcheota and Firmicutes in Group-IV were designated as the top abundant phyla with a cutoff of 1.0 (Fig. 6). However, when moving down to the species-level in microbiome co-occurrence in the AD, keystone taxa were much more consistent between networks with different correlation cutoffs. These results reveal that applying the same conditions in the AD for energy production, network elements must happen under careful consideration of the parameters used to delineate co-occurrence relationships. The positive correlations between Group-I and Group-II were observed among the microbiomes of the AD while Group-III and Group-IV showed negative correlations in terms of energy production with the microbial taxa of other two groups (Fig. 6). These findings therefore suggest that different strains of Euryarcheota and Firmicutes phyla were negatively correlated but associated with highest level of energy production (highest % of CH₄; Group-IV).

Genomic functional potentials of the anaerobic microbiomes

In this study, there was a broad variation in the diversity and composition of the antimicrobial resistance genes (ARGs) (Fig. 7, Data S2). The results of the present study revealed significant correlation (p = 0.0411, Kruskal–Wallis test) between the relative abundances of the detected ARGs and the relative abundance of the associated bacteria found in four metagenomes (Data S2). ResFinder identified 45 ARGs belonged to eight antibiotic classes distributed in 2,513 bacterial strains (Data S2). The Group-III microbiomes harbored the highest number of ARGs (42), followed by Group-II (38), Group-IV (29) and Group-I (22) microbes (Fig. 7, Data S2). The tetracyclines (doxycycline and tetracycline) resistant gene, tetQ had the highest relative abundance (23.81%) in Group-I associated bacteria followed by Group-II (22.85%), Group-III (16.49%) and Group-IV (6.73%)–microbes. Macrolides (erythromycin and streptogramin B) resistant genes such as mefA (16.80%), mefB (15.32%) and msrD (11.10%) had higher relative abundances in highest CH₄ producing metagenome compared to other metagenome groups (Fig. 7A). The broad-spectrum beta-lactams resistant gene, cfxA2-6 was found as the common ARG among the microbiomes of four metagenomes, displaying the highest relative abundance (35.58%) in inoculum (Group-I) microbiota followed by Group-II (23.09%), Group-III (8.02%) and Group-IV (0.14%) microbiomes. The rest of the ARGs also varied in their expression levels across the four metagenomes, being more prevalent in the Group-III microbiomes (Fig. 7A, Data S2).

Most of the dominant archaeal species had stronger positive correlation with sul4, tet(W), tet(44), tet(C), Inu(B), tet(32), catQ, tetM) and Inu(D) (Spearman correlation; r > 0.5, p < 0.01), and were negatively correlated with tet(Q), tet(40), Inu(C), cfxA, cfxA2, cfxA3, cfxA4, cfxA5 and cfxA6 (Spearman correlation; r > 0.6, p < 0.01) (Fig. 7B, Data S2, Fig. S6). In contrast, the dominating bacterial species of the AD had significant positive correlation with cfxA, cfxA2, cfxA3, cfxA4, cfxA5, cfxA6, tet(Q), blaACI-1, tet(40) and Inu(C) (Spearman correlation; r > 0.6, p < 0.01) (Fig. 7B, Data S2, Fig. S6). Analyzing the correlation between physicochemical parameters and ARGs classes, we found that the digester pH, CH₄ concentration, pressure and temperature were positively correlated with macrolide-lincosamide-streptogramin (MLS), tetracyclines and phenicol while most of the physicochemical parameters were negatively correlated with sulfonamides class of antibiotics (Spearman correlation; r > 0.6, p < 0.01) (Fig. 7C, Data S2, Fig. S7A). Likewise, the digester pH, CH₄ concentration, pressure and temperature showed strongest positive correlation with MLS resistance MFS and ABC efflux pumps associated mechanisms (Spearman correlation; r > 0.5, p < 0.01) followed by chloramphenicol acetyltransferases and tetracycline resistance ribosomal protection proteins related functions (Fig. 7D, Data S2). Conversely, Class A betalactamases and sulfonamide resistance dihydropteroate synthases were the negatively correlated mechanisms with digester physicochemical parameters like pH and CH₄ (Fig. 7D, Data S2). The mechanisms of the antimicrobial resistance also varied between the dominant bacterial and archaeal species (Fig. S7B). For instance, most of the bacterial species showed highest positive correlation Class A betalactamases (Spearman correlation; r > 0.5, p < 0.01) while the enriched archaeal species showed negative correlation with this functional mechanism of antibiotics. The methanogenic archaeal species predominantly identified however had significant positive correlation with tetracycline resistance ribosomal protection proteins related functions, 23S rRNA methyltransferases and MLS resistance MFS efflux pumps (Fig. S7B, Data S2). In addition to these ARGs, the highest CH₄ producing microbiomes were enriched with the higher relative abundance of genes coding for cobalt-zinc-cadmium resistance (18.85%), resistance to chromium compounds (12.17%), arsenic (6.29%), zinc (4.96%) and cadmium (3.26%) resistance compared to the microbes of other three metagenomes (Fig. S8, Data S2). By comparing the possible mechanisms of the detected ARGs, we found that antibiotic efflux pumps associated resistance had the highest level of expression in the anaerobic microbiomes of the AD followed by antibiotic inactivation, enzymatic inactivation and modification, antibiotic target protection/alteration, and folate pathway antagonist-attributed resistance mechanisms (Fig. S8, Data S2). Notably, the physicochemical properties of the AD including pH, CH₄ concentration (%), pressure, temperature and environmental temperature were found to be positively correlated with metal such as arsenic, copper, cadmium, mercury, chromium compounds and zinc resistance pathways (Spearman correlation; r > 0.5, p < 0.01). (Fig. 8A, Data S2). Remarkably, most of the methane producing dominant archaeal species showed significant positive correlation with these metal resistance pathways (Spearman correlation; r > 0.7, p < 0.01). (Fig. 8B, Data S2) while the predominant bacterial species to be associated with methane production showed stronger negative correlation with most of the metal resistance pathways (Spearman correlation; r > 0.7, p < 0.01). (Fig. 8B, Data S2).

Functional metabolic profiling of the gene families of the same KEGG pathway for AD microbiomes revealed significant differences (p = 0.012, Kruskal–Wallis test) in their relative abundances, and positive correlation with level of energy production (Fig. 9, Data S2). Among the detected KO modules, genes coding for CHO metabolism and genetic information and processing were top abundant, however did not vary significantly across the metagenome groups. Remarkably, the relative abundance of genes coding for energy metabolism, xenobiotics biodegradation and metabolism, butanoate metabolism, citrate synthase (gltA), succinyl-CoA synthetase subunits (sucC/D), pyruvate carboxylase subunits (pycA) and nitrogen metabolism gradually increased with the increasing rate of CH₄ production, and had several-fold over expression among the microbiomes of Group-IV. Conversely, fumarate hydratase (fumA/B), malate dehydrogenase (mdh) and bacterial secretion system associated genes were predominantly overexpressed in Group-I related microbiomes which gradually decreased with advance of digestion process, and remained more than two-fold lower expressed in the peak level of CH₄ production (lowest in Group-IV) (Fig. 9A, Data S2). We also detected 41 statistically different (p = 0.033, Kruskal–Wallis test) SEED functions in the AD microbiomes. Overall, the top CH₄ producing microbiomes (Group-III and Group-IV) had higher relative abundances of these SEED functions compared lower CH₄ producing microbiomes (Group-I and Group-II), except for regulation of virulence (highest in Group-I microbes; 17.08%), gluconeogenesis (highest in Group-I microbes; 16.27%) and transposable elements (highest in Group-I microbes; 17.28%) (Fig. 9B, Data S2). The Group-IV-microbiomes (highest CH₄ producing) were enriched in genes coding for tetrapyrroles (17.42%), one carbon (10.29%) and biotin (4.55%) metabolism, oxidative (18.76%) and osmotic (9.94%) stress, proteolytic pathway (7.74%), MT1-MMP pericellular network (6.45%), acetyl-CoA production (5.33%) and motility and chemotaxis (3.13%) compared to the microbes of the other metagenomes. The Group-I microbiomes however had a higher abundance of SEED functions involved in protection from ROS (16.28%), heat shock (18.31%) and NAD and NADP (19.03%) (Fig. 9B, Data S2). Analyzing correlation between AD physicochemical parameters and microbial genomic functions, we found that SEED subsystems such as cofactors, vitamins, prosthetic groups, pigments, membrane transport, motility and chemotaxis, respiration, stress response, dehydrogenase_complexes, dehydrogenase_ kinases, glycolysis and gluconeogenesis including archaeal enzymes, glyoxylate bypass, pentose phosphate pathway, pyruvate:ferredoxin oxidoreductase, pyruvate metabolism I and II had significant positive correlation with digester pH and CH₄ concentration (%) (Spearman correlation; r > 0.6, p < 0.01). (Fig. 10A, Data S2). Simultaneously, the Kos like ascorbate and aldarate metabolism, citrate synthase (gltA), energy, glyoxylate, dicarboxylate, methane and nitrogen metabolism, xenobiotics biodegradation and metabolism, succinyl-CoA synthetase subunits (sucC/D) and oxidative phosphorylation were significantly positively correlated with digester pH, CH₄ concentration (%), pressure and temperature (Spearman correlation; r > 0.6, p < 0.01). (Fig. 10B, Data S2).