==== Front Microbiome Microbiome Microbiome 2049-2618 BioMed Central London 1197-05-6170/s40168-022-01316-w Research Analysis of the anaerobic digestion metagenome under environmental stresses stimulating prophage induction Rossi Alessandro 1 Morlino Maria Silvia 1 Gaspari Maria 2 Basile Arianna 1 Kougias Panagiotis 3 Treu Laura gestrada@example.com 1 Campanaro Stefano 14 1 grid.5608.b 0000 0004 1757 3470 Department of Biology, University of Padua, via U. Bassi 58/b, 35131 Padova, Italy 2 grid.4793.9 0000000109457005 Department of Hydraulics, Soil Science and Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece 3 Soil and Water Resources Institute, Hellenic Agricultural Organisation Demeter, Thermi, 57001 Thessaloniki, Greece 4 grid.5608.b 0000 0004 1757 3470 CRIBI biotechnology center, University of Padua, via U. Bassi 58/b, 35131 Padova, Italy 15 8 2022 15 8 2022 2197-05-61707 3 2022 25 6 2022 © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Background The viral community has the potential to influence the structure of the microbiome and thus the yield of the anaerobic digestion process. However, the virome composition in anaerobic digestion is still under-investigated. A viral induction experiment was conducted on separate batches undergoing a series of DNA-damaging stresses, in order to coerce temperate viruses to enter the lytic cycle. Results The sequencing of the metagenome revealed a viral community almost entirely composed of tailed bacteriophages of the order Caudovirales. Following a binning procedure 1,092 viral and 120 prokaryotic genomes were reconstructed, 64 of which included an integrated prophage in their sequence. Clustering of coverage profiles revealed the presence of species, both viral and microbial, sharing similar reactions to shocks. A group of viral genomes, which increase under organic overload and decrease under basic pH, uniquely encode the yopX gene, which is involved in the induction of temperate prophages. Moreover, the in-silico functional analysis revealed an enrichment of sialidases in viral genomes. These genes are associated with tail proteins and, as such, are hypothesised to be involved in the interaction with the host. Archaea registered the most pronounced changes in relation to shocks and featured behaviours not shared with other species. Subsequently, data from 123 different samples of the global anaerobic digestion database was used to determine coverage profiles of host and viral genomes on a broader scale. Conclusions Viruses are key components in anaerobic digestion environments, shaping the microbial guilds which drive the methanogenesis process. In turn, environmental conditions are pivotal in shaping the viral community and the rate of induction of temperate viruses. This study provides an initial insight into the complexity of the anaerobic digestion virome and its relation with the microbial community and the diverse environmental parameters. Video Abstract Supplementary Information The online version contains supplementary material gestrada@example.com/s40168-022-01316-w. Hellenic Foundation for Research and Innovation,Greece580 Università degli Studi di Padova,ItalyBIRD198423 Ministero dell'Istruzione, dell'Università e della Ricerca,Italyex D.M.738 dd 08/08/19 issue-copyright-statement© The Author(s) 2022 ==== Body pmcBackground Anaerobic digestion (AD) is a functional process carried out by microbial communities composed of Bacteria and Archaea which degrade organic matter in anoxic conditions. AD occurs in natural environments such as aquatic sediments, wetlands, and animal gut, but it is also widely employed in industrial processes. It is particularly valuable as a way to produce methane while disposing of organic waste, playing an important role in the reduction of the dependence from fossil fuels and the development of a circular economy approach [1]. The composition of AD microbiomes is extremely variable, and it reflects the wide variety of substrates and physicochemical conditions under which this degradation process occurs, both in natural and technical environments [2]. In AD, polymers are first broken down into simple molecules, which are then converted into Volatile Fatty Acids (VFA), then into acetate and finally into methane in the four steps of hydrolysis, acidogenesis, acetogenesis and methanogenesis. The first three steps are conducted by the bacterial community which, despite the great variation across different conditions, is dominated by the phylum Firmicutes, usually followed by Bacteroidetes and Proteobacteria. Archaeal species, mostly belonging to the phylum Euryarchaeota, are involved in the conversion of simple molecules to methane and usually account for a much smaller part of the community [3]. The microbial species present and their balance are crucial for optimisation of biogas production, and they have been extensively studied in the last two decades [4]. Among the numerous factors concurring to shape microbial communities, the importance of viruses, in particular bacteriophages, is increasingly recognised [5]. Viral concentration in samples from wastewater treatment plants (WWTPs) has been estimated to be greater in comparison to aquatic environments by one to three orders of magnitude [6, 7]. Furthermore, it has been observed that bacteriophages have a strong correlation with prokaryotic species across time in wastewater-treating bioreactors [8]. Despite the importance of this, most of the existing articles regarding the AD virome are limited to characterisations of the community and do not assess the impact of viruses on the microbial community [9–11]. Zhang and colleagues showed that there is a correlation between the viral community composition and the production of methane in anaerobic digesters of WWTPs and argued that the viral shunt has a positive impact on the production of methane [12], but such conclusions are drawn on a broad scale analysis, leaving many of the actual dynamics unaddressed. The transition of a temperate phage from lysogenic to lytic cycle is known as induction. Temperate viruses spontaneously undergo induction at a low rate, but in several species of bacteriophages and archaeal viruses, this phenomenon is known to increase with DNA-damaging stresses [13]. For example, in a study targeting the response to different types of anaerobic stresses in Nitrosospira multiformis 25196, it was observed how N. multiformis cells reacted to a wide range of environmental stresses through prophage induction [14]. A prime example of the importance of bacteriophages in engineered systems is the dairy industry, which is threatened by bacteriophages attacking Streptococcus thermophilus strains [15]. Moreover, viruses are known to be players in the regulation of global carbon and nitrogen cycles in natural ecosystems [12], e.g. aquifer sediments [16], and in phytoplankton dynamics and diversity [17]. As parasites, viruses apply strong selective pressures on their hosts. It has been estimated that in marine ecosystems, viruses kill about 20% of the microbial biomass daily [18]. The recycling of organic matter from lysed microbes, called viral shunt, plays a relevant role in the regulation of global carbon and nitrogen cycles. In biogas plants, phage-induced bacterial cell lysis can decrease biogas production when the key species associated with biogas production are affected. At the same time, auxotrophic microorganisms are benefitted as lysis serves as a source of cofactors, vitamins and amino acids [11]. Furthermore, as mobile genetic elements, viruses enact horizontal gene transfer (HGT) across microbes at different taxonomic ranks, from species to phyla. This potentially endows hosts with beneficial functions, increases the genetic diversity of the population, and plays a role in the complex co-evolutionary dynamics between viruses and hosts [19]. However, both HGT and viral lysis rates in engineered systems are still overlooked. Despite the elucidation of virus-mediated mechanisms, most of the viral diversity remains unknown [20]. However, the advent of metagenomics has brought large advances in the description of microbial environmental communities. The introduction of binning methods in standard metagenomic data analysis pipelines has allowed for the recovery of many uncultivable AD microbial species [2] and the detailed description of key organisms of the microbiome. The exponential increase of available sequences from both bulk metagenomes and metaviromes has led to the creation of numerous databases of viral sequences [21, 22], paralleled by the development of predictors able to effectively find new phages [23–26]. All viral prediction algorithms depend to some extent on the previous knowledge associated with taxonomically assigned genomes reported in public databases. This applies to all the software, whether they are based on homology search, like CheckV and PHASTER, or leverage k-mer usage like VirFinder, or analyse sequence features within machine learning frameworks, like VIBRANT, VirSorter2 and PPR-Meta [23–28]. However, their application has proven effective in discovering novel viral clades, the most emblematic case being the crAss-like phage family [29–31]. In the light of the relevant results obtained from metaviromics the International Committee on Taxonomy of Viruses proposed the establishment of new classification methods based solely on genomic features [32]. Unravelling the "dark matter" of novel viral diversity is a daunting task, and the aforementioned exploratory studies conducted on the AD virome showed the potential that phages have in shaping the prokaryotic community [9, 11, 12]. The DNA virome of AD has been described as dominated by tailed bacteriophages of the Siphoviridae, Podoviridae and Myoviridae families, with a minor presence of Tectiviridae, Inoviridae and other families. The AD microbiome is extremely complex and composed of species involved in different functional tasks, including the hydrolysis of organic matter and the conversion of the derived by-products in simple organic molecules (e.g. volatile fatty acids and methane). However, little is known on which prokaryotic species can be potentially affected by phages and, therefore, which are the functional processes potentially influenced by lytic cycles. Heyer and colleagues [11] reported that species belonging to Bacillaceae, Enterobacteriaceae, and Clostridiaceae are among the favourite targets of bacteriophages, but these findings are not conclusive to determine whether specific parts of the AD funnel are more impacted by viruses. Identifying and characterising the viruses and their hosts in this system can lead not only to a better comprehension of AD microbial dynamics but also to applications such as phage-mediated treatment of the reactors in order to increase process performance. Bacteriophages are already used as tools for manipulating microbial communities in different fields, such as phage therapy and pathogen control in food and water, and have been used as control for biomass bulking in wastewater treatment [33–36]. An increased attention towards the AD viral community could lead to the development of similar techniques for the improvement of the AD process as well. This could be achieved by removing species like the sulphate reducers which compete with key players in pivotal steps of methanogenesis, or leveraging bacteriophage-mediated HGT in order to confer desirable metabolic characteristics to microbial species of interest. In this experiment, we attempted to use induction to explore the effect of diverse conditions potentially affecting the AD process on both the microbial and viral community. We then assessed the presence of the retrieved genomes in other AD metagenomes from the Biogas Microbiome collection [2, 37] (microbial-genomes.org). Materials and methods Inoculum and feedstock Active inoculum was obtained from a lab-scale Continuous Stirred Tank Reactor (CSTR) (Waste Management and Bioprocessing Lab, Thessaloniki, Greece), treating cattle manure at mesophilic conditions (37 ± 1 °C). Cattle manure was collected from a full-scale biogas plant located in northern Greece (Biogas Lagada S.A., Thessaloniki, Greece). The raw substrate was sieved using a separating net with a 2 mm opening to remove large particles and stored until usage at −20 °C to prevent alterations in its composition. Batch assays experimental setup In order to test for perturbations of the AD process, 21 anaerobic batch experiments were performed aiming to define the microbial and viral composition in reactors under different conditions. These included addition of mitomycin, temperature shifts, high salt concentration, oxidative stress, pH shifts, and organic overload (details regarding the application of each condition are listed in Additional file 1). Four of these assays involved a combination of temperature change with salt or oxidation stresses. Finally, a control assay was conducted by incubating the inoculum without imposing any stressing condition. All the experiments were performed in triplicate using 300 mL serum glass bottles with a working volume of 50 mL and an organic load of 2 g VS/L, for a total of 66 batches. In addition to the batch experiments, an aliquot of inoculum was saved by storing it at −20 °C immediately after sampling. Prior to incubation, bottles were flushed with nitrogen to achieve anaerobic conditions. Thereafter, the bottles were hermetically closed with butyl rubber stoppers and screw caps. The batch reactors were maintained at 37 °C in a temperature-controlled incubator (BINDER BD260, Tuttlingen, Germany) for 24 h. Analytical methods At the end of each treatment, after 24 h of operation, biogas composition and VFA concentration were measured on all 21 assays plus the control bottles, with the aim of evaluating the effect of the different conditions in the digesters. To determine biogas composition, a gas chromatograph (Shimadzu GC-2014, Kyoto, Japan) equipped with a thermal conductivity detector (TCD) and a packed column (Molecular Sieve 5A, 1.8 m × 2 mm ID) was used. The VFA concentrations were defined with a gas chromatograph (Shimadzu GC-2010 Pro, Kyoto, Japan) provided with a flame ionisation detector (FID) and equipped with fused silica capillary column (30 m × 0.53 mm ID, 1 μm film thickness). The oven temperature was initially set at 50oC for 3.5 min, subsequently increased at a rate of 25 °C/min to 130 °C and, finally, increased at a rate of 10°C/min until reaching the final temperature of 210 °C, which was maintained stable for 2 min. The temperature in the injection port was 150 °C and in the detector 230 °C. Helium was used as carrier gas for the gas chromatograph. DNA extraction and sequencing The most promising conditions according to the literature and methane yield variation were selected for DNA extraction, along with the control bottles and the inoculum. Specifically, conditions with a decrease in methane yield between 0 and 30% compared to the control were selected, under the assumption that they were affected enough to potentially observe phage induction, but not to the point of killing the majority of cells (Fig. 1). For each bottle and the inoculum, pellet and supernatant samples were collected as described below for analysing the microbial and viral community, respectively. Centrifugation at high speed was used to separate the viral and bacterial fractions [38, 39]. For the microbial-enriched community (pellet) samples, before starting the extraction, 3 mL of the inoculum were centrifuged at 15000 rpm for 10 min in order to obtain the acquired quantity of 0.2–0.8 g pellet, while the supernatant was discarded. Hereupon, the genomic DNA was extracted. For the viral-enriched community (supernatant) samples, 45 mL of each bottle's content (or of inoculum from the reactor) were centrifuged (Thermo Scientific SL 16R, New York, USA) at 15,000×g for 10 min at 4 °C. In order to further enrich supernatant samples in viral content, an attempt was performed to filter the supernatant with 0.22 μm syringe filters [40]. However, due to the high content of suspended and dissolved solids of the substrate, filtering was only possible with 1 μm syringe filters (Millex-GP, Merck Millipore Ltd). With the intention of reducing the final volume while using the whole viral content, the filtered flowthroughs were frozen overnight and lyophilised using a freeze dryer (Christ Alpha 1–2, Martin Christ Gefriertrocknungsanlagen GmbH, Germany) coupled with a vacuum pump (rotary vane vacuum pump, Vacuumbrand RZ 2.5, Vacuumbrand GmbH + CO KG, Germany) for 48 gestrada@example.com mbar. Before the extraction of the obtained enriched viral community, the lyophilised samples were resuspended in 3 mL PCR water. Subsequently, the samples underwent DNA extraction with DNeasy® PowerSoil® Kit (QIAGEN, Hilden, Germany) following the manufacturer's protocol. Recovery of DNA from pellet and supernatant samples was ensured by qualitative and quantitative analyses on the samples, using NanoDrop Microvolume UV-Vis spectrophotometer (Thermo Fisher Scientific, USA) and Qubit Fluorometer (Thermo Fisher Scientific, USA). Importantly, DNA yield was limiting for the supernatant samples. Indeed, among all samples, only four tested conditions and the inoculum yielded enough DNA for library preparation, but only upon pooling the replicates for the supernatant. To ensure a coherent comparison, replicates for pellet samples were also pooled before sequencing, and the four conditions and inoculum were further processed.Fig. 1 Biochemical measurements of perturbation assays. A VFA measurements of bottles after 24 h of perturbation. Error bars represent the standard deviation of the total VFA concentration across triplicate bottles (Additional file 2). B Percent difference in methane yield of each perturbation compared to the control assay. The dashed line represents the -30% threshold used to choose which samples to process further DNA samples underwent library preparation using the Nextera DNA Flex Library Prep Kit (Illumina Inc., San Diego CA) and were sequenced using the Illumina Novaseq platform at the CRIBI Biotechnology Center sequencing facility (University of Padova, Italy). The sequencing run yielded 6.3 million 150 bp reads on average per sample. Raw data have been deposited at NCBI, BioProject PRJNA767833. Assembly and binning Reads were filtered with Trimmomatic v0.39 [41] and cleaned with BBDuk v38.86. The reads of all pellet and supernatant samples were co-assembled using MEGAHIT v1.2.9 [42]. The quality of the co-assembly was assessed with QUAST v5.0.2 [43]. The filtered and cleaned reads were then mapped back on the assembly with Bowtie 2 v30.255.87.135 [44]. Details and parameters used for these programs are reported in Additional file 1. The assembled contigs were analysed with PPR-Meta v1.1, CheckV v0.7.0, VIBRANT v1.2.0 and the PHASTER web server [23, 24, 27, 28]. CheckV results were filtered excluding predictions with "not determined" quality and no viral genes detected. PPR-Meta predictions were filtered for viral scores of 0.75 or higher. VIBRANT and PHASTER predictions were carried forward with no pre-filtering. A first list of contigs classified as viral was defined by considering predictions made by either PHASTER alone, or at least two of the other programs (Additional file 1: Figure 1). A binning procedure was performed with MetaBAT2 v2.12.1 [45, 46] using a minimum bin size of 10,000 bp. Here, we refer to the output of the binning algorithm as "bins" and to bins which have passed quality control and are thus considered representative of prokaryotic genomes as "MAGs". The bins yielded by MetaBAT2 were evaluated and divided into Metagenome-Assembled Genomes (MAGs), viral MAGs and unclassified contigs, according to a procedure described in Additional file 1. For prokaryotes, bin quality, completeness and contamination were measured with CheckM v1.1.2 [47]. Finally, CheckV was run again on the viral MAGs recovered from binning in order to calculate genome quality and completeness. Coverage profiles Relative abundance of prokaryotic and viral MAGs was calculated by performing genome count per million (CPM) normalisation, which takes into account genome length and sequencing depth, on read counts obtained from the reads mapped on the assembly. The values obtained were highly similar with those obtained using CheckM software v1.1.2 (Additional file 1: Figure 2). For the 50 most abundant MAGs, the coefficient of variation, defined as the standard deviation divided by the mean, was calculated. The mean was calculated across pellet samples. The effect of the different shocks was evaluated by calculating the log ratio between the relative abundance of genomes in each shock and the mean relative abundance across all the conditions considered. In this context, a positive log ratio refers to an abundance higher than average and vice versa. For an overall comparison of the conditions, Spearman correlation was calculated between log ratio values in different samples. The calculation of the Spearman correlation coefficients and the corresponding P-values was carried out with SciPy v1.3.1 [48]. The same analysis was performed by using the control sample as a reference. The 50 most abundant MAGs and viral genomes were clustered by computing the Euclidean distance from the log ratios under different conditions and using an average linkage method. Correlation values between MAGs were computed by using all eight treated samples (four pellet, four supernatant). The software SparCC (commit 2ddc13f, February 2020) was used to calculate correlation coefficients while taking into account the compositional nature of the data [49]. The input was a matrix of mapped reads on each genome in the eight samples. Significance of the obtained correlation values was assessed by generating 1000 bootstraps and calculating two-sided pseudo P-values. Taxonomic assignment and functional annotation Prokaryotic MAGs were taxonomically assigned using GTDB-Tk v1.4.1 and converted to NCBI taxonomy with the script gtdb_to_ncbi_majority_vote.py [50]. Viral genomes were assigned via Hidden Markov Model against the Prokaryotic Virus Orthologous Groups (pVOGs) database using hmmsearch from the HMMER v3.3.2 suite [51, 52]. ORFs were predicted using Prodigal v2.6.3 [53]. Taxonomy was assigned on the basis of a consensus rule, as previously reported [21]. Taxonomy assignment is explained in detail in Additional file 1. Prodigal was also used to detect the presence of alternative stop codons in viral sequences, following the method used by Borges and colleagues [54]. Functional annotation was carried out on protein encoding genes predicted on prokaryotic and viral genomes using the eggnog-mapper server [55]. The completeness of KEGG modules in each microbial genome was calculated with KEMET [56]. Furthermore, ORFs annotated with KEGG orthologs belonging to putative alternatives to the Wood-Ljungdahl pathway (WLP) were counted in each MAG to identify potential syntrophic acetate oxidising bacteria. Proteins involved in carbohydrate hydrolysis were searched against the dbCAN database [57] using hmmsearch and annotated. Proteins were also analysed with gutSMASH [58], in order to find gene clusters related to VFA production and metabolism. Fisher's exact test, implemented in SciPy v1.3.1, was employed in order to assess whether the occurrence of the GH33 enzymatic family was significantly higher in viral genomes than in microbial genomes. Detection of induction in integrated prophages With the aim of evaluating the induction of putative integrated phages, an analysis was carried out on the eight samples analysed in this work and extended to 110 samples of the AD database [2]. The aim of the analysis was to check whether prophages detected in the induction experiment reported in this study were also present (and, possibly, induced) in other, unrelated AD communities. MAGs featuring integrated viruses were split into viral and nonviral sequences by extracting the viral sequence predicted within MAGs. This approach resulted in a dataset of 64 prokaryotic MAGs and 64 corresponding integrated viral sequences. Ten million reads were randomly extracted from fastq files and mapped on the database generated from the extracted prophages and MAGs using Bowtie 2. Genome coverage of MAGs and prophages in the different samples was calculated with CheckM coverage. One coverage value was obtained for each contig. Coverage values for each genome were obtained by averaging the values of individual contigs. The coverage threshold for a species to be considered was set to 0.01. The virus/MAG coverage ratios were calculated and the distributions of their values across samples and across genomes were inspected. Finally, log ratios were clustered with an average linkage algorithm based on Euclidean distance. Prophages were considered putatively induced in a sample when the log ratio was greater than 10. This threshold was chosen to exclude values resulting from noise, based on the exponential-like distribution which reaches a plateau around the value of 10 (Additional file 1). Results Anaerobic digestion perturbation assays The current study investigated the effect of environmental stresses on viral and microbial composition of AD communities present in laboratory-scale reactors. The experimental plan included 21 different environmental perturbations known to affect the AD microbial community and to stimulate the induction of integrated prophages (Additional file 1) [14, 59–65]. Some of these conditions occur in biogas plants and negatively impact the reactor performance, but they do not completely disrupt the microbial community, and methane production can be recovered if the conditions are removed. Overall, the highest decreases in methane yield were observed with low pH and temperature shifts to 55 °C or 60 °C (Fig. 1). Temperature shifts had a similarly strong effect if combined with high salt concentration and a much stronger one when paired with oxidation. A moderate temperature increase, on the other hand, registered positive effects on the production of methane, even when combined with other factors. Interestingly, the treatment with mitomycin did not result in a reduction of methanogenesis. In fact, for both concentrations of mitomycin assessed, the methane yield increased. Importantly, the aim was to identify the perturbations where viral induction was more likely to be detectable. A condition for this was that the microbial community had to be perturbed, but not completely disrupted. The most promising batches, either according to data found in literature, or according to the variation in methane yield, were carried over to DNA extraction and sequencing. DNA yields for the viral fraction were often exceptionally low, despite using all the available volume to perform the extraction. In several cases, including the control bottle, the insufficient yield of viral DNA made it impossible to generate sequencing libraries or provided raw reads of low quality that were discarded (data not shown). Ultimately, four conditions were successfully sequenced and analysed. All of them are characterised by a moderate decrease in the methane yield (up to −30%) with respect to the control bottle. These were organic overload of 8 g VS/L, exposure to atmospheric air at a concentration of 15 mL O2/g VS, pH increase to the value of 8.5, and exposure to H2O2 at a concentration of 3 mM. The fourth condition, which obviously does not occur in biogas plants, was set up in order to mimic a strong oxidative shock, possibly happening during a massive oxygen influx in the system. Viral and microbial community Metagenomic analysis allowed the recovery of 1,092 viral genomes (virMAGs). A parallel binning approach recovered 120 microbial MAGs, 72 of them being of high quality according to MIMAG standards. It is reported in the literature that about half of known bacterial genomes feature integrated prophages in their sequence [66]; similarly, 64 of the 120 MAGs identified in this study harbour prophages (Additional file 3). The normalised relative abundance of the viral component is very high, reaching almost 70% of the total community (viral + microbial) in some samples (Fig. 2). Viral MAGs and single-scaffolds viral genomes ranged in size from 1502 to 195,329 bp, covering the vast majority of the sequence lengths space occupied by prokaryotic viruses with the exclusion of jumbophages [67] (Additional file 3). This was done by design, as the three viral bins longer than 200 kpb were divided into single contigs (Additional file 1). Furthermore, CheckV showed that these bins featured a low completeness and high contamination, justifying the approach taken in their regard. The combined approach of viral prediction and binning yielded a total of 16 high-quality and five complete viral MAGs according to MIUViG standards [68]. Other metaviromic studies report similar numbers of high quality viral genomes per dataset [9, 21, 69].Fig. 2 Overview of the prokaryotic and viral community. A Relative abundance of prokaryotic phyla (left) and viral families (right), expressed in genome count per million of genomes (CPM) in each sample. The pie charts in the middle represent the overall CPM abundance of viruses (blue), prokaryotes (orange) and unclassified (green) contigs. The term "Candidatus" in the legend refers to the sum of all the candidate phyla. B Genome map of two significant viruses retrieved in this study. Annotated ORFs are depicted in orange; uncharacterised ORFs in blue The DNA virome is largely dominated by Caudovirales phages belonging to the Myoviridae, Podoviridae and Siphoviridae families, confirming similar results observed in previous studies investigating AD viromes [9, 11]. A few small contigs (< 5 kb) were classified as Inoviridae or Microviridae. This is consistent with the fact that species within these taxa tend to have very small genomes, in the order of kilobases. As they are short and underrepresented in databases, it is difficult both to detect them in metagenomes and to estimate completeness and contamination. Among the viral sequences investigated (considering all viral MAGs and prophages integrated in the prokaryotic MAGs), 4.5% were unequivocally assigned at species level. Furthermore, the vast majority (88%) of the viral genomes were assigned at family level. The distribution of relative abundances among viral genomes is very skewed, with a few prominent viruses (including Siphoviridae sp. 0304, Siphoviridae sp. 0142, Virus sp. 0026, Siphoviridae sp. 0307; Additional file 1: Figure 3) and the majority having very low values. In particular, Siphoviridae sp. 0304 (Fig. 2B) is by far the most abundant, with a relative abundance of about 5.3% on average across samples. Single-scaffold phage genome Siphoviridae sp. 0431, while not being as abundant as the virMAGs previously mentioned, is a prominent component of the viral community, with an average abundance of 5400 CPM (0.54%) across samples, which spikes at 13,009 CPM in the supernatant part of the sample subjected to alkaline condition. The prokaryotic composition is consistent with the results of previous works describing anaerobic digestion communities [70, 71]. Firmicutes was the most abundant bacterial phylum (25–30% in relative abundance) followed by Candidatus Cloacimonetes (20%), Bacteroidetes (18%) and Proteobacteria (9.4%) (Additional file 3). Four of the 120 prokaryotic MAGs were classified as Archaea, accounting for 3 to 4% of the microbiome (Additional file 3). This set is represented by: Methanoculleus sp. 0064, Methanothrix sp. 0024, Methanosarcina flavescens 0114 and Methanosarcina mazei 0049. Methanoculleus species perform hydrogenotrophic methanogenesis [72, 73], archaea of Methanothrix genus perform aceticlastic methanogenesis, while the Methanosarcina are generalists [74]. The two Methanosarcina identified in this work harbour integrated viral sequences, belonging to the Siphoviridae family. The two genomes Candidatus Cloacimonetes spp. 057 and 073 are highly abundant, accounting for a substantial percentage of the bacterial community, from 16% in the pH 8.5 supernatant sample up to 23% in the organic overload supernatant sample. Other examples of AD communities dominated by members of Candidatus Cloacimonetes, not included in the AD database, have been described in literature [75–77]. Members of this phylum have been suggested as glycolytic in previous studies [78]. Effect of tested conditions on the metagenome The effect of the different treatments on MAGs and viruses was evaluated by calculating the log ratio of the relative abundances with respect to the average value (see the "Materials and methods" section). The same analysis was performed by comparing the treated samples with the inoculum, the results of which are reported in Additional file 1: Figure 4 due to the marked differences in the microbial profiles existing between the control and the other conditions. A hierarchical clustering performed on the most abundant microorganisms and viruses highlighted groups of species with similar behaviours (Fig. 3). Correlation values between all genomes were calculated considering the compositional nature of the data and are reported in Additional file 3.Fig. 3 Heatmaps displaying the log ratios of MAGs and virMAGs relative abundance in each condition over their mean relative abundance across samples. A The 50 most abundant viral MAGs are displayed, and the average abundance is calculated on the supernatant samples. Four clusters are highlighted: cluster "1" is exclusively composed of Siphoviridae phages undergoing a mild increase during microaeration; cluster "2" is characterised by a marked increase during overload as well as a decrease in pH; cluster "3" is similar to cluster "2", but the variations are less evident; cluster "4" is composed of phages sharply decreasing during overload and increasing when exposed to basic pH. B The 50 most abundant prokaryotic MAGs are displayed, and the average relative abundance is calculated across pellet samples. Coherent behaviours in terms of response to conditions are depicted as clusters: cluster "A" comprises MAGs increasing in relative abundance when exposed to basic pH and negatively affected by microaeration; cluster "B" is composed of MAGs with slightly increased average relative abundance under microaeration; in cluster "C" and cluster "D", respectively, a sharp decrease and increase during overload treatment are evidenced. C The summary table contains some functionally relevant species discussed in the main text Groups of phages with very definite behaviours emerged from the hierarchical clustering (Fig. 3). These groups are heterogeneous in terms of viral taxonomies, yet certain genes are characteristic of each group. The 50 most abundant viral genomes match against a total of 371 HMM profiles of the pVOG database. Of these, 266 (72%) are cluster-specific. It is evident that basic pH and overload tend to have opposite effects on phages (Spearman's ⍴ = − 0.28, p < 0.05), particularly in clusters "2" and "4". The former is characterised by eight phages which decrease in relative abundance during overload treatment (log ratios between − 0.5 and − 1, see Additional file 3). Three of these genomes have an increase in relative abundance only when exposed to basic pH, with Siphoviridae sp. 0431 and Virus sp. 0283 almost doubling. It should be noted that increased abundance could have different biological explanations including phage induction and increased abundance of the host. In the latter cluster, the opposite effect can be seen, and in particular two genomes (Virus sp. 0026 and Virus sp. 0212) are those most heavily affected by basic pH (log ratios − 0.68 and − 0.59, respectively) and overload (log ratio 0.64 and 0.66, respectively). Viruses of cluster "3", similarly, increase in abundance during organic overload and decrease when exposed to basic pH, but less markedly (average log ratios 0.20 and -0.31, respectively). This cluster includes Siphoviridae sp. 0307 and Siphoviridae sp. 0142, which are the second and third most abundant viruses present in the dataset. The Yersinia outer protein gene (yopX), which is likely to be involved in life cycle regulation in temperate bacteriophages [79], is only present in genomes belonging to cluster "3", chiefly Siphoviridae sp. 0142, contributing to the hypothesis that these viruses are temperate. The main dominant viral genome, Siphoviridae sp. 0304, is part of cluster "1", entirely composed of Siphoviridae and the only cluster of genomes whose relative abundance increases during microaeration (log ratios from 0.11 to 0.31). Cluster "1" is the only one where matches against pVOGs VOG3653, VOG3654, and VOG9328 were found, all of which are annotated as tail proteins. The ORFs matching against these HMM profiles are always found in a duo: one matches with VOG3653, the other both with VOG3654 and VOG9328 (Fig. 2B). This result suggests that the proteins encoded by these two genes are complementary tail components. Overall, basic pH and overload affect phages the most. Basic pH is the condition with the most negative influence on the viruses considered, while overload is mostly associated with positive log ratio values. Unexpectedly, exposure to H2O2 is not responsible for great variations in relative abundance of phages. Considering the 50 most abundant phages, most log ratio values under hydrogen peroxide exposition fall between − 0.1 and 0.1, and only a small set of viruses shows a mild increase, including the aforementioned Siphoviridae sp. 0096 and Siphoviridae sp. 0307. Microaeration and exposure to hydrogen peroxide have strong effects on MAGs and seem to have opposite effects on the whole microbial community, displaying a slight anticorrelation (Spearman's ⍴ = − 0.40, p < 0.005). Basic pH is another condition that predominantly affects the microbial community. Genomes in cluster "A" (Fig. 3), sharply increase in relative abundance under basic pH and decrease under microaeration. This cluster shows a relative lack of integrated proviruses in the MAGs' genomes: only two MAGs out of 14 include viral sequences (14%), whereas in the entire dataset, 64 MAGs out of 120 do (53%). Contrariwise, in cluster "B" 6 out of 8 MAGs carry integrated prophages. However, the small number of genomes made it difficult to draw statistically sound conclusions regarding these differences. The behaviour of the three archaeal MAGs included in the analysis is peculiar and, although they end up in distant places in the clustering (Fig. 3), they have common characteristics. First, they do not cluster with bacterial genomes, but rather exhibit a unique behaviour. Secondly, their relative abundance varies markedly (coefficients of variation between 0.34 and 0.46, higher than 40 MAGs out of the 50 included in the heatmap), evidencing a marked response to the changes in the experimental conditions. For example, M. flavescens 114 and Methanothrix sp. 24 show a marked increase under organic overload, with log ratios of 0.71 and 0.56, respectively. Another common trend of archaeal MAG abundance in relation to shocks is a decrease in response to H2O2 exposure. This is coherent with biochemical measurements, as the sample is characterised by the highest concentration of acetate and VFA, and a substantially lower methane yield (Additional file 2, Fig. 1). A kinetic imbalance between acid producers and consumers, reflected by low methane production, is revealed by VFA accumulation [62, 80]. This sample shows the highest concentration of VFA, including acetate. This is coherent with the idea that a halt in the activity of acetoclastic archaea leads to an accumulation of acetate and a decrease in methane production. Two isolated small clusters dubbed "C" and "D", comprising two MAGs each, comprise species which display marked variations in relative abundance among different conditions. Cluster "C" comprises Thiopseudomonas sp. 046 and Propionibacterium sp. 081, both decreasing with organic overload, but the former sharply increased with air. The Thiopseudomonas genus has been described as a facultative anaerobe, catalase- and oxidase-positive [81]. Furthermore, five ORFs in the two Thiopseudomonas MAGs are functionally related to oxidative stress (EC numbers +1-518-422-2941, +1-518-422-2941, and +1-518-422-2941, Additional file 4), which could explain a possible air tolerance. Cluster "D" comprises Actinomyces oricola 091 and M. flavescens 114. The bacterium is negatively affected by basic pH, with its relative abundance almost halved with respect to the mean (mean abundance 6,631 CPM; abundance in basic pH 3541 CPM; log ratio − 0.90). Its relative abundance also suggests an increase during H2O2 exposure. Finally, Pseudomonas formosensis 084 and Thiopseudomonas sp. 083 display extreme variation between conditions. Their relative abundance is exceptionally low under overload (log ratios -4.8 and -2.2 respectively) which is their most striking characteristic. Furthermore, both revealed a higher relative abundance under microaeration, and a lower relative abundance with H2O2 in comparison to the average value across conditions. Functional categories of proteins encoded in MAGs and viral MAGs The tested conditions appear to have an important effect in shaping the structure of both the viral and microbial communities. Functional annotation was employed to investigate the link between the variation in community composition and the tested conditions. The AD process is carried out by a multitude of microorganisms, each one playing a number of roles in the degradation and conversion of organic matter [82]. A starting point for the metabolic characterisation is the functional annotation of genes. Analysis of the functional categories was performed on protein-coding genes identified in all predicted viruses and microbial genomes. Notably, 70% of the ORFs encoded by prokaryotic genomes registered a match in the KEGG Orthology database, while this percentage is as low as 30% in viral genomes. Viruses, although they do not perform metabolic activities in the community, can influence and modulate microbial functionality via infection, induction, and HGT. It was recently reported that, in the design of synthetic microbial communities, it is of utmost importance to determine the absence of integrated, putative inducible prophages, to ensure the stability of the process [83, 84]. According to this approach, the presence of prophages was verified, in order to determine the putative level of vulnerability of specific steps of the AD process. AD is divided into four main steps: hydrolysis, acidogenesis, acetogenesis, and methanogenesis [85]. In the hydrolysis step, complex organic molecules are broken down to their monomers, which are then converted into VFAs by the guild of acidogenic bacteria. VFAs are then employed by the acetogenic species in the production of acetate, hydrogen and CO2, upon which the methanogenic archaea feed, producing methane. In an attempt to categorise the MAGs according to their role in the AD process, particular attention was paid to gene categories that are important in each of these steps. These gene categories were grouped as follows: (I) genes involved in binding and degradation of polysaccharides, especially cellulosome-related protein families; (II) genes related to VFA production or metabolism; (III) genes pertaining to the Wood-Ljungdahl pathway (WLP) or pathways proposed as alternatives and potentially involved in syntrophic acetate oxidation; and (IV) genes involved in methanogenesis (Additional file 4). Viruses have a double role in the funnel-shaped web of interaction of the AD. On one hand, they may carry genes conferring additional enzymatic functions to their hosts; on the other hand, they represent a threat to the host, as their lifestyle often involves the hijacking of the host metabolism and its death. For this reason, genes of interest were searched in the prophages integrated in the aforementioned MAGs, both with a positive and a negative impact on the host metabolism. Free viral genomes were also investigated as they could represent temperate viruses. The MAGs encoding the largest number of ORFs annotated with carbohydrate-binding functions (guild I) belong to the candidate phyla Cloacimonetes and Hydrogenedentes. The two Candidatus Cloacimonetes genomes are characterised by the occurrence of ORFs annotated as CBM56 by dbCAN (Fig. 4A). This enzymatic family is associated with a beta-1-3-glucan binding function; hence, it is probably involved in favouring the binding of Bacteria to cellulose substrates. A glycolytic role has been suggested for Candidatus Cloacimonetes bacteria in a previous study [78]. Moreover, Candidatus Hydrogenedentes sp. 010 and Candidatus Hydrogenedentes sp. 026 feature, respectively, 13 and 27 genes annotated as dockerins, i.e., proteins that take part in the formation of cellulosomes.Fig. 4 Distribution of metabolic genes in the microbiome. A Tree map showing the number of ORFs annotated as CBM56, a family of cellulose-binding enzymes, in each genome. More than half of the CBM56 ORFs of the whole dataset are found in the two Candidatus Cloacimonetes MAGs. B Upset plot summarising the MAGs coding for VFA-related metabolic pathways. The horizontal bar plot indicates how many species carry out each process, the vertical bar plot shows how many and which pathway each species codes for. C The 20 most frequent families of carbohydrate-active enzymes of the CAZy database both in viral (top) and prokaryotic genomes (bottom) Genes encoding enzymes related to carbohydrate synthesis, degradation and binding were found in 18 out of 64 integrated viral genomes. Three enzymatic families largely outnumber the others: GH33, GT4 and GT2 are found in 42, 41 and 40 genomes respectively, with each single family representing more than 15% of the 262 hydrolytic enzymes found in viral genomes. GT4 and GT2 are glycosyl transferase families and are respectively the first and the third most frequent enzymatic families found in prokaryotic genomes, with 580 and 514 matches out of a total of 7073. It is known that mobile genetic elements can provide novel metabolic functionalities to their hosts [86]. GH33, instead, is a family of sialidases and neuraminidases, and is represented by 161 ORFs among prokaryotic sequences, which ranks it as the twelfth most represented. It is thus more frequent in viruses than in prokaryotic sequences (p = 10−22, Fisher's exact test), hinting at their importance in the viral physiology (Fig. 4C). The presence of genes related to VFA metabolism and acetate formation (guild II) was evaluated by checking the completeness of the beta-oxidation module and the presence of a selection of clusters of genes [58]. The beta-oxidation module (M00087) was complete in five genomes (Additional file 4). Three of these genomes, as well as Ruminococcaceae sp. 120, encode enzymes involved in acetate, pyruvate and butyrate metabolism (Table 1). In particular, the genome of Deltaproteobacteria sp. 006 features the complete M00087 beta-oxidation module, genes belonging to the "Acetate to butyrate" and "Acetyl-CoA pathway" metabolisms and an integrated prophage of the Myoviridae family (Table 1).Table 1 Bacteria related to VFA metabolism. The table reports the phylum of belonging, the family of integrated prophages, the completeness of the beta-oxidation KEGG module (M00087) and the presence of relevant metabolic gene clusters detected using the gutSMASH software Genome ID Phylum Prophage M00087 Metabolic gene clusters Deltaproteobacteria sp. 006 Proteobacteria Myoviridae Complete 3 acetate to butyrate; 1 acetyl-CoA pathway Myxococcales sp. 007 Proteobacteria Absent Complete Absent Pseudomonas formosensis 084 Proteobacteria Myoviridae Complete Absent Paracandidimonas sp. 097 Proteobacteria Siphoviridae Complete 3 Acetate to butyrate Bacillus ginsengihumi 031 Firmicutes Siphoviridae Complete 3 Acetate to butyrate Ruminococcaceae sp. 120 Firmicutes Absent Incomplete 1 Pyruvate to acetate-formate; 3 acetate to butyrate Bacteria potentially involved in syntrophic acetate oxidation (guild III) were identified by evaluating the presence of genes belonging to the Wood-Ljungdahl pathway (WLP) or its putative alternatives, the glycine synthase-reductase pathway (GSRP), and the reductive glycine pathway (RGP). In this dataset, these alternative WLP modules seem to be exclusive of Firmicutes (Additional file 4). Eleven MAGs comprise genes belonging to oxidative pathways, ten of which belong to Firmicutes (e.g. Firmicutes sp. 0060 and Catabacter sp. 0112) and one to Chloroflexi (Anaerolineaceae sp. 0082). Firmicutes bacteria have already been reported as capable of converting acetate to CO2 through the reverse WL pathway [87, 88]. Furthermore, gene annotations in previous works [89] have already identified bacteria from the Chloroflexi phylum as potential syntrophic acetate oxidising bacteria. In Firmicutes sp. 0060, both the glycine cleavage system and the GSRP were fully complete, while the RGP was 83% complete. A noteworthy viral genome recovered is Siphoviridae sp. 0243, which is an 84-kb phage with an estimated completeness between 80 and 100% and harbours a section of the glycine synthase-reductase pathway (GSRP). This suggests that this virus can confer additional enzymatic capabilities to its host, giving it an alternative to the Wood-Ljungdahl pathway. Methanogenesis (guild IV) is exclusively carried out by methanogenic archaea. This guild is represented by a heterogeneous population of hydrogenotrophic (Methanoculleus sp. 0064), acetoclastic (Methanothrix sp. 0024) and generalist methanogens (M. flavescens 0114; M. mazei 49). The two MAGs assigned to Methanosarcina genus have integrated Siphoviridae proviruses. Evaluation of selected prophages abundance in the AD database Given the wide presence and importance of integrated proviruses across all metabolic guilds, the search was broadened by considering 123 additional shotgun sequencing experiments deposited in public AD databases [2]. Many of these experiments investigated AD reactors operating under a variety of parameters including temperatures ranging from 35 to 55 °C, different feedstocks and stressful conditions such as lipids overload [90] or high concentration of ammonia [91]. For each MAG featuring integrated proviruses, the viral/host ratio was defined as the ratio between the read coverage of the viral and the non-viral components. This proportion was calculated separately for each experiment, with the aim of showing whether a specific prophage increases in abundance with respect to the host under specific environmental conditions. Microbial and viral diversity across the AD database In the samples from the present study, the average prophage/host ratio was equal to 1.1 and the maximum value was 13.3. Contrariwise, considering all experiments from the AD database, the average ratio rises to 12.0 and the maximum is over 4200 (Additional file 5). These data reflect the diversity of environmental conditions across the AD database and underline their importance in shaping the microbial and viral community. The read coverage across the whole dataset shows that viruses and hosts are not always present in the same community. There are 64 MAGs featuring integrated proviruses, on which reads from the additional experiments were mapped, resulting in 7872 values of coverage ratio. In 2722 cases (34.6%) the integrated virus was not found, despite the presence of the host (Additional file 5). The opposite is much rarer: only in 22 cases (0.3%) reads map only onto the viral part of the MAG. These cases may be explained by the ability of the viral species itself or related strains to infect a different host [92]. In most cases (1221 occurrences, 15.5%) where the host microbe is not present, the bacteriophage is not present either. Effects of temperature Temperature is the strongest driver of clustering: most of the mesophilic samples end up in four sub-clusters of respectively 29, 6, 9 and 8 sequencing experiments (Fig. 5). One large cluster is entirely composed of thermophilic samples. Here, 73-98% of MAGs are still present, although their coverage is often lower than those registered in our samples. However, only 26–53% of the respective phages are present and the prophage/host ratio is 7.74 on average.Fig. 5 Coverage of the MAGs with integrated phages in the 8 samples from the present experiment and in 110 samples from the AD database. The colour scale of coverage is logarithmic. The sample names are reported in Additional file 5 in the order in which they appear in the heatmap. MAGs are identified by the same sequential numbers that appear in their complete name (e.g., "Firmicutes sp. 043" is here "43 M"). "M" denotes the prokaryotic fraction of the MAGs, while "V" indicates the integrated viral part. Feedstock and temperature range are displayed as coloured labels at the top of the heatmap. Samples are grouped into clusters based on Euclidean distance. Clusters
{'source': 'PubMedDataset'}