Dynamics of microbiome composition during anaerobic digestion of different renewable resources

Dynamics of microbiome composition during anaerobic digestion of different renewable resources | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Dynamics of microbiome composition during anaerobic digestion of different renewable resources Nicoletta Favale, Stefania Costa, Daniela Summa, Silvia Sabbioni, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4003924/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND This study, using the whole metagenomic sequencing approach, provides an insight in the microbial dynamics that occurred during the anaerobic digestion of two crop substrates with different lignocellulose composition: cereal grains and grape pomace. RESULTS A total of 15 strains were identified as specifically characterising the two substrates. Among them some strains never detected in biogas reactors were identified: Clostridium isatidis, Methanothermobacter wolfeii and Methanobacter sp MB1 in cereal grains samples and Acetomicrobium hydrogeniformans, Acetomicrobium thermoterrenum in grape pomace samples. CONCLUSIONS The presence of bacteria as Acetomicrobium sp. and P.mucosa , involved in the degradation of lipids and protein-rich substrates, together with Methanosarcina sp. and P.bacterium 1109, able to tolerate high hydrogen pressures and high ammonia concentration derived by aminoacids degradation, suggest that a more complex syntrophic community is established in lignin-cellulose-enriched substrates; this evidence may help the development of new strategies to optimize the anaerobic digestion process of these kind of biomasses. Anaerobic digestion metagenomics lignocellulosic biomasses lignin biodegradation biogas production Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND The reorientation of the global energy industry towards renewable sources is one of the major challenges of this century and has become an excellent component to any alternative energy portfolio ( 1 ). In particular, anaerobic digestion (AD) among the biochemical conversion of agricultural still represents an economically attractive technology to generate bioenergy and reduce climalterant emissions ( 2 ). Energy crops such as cereal grains (CG) are a common substrate for bioenergy production due to their high biogas potential ( 3 ). However, they may cause important environmental burdens due to the requirement of intensive agricultural activities and fertilisers, with negative impacts on soils and water, as well as land use constraints and the impact on other non-energy commodities, such as food ( 4 ). Currently, lignocellulosic residual biomass has received extensive attention because it is one of the most abundant organic compounds on earth, it is cheap and geographically distributed ( 5 ). Although anaerobic co-digestion of various agricultural feedstock is a common approach in terms of sustainable biofuel production, the presence of poorly biodegradable components in lignocellulosic biomass dramatically limits the methane recovery in AD systems. The main challenge in the use of lignocellulosic materials for biogas production, remains their structure and composition, because it primarily consists of cellulose, hemicellulose and lignin, that is extremely recalcitrant against microbial degradation ( 6 ). Grape pomace (GP), the main solid vinery waste, contains up to 45% lignin ( 7 ). Considering that averagely 18 kg of GP is generated per 100 L of wine produced, about 5 million tons of such residue are annually yielded ( 8 ). Nowadays, a recent European reform in the wine sector (EC Regulation 479/2008) promotes the gradual withdrawal of distillation subsidies and consequently revokes the compulsory distillation. This should drive the promotion of integrated, sustainable and standardized alternative protocols for the valorisation of solid winery waste. An alternative valorisation of GP could be represented by the production of biogas by anaerobic digestion (AD) processes. However, low performances were generally achieved, due to the high content of lignin, which is not readily fermentable ( 9 ). Moreover, it has been reported the detection of a significant lag phase during methane production ( 10 ). The GP moisture after pressing is around 20–30% w/w, and the material is usually characterized by C:N ratio ranges from 40 to 45:1, pH ranges from 3 to 6, and low electrical conductivity ( 11 ). Several mechanical and chemical procedures for saccharification have been established, but definitely there is no turn-key bioenergy lignocellulosic feedstock solution at this time ( 12 ). An approach based on metagenomic has been in recent times proposed in order to study the microbial communities and biodiversity involved in the AD process, being the majority of members participating in the biogas production process still unknown ( 13 ). Metagenomics offers the possibility of studying the genetic material of difficult-to-culture species within microbial communities with the competence to degrade lignocellulosic biomass ( 14 ). This approach could provide a new opportunity to elucidate lignocellulose degradation mechanisms used by currently lesser-known microbial species. These techniques also provide insights into the composition, functional gene profiling, and metabolic pathway reconstruction of microbial communities, enabling a more comprehensive understanding of their roles in biogas production ( 15 ). A knowledge of the microbiome residing in the anaerobic digester can be further used for the development of more efficient processes in conjunction with the identified consortium and could be essential to minimize process failures and create new strategies for optimizing lignocellulose-based AD processes ( 16 ). The focus on microbiomes and comparative genomic analyses of the microbial communities present in different organic wastes is critical to better understand the molecular basis of AD activities and their use for lignocellulosic degradation without further pre-treatment and to achieve more effective and efficient AD performance for this biomass ( 17 ). In this research, we therefore aimed to 1) determine the biochemical methane potential (BMP) of lignocellulosic grape pomace (GP) as carbon source and compare it with the BMP of cereal grains (CG) in AD trials; 2) apply the high-resolution whole metagenomic shotgun (WMS) analysis to explore the composition of microbiome during AD of CG and GP and compare microbial community dynamics; 3) relate microbiology to biogas performance, and investigate differences in reactors’ microbial community dynamics compared to the original inoculum culture. The results of the present study provide evidence in favour of the hypothesis that integrating genomics with AD process monitoring and control strategies offers new possibilities for optimizing biogas production and ensuring process stability. In addition, they could lay the foundation for the production of starter microbial mixtures for AD of specific lignocellulosic biomasses, thereby improving biogas production. MATERIALS AND METHODS 2.1 Set up and experimental design Inoculum sludge, CG and GP were obtained from a local biogas production facility (Sesa Spa, Padua, Italy). CG was a mixture of corn silage, ground whole flour and corn cob. Lab scale batch reactor was set up with glass digesters of 250 ml of capacity, equipped with two-ways screw caps. The average pH of the inoculum was 8.0, VS/TS ratio (Volatile Solids, VS; Total Solids, TS) and TS were 82.6% and 73.5 ± 2.0 g/kg (wet base) respectively. In each digestion test, 60 g of inoculum (seed bacterial culture sludge) was used and mixed with the substrate resulting in 14 trial reactors (seven with CG and seven with GP) to be analysed in parallel during AD and compared with the inoculum in the metagenomic profiles. The VS/TS ratio of sample and inoculum in each bottle was 1:1. All reactors were tightly closed with rubber septa, incubated in a water bath (Argolab WB 22, Giorgio Bormac Srl, Modena, Italy) at 55°C ± 1°C and manually mixed for about 1 min once a day prior to measurement of biogas volume. The formed biogas was led into 1% NaOH solution to remove CO 2 and the volume of methane determined every 24 hours using Dietrich–Frühling calcimeter ( 18 ). For reference purposes, the methane yield produced by the inoculum alone was determined and subtracted from the sample yields. Methane yields were expressed as volume of methane Nm3/ton (normal cube meter per ton) per corrected unit mass of VS ( 19 ). Every 5 days, a bottle was withdrawn and used as sample for chemical and genomic analysis. Sampling was suspended after 30 days, when chemical assays revealed that AD activity had concluded. 2.2 Chemical assays pH was measured by a calibrated Crison basic 20 pH meter (Crison, Barcelona, Spain) at 20°C. Total solids (TS), volatile solids (VS) and ashes on dry basis were determined using the combustion method at 105 ± 1°C for 24 hours according to “Standard Methods” ( 20 ). Lignin content was determined by means of the INNVENTIA Test Methods L 2:2016 ( 21 ). Sugars and volatile acids have been analysed using HPLC LC4000 (Jasco Inc., MD, USA), equipped with refractive index detector ( 22 ). 2.3 DNA Sample processing Sludge samples collected from the reactors were labelled "cg0" to "cg30" and "gp0" to "gp30" for the different experimental time intervals for CG and GP, respectively, and stored at 20°C until centralized analysis. Whole genomic DNA was extracted using the GenElute™ Bacterial Genomic DNA Kit (Sigma), in accordance with the manufacturer's instructions. Purified DNA was quantified by spectrophotometer (Shimadzu BioSpec-nano) and by Qubit 2.0 Fluorometer (Life Technologies) by using the Qubit dsDNA HS Assay Kit (Life Technologies). DNA was fragmented using NEBNext® dsDNA Fragmentase for 30 min. NEBNext® Multiplex Oligos for Illumina® (Dual Index Primers set 1) were used to produce the library and label it with specific molecular barcodes. The library was generated from 100 ng of genomic DNA, in accordance with the NEBNext® Ultra™ II DNA Library Prep Kit for Illumina protocol (New England Biolabs). Then, AMPure XP beads (Beckman Coulter) were used for library purification. Finally, the library was quantified using the High Sensitivity DNA Kit (Agilent Technologies) on the Bioanalyzer instrument (Agilent). Sequencing was performed with an Illumina NextSeq 500 sequencer with 2 × 150-bp read, using NextSeq® 500/550 Mid Output Kit v2. 2.4 Taxonomic profiling After QC (detailed in Supplementary materials), community taxonomic analysis of the processed data was performed using BLASTN ( 23 ) and Metagenomic Phylogenetic Analysis softwares (MetaPhlAn3 v. 3.0.11,( 24 ); MetaPhlAn4 v.4.0.6,( 25 )). BLASTN analysis was conducted using the NCBI “nt” reference database, the parameters were set to exclude matches with an e-value > 1x10 − 6 , a percentage of identity ≥ 95% and a minimum length > 100 bp. Then, MEGAN6 (v.6.21.5, ( 26 )) with default parameters was used to perform the taxonomical analysis of the data, ranked for genus and species. The processed data were also analysed with MetaPhlAn3, by aligning reads to the internal custom microbial database (mpa_v30_CHOCOPhlAn_201901) with default parameters, and with MetaPhlAn4 aligning reads with default parameters to the latest version available of the dedicated database (mpa_vOct22_CHOCOPhlAnSGB_202212). Since there is no official classification of prokaryotes, the names of taxa provided by MetaPhlAn3 have been used throughout the main text, figures and tables, but the names established by the International Code of Nomenclature of Bacteria and Prokaryotes were also indicated. 2.5 Statistical analysis Alpha-diversity was used to describe the microbiome diversity within sample and was measured with the Shannon H’ diversity index, Pielou evenness J index and the Margalef d richness index ( 27 ). In order to identify genera and specie that define the taxonomical differences in the two matrices the linear discriminant analysis (LDA) Effect Size (LEfSe) algorithm ( 28 ) was applied. The threshold on the logarithmic LDA score was set to 2.5 to be more conservative. Rstudio (v.4.2.2,( 29 )) was used for constructing the Neighbour Joining (NJ) tree, for principal coordinates analysis (PCoA) with the Bray-Curtis distance and to create heatmaps. RESULTS AND DISCUSSION 3.1 Biochemical Methane Potential and Microbial diversity Average daily biogas production from cereal grains and grape pomace AD are shown in Fig. 1 . The CG had the highest biogas yield, compared with GP. After 5 days of digestion, CG have completely exhausted the substrate, whereas GP showed a very slow trend and a minimal biogas production within the 30 days. Characterization of CG- and GP-based substrates has been reported in Table 1 . For CG-based substrates (Table 1 A), all parameters evidenced the rapid digestion within the first 5 days. Residual acetic acid from day 5 to day 30 may indicate cessation of methanogenesis due to the complete depletion of fermentable substrate. Table 1 B shows compositional parameters of GP-based substrate. As evidenced by the low and slow biogas production rate, high lignin content inhibited anaerobic digestion. Table 1 – Chemical characterization of the AD of cereal grains (A) and grape pomace (B) over 30 days. Data are expressed as fresh matter (FM). (A) Cereal grains Day TS (g/kg FM) VS (g/kg FM) pH Lignin (g/kg FM) Cellobiose (% FM) Glucose (% FM) Acetic acid (% FM) 0 145.0 121.5 8.0 0.20 0.08 0.04 0 5 57.9 31.5 6.0 0.32 0 0 0.16 10 74.0 33.7 5.8 0.24 0 0 0.17 15 64.1 29.4 6.0 0.27 0 0 0.19 20 59.0 35.1 5.9 0.28 0 0 0.16 25 55.9 31.8 5.9 0.29 0 0 0.20 30 54.5 27.5 6.0 0.29 0 0 0.17 (B) Grape pomace Day TS (g/kg FM) VS (g/kg FM) pH Lignin (g/kg FM) Cellobiose (% FM) Glucose (% FM) Acetic acid (% FM) 0 150 122.2 8.0 0.59 0 0.04 0.009 5 142 115.1 8.4 0.56 0 0.05 0 10 140 113.3 8.2 0.57 0 0 0 15 131 101.2 8.3 0.56 0 0 0 20 138 107.9 8.2 0.58 0 0 0 25 144 113.4 8.3 0.60 0 0 0 30 145 117.3 8.2 0.61 0 0 0 The Alpha diversity along with Evenness (Fig. 2 A and 2 B) calculated on the different samples showed a different trend in the two substrates: the CG samples (cg0-30) present a peak corresponding to the fifth day of fermentation and then decreased until the 30th day, while the GP samples show a linear growth over time. Richness index (Fig. 2 C) shows a similar trend in both matrices, growing in the first 10 days, then remaining more or less stable until day 30. A higher J Pielou's evenness at increased biogas production in both group of samples was observed. Compared with biogas production (see Fig. 1 ), it indicates that the better performing process (in terms of biogas production and methane content) is always related with higher community evenness ( 30 ). Community evenness is particularly important in a system such as AD, as it signposts equitable distribution between the various AD functional groups; this enables the community to fully exploit all metabolic pathways, as well as the co-metabolic pathways, which are known to play an important in AD performance ( 31 ). Further to this, communities with uneven distributions of diversity tend to be dominated by groups of microorganisms specialised to the current conditions, when exposed to external changes (e.g. pH) they are unable to adapt rapidly and require long recovery times. CG showed a rapid progressive loss of diversity over time, maintaining the number of species but with a prevalence of few taxa. Otherwise, in GP substrate samples a smooth and slow increase of diversity over time was evidenced, but with a good equipartition. 3.2 Taxonomical profiling The processing details of WMS sequencing for the 15 samples, seven for both CG and GP plus inoculum, are shown in supplementary materials. The throughput of sequencer was quite similar among the 15 samples, even after filtering out low quality sequence (see supplementary material). Three different software, differing in their ability to assign reads to specific taxonomic units, were used to build the taxonomical profiles: in terms of number of reads classified, BLASTN and MetaPhlAn3 showed similar values, while the classification rates increased by an order of magnitude with MetaPhlAn4 (see supplementary material), which is able to improve the metagenomic taxonomic profiling using the metagenome-assembled genomes (MAGs) to define an expanded set of species-level genome bins ( 25 ). All the three classification showed the same trend, with sample cg0 with the minor number of reads classified and sample cg25 with the highest classification rate. One of the main problems in the taxonomic profile reconstruction, obtained from metagenomic analyses, lies in the high percentage of reads that fail to be uniquely assigned to a species and are therefore classified as "unclassified organism" or "uncultured bacterium/archeon”. For BLASTN + MEGAN6 and Methaphlan3 the percentage of unclassified reads reaches 80–90%, whereas Methaphlan4 allows a deeper assignment of reads, through the adoption of the species-level genome bins system that makes it possible to drastically reduce the number of unassigned reads (see supplementary material). For this specific study design, despite the great potential of MetaPhlAn4 in metagenome analysis, it proved to be less useful because, by targeting generic OTUs instead of specific cultivable strains, it is less successful in identifying specific strains that could be subsequently used to improve and optimize the fermentation system. On the other hand, BLAST-based analysis also has some disadvantages, mainly related to the high computational time requested for analyses and to the redundancy of the database used by BLASTN. Furthermore, as a first descriptive approach, community progression analysis during biogas production was performed by NJ tree construction on BLASTN results (Fig. 3 A) and by PCoA (Fig. 3 B) for MetaPhlAn3. The two analyses highlighted exactly the same trend, showing two different dynamics for the two substrates during the fermentation process: in GP the microbial community changes slowly over time, while CG shows a rapid initial change in microbiome structure, corresponding to a high rate of biogas production, and then the community tends to return to a composition similar to the initial inoculum composition. In fact, the microbial community retrieved in the samples from CG fermentation after 5 days (cg5) was distant from the start point (inoculumu-cg0) in both representation; then through time the bacterial composition in CG returned similar to cg0. Conversely, the microbial composition of the samples obtained from GP fermentation gradually deviated from the starting point (inoculum-gp0) until day 30 during the biogas production process. Therefore, based on the above considerations and on the initial descriptive analyses of microbial community dynamics, which were found to be widely overlapping between BLASTN and MetaPhlAn3, only the taxonomic profile generated with MetaPhlAn3 will be discussed in detail below, while the results of the taxonomic analysis performed with BLASTN + MEGAN6 and MetaPhlAn4 are presented in the supplementary materials. The taxonomical composition of inoculum, in term of phylum and species, is detailed in the supplementary material; this sample was characterized by the presence of 5 phyla, the most abundant was Thermotogae (over than 65% of assigned reads), followed by Firmicutes (renamed as Bacillota), Euryarcheota (the only Archaea phylum identified), Synergistetes and Bacteroidetes. Eleven genera and 14 species were identified in the inoculum: Defluviitoga was the most abundant genera and Defluviitoga tunisiensis the dominant species (see supplementary material). The taxonomic profiles from time 0 to day 30 of the samples from both CG and GP are also showed in the supplementary material, where the distribution of phyla and species were, respectively, presented. As in the inoculum, in all samples the sequences were classified into 5 phyla (Thermogae, Firmicutes, Euryarchaeota, Synergistetes, and Bacteroidetes), with the Thermogae always being the most abundant followed by the Firmicutes, while the other three phyla are present with lower relative abundances. A total of 16 genera and 22 species were detected, the most abundant genera were Defluviitoga, Hungateiclostridium (reclassified as Acetovibrio) and Herbinix; the most represented species in all the samples were D. tunisiensis (over 65% of all samples), Hungateiclostridium saccincola (reclassified as Acetovibrio saccincola in GP samples, and Herbinix luporom in CG samples; all the other species had a relative abundance lower than 1% (see supplementary material). 3.3 Differences in microbial composition among different substrate Overall 12 genera and 15 species were identified: 6 genera and 7 species enriched in samples from CG; 6 genera and 8 species enriched in samples from GP. The relative abundance among all samples of these 15 species was used to generate the heatmap represented in Fig. 4 B. As previously described, D. tunisiensis was largely (relative abundance > 65%) present in all the samples during all the phases of biogas production (Fig. 4 B), but resulted statistical most abundant in the CG community samples (Fig. 4 A). Samples from CG were also significantly enriched by the presence of Clostridium cellulosi , Clostridium sp. N3C , Clostridium thermopalmarium , Methanobacte rium sp. MB1 and Methanothermobacter wolfeii (Fig. 4 A). During the first days (cg5-15) of production of biogas in samples from cereal grains there was a higher abundance of H. luporum , C. sp. N3C , C. thermopalmarium and M. wolfeii (Fig. 4 B). Conversely, the same substrate showed an increase of C. cellulosi an M. sp. MB1 during the late phases of biogas production in CG samples (cg20-30, Fig. 4 B). The second most abundant species detected in all samples was H. saccincola , which was one of the most common species at the beginning of the biogas production process (inoculum, cg0, gp0) and remained significantly highly abundant during all time in grape pomace samples (Fig. 4 B). Samples from GP were also enriched by Acetomicrobium hydrogeniformans , Acetomicrobium thermoterrenum, Methanoculleus bourgensis, Methanosarcina flavescens, Methanosarcina thermophila, Peptococcaceae bacterium 1109 and Petrimonas mucosa (Fig. 4 A and 4 B). A. hydrogeniformans, M. bourgensis and P. bacterium 1109 presented all the same dynamics: they were abundant during the first stages (gp5-15) of fermentation and then their abundance decrease. Conversely A. thermoterrenum , M. thermophila and P.mucosa showed a progressive increase during last days of biogas production (gp20-25). M. flavescens exhibited a different growth dynamic: it was very abundant at the beginning (gp0), its abundance slightly decreased during the later stages of fermentation (gp5-15) and then increased again in the last ten days (gp20-30, Fig. 4 B). Since the biogas production rate had its peak during the first days of the fermentation process a comparison of early time samples (5-10-15 days), on both substrates, were performed with LEfSe (see supplementary material). The principal results of this analysis were similar to the general comparison between samples (CG vs GP Fig. 4 A), but this analysis also revealed the presence of Clostridium isatidis as significantly enriched in the CG samples, and Petrotoga halophila enriched in GP samples. 3.4 Structure of microbial community in the key microbiological steps of AD process In recent years, various molecular biological techniques (including genomics, metagenomics, meta-transcriptomics) have been applied to investigate the composition and the dynamics of the AD microbiome and to understand its implications for the biogas process ( 15 ). Among the organic biomasses of interest for biogas production, GP is one of the most abundant waste products in the agri-food industry, but fermentation of this type of substrate has a low yield in biogas production; the purpose of this work was to compare, through the WMS approach, the different dynamics of the microbiome during GP and CG fermentation with the aim of identifying effective approaches to optimize biogas production and ensure process stability in GPs. The two different substrates show different microbiome structure and dynamics over time, although the two processes were both characterized during all experimental phases by very high levels of D. tunisiensis (Fig. 4 B). High abundances of D. tunisiensis were already detected in a thermophilic laboratory fermenter ( 32 ), highlighting the broad spectrum of substrates (polymers, oligosaccharide, acids and alcohols) that D. tunisiensis is capable of metabolizing therein including the ability to degrade cellulose since genes encoding non-cellulosomal cellulases were identified in its genome. Li et al. ( 33 ) confirmed the cellulose-degrading ability of D. tunisiensis , showing how this microorganism is predicted to participate in the AD of a variety of carbohydrates and produces acetate, H 2 and CO 2 . Other researches support the idea that the high H 2 -producing ability of D. tunisiensis significantly influenced the proportion of hydrogenotrophic archaea species that, syntrophically associated with this bacterium, can utilise CO 2 and H 2 for methanogenesis ( 34 ). Next to D. tunisiensis , two other bacteria, H. luporum and H. saccincola , also characterized at high levels CG and GP respectively (Fig. 4 B). With regard to H. luporum , although its relative abundance is significantly higher in CG, its levels in GP are more than 3% making it a relatively abundant species in this matrix as well. The complete genome sequence of H. luporum was reported in 2016 ( 35 ) and its characterization has shown that the bacterium is able to digest cellulosic and hemicellulosic substrates. Also, Maus et al. ( 36 ) described H. luporum to be involved in thermophilic degradation of lignocellulosic biomass representing together with C.cellulosi an important cellulose degrader. On H. saccincola there is not much information, in 2019 Rettenmaier et al. ( 37 ) showed that H. saccincola is closely related to Hungateiclostridium thermocellum a well-known cellulolytic key players such as C.cellulosi and Herbinix hemicellulosilytica . Recently, a study aimed to characterize the synergism of a hydrolytic/cellulolytic bacterial consortium isolated from biogas fermenters proposed that enzymatic activity of H. thermocellum liberates soluble mono- and oligosaccharides from cellulose and hemicellulose, thereby promoting growth of saccharolytic bacteria. Bacterial synergism is supposed to accelerate biomethane production in AD of plant fibers by increasing the overall cellulose hydrolysis rates and increasing the amounts of produced volatile metabolites ( 38 ). Present results seem to confirm the importance of this metabolic synergy, in fact in both matrices the co-presence of cellulolytic, saccharolytic and hydrolytic bacteria was confirmed: in the CG, H. luporum was flanked by a consortium of clostridia among which the non-cellulolytic C. sp. N3C and C. thermopalmarium strains, while the cellulolytic H. saccincola and H. luporum in GP are accompanied with non-cellulolytic Acetomicrobium sp and P. mucosa (Fig. 5 ) as we will discuss below. A further general consideration of the overall microbiome dynamics of the two different biomasses concerns methanogenesis by two different types of Archea: M. wolfeii and M. sp. MB1 characterise the CG while, the M.thermophila and M.flavescens are more represented in GP and, reasonably, all grow in abundance toward the terminal phase of the AD cycle, indicating their role in the aceticlastic and hydrogeno-trophic pathways (Fig. 5 ). To date, while the role of Methanosarcina in AD is fairly well described in the literature, not much is known about the two strains observed in CG, and the present analysis is one of the first studies that identify M. wolfeii and M. sp. MB1 as two prokaryotes with a main role in methanogenesis from CG. 3.4.1 Dynamics of microbial community in AD process for cereal grains Exploring the structure and dynamics of CG microbial community in more detail, from the comparison of early time samples (5-10-15 days) between CG and GP, C. isatidis results significantly enriched in CG. Little is known about this clostridium strain, but its role in AD results from a 2010 study indicating the C.isatidis strain as capable of directly converting cellulose to ethanol ( 39 ). Moreover, we observed that during the early days (cg5-15) there was a higher abundance of C. sp. N3C and C. thermopalmarium (see supplementary material), both are non-cellulolytic, hydrogen-producing bacteria and both seemed to contribute to butyrate production ( 36 , 40 ). The co-existence of cellulolytic strains, such C.isatidis and H.luporum , and hydrogen producers, such as the C. sp. N3C and C. thermopalmarium as seen in the present study highlights the idea that these strains, by taking advantage of their specific metabolic capacities, offers a promising new way to improve the conversion efficiency of cellulose to hydrogen. In fact, the use of cellulolytic bacteria for hydrogen production is often limited by low hydrogen yields, due to the bacteria's poor growth rates and pH sensitivity. The present analysis is one of the first studies that identify M. wolfeii and M. sp. MB1 as two prokaryotes with a major role in methanogenesis from CG (cg20-30, Fig. 4 B). These two archaea are both hydrogenotrophic methanogens that use formate, hydrogen, and carbon dioxide as substrates for methanogenesis and require acetate for growth ( 36 ). Furthermore, it cannot be ruled out that C. cellulosi might form a syntrophic association with these hydrogenotrophic methanogens, in particular with M. sp. MB1 since both these prokaryotes show a similar growth in their abundance in the final stage of the fermentation process. 3.4.2 Dynamics of microbial community in AD process for grape pomace As described (Fig. 3 A and 3 B) the structure of the microbial community changes slowly over time in GP, maintaining biogas production capacity longer, albeit at lower levels, than CGs (Fig. 1 ). Next to the two cellulolytic bacteria already discussed, D. tunisiensis and H. saccincola , present at high abundance throughout GP digestion as well, this matrix is characterized by the presence of A.hydrogeniformans, M.bourgensis and P. bacterium 1109, that typify the first days of AD and A. thermoterrenum, M. thermophila, M. flavescent and P.mucosa showing a progressive increase during last days of AD (Fig. 4 B). The Acetomicrobium genus, identified in 2016 and initially classified as Anaerobaculum ( 41 ), is reported in biogas production reactors, and their role could be attributed to the digestion of fats and proteins ( 42 ), as well as glucose fermentation to acetate, CO 2 and H 2 ( 41 ). However, to date, little or no information is available in the literature about the two species observed in the present study, i.e. A.hydrogeniformans and A. thermoterrenum. Their presence in GP digestion could be explained considering the high concentration of promptly digestible lipids (7–15%) and proteins (8–16%) that characterized this substrate ( 43 ). In fact, in A. hydrogeniformans genome a thermostable esterase was identified presenting high catalytic activity with a preference towards short-acyl-chain esters ( 44 ), while A.thermoterrenum is involved in glycerol conversion ( 45 ), both processes being related to lipids metabolism during AD. Moreover, is interesting to notice that both strains of Acetomicrobium observed in this study potentially act as syntrophic partner with homoacetogenic or syntrophic acetate-oxidizing bacteria (SAOB)( 46 ), and with acetoclastic or hydrogenotrophic methanogens (HM) even if their role in these syntrophic communities still remains enigmatic ( 47 ). The strong association between SAOB and HM is well documented and, compared to acetoclastic methanogenesis, methane production from the association between SAOB and HM is thermodynamically favoured at high temperatures ( 48 ). In this regard, the co-presence of A. hydrogeniformans and M.bourgensis , a known syntrophic partner of SAOB bacteria ( 49 ), detected in this study at the early stages of GP AD (Fig. 4 B and 5 ) elucidates how the system is capable of synthesizing methane via the hydrogenotrophic pathway even in the initial stages of fermentation of this biomass. P. halophila and P. bacterium 1109 species have been identified in our GP substrate. So far, Petrotoga species has been isolated only from oil reservoirs, whereas P. bacterium 1109 is typically classified as belonging to acetogenic community, highly abundant in most biogas plants Singh et al. ( 50 ). Buettner et al. ( 51 ) with a network analyses approach demonstrated that P.bacterium 1109 was a key module in both HM and acetoclastic methanogenesis, highlighting a possible SAOB behaviour. An additional ability of the P.bacterium 1109 appears to be the conversion of propionate in methane, through cooperation between the P. bacterium and the methanogen M.bourgensis , even in inhibiting ammonia concentration Singh et al. ( 50 ), deriving from proteins catabolism. The later GP fermentation stages, are characterized by the presence of two relevant Methanosarcina species, M. flavescens and M.thermophila . M. flavescens is a hydrogenotrophic and acetoclastic methanogen species that was recently identified and proliferates at increased shear velocity when acetic acid is the major VFA component ( 52 ); conversely, M.thermophila is a more established thermophilic archea that is referred to as acetoclastic methanogen but that can grow well also by utilizing methanol or methylated amines and slowly utilizing H 2 /CO 2 ( 53 ). All Methanosarcina selected from GP substrate contain cytochrome c, a high intermembrane electron transfer medium, which enables them to withstand high hydrogen partial pressures, compared with methanogens present in CG substrate, lacking cytochrome c ( 54 ). This could be presumably due to the prevalence of catabolism pathways of protein and lipids in GP that increase the hydrogen partial pressure (Fig. 5 ). Another bacterium that could play an important role in this microbial community is P.mucosa , also observed at higher abundance in the later stage of AD of GP in the present investigation (Fig. 4 B). Its function in the AD process most probably is associated with acidogenesis. Based on integrated omics analyses it has also been observed that P. mucosa encodes a diverse set of glycosyl-hydrolyses involved in carbohydrate metabolism. Under these conditions, it may play an important role in conversion of lignocellulosic biomass ( 55 ). It’s worth noting as digestion GP substrate leads to the development of a more complex bacterial consortium than CG and a combination of synergistic behaviours. The presence of bacteria as Acetomicrobium sp. and P.mucosa , involved in the degradation of lipids and protein-rich substrates, together with Methanosarcina sp. and P.bacterium 1109, able to tolerate high hydrogen pressures and high ammonia concentration derived by aminoacids degradation, seems to confirm the preference towards more rapidly-fermentable biomass components, as proteins and lipids, rather than lignocellulose. Moreover, in the late AD stages, the co-presence here observed of M.bourgensis with P.bacterium 1109, which relative abundance was seen as positively correlated to pH and is able to hydrolyze substrates efficiently even at pH ≥ 7.7, could be indicative of an adapted microbial communities ( 51 ). The present results pave the ground on the perspective to build a tailored microbial consortium to be inoculated from the beginning of the process in view of improve biogas yield during lignocellulosic biomass AD. CONCLUSION Comparing CG and GP substrates with WMS analysis, we identified 15 specific species, detecting some new strains: in GP, showing higher species-complexity, potentially syntrophic interactions between cellulolytic H.saccincola , non-cellulolytic Acetomicrobium sp. , and HMs M.bourgensis and Methanosarcina sp . in both early and late AD stages were detected; in CG: the cellulolytic C.isatidis , a Clostridium that could play an important role during early-stage of cellulose degradation in synergy with M.wolfeii was observed. The results here presented represent a preliminary effort to develop a tailor-made microbial consortium to be inoculated from the beginning of AD process in order to accelerate and improve bioenergy production from lignocellulosic feedstocks. Declarations Author Contribution Conceptualization, C.S. and E.T; methodology, N.F., S.C., S.S., E.M., C.S. and E.T.; formal analysis, N.F., S.C., D.S. and C.S.; writing—original draft preparation, N.F., S.C., E.T. and C.S.; writing—review and editing, N.F., D.S., S.C., E.M., S.S., C.S. and E.T; supervision, C.S. and E.T.; funding acquisition, C.S., S.S. and E.T. ACKNOWLEDGMENTS The study was supported by research grants of the University of Ferrara (Scapoli, FAR 2019–2020; Sabbioni, FAR 2019-20; Tamburini, FAR 2020-21). DATA AVAILABILITY WMS sequencing data has been deposited into public database NCBI, and the BioProject accession number is PRJNA1037512. References Ho DP, Ngo HH, Guo W. A mini review on renewable sources for biofuel. Bioresour Technol. 2014;169:742–9. Dewil R, Appels L, Baeyens J. Energy use of biogas hampered by the presence of siloxanes. Energy Convers Manag. 2006;47(13–14):1711–22. Babu S, Singh Rathore S, Singh R, Kumar S, Singh VK, Yadav SK, et al. 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The Potential of Grape Pomace Varieties as a Dietary Source of Pectic Substances. Foods. 2021;10(4):867. Cook LE, Gang SS, Ihlan A, Maune M, Tanner RS, McInerney MJ, et al. Genome Sequence of Acetomicrobium hydrogeniformans OS1. Genome Announc. 2018;6(26):e00581-18. Magalhães CP, Ribeiro JA, Guedes AP, Arantes AL, Sousa DZ, Stams AJM, et al. Co-cultivation of Thermoanaerobacter strains with a methanogenic partner enhances glycerol conversion. Microb Biotechnol. 2020;13(4):962–73. Li C, Hao L, Lü F, Duan H, Zhang H, He P. Syntrophic Acetate-Oxidizing Microbial Consortia Enriched from Full-Scale Mesophilic Food Waste Anaerobic Digesters Showing High Biodiversity and Functional Redundancy. mSystems. 2022;7(5):e00339-22. Dyksma S, Jansen L, Gallert C. Syntrophic acetate oxidation replaces acetoclastic methanogenesis during thermophilic digestion of biowaste. Microbiome. 2020;8(1):105. Dolfing J. Thermodynamic Constraints on Syntrophic Acetate Oxidation. Appl Environ Microbiol. 2014;80(4):1539–41. Manzoor S, Schnürer A, Bongcam-Rudloff E, Müller B. Complete genome sequence of Methanoculleus bourgensis strain MAB1, the syntrophic partner of mesophilic acetate-oxidising bacteria (SAOB). Stand Genomic Sci. 2016;11(1):80. Singh A, Müller B, Schnürer A. Profiling temporal dynamics of acetogenic communities in anaerobic digesters using next-generation sequencing and T-RFLP. Sci Rep. 2021;11(1):13298. Buettner C, Von Bergen M, Jehmlich N, Noll M. Pseudomonas spp. are key players in agricultural biogas substrate degradation. Sci Rep. 2019;9(1):12871. Cayetano RDA, Park JH, Kim SH. Effect of shear velocity and feed concentration on the treatment of food waste in an anaerobic dynamic membrane Bioreactor: Performance Monitoring and microbial community analysis. Bioresour Technol. 2020;296:122301. Lerm S, Kleyböcker A, Miethling-Graff R, Alawi M, Kasina M, Liebrich M, et al. Archaeal community composition affects the function of anaerobic co-digesters in response to organic overload. Waste Manag. 2012;32(3):389–99. Thauer RK, Kaster AK, Seedorf H, Buckel W, Hedderich R. Methanogenic archaea: ecologically relevant differences in energy conservation. Nat Rev Microbiol. 2008;6(8):579–91. Maus I, Tubbesing T, Wibberg D, Heyer R, Hassa J, Tomazetto G, et al. The Role of Petrimonas mucosa ING2-E5AT in Mesophilic Biogas Reactor Systems as Deduced from Multiomics Analyses. Microorganisms. 2020;8(12):2024. Additional Declarations No competing interests reported. Supplementary Files DynamicMicrobiomeADBiomassGraphicalAbstBMCScapoli.jpeg DynamicMicrobiomeADBiomassSupplMaterialFavale2024.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4003924","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":276338178,"identity":"3064f77f-3e5f-4a1c-bcc6-8e98f8ba16dd","order_by":0,"name":"Nicoletta Favale","email":"","orcid":"","institution":"University of Ferrara","correspondingAuthor":false,"prefix":"","firstName":"Nicoletta","middleName":"","lastName":"Favale","suffix":""},{"id":276338179,"identity":"c3fdcb02-ff2a-4760-9196-382cdccb7192","order_by":1,"name":"Stefania Costa","email":"","orcid":"","institution":"University of Ferrara","correspondingAuthor":false,"prefix":"","firstName":"Stefania","middleName":"","lastName":"Costa","suffix":""},{"id":276338180,"identity":"628ef067-e4c3-4471-87f3-bc59aa9b5a26","order_by":2,"name":"Daniela Summa","email":"","orcid":"","institution":"University of Ferrara","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Summa","suffix":""},{"id":276338181,"identity":"2d4d1755-66da-46af-9b1c-9362ba4a3f5f","order_by":3,"name":"Silvia Sabbioni","email":"","orcid":"","institution":"University of Ferrara","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Sabbioni","suffix":""},{"id":276338182,"identity":"6a56218b-5fad-42db-a9e1-f7dcdf3b9373","order_by":4,"name":"Elisabetta Mamolini","email":"","orcid":"","institution":"University of Ferrara","correspondingAuthor":false,"prefix":"","firstName":"Elisabetta","middleName":"","lastName":"Mamolini","suffix":""},{"id":276338183,"identity":"0b981029-eb9d-43c1-b346-05602df96e7c","order_by":5,"name":"Elena Tamburini","email":"","orcid":"","institution":"University of Ferrara","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Tamburini","suffix":""},{"id":276338184,"identity":"6a5b8333-8d0b-4c17-8cb6-19067720309c","order_by":6,"name":"Chiara Scapoli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCcYGBOeBgQ0DAzPjAwYGA2K1JBikAbUwA9Ub4NEjgcxJYDgMJEFa8FjDP7u58XNFDYM9f/vZgw8SCs4nbmcHuazgD25L7hxsljxzjCFxxpm8ZIMEg9uJO5tBLsPjMAOJxAbJBjagLxhyzCRAWjYc5j8mQUBL88+Gfwz2BvxvQFrOAbUws/8goKVNsrGNgXGDBNiWAyAtbHhDTOJGYptlY59E4owb70B+STYGamEG6jXGqYV/Rvrjmw3fbOz5+3MPPvjwx052w/nDjB8+/JHDqQVmGRDzIPETCGmAAB7CSkbBKBgFo2BkAgB4sU9UxpqYNQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ferrara","correspondingAuthor":true,"prefix":"","firstName":"Chiara","middleName":"","lastName":"Scapoli","suffix":""}],"badges":[],"createdAt":"2024-03-01 17:00:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4003924/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4003924/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52067406,"identity":"e308e05b-ffb9-4e3e-a4ed-40f0a2c4806e","added_by":"auto","created_at":"2024-03-06 07:12:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62444,"visible":true,"origin":"","legend":"\u003cp\u003eBiogas yield from cereal grains and grape pomace AD, expressed as Nm\u003csup\u003e3\u003c/sup\u003e/ton FM. Each data point is the average of the measurements of six reactors.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4003924/v1/45be3bb178c68132bb83599a.png"},{"id":52067405,"identity":"caa317ee-1c61-47d7-afc1-6104569efa3b","added_by":"auto","created_at":"2024-03-06 07:12:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43860,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity inside the different matrices during time: 2A) alpha-diversity (H’) index, 2B) Evenness index, 2C) Richness index.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4003924/v1/9eda43b6007597eee3fb4ff5.png"},{"id":52067408,"identity":"1a795a58-7112-4181-9de1-a7a873c4cf8a","added_by":"auto","created_at":"2024-03-06 07:12:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126101,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial community progression during biogas production process: \u003cstrong\u003e3A)\u003c/strong\u003e Neighbor Joining tree from BLASTN output; \u003cstrong\u003e3B)\u003c/strong\u003e plot of principal coordinates analysis (PCoA) of microbial composition from MetaPhlAn3 output, the analysis was based on Bray-Curtis matrix distance (species).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4003924/v1/b54d364a64f8bbaead3371a9.png"},{"id":52067411,"identity":"07ed0c94-bc57-4b5d-9457-ccc7522c2bd8","added_by":"auto","created_at":"2024-03-06 07:12:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":431369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e4A) \u003c/strong\u003eCladogram plotted from the LEfSe analysis, comparing all CG samples with all GP samples. Taxonomic levels are represented by rings with phyla in the outermost ring and species in the innermost ring. Each circle represents a member within that level. Taxa with enriched level in samples from CG are coloured in red, those enriched in samples from GP are coloured in green; unenriched taxa are ochre coloured\u003cstrong\u003e. 4B) \u003c/strong\u003eRelative abundance of strains resulted significantly different in the LEfSe analysis between the two substrate, represented as an heatmap. Abundancy value were showed with a shade color scale from white (0%) to red (100%).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4003924/v1/b42ff0c6fc021aefc41ab1bb.png"},{"id":52067407,"identity":"f5511418-5799-4bba-8ef4-ec0af5867492","added_by":"auto","created_at":"2024-03-06 07:12:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":332063,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional groups of microorganisms in the hydrolysis, acidogenesis, acetogenesis and methanogenesis processes. The microorganisms which increased in abundance in CG are marked in red and those which increased in abundance in GP are marked in green.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4003924/v1/16391ba157109daa794e8cf9.png"},{"id":63192100,"identity":"931e1331-5315-400e-9758-33e559af9669","added_by":"auto","created_at":"2024-08-24 15:52:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1633634,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4003924/v1/83860e8a-ebca-47c3-8584-1144750fdf91.pdf"},{"id":52067410,"identity":"009c91a7-a4c2-4b3b-812c-dae09387f02a","added_by":"auto","created_at":"2024-03-06 07:12:46","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2984935,"visible":true,"origin":"","legend":"","description":"","filename":"DynamicMicrobiomeADBiomassGraphicalAbstBMCScapoli.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4003924/v1/8f819a1ac7dffefd2bd7d479.jpeg"},{"id":52067412,"identity":"7165fc70-344c-426e-8803-b55b7e7d6c7c","added_by":"auto","created_at":"2024-03-06 07:12:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1940784,"visible":true,"origin":"","legend":"","description":"","filename":"DynamicMicrobiomeADBiomassSupplMaterialFavale2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4003924/v1/0345be57d92d29086d1ae764.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamics of microbiome composition during anaerobic digestion of different renewable resources","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe reorientation of the global energy industry towards renewable sources is one of the major challenges of this century and has become an excellent component to any alternative energy portfolio (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In particular, anaerobic digestion (AD) among the biochemical conversion of agricultural still represents an economically attractive technology to generate bioenergy and reduce climalterant emissions (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Energy crops such as cereal grains (CG) are a common substrate for bioenergy production due to their high biogas potential (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, they may cause important environmental burdens due to the requirement of intensive agricultural activities and fertilisers, with negative impacts on soils and water, as well as land use constraints and the impact on other non-energy commodities, such as food (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Currently, lignocellulosic residual biomass has received extensive attention because it is one of the most abundant organic compounds on earth, it is cheap and geographically distributed (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Although anaerobic co-digestion of various agricultural feedstock is a common approach in terms of sustainable biofuel production, the presence of poorly biodegradable components in lignocellulosic biomass dramatically limits the methane recovery in AD systems. The main challenge in the use of lignocellulosic materials for biogas production, remains their structure and composition, because it primarily consists of cellulose, hemicellulose and lignin, that is extremely recalcitrant against microbial degradation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Grape pomace (GP), the main solid vinery waste, contains up to 45% lignin (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Considering that averagely 18 kg of GP is generated per 100 L of wine produced, about 5\u0026nbsp;million tons of such residue are annually yielded (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Nowadays, a recent European reform in the wine sector (EC Regulation 479/2008) promotes the gradual withdrawal of distillation subsidies and consequently revokes the compulsory distillation. This should drive the promotion of integrated, sustainable and standardized alternative protocols for the valorisation of solid winery waste.\u003c/p\u003e \u003cp\u003eAn alternative valorisation of GP could be represented by the production of biogas by anaerobic digestion (AD) processes. However, low performances were generally achieved, due to the high content of lignin, which is not readily fermentable (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Moreover, it has been reported the detection of a significant lag phase during methane production (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The GP moisture after pressing is around 20\u0026ndash;30% w/w, and the material is usually characterized by C:N ratio ranges from 40 to 45:1, pH ranges from 3 to 6, and low electrical conductivity (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Several mechanical and chemical procedures for saccharification have been established, but definitely there is no turn-key bioenergy lignocellulosic feedstock solution at this time (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn approach based on metagenomic has been in recent times proposed in order to study the microbial communities and biodiversity involved in the AD process, being the majority of members participating in the biogas production process still unknown (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Metagenomics offers the possibility of studying the genetic material of difficult-to-culture species within microbial communities with the competence to degrade lignocellulosic biomass (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This approach could provide a new opportunity to elucidate lignocellulose degradation mechanisms used by currently lesser-known microbial species. These techniques also provide insights into the composition, functional gene profiling, and metabolic pathway reconstruction of microbial communities, enabling a more comprehensive understanding of their roles in biogas production (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). A knowledge of the microbiome residing in the anaerobic digester can be further used for the development of more efficient processes in conjunction with the identified consortium and could be essential to minimize process failures and create new strategies for optimizing lignocellulose-based AD processes (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe focus on microbiomes and comparative genomic analyses of the microbial communities present in different organic wastes is critical to better understand the molecular basis of AD activities and their use for lignocellulosic degradation without further pre-treatment and to achieve more effective and efficient AD performance for this biomass (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this research, we therefore aimed to 1) determine the biochemical methane potential (BMP) of lignocellulosic grape pomace (GP) as carbon source and compare it with the BMP of cereal grains (CG) in AD trials; 2) apply the high-resolution whole metagenomic shotgun (WMS) analysis to explore the composition of microbiome during AD of CG and GP and compare microbial community dynamics; 3) relate microbiology to biogas performance, and investigate differences in reactors\u0026rsquo; microbial community dynamics compared to the original inoculum culture.\u003c/p\u003e \u003cp\u003eThe results of the present study provide evidence in favour of the hypothesis that integrating genomics with AD process monitoring and control strategies offers new possibilities for optimizing biogas production and ensuring process stability. In addition, they could lay the foundation for the production of starter microbial mixtures for AD of specific lignocellulosic biomasses, thereby improving biogas production.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Set up and experimental design\u003c/h2\u003e \u003cp\u003eInoculum sludge, CG and GP were obtained from a local biogas production facility (Sesa Spa, Padua, Italy). CG was a mixture of corn silage, ground whole flour and corn cob. Lab scale batch reactor was set up with glass digesters of 250 ml of capacity, equipped with two-ways screw caps. The average pH of the inoculum was 8.0, VS/TS ratio (Volatile Solids, VS; Total Solids, TS) and TS were 82.6% and 73.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 g/kg (wet base) respectively. In each digestion test, 60 g of inoculum (seed bacterial culture sludge) was used and mixed with the substrate resulting in 14 trial reactors (seven with CG and seven with GP) to be analysed in parallel during AD and compared with the inoculum in the metagenomic profiles. The VS/TS ratio of sample and inoculum in each bottle was 1:1. All reactors were tightly closed with rubber septa, incubated in a water bath (Argolab WB 22, Giorgio Bormac Srl, Modena, Italy) at 55\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C and manually mixed for about 1 min once a day prior to measurement of biogas volume. The formed biogas was led into 1% NaOH solution to remove CO\u003csub\u003e2\u003c/sub\u003e and the volume of methane determined every 24 hours using Dietrich\u0026ndash;Fr\u0026uuml;hling calcimeter (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). For reference purposes, the methane yield produced by the inoculum alone was determined and subtracted from the sample yields. Methane yields were expressed as volume of methane Nm3/ton (normal cube meter per ton) per corrected unit mass of VS (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Every 5 days, a bottle was withdrawn and used as sample for chemical and genomic analysis. Sampling was suspended after 30 days, when chemical assays revealed that AD activity had concluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Chemical assays\u003c/h2\u003e \u003cp\u003epH was measured by a calibrated Crison basic 20 pH meter (Crison, Barcelona, Spain) at 20\u0026deg;C. Total solids (TS), volatile solids (VS) and ashes on dry basis were determined using the combustion method at 105\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C for 24 hours according to \u0026ldquo;Standard Methods\u0026rdquo; (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Lignin content was determined by means of the INNVENTIA Test Methods L 2:2016 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Sugars and volatile acids have been analysed using HPLC LC4000 (Jasco Inc., MD, USA), equipped with refractive index detector (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 DNA Sample processing\u003c/h2\u003e \u003cp\u003eSludge samples collected from the reactors were labelled \"cg0\" to \"cg30\" and \"gp0\" to \"gp30\" for the different experimental time intervals for CG and GP, respectively, and stored at 20\u0026deg;C until centralized analysis. Whole genomic DNA was extracted using the GenElute\u0026trade; Bacterial Genomic DNA Kit (Sigma), in accordance with the manufacturer's instructions. Purified DNA was quantified by spectrophotometer (Shimadzu BioSpec-nano) and by Qubit 2.0 Fluorometer (Life Technologies) by using the Qubit dsDNA HS Assay Kit (Life Technologies).\u003c/p\u003e \u003cp\u003eDNA was fragmented using NEBNext\u0026reg; dsDNA Fragmentase for 30 min. NEBNext\u0026reg; Multiplex Oligos for Illumina\u0026reg; (Dual Index Primers set 1) were used to produce the library and label it with specific molecular barcodes. The library was generated from 100 ng of genomic DNA, in accordance with the NEBNext\u0026reg; Ultra\u0026trade; II DNA Library Prep Kit for Illumina protocol (New England Biolabs). Then, AMPure XP beads (Beckman Coulter) were used for library purification. Finally, the library was quantified using the High Sensitivity DNA Kit (Agilent Technologies) on the Bioanalyzer instrument (Agilent). Sequencing was performed with an Illumina NextSeq 500 sequencer with 2 \u0026times; 150-bp read, using NextSeq\u0026reg; 500/550 Mid Output Kit v2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Taxonomic profiling\u003c/h2\u003e \u003cp\u003eAfter QC (detailed in Supplementary materials), community taxonomic analysis of the processed data was performed using BLASTN (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and Metagenomic Phylogenetic Analysis softwares (MetaPhlAn3 v. 3.0.11,(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e); MetaPhlAn4 v.4.0.6,(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)). BLASTN analysis was conducted using the NCBI \u0026ldquo;nt\u0026rdquo; reference database, the parameters were set to exclude matches with an e-value\u0026thinsp;\u0026gt;\u0026thinsp;1x10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, a percentage of identity\u0026thinsp;\u0026ge;\u0026thinsp;95% and a minimum length\u0026thinsp;\u0026gt;\u0026thinsp;100 bp. Then, MEGAN6 (v.6.21.5, (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)) with default parameters was used to perform the taxonomical analysis of the data, ranked for genus and species.\u003c/p\u003e \u003cp\u003eThe processed data were also analysed with MetaPhlAn3, by aligning reads to the internal custom microbial database (mpa_v30_CHOCOPhlAn_201901) with default parameters, and with MetaPhlAn4 aligning reads with default parameters to the latest version available of the dedicated database (mpa_vOct22_CHOCOPhlAnSGB_202212).\u003c/p\u003e \u003cp\u003eSince there is no official classification of prokaryotes, the names of taxa provided by MetaPhlAn3 have been used throughout the main text, figures and tables, but the names established by the International Code of Nomenclature of Bacteria and Prokaryotes were also indicated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eAlpha-diversity was used to describe the microbiome diversity within sample and was measured with the Shannon H\u0026rsquo; diversity index, Pielou evenness J index and the Margalef d richness index (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to identify genera and specie that define the taxonomical differences in the two matrices the linear discriminant analysis (LDA) Effect Size (LEfSe) algorithm (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) was applied. The threshold on the logarithmic LDA score was set to 2.5 to be more conservative. Rstudio (v.4.2.2,(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)) was used for constructing the Neighbour Joining (NJ) tree, for principal coordinates analysis (PCoA) with the Bray-Curtis distance and to create heatmaps.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Biochemical Methane Potential and Microbial diversity\u003c/h2\u003e \u003cp\u003eAverage daily biogas production from cereal grains and grape pomace AD are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The CG had the highest biogas yield, compared with GP. After 5 days of digestion, CG have completely exhausted the substrate, whereas GP showed a very slow trend and a minimal biogas production within the 30 days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCharacterization of CG- and GP-based substrates has been reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For CG-based substrates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), all parameters evidenced the rapid digestion within the first 5 days. Residual acetic acid from day 5 to day 30 may indicate cessation of methanogenesis due to the complete depletion of fermentable substrate. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB shows compositional parameters of GP-based substrate. As evidenced by the low and slow biogas production rate, high lignin content inhibited anaerobic digestion.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Chemical characterization of the AD of cereal grains (A) and grape pomace (B) over 30 days. Data are expressed as fresh matter (FM).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e(A) Cereal grains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003cp\u003e\u003cb\u003e(g/kg FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVS\u003c/p\u003e \u003cp\u003e\u003cb\u003e(g/kg FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLignin\u003c/p\u003e \u003cp\u003e\u003cb\u003e(g/kg FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCellobiose\u003c/p\u003e \u003cp\u003e\u003cb\u003e(% FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003cp\u003e\u003cb\u003e(% FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eAcetic acid\u003c/p\u003e \u003cp\u003e\u003cb\u003e(% FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e(B) Grape pomace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003cp\u003e\u003cb\u003e(g/kg FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVS\u003c/p\u003e \u003cp\u003e\u003cb\u003e(g/kg FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLignin\u003c/p\u003e \u003cp\u003e\u003cb\u003e(g/kg FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCellobiose\u003c/p\u003e \u003cp\u003e\u003cb\u003e(% FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003cp\u003e\u003cb\u003e(% FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAcetic acid\u003c/p\u003e \u003cp\u003e\u003cb\u003e(% FM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Alpha diversity along with Evenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) calculated on the different samples showed a different trend in the two substrates: the CG samples (cg0-30) present a peak corresponding to the fifth day of fermentation and then decreased until the 30th day, while the GP samples show a linear growth over time. Richness index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) shows a similar trend in both matrices, growing in the first 10 days, then remaining more or less stable until day 30.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA higher J Pielou's evenness at increased biogas production in both group of samples was observed. Compared with biogas production (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), it indicates that the better performing process (in terms of biogas production and methane content) is always related with higher community evenness (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Community evenness is particularly important in a system such as AD, as it signposts equitable distribution between the various AD functional groups; this enables the community to fully exploit all metabolic pathways, as well as the co-metabolic pathways, which are known to play an important in AD performance (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Further to this, communities with uneven distributions of diversity tend to be dominated by groups of microorganisms specialised to the current conditions, when exposed to external changes (e.g. pH) they are unable to adapt rapidly and require long recovery times. CG showed a rapid progressive loss of diversity over time, maintaining the number of species but with a prevalence of few taxa. Otherwise, in GP substrate samples a smooth and slow increase of diversity over time was evidenced, but with a good equipartition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Taxonomical profiling\u003c/h2\u003e \u003cp\u003eThe processing details of WMS sequencing for the 15 samples, seven for both CG and GP plus inoculum, are shown in supplementary materials. The throughput of sequencer was quite similar among the 15 samples, even after filtering out low quality sequence (see supplementary material).\u003c/p\u003e \u003cp\u003eThree different software, differing in their ability to assign reads to specific taxonomic units, were used to build the taxonomical profiles: in terms of number of reads classified, BLASTN and MetaPhlAn3 showed similar values, while the classification rates increased by an order of magnitude with MetaPhlAn4 (see supplementary material), which is able to improve the metagenomic taxonomic profiling using the metagenome-assembled genomes (MAGs) to define an expanded set of species-level genome bins (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). All the three classification showed the same trend, with sample cg0 with the minor number of reads classified and sample cg25 with the highest classification rate.\u003c/p\u003e \u003cp\u003eOne of the main problems in the taxonomic profile reconstruction, obtained from metagenomic analyses, lies in the high percentage of reads that fail to be uniquely assigned to a species and are therefore classified as \"unclassified organism\" or \"uncultured bacterium/archeon\u0026rdquo;. For BLASTN\u0026thinsp;+\u0026thinsp;MEGAN6 and Methaphlan3 the percentage of unclassified reads reaches 80\u0026ndash;90%, whereas Methaphlan4 allows a deeper assignment of reads, through the adoption of the species-level genome bins system that makes it possible to drastically reduce the number of unassigned reads (see supplementary material).\u003c/p\u003e \u003cp\u003eFor this specific study design, despite the great potential of MetaPhlAn4 in metagenome analysis, it proved to be less useful because, by targeting generic OTUs instead of specific cultivable strains, it is less successful in identifying specific strains that could be subsequently used to improve and optimize the fermentation system. On the other hand, BLAST-based analysis also has some disadvantages, mainly related to the high computational time requested for analyses and to the redundancy of the database used by BLASTN.\u003c/p\u003e \u003cp\u003eFurthermore, as a first descriptive approach, community progression analysis during biogas production was performed by NJ tree construction on BLASTN results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and by PCoA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) for MetaPhlAn3. The two analyses highlighted exactly the same trend, showing two different dynamics for the two substrates during the fermentation process: in GP the microbial community changes slowly over time, while CG shows a rapid initial change in microbiome structure, corresponding to a high rate of biogas production, and then the community tends to return to a composition similar to the initial inoculum composition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn fact, the microbial community retrieved in the samples from CG fermentation after 5 days (cg5) was distant from the start point (inoculumu-cg0) in both representation; then through time the bacterial composition in CG returned similar to cg0. Conversely, the microbial composition of the samples obtained from GP fermentation gradually deviated from the starting point (inoculum-gp0) until day 30 during the biogas production process.\u003c/p\u003e \u003cp\u003eTherefore, based on the above considerations and on the initial descriptive analyses of microbial community dynamics, which were found to be widely overlapping between BLASTN and MetaPhlAn3, only the taxonomic profile generated with MetaPhlAn3 will be discussed in detail below, while the results of the taxonomic analysis performed with BLASTN\u0026thinsp;+\u0026thinsp;MEGAN6 and MetaPhlAn4 are presented in the supplementary materials.\u003c/p\u003e \u003cp\u003eThe taxonomical composition of inoculum, in term of phylum and species, is detailed in the supplementary material; this sample was characterized by the presence of 5 phyla, the most abundant was Thermotogae (over than 65% of assigned reads), followed by Firmicutes (renamed as Bacillota), Euryarcheota (the only Archaea phylum identified), Synergistetes and Bacteroidetes. Eleven genera and 14 species were identified in the inoculum: Defluviitoga was the most abundant genera and \u003cem\u003eDefluviitoga tunisiensis\u003c/em\u003e the dominant species (see supplementary material). The taxonomic profiles from time 0 to day 30 of the samples from both CG and GP are also showed in the supplementary material, where the distribution of phyla and species were, respectively, presented. As in the inoculum, in all samples the sequences were classified into 5 phyla (Thermogae, Firmicutes, Euryarchaeota, Synergistetes, and Bacteroidetes), with the Thermogae always being the most abundant followed by the Firmicutes, while the other three phyla are present with lower relative abundances.\u003c/p\u003e \u003cp\u003eA total of 16 genera and 22 species were detected, the most abundant genera were Defluviitoga, Hungateiclostridium (reclassified as Acetovibrio) and Herbinix; the most represented species in all the samples were \u003cem\u003eD. tunisiensis\u003c/em\u003e (over 65% of all samples), \u003cem\u003eHungateiclostridium saccincola\u003c/em\u003e (reclassified as \u003cem\u003eAcetovibrio saccincola\u003c/em\u003e in GP samples, and \u003cem\u003eHerbinix luporom\u003c/em\u003e in CG samples; all the other species had a relative abundance lower than 1% (see supplementary material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differences in microbial composition among different substrate\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall 12 genera and 15 species were identified: 6 genera and 7 species enriched in samples from CG; 6 genera and 8 species enriched in samples from GP. The relative abundance among all samples of these 15 species was used to generate the heatmap represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. As previously described, \u003cem\u003eD. tunisiensis\u003c/em\u003e was largely (relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;65%) present in all the samples during all the phases of biogas production (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), but resulted statistical most abundant in the CG community samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Samples from CG were also significantly enriched by the presence of \u003cem\u003eClostridium cellulosi\u003c/em\u003e, \u003cem\u003eClostridium sp. N3C\u003c/em\u003e, \u003cem\u003eClostridium thermopalmarium\u003c/em\u003e, \u003cem\u003eMethanobacte\u003c/em\u003erium \u003cem\u003esp. MB1\u003c/em\u003e and \u003cem\u003eMethanothermobacter wolfeii\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). During the first days (cg5-15) of production of biogas in samples from cereal grains there was a higher abundance of \u003cem\u003eH. luporum\u003c/em\u003e, \u003cem\u003eC. sp. N3C\u003c/em\u003e, \u003cem\u003eC. thermopalmarium\u003c/em\u003e and \u003cem\u003eM. wolfeii\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Conversely, the same substrate showed an increase of \u003cem\u003eC. cellulosi\u003c/em\u003e an \u003cem\u003eM. sp. MB1\u003c/em\u003e during the late phases of biogas production in CG samples (cg20-30, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe second most abundant species detected in all samples was \u003cem\u003eH. saccincola\u003c/em\u003e, which was one of the most common species at the beginning of the biogas production process (inoculum, cg0, gp0) and remained significantly highly abundant during all time in grape pomace samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Samples from GP were also enriched by \u003cem\u003eAcetomicrobium hydrogeniformans\u003c/em\u003e, \u003cem\u003eAcetomicrobium thermoterrenum, Methanoculleus bourgensis, Methanosarcina flavescens, Methanosarcina thermophila, Peptococcaceae bacterium 1109\u003c/em\u003e and \u003cem\u003ePetrimonas mucosa\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). \u003cem\u003eA. hydrogeniformans, M. bourgensis and P. bacterium 1109\u003c/em\u003e presented all the same dynamics: they were abundant during the first stages (gp5-15) of fermentation and then their abundance decrease. Conversely \u003cem\u003eA. thermoterrenum\u003c/em\u003e, \u003cem\u003eM. thermophila\u003c/em\u003e and \u003cem\u003eP.mucosa\u003c/em\u003e showed a progressive increase during last days of biogas production (gp20-25). \u003cem\u003eM. flavescens\u003c/em\u003e exhibited a different growth dynamic: it was very abundant at the beginning (gp0), its abundance slightly decreased during the later stages of fermentation (gp5-15) and then increased again in the last ten days (gp20-30, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eSince the biogas production rate had its peak during the first days of the fermentation process a comparison of early time samples (5-10-15 days), on both substrates, were performed with LEfSe (see supplementary material). The principal results of this analysis were similar to the general comparison between samples (CG vs GP Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), but this analysis also revealed the presence of \u003cem\u003eClostridium isatidis\u003c/em\u003e as significantly enriched in the CG samples, and \u003cem\u003ePetrotoga halophila\u003c/em\u003e enriched in GP samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Structure of microbial community in the key microbiological steps of AD process\u003c/h2\u003e \u003cp\u003eIn recent years, various molecular biological techniques (including genomics, metagenomics, meta-transcriptomics) have been applied to investigate the composition and the dynamics of the AD microbiome and to understand its implications for the biogas process (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the organic biomasses of interest for biogas production, GP is one of the most abundant waste products in the agri-food industry, but fermentation of this type of substrate has a low yield in biogas production; the purpose of this work was to compare, through the WMS approach, the different dynamics of the microbiome during GP and CG fermentation with the aim of identifying effective approaches to optimize biogas production and ensure process stability in GPs.\u003c/p\u003e \u003cp\u003eThe two different substrates show different microbiome structure and dynamics over time, although the two processes were both characterized during all experimental phases by very high levels of \u003cem\u003eD. tunisiensis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). High abundances of \u003cem\u003eD. tunisiensis\u003c/em\u003e were already detected in a thermophilic laboratory fermenter (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), highlighting the broad spectrum of substrates (polymers, oligosaccharide, acids and alcohols) that \u003cem\u003eD. tunisiensis\u003c/em\u003e is capable of metabolizing therein including the ability to degrade cellulose since genes encoding non-cellulosomal cellulases were identified in its genome. Li et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) confirmed the cellulose-degrading ability of \u003cem\u003eD. tunisiensis\u003c/em\u003e, showing how this microorganism is predicted to participate in the AD of a variety of carbohydrates and produces acetate, H\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e. Other researches support the idea that the high H\u003csub\u003e2\u003c/sub\u003e-producing ability of \u003cem\u003eD. tunisiensis\u003c/em\u003e significantly influenced the proportion of hydrogenotrophic archaea species that, syntrophically associated with this bacterium, can utilise CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e for methanogenesis (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNext to \u003cem\u003eD. tunisiensis\u003c/em\u003e, two other bacteria, \u003cem\u003eH. luporum\u003c/em\u003e and \u003cem\u003eH. saccincola\u003c/em\u003e, also characterized at high levels CG and GP respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). With regard to \u003cem\u003eH. luporum\u003c/em\u003e, although its relative abundance is significantly higher in CG, its levels in GP are more than 3% making it a relatively abundant species in this matrix as well. The complete genome sequence of \u003cem\u003eH. luporum\u003c/em\u003e was reported in 2016 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and its characterization has shown that the bacterium is able to digest cellulosic and hemicellulosic substrates. Also, Maus et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) described \u003cem\u003eH. luporum\u003c/em\u003e to be involved in thermophilic degradation of lignocellulosic biomass representing together with \u003cem\u003eC.cellulosi\u003c/em\u003e an important cellulose degrader.\u003c/p\u003e \u003cp\u003eOn \u003cem\u003eH. saccincola\u003c/em\u003e there is not much information, in 2019 Rettenmaier et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) showed that \u003cem\u003eH. saccincola\u003c/em\u003e is closely related to \u003cem\u003eHungateiclostridium thermocellum\u003c/em\u003e a well-known cellulolytic key players such as \u003cem\u003eC.cellulosi\u003c/em\u003e and \u003cem\u003eHerbinix hemicellulosilytica\u003c/em\u003e. Recently, a study aimed to characterize the synergism of a hydrolytic/cellulolytic bacterial consortium isolated from biogas fermenters proposed that enzymatic activity of \u003cem\u003eH. thermocellum\u003c/em\u003e liberates soluble mono- and oligosaccharides from cellulose and hemicellulose, thereby promoting growth of saccharolytic bacteria. Bacterial synergism is supposed to accelerate biomethane production in AD of plant fibers by increasing the overall cellulose hydrolysis rates and increasing the amounts of produced volatile metabolites (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePresent results seem to confirm the importance of this metabolic synergy, in fact in both matrices the co-presence of cellulolytic, saccharolytic and hydrolytic bacteria was confirmed: in the CG, \u003cem\u003eH. luporum\u003c/em\u003e was flanked by a consortium of clostridia among which the non-cellulolytic \u003cem\u003eC. sp. N3C\u003c/em\u003e and \u003cem\u003eC. thermopalmarium\u003c/em\u003e strains, while the cellulolytic \u003cem\u003eH. saccincola\u003c/em\u003e and \u003cem\u003eH. luporum\u003c/em\u003e in GP are accompanied with non-cellulolytic \u003cem\u003eAcetomicrobium\u003c/em\u003e sp and \u003cem\u003eP. mucosa\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e) as we will discuss below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA further general consideration of the overall microbiome dynamics of the two different biomasses concerns methanogenesis by two different types of Archea: \u003cem\u003eM. wolfeii\u003c/em\u003e and \u003cem\u003eM. sp. MB1\u003c/em\u003e characterise the CG while, the \u003cem\u003eM.thermophila\u003c/em\u003e and \u003cem\u003eM.flavescens\u003c/em\u003e are more represented in GP and, reasonably, all grow in abundance toward the terminal phase of the AD cycle, indicating their role in the aceticlastic and hydrogeno-trophic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e). To date, while the role of Methanosarcina in AD is fairly well described in the literature, not much is known about the two strains observed in CG, and the present analysis is one of the first studies that identify \u003cem\u003eM. wolfeii\u003c/em\u003e and \u003cem\u003eM. sp. MB1\u003c/em\u003e as two prokaryotes with a main role in methanogenesis from CG.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Dynamics of microbial community in AD process for cereal grains\u003c/h2\u003e \u003cp\u003eExploring the structure and dynamics of CG microbial community in more detail, from the comparison of early time samples (5-10-15 days) between CG and GP, \u003cem\u003eC. isatidis\u003c/em\u003e results significantly enriched in CG. Little is known about this clostridium strain, but its role in AD results from a 2010 study indicating the \u003cem\u003eC.isatidis\u003c/em\u003e strain as capable of directly converting cellulose to ethanol (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Moreover, we observed that during the early days (cg5-15) there was a higher abundance of \u003cem\u003eC. sp. N3C\u003c/em\u003e and \u003cem\u003eC. thermopalmarium\u003c/em\u003e (see supplementary material), both are non-cellulolytic, hydrogen-producing bacteria and both seemed to contribute to butyrate production (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The co-existence of cellulolytic strains, such \u003cem\u003eC.isatidis\u003c/em\u003e and \u003cem\u003eH.luporum\u003c/em\u003e, and hydrogen producers, such as the \u003cem\u003eC. sp. N3C\u003c/em\u003e and \u003cem\u003eC. thermopalmarium\u003c/em\u003e as seen in the present study highlights the idea that these strains, by taking advantage of their specific metabolic capacities, offers a promising new way to improve the conversion efficiency of cellulose to hydrogen. In fact, the use of cellulolytic bacteria for hydrogen production is often limited by low hydrogen yields, due to the bacteria's poor growth rates and pH sensitivity.\u003c/p\u003e \u003cp\u003eThe present analysis is one of the first studies that identify \u003cem\u003eM. wolfeii\u003c/em\u003e and \u003cem\u003eM. sp. MB1\u003c/em\u003e as two prokaryotes with a major role in methanogenesis from CG (cg20-30, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These two archaea are both hydrogenotrophic methanogens that use formate, hydrogen, and carbon dioxide as substrates for methanogenesis and require acetate for growth (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Furthermore, it cannot be ruled out that \u003cem\u003eC. cellulosi\u003c/em\u003e might form a syntrophic association with these hydrogenotrophic methanogens, in particular with \u003cem\u003eM. sp. MB1\u003c/em\u003e since both these prokaryotes show a similar growth in their abundance in the final stage of the fermentation process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Dynamics of microbial community in AD process for grape pomace\u003c/h2\u003e \u003cp\u003eAs described (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) the structure of the microbial community changes slowly over time in GP, maintaining biogas production capacity longer, albeit at lower levels, than CGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Next to the two cellulolytic bacteria already discussed, \u003cem\u003eD. tunisiensis\u003c/em\u003e and \u003cem\u003eH. saccincola\u003c/em\u003e, present at high abundance throughout GP digestion as well, this matrix is characterized by the presence of \u003cem\u003eA.hydrogeniformans, M.bourgensis\u003c/em\u003e and \u003cem\u003eP. bacterium\u003c/em\u003e 1109, that typify the first days of AD and \u003cem\u003eA. thermoterrenum, M. thermophila, M. flavescent\u003c/em\u003e and \u003cem\u003eP.mucosa\u003c/em\u003e showing a progressive increase during last days of AD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe Acetomicrobium genus, identified in 2016 and initially classified as Anaerobaculum (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), is reported in biogas production reactors, and their role could be attributed to the digestion of fats and proteins (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), as well as glucose fermentation to acetate, CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). However, to date, little or no information is available in the literature about the two species observed in the present study, i.e. \u003cem\u003eA.hydrogeniformans\u003c/em\u003e and \u003cem\u003eA. thermoterrenum.\u003c/em\u003e Their presence in GP digestion could be explained considering the high concentration of promptly digestible lipids (7\u0026ndash;15%) and proteins (8\u0026ndash;16%) that characterized this substrate (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In fact, in \u003cem\u003eA. hydrogeniformans\u003c/em\u003e genome a thermostable esterase was identified presenting high catalytic activity with a preference towards short-acyl-chain esters (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), while \u003cem\u003eA.thermoterrenum\u003c/em\u003e is involved in glycerol conversion (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), both processes being related to lipids metabolism during AD.\u003c/p\u003e \u003cp\u003eMoreover, is interesting to notice that both strains of Acetomicrobium observed in this study potentially act as syntrophic partner with homoacetogenic or syntrophic acetate-oxidizing bacteria (SAOB)(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), and with acetoclastic or hydrogenotrophic methanogens (HM) even if their role in these syntrophic communities still remains enigmatic (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The strong association between SAOB and HM is well documented and, compared to acetoclastic methanogenesis, methane production from the association between SAOB and HM is thermodynamically favoured at high temperatures (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). In this regard, the co-presence of \u003cem\u003eA. hydrogeniformans\u003c/em\u003e and \u003cem\u003eM.bourgensis\u003c/em\u003e, a known syntrophic partner of SAOB bacteria (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), detected in this study at the early stages of GP AD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e) elucidates how the system is capable of synthesizing methane via the hydrogenotrophic pathway even in the initial stages of fermentation of this biomass.\u003c/p\u003e \u003cp\u003e \u003cem\u003eP. halophila\u003c/em\u003e and \u003cem\u003eP. bacterium 1109\u003c/em\u003e species have been identified in our GP substrate. So far, \u003cem\u003ePetrotoga\u003c/em\u003e species has been isolated only from oil reservoirs, whereas \u003cem\u003eP. bacterium 1109\u003c/em\u003e is typically classified as belonging to acetogenic community, highly abundant in most biogas plants Singh et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Buettner et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) with a network analyses approach demonstrated that \u003cem\u003eP.bacterium 1109\u003c/em\u003e was a key module in both HM and acetoclastic methanogenesis, highlighting a possible SAOB behaviour. An additional ability of the \u003cem\u003eP.bacterium 1109\u003c/em\u003e appears to be the conversion of propionate in methane, through cooperation between the \u003cem\u003eP. bacterium\u003c/em\u003e and the methanogen \u003cem\u003eM.bourgensis\u003c/em\u003e, even in inhibiting ammonia concentration Singh et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), deriving from proteins catabolism.\u003c/p\u003e \u003cp\u003eThe later GP fermentation stages, are characterized by the presence of two relevant Methanosarcina species, \u003cem\u003eM. flavescens\u003c/em\u003e and \u003cem\u003eM.thermophila\u003c/em\u003e. \u003cem\u003eM. flavescens\u003c/em\u003e is a hydrogenotrophic and acetoclastic methanogen species that was recently identified and proliferates at increased shear velocity when acetic acid is the major VFA component (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e); conversely, \u003cem\u003eM.thermophila\u003c/em\u003e is a more established thermophilic archea that is referred to as acetoclastic methanogen but that can grow well also by utilizing methanol or methylated amines and slowly utilizing H\u003csub\u003e2\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). All Methanosarcina selected from GP substrate contain cytochrome c, a high intermembrane electron transfer medium, which enables them to withstand high hydrogen partial pressures, compared with methanogens present in CG substrate, lacking cytochrome c (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This could be presumably due to the prevalence of catabolism pathways of protein and lipids in GP that increase the hydrogen partial pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother bacterium that could play an important role in this microbial community is \u003cem\u003eP.mucosa\u003c/em\u003e, also observed at higher abundance in the later stage of AD of GP in the present investigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Its function in the AD process most probably is associated with acidogenesis. Based on integrated omics analyses it has also been observed that \u003cem\u003eP. mucosa\u003c/em\u003e encodes a diverse set of glycosyl-hydrolyses involved in carbohydrate metabolism. Under these conditions, it may play an important role in conversion of lignocellulosic biomass (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt\u0026rsquo;s worth noting as digestion GP substrate leads to the development of a more complex bacterial consortium than CG and a combination of synergistic behaviours. The presence of bacteria as \u003cem\u003eAcetomicrobium\u003c/em\u003e sp. and \u003cem\u003eP.mucosa\u003c/em\u003e, involved in the degradation of lipids and protein-rich substrates, together with \u003cem\u003eMethanosarcina\u003c/em\u003e sp. and \u003cem\u003eP.bacterium\u003c/em\u003e 1109, able to tolerate high hydrogen pressures and high ammonia concentration derived by aminoacids degradation, seems to confirm the preference towards more rapidly-fermentable biomass components, as proteins and lipids, rather than lignocellulose. Moreover, in the late AD stages, the co-presence here observed of \u003cem\u003eM.bourgensis\u003c/em\u003e with \u003cem\u003eP.bacterium\u003c/em\u003e 1109, which relative abundance was seen as positively correlated to pH and is able to hydrolyze substrates efficiently even at pH\u0026thinsp;\u0026ge;\u0026thinsp;7.7, could be indicative of an adapted microbial communities (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present results pave the ground on the perspective to build a tailored microbial consortium to be inoculated from the beginning of the process in view of improve biogas yield during lignocellulosic biomass AD.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eComparing CG and GP substrates with WMS analysis, we identified 15 specific species, detecting some new strains: in GP, showing higher species-complexity, potentially syntrophic interactions between cellulolytic \u003cem\u003eH.saccincola\u003c/em\u003e, non-cellulolytic \u003cem\u003eAcetomicrobium sp.\u003c/em\u003e, and HMs \u003cem\u003eM.bourgensis\u003c/em\u003e and \u003cem\u003eMethanosarcina sp\u003c/em\u003e. in both early and late AD stages were detected; in CG: the cellulolytic \u003cem\u003eC.isatidis\u003c/em\u003e, a Clostridium that could play an important role during early-stage of cellulose degradation in synergy with \u003cem\u003eM.wolfeii\u003c/em\u003e was observed.\u003c/p\u003e \u003cp\u003eThe results here presented represent a preliminary effort to develop a tailor-made microbial consortium to be inoculated from the beginning of AD process in order to accelerate and improve bioenergy production from lignocellulosic feedstocks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, C.S. and E.T; methodology, N.F., S.C., S.S., E.M., C.S. and E.T.; formal analysis, N.F., S.C., D.S. and C.S.; writing\u0026mdash;original draft preparation, N.F., S.C., E.T. and C.S.; writing\u0026mdash;review and editing, N.F., D.S., S.C., E.M., S.S., C.S. and E.T; supervision, C.S. and E.T.; funding acquisition, C.S., S.S. and E.T.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eThe study was supported by research grants of the University of Ferrara (Scapoli, FAR 2019\u0026ndash;2020; Sabbioni, FAR 2019-20; Tamburini, FAR 2020-21).\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003eWMS sequencing data has been deposited into public database NCBI, and the BioProject accession number is PRJNA1037512.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHo DP, Ngo HH, Guo W. 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Microorganisms. 2020;8(12):2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anaerobic digestion, metagenomics, lignocellulosic biomasses, lignin biodegradation, biogas production","lastPublishedDoi":"10.21203/rs.3.rs-4003924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4003924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eThis study, using the whole metagenomic sequencing approach, provides an insight in the microbial dynamics that occurred during the anaerobic digestion of two crop substrates with different lignocellulose composition: cereal grains and grape pomace.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eA total of 15 strains were identified as specifically characterising the two substrates. Among them some strains never detected in biogas reactors were identified: \u003cem\u003eClostridium isatidis, Methanothermobacter wolfeii\u003c/em\u003e and \u003cem\u003eMethanobacter sp\u003c/em\u003e MB1 in cereal grains samples and \u003cem\u003eAcetomicrobium hydrogeniformans, Acetomicrobium thermoterrenum\u003c/em\u003e in grape pomace samples.\u003c/p\u003e\u003ch2\u003eCONCLUSIONS\u003c/h2\u003e \u003cp\u003eThe presence of bacteria as \u003cem\u003eAcetomicrobium\u003c/em\u003e sp. and \u003cem\u003eP.mucosa\u003c/em\u003e, involved in the degradation of lipids and protein-rich substrates, together with \u003cem\u003eMethanosarcina\u003c/em\u003e sp. and \u003cem\u003eP.bacterium\u003c/em\u003e 1109, able to tolerate high hydrogen pressures and high ammonia concentration derived by aminoacids degradation, suggest that a more complex syntrophic community is established in lignin-cellulose-enriched substrates; this evidence may help the development of new strategies to optimize the anaerobic digestion process of these kind of biomasses.\u003c/p\u003e","manuscriptTitle":"Dynamics of microbiome composition during anaerobic digestion of different renewable resources","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 07:12:41","doi":"10.21203/rs.3.rs-4003924/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6549092e-3e1f-485f-8884-036fdfee4673","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-31T12:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 07:12:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4003924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4003924","identity":"rs-4003924","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}