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Pope, P. B.

Publications and source records attributed to Pope, P. B..

At least 19 recordsLinked to original sources

Rumen transfaunation between low- and high-methane-yielding dairy cows reveals asymmetric microbiome reconstitution patterns: a pilot study

BackgroundThis study investigated rumen microbiome reconstitution and methane (CH4) emissions following a complete exchange of rumen contents between low- and high-CH4-yielding Norwegian Red dairy cows. Twenty cows were screened for CH4 yield, and two low and two high emitters were selected for rumen cannulation and content swap. Total rumen contents were swapped after complete evacuation and washing of both the rumen and omasum. Rumen samples were collected twice in weeks -1, 1, 3, and 7 for fermentation analysis, metagenomics, and metaproteomics, and at week 8 CH4 production was measured. ResultsPrior to the swap, low and high emitters produced 21.2 {+/-} 0.7 and 26.3 {+/-} 1.4 g CH4/kg dry-matter intake (DMI), respectively. Eight weeks after swap, CH4 yields were 12.7 {+/-} 0.3 and 28.9 {+/-} 0.3 g CH4/kg DMI, respectively, showing that the CH4 phenotype of each cow was maintained. Analysis of metagenome-derived 16S rRNA gene sequences showed that low emitters gradually re-established their original microbial community, whereas high emitters retained donor-like microbiota. Metaproteomic mapping suggested higher expression of Prevotella-associated succinate-propionate pathway enzymes in low emitters at week 7, though these differences were modest. ConclusionThese findings suggest that host factors influence CH4 output and microbial reconstitution, with low emitters restoring their native microbiome while high emitters retained a donor-associated community yet continued to emit high CH4. Results should be interpreted with caution given the small sample size (n = 2 per phenotype) and require confirmation in larger studies. ImportanceReducing enteric methane from cattle requires understanding whether the rumen microbiome or the host animal is the primary driver of methane output. We exchanged the entire rumen contents between low- and high-methane-yielding dairy cows and measured methane production alongside metagenomic and metaproteomic profiling over two months. Despite receiving each others microbiomes, each cows methane phenotype persisted--low emitters stayed low and high emitters stayed high. Microbiome reconstitution was asymmetric: low emitters restored their original microbial community, while high emitters retained the donor microbiota. Methanogen communities did not differ between phenotypes, pointing to host-level rather than microbial-level control of methane yield. These pilot findings suggest that breeding for favorable host traits may be essential for lasting methane reduction, and that microbiome transfer alone is unlikely to shift an animals methane phenotype. Larger studies are needed to confirm these observations.

microbiology↗

Ruminosignatures associated with methane emissions and feed efficiency across geographies and cattle breeds

The cattle rumen microbiota represents a highly complex and dynamic ecosystem, whose organization and connection to host phenotypes are of the highest importance to food security and the environment. In this study, we analyzed the rumen microbiota, from 2,492 cattle belonging to five different breeds and production systems across five countries, categorizing them into microbial co-abundance groups referred to as Ruminosignatures. We identified twelve distinct Ruminosignatures, including two that were consistently observed across all populations and were dominated by the genus Prevotella and UBA2810. Additional Ruminosignatures showed breed-and diet-specific patterns and collectively explained 96-99% of the variance in rumen microbial composition. The abundances of several Ruminosignatures were associated with methane emissions and feed efficiency, and were influenced by host genetics, with heritability estimates ranging from 0.09 to 0.51. The Ruminosignature dominated by UAB2810 was negatively associated with methane emissions across all datasets and positively linked to feed efficiency in Holstein from Italy and crossbred from Ireland. Additionally, the type of production system affects both the occurrence of Ruminosignatures and their impact on host phenotypes, emphasizing the need for context-specific approaches to modulate the rumen microbiome. Overall, our results offer new perspectives on the assembly of ruminal microbes and underscore the potential of the Ruminosignatures framework for microbiome-informed precision agriculture and breeding initiatives aimed at enhancing feed efficiency and minimizing the environmental impact of cattle farming.

microbiology↗

In vitro model reveals structural and metabolic insights to the porcine caecal microbiota in response to β-mannan exposure

The gastrointestinal microbiota plays a pivotal role in shaping host physiology and health. By selectively promoting bacteria associated with improved host health, microbiota-directed fibres offer a strategy to enhance the beneficial functions of the microbiota. In this work, we developed a pH-controlled in vitro fermentation system (InVitSim) as a model to evaluate the effects of such a fibre - acetylated galactoglucomannan from Norway spruce - on the composition and functionality of porcine caecal microbial communities. We validated the experimental outcomes by comparing the response of the in vitro model to a previous in vivo feeding trial utilising the same {beta}-mannan fibres. Long-read sequencing with Oxford Nanopore, metatranscriptomics, and short-chain fatty acid measurements were undertaken to survey microbial community dynamics and functionality. Microbial communities in pigs and InVitSim responded similarly to {beta}-mannan supplementation, with taxa like Prevotella, Catenibacterium, and Faecalibacterium increasing in abundance. Intriguingly, some taxa were observed to be more affected by {beta}-mannan supplementation in InVitSim than in vivo. These taxa included several bacterial species that were not previously known to utilise {beta}-mannan, yet exhibited upregulated genes encoding carbohydrate-active enzymes involved in the degradation of this substrate. ImportanceIn this study, we establish a fermenter system able to preserve more than 70% of over 300 distinct microbial taxa identified in the porcine caecal gut. The in vitro model and the functional omic data generated from it enabled us to identify relevant microbial populations that responded to the presence of AcGGM by upregulating {beta}-mannan-specific polysaccharide utilisation loci. Our results highlight the value of in vitro approaches as a complementary tool to in vivo trials for learning about the gastrointestinal microbiomes response to dietary interventions on the host level. Description of supplementary filesO_LIIn-depth analyses for in vitro model validation and investigations. C_LIO_LICommon taxa between in vivo and in vitro systems exposed to {beta}-mannans. C_LIO_LIDifferential abundance analysis result visualisations. C_LIO_LIShort-chain fatty acid concentrations and correlation with microbial abundances. C_LI

microbiology↗

Intricate microbiome differences observed in lactating cows across methane intensity phenotypes

Methane emissions from ruminants can be expressed through several metrics as total production, yield, or intensity, each reflecting distinct aspects of energy utilization. Among these, methane intensity defined as grams of methane emitted per kilograms of energy-corrected milk, directly links emissions to productive efficiency; however, the microbial mechanisms underlying variation in this trait remain unclear. Here, we applied genome-resolved metagenomics and metatranscriptomics to characterize rumen microbial identity, functional potential, and transcriptional activity in lactating cows differing in methane intensity while sharing breed and diet. Microbial community composition and diversity were comparable across phenotypes. However, rumen microbial gene expression revealed marked functional divergence. The rumen content of low-methane intensity cows showed enriched transcription of fructan-degrading carbohydrate-active enzymes and butyrate-forming pathways, primarily encoded by RUG440 (Atopobiaceae) and Sodaliphilus-affiliated metagenome-assembled genomes. These functions suggest a fructan-butyrate metabolic axis supported by potential cross-feeding between primary degraders and butyrate producers. Conversely, the high-methane intensity rumen exhibited greater transcription of pectin-degrading carbohydrate-active enzymes, mainly carried by Prevotella metagenome-assembled genomes, suggesting methyl-ester hydrolysis and methanol release. Despite higher methanogenesis transcript levels in high-methane intensity cows, total methane production did not differ between groups. Together, these findings reveal two contrasting functional configurations of the rumen microbiome in Norwegian Red dairy cattle: a fructan-butyrate-oriented metabolism in low-methane intensity cows and a pectin-methanol-oriented metabolism in high-methane intensity counterparts. This study provides genome-resolved, multi-omic evidence that microbial carbohydrate specialization and fermentation routing contribute to methane intensity phenotypes in dairy cows, offering mechanistic insights for improving ruminant climate efficiency.

microbiology↗

Integrative holo-omic data analysis predicts interactions across the host-microbiome axis

Understanding the interplay between host organisms and their microbiomes is central to the development of sustainable food systems. However, high dimensionality and spurious associations remain major obstacles to extracting meaningful biological insight from multi-omic host-associated microbiome data; a challenge further exacerbated when "holo-omic" analyses across the host-microbiome boundary is considered. Here, we show that a computational method designed for multi-omic analysis in eukaryotes can be leveraged to integrate and analyse five layers of holo-omic data from porcine hosts and their gut microbiomes. We collected caecal tissue and digesta samples during a feeding trial that tested the impact of microbiota-directed fibres (acetylated galactoglucomannan) at critical developmental stages. From 800,000 features including microbial and host genes, metagenome-assembled genomes, and metabolites from caecal tissue and digesta, we used multiset correlation and factor analysis to select the most relevant features for capturing coordinated patterns across omic layers. From these features, we predicted over 2,000 putative host-microbiome interactions based on co-occurrence. Some of them reflected previously known relationships between animal and microbiome features, such as microbial genes for carbohydrate metabolism being linked to glycoside abundances in host tissue. Other predicted co-occurrences included features that were not detected in single-omic analysis and offer new hypotheses of host-microbiome interactions that warrant future investigation. Hence, we showcase an application of holo-omic analysis that avoids common pitfalls in high-dimensional data analysis; identifies known interactions as a form of validation; and most importantly, predicts new leads for understanding host-microbiome symbiosis. ImportanceWhile study systems involving mammalian hosts and their microbiomes are inherently complex, multi- and holo-omic analyses promise to provide interpretable results with translational value for the animal production industry. Unfortunately, computational methods capable of this kind of integration are currently scarce, as most existing multi-omics approaches have been developed for analysis of data layers within a single multicellular organism. We propose to adapt existing multi-omic methods for holo-omics by combining feature selection and interaction inference. This two-step analysis approach addresses common challenges in data-driven studies and can be implemented with a variety of tools for feature selection and interaction modelling. Through this holistic approach, we show that both known and novel relationships across the holobiont axis can be identified in a data-driven manner, offering new targets for the continued study of host-microbiome interactions and the effect of dietary interventions on production animals.

bioinformatics↗

OmniCorr: An R-package for visualizing putative host-microbiota interactions using multi-omics data

Holo-omics leverages omics datasets to explore the interactions between hosts and their associated microbiomes. Although the generation of omics data from matching host and microbiome samples is steadily increasing, there remains a scarcity of computational tools capable of integrating and visualizing this data to facilitate the interpretation and prediction of host-microbiota interactions. We present OmniCorr, an R package designed to: (1) manage the complexity of omics data by clustering similar observations into modules, (2) visualize correlations of these modules across different omics layers, host-microbiota interfaces, and meta-data, and (3) identify statistically significant associations indicative of putative host-microbiota interactions. OmniCorrs utility is demonstrated using datasets from two systems: (i) Atlantic salmon, integrating host transcriptomics with metagenomics and metatranscriptomics to explore dietary impacts, and (ii) cattle, combining host proteomics with metaproteomics and metabolomics to investigate methane emission variability. Availability and ImplementationOmniCorr is freely available at: https://github.com/shashank-KU/OmniCorr.

bioinformatics↗

Global distribution of microbial carrageenan foraging pathways reveals widespread latent traits within the genetic dark matter of ruminant intestinal microbiomes

Seaweeds represent a promising source of sustainable, alternative feeds for livestock. Despite their increasing popularity in agriculture, the dietary fate of seaweed polysaccharides, such as carrageenan, is unknown. Here, we applied functional microbiome analyses of ruminant gastrointestinal tract microbiomes to discover catabolic enzymes specific for carrageenan digestion from the red seaweed Mazzaella japonica. M. japonica preferentially increased Bacteroides abundance within the distal gut over the rumen, and bacterial isolates had capacity to use carrageenans as a sole carbon source. Carrageenan-active polysaccharide utilization loci (CarPULs) were identified and recombinant enzymes were characterized to provide insights into pathway specialization of divergent CarPULs. Selective enrichment and metagenomic mining revealed that carrageenan catabolism is widespread among geographically and taxonomically distinct ruminants, suggesting it is a globally distributed latent trait within the order Ruminantia and carried within microbiome as part of the microbial "dark matter". These pathways are structurally distinct from those found in marine bacteria, highlighting a complex and ancient evolutionary history of CarPULs in ruminant microbiomes.

microbiology↗

Microbial alginate foraging is conserved in geographically and taxonomically distinct ruminant microbiomes

Seaweed plays a crucial role in carbon cycling and is expected to be a valuable resource for sustainable biomass, with applications in biofuel production, human nutrition, and animal feed. Although seaweed has historically been used as a feed source for livestock grazing near coastlines, the process by which it is digested in the rumen remains unknown. Here, we show how the brown algae Saccharina latissima is catabolized in the rumen ecosystem of two different species using in vivo and in vitro experimental systems. We determined that the ruminal decomposition of alginate, a prominent component of the brown algae cell wall, requires microbial catabolic pathways complete with alginate lyases and transport proteins. Evidence of digestion was obtained through a combination of animal models, bacterial imaging, multilayered meta-omics, and enzyme biochemistry. The evolution of and implications for acquisition of alginate utilization loci within geographically and taxonomically distinct ruminants are considered. Graphical abstractSaccharina latissima is a brown alga commonly found in the North Atlantic, Arctic and Pacific oceans. S. latissima was collected from the west coast and Canada and Norway for microbiome studies. Alginate constitutes a substantial portion of the cell wall of S. latissima (SL), and its digestion requires a specific set of enzymes, alginate lyases. We investigated if and how S. latissima is metabolized in geographically distinct rumen ecosystems through in vivo lamb feeding experiments (2.5 and 5% inclusion, DM basis) and in vitro cattle-based rumen simulation technique, RUSITEC, experiments (up to 50% inclusion). Evidence supporting ruminal degradation of alginate was explored using a combination of multilayered meta-omics, physiology (fluorescently labelled S. latissima hot water extracts (FLA-SLAT)) and biochemical characterization of PL6 alginate lyases. O_FIG O_LINKSMALLFIG WIDTH=161 HEIGHT=200 SRC="FIGDIR/small/628917v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@eebe40org.highwire.dtl.DTLVardef@c6fb8org.highwire.dtl.DTLVardef@7a9b1borg.highwire.dtl.DTLVardef@15d30d7_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

Protozoal populations drive system-wide variation in the rumen microbiome

While rapid progress has been made to characterize the bacterial and archaeal populations of the rumen microbiome, insight into how they interact with keystone protozoal species remains elusive. Here, we reveal two distinct rumen community types (RCT-A and RCT-B) that are not strongly associated with host phenotype nor genotype but instead linked to protozoal community patterns. We leveraged a series of multi-omic datasets to show that the dominant Epidinium spp. in animals with RCT-B employ a plethora of fiber-degrading enzymes that present enriched Prevotella spp. a favorable carbon landscape to forage upon. Conversely, animals with RCT-A, dominated by genera Isotricha and Entodinium, harbor a more even distribution of fiber, protein, and amino acid metabolizers, reflected by higher detection of metabolites from both protozoal and bacterial activity. We reveal microbiome variation across key protozoal and bacterial populations is interlinked, which should act as an important consideration for future development of microbiome-based technologies.

microbiology↗

Metagenomic analysis of marsupial gut microbiomes provides a genetic basis for the low methane economy

The potent greenhouse gas methane is an end-product of plant biomass digestion by gut microbiota, though the amount produced and/or released varies among herbivorous animals. On a per unit of feed basis, macropodid marsupials (e.g. kangaroos) are widely thought to be low methane-emitting herbivores compared to high methane-producing ruminant livestock. How the gut microbiome contributes to the low methane status of marsupials is not well understood but of high potential value for a low methane economy. Here, we analyse the faecal metagenomes of 14 different marsupial species and 1,394 derived metagenome-assembled genomes (MAGs), focusing on the functional distinction of the bacterial and archaeal communities compared to ruminant faecal microbiomes. Though composition and function of the marsupial gut microbiome considerably varied across and within animal species, there was a clear host-associated bacterial signature for the community that differed significantly between marsupial hosts and compared to ruminants. Of particular note was a range of Bacteroidota, Campylobacterota, Desulfobacterota, Pseudomonadota and Verrucomicrobiota species that were enriched in marsupials and encode H2-uptake hydrogenases that mediate hydrogenotrophic respiration. Additionally, in support of an enrichment of electron sinks, enzymes for butyrate, propionate, and glutamate production, as well as nitrate, nitrite, and fumarate respiration were enriched in marsupials. Collectively, these data suggest that, by favoring an enrichment of alternate hydrogen sinks of bacterial origin, the low methane phenotype reported for marsupials is feasible and offers a genetic basis to pursue reductions of livestock methane emissions.

microbiology↗

Methanogenesis inhibition remodels microbial fermentation and stimulates acetogenesis in ruminants

Rumen microbiota enable ruminants to grow on fibrous plant materials but also produce methane, driving 5% of global greenhouse gas emissions and leading to a loss of gross energy content. Methanogenesis inhibitors such as 3-nitrooxypropanol (3-NOP) decrease methane emissions in ruminants when supplemented in feed. Yet we lack a system-wide, species-resolved understanding of how the rumen microbiota remodels following inhibition and how this influences animal production. Here, we conducted a large-scale trial with 51 dairy calves to analyse microbiota responses to 3-NOP, pairing host performance, emissions, and nutritional profiles with genome-resolved metagenomic and metatranscriptomic data. 3-NOP supplementation decreased methane emissions by an average of 62%, modulated short-chain fatty acid and H2 levels, and did not affect dietary intake or animal performance. We created a rumen microbial genome catalogue with an unprecedented mapping rate. We observed a strong reduction of methanogens and stimulation of reductive acetogens, primarily novel uncultivated lineages such as Candidatus Faecousia. However, there was a shift in major fermentative communities away from acetate production in response to hydrogen gas accumulation. Thus, the divergent responses of the fermentative and hydrogenotrophic communities limit potential productivity gains from methane reduction. Reporting one of the largest reductions in methane emissions in a field trial to date, this study links ruminant greenhouse gas emissions and productivity to specific microbial species. These findings also emphasise the importance of microbiota-wide analysis for optimising methane mitigation strategies and identify promising strategies to simultaneously reduce emissions while increasing animal production. Significance StatementOne strategy to increase the sustainability and productivity of livestock production is to modulate ruminant microbiota to produce absorbable nutrients rather than the potent greenhouse gas methane. Previous studies show supplementing feed with methanogenesis inhibitors such as 3-nitrooxypropanol reduces methane emissions, but also leads to inconsistent productivity gains. Here we report a definitive field trial, combining animal data, meta-omics, and structural modelling, to resolve the key microbes and pathways controlling nutrient and methane production in ruminants. We show that shifts in composition and gene expression of hydrogen-cycling microbes reduce emissions but limit productivity gains. These findings offer insights at unprecedented resolution, while the data and analytical framework provide valuable resources to develop solutions to enhance livestock productivity and sustainability.

microbiology↗

Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases

Metagenomics, particularly genome-resolved metagenomics, has significantly deepened our understanding of microbes, illuminating their taxonomic and functional diversity and roles in ecology, physiology, and evolutions. However, eukaryotic populations within various microbiomes, including those in the mammalian gastrointestinal (GI) tract, remain relatively underexplored in metagenomic studies due to the lack of comprehensive reference genome databases and robust bioinformatics tools. The GI tract of ruminants, particularly the rumen, contains a high eukaryotic biomass although a relatively low diversity of ciliates and fungi, which significantly impact feed digestion, methane emissions, and rumen microbial ecology. In the present study, we developed GutEuk, a bioinformatics tool that improves upon the currently available Tiara and EukRep in accurately identifying metagenome eukaryotic sequences. GutEuk is optimized for high precision across different sequence lengths. It can also distinguish fungal and protozoal sequences, facilitating further elucidation of their unique ecological and physiological impacts. GutEuk was shown to facilitate a comprehensive analysis of protozoa and fungi within more than one thousand rumen metagenomes, revealing a greater genomic diversity among protozoa than previously documented. We further curated several ruminant eukaryotic protein databases, significantly enhancing our ability to distinguish the functional roles of ruminant fungi and protozoa from those of prokaryotes. Overall, the newly developed package GutEuk and its associated databases create new opportunities for in-depth study of GI tract eukaryotes.

microbiology↗

CompareM2 is a genomes-to-report pipeline for comparing microbial genomes

Here, we present CompareM2, a genomes-to-report pipeline for comparative analysis of bacterial and archaeal genomes derived from isolates and metagenomic assemblies. CompareM2 is easy to install and operate, and integrates community-adopted tools to perform genome quality control and annotation, taxonomic and functional predictions, as well as comparative analyses of core- and pan-genome partitions and phylogenetic relations. The central results generated via the CompareM2 workflow are emphasized in a portable dynamic report document. CompareM2 is free software and welcomes modifications and pull requests from the community on its Git repository at https://github.com/cmkobel/comparem2.

bioinformatics↗

The need for high-resolution gut microbiome characterization to design efficient strategies for sustainable aquaculture production

Microbiome-directed dietary interventions such as microbiota-directed fibers (MDFs) have a proven track record in eliciting responses in beneficial gut microbes and are increasingly being promoted as an effective strategy to improve animal production systems. Here we used initial metataxonomic data on fish gut microbiomes as well as a wealth of a priori mammalian microbiome knowledge on -MOS and {beta}-mannan-derived MDFs to study effects of such feed supplements in Atlantic salmon (Salmo salar) and their hitherto poorly characterized gut microbiomes. Our multi-omic analysis revealed that the investigated MDFs (two -mannans and an acetylated {beta}-galactoglucomannan), at a dose of 0.2%, had negligible effects on both host gene expression, and gut microbiome structure and function under studied conditions. While a subsequent trial using a higher (4%) dietary inclusion of {beta}-mannan significantly shifted the gut microbiome composition, there were still no biologically relevant effects on salmon metabolism and physiology. Only a single Burkholderia-Caballeronia-Paraburkholderia (BCP) population demonstrated consistent and significant abundance shifts across both feeding trials, although with no evidence of {beta}-mannan utilization capabilities or changes in gene transcripts for producing metabolites beneficial to the host. In light of these findings, we revisited our omics data to predict and outline novel and potentially beneficial endogenous lactic acid bacteria that should be targeted with future, conceivably more suitable, MDF strategies for salmon. IMPORTANCEThis study focuses on the potential of MDFs to improve aquaculture production. Despite preliminary 16S rRNA amplicon data suggested that populations in the salmon gut microbiome could utilize structurally complex mannans, our findings indicates that endogenous microbes could not metabolize it, nor the host responds to its dietary inclusion, at least not under the trial conditions investigated in this study. We highlight that high-resolution and host-specific microbiome characterization can greatly improve trial design and selection of candidate MDFs for future nutritional interventions. Understanding the intricate interplay between host and its gut microbiome is paramount in studies seeking to leverage endogenous microbial communities to benefit the host. While each new condition, whether it is a disease onset or a nutritional stressor, has the potential to profoundly reshape the microbial diversity, composition and outputs, the functional microbiome information gained under healthy conditions represent a pivotal step towards designing more effective trials involving microbiome-reprogramming feed additives. Overall, we envisage that these results will lead to improved focus on coupling fundamental microbiome characterization to the design of next-generation feeds for salmon aquaculture.

microbiology↗

Predicting microbial genome-scale metabolic networks directly from 16S rRNA gene sequences

Genome-scale metabolic models are key biotechnology tools that can predict metabolic capabilities and growth for an organism. In particular, these models have become indispensable for metabolic analysis of microbial species and communities such as the gut microbiomes of humans and other animals. Accurate microbial models can be built automatically from genomes, but many microbes have only been observed through sequencing of marker genes such as 16S rRNA and thus remain inaccessible to genome-scale modeling. To extend the scope of genome-scale metabolic models to microbes that lack genomic information, we trained an artificial neural network to build microbial models from numeric representations of 16S rRNA gene sequences. Specifically, we built models and extracted 16S rRNA gene sequences from more than 15,000 reference and representative microbial genomes, computed multiple sequence alignments and large language model embeddings for the 16S rRNA gene sequences, and trained the neural network to predict metabolic reaction probabilities from sequences, alignments, or embeddings. Training was fast on a single graphics processing unit and trained networks predicted reaction probabilities accurately for unseen archaeal and bacterial sequences and species. This makes it possible to reconstruct microbial genome-scale metabolic networks from any 16S rRNA gene sequence and enables simulation of metabolism and growth for all observed microbial life.

systems biology↗

The Salmon Microbial Genome Atlas enables novel insights into bacteria-host interactions via functional mapping

The essential role of the gut microbiota for host health and nutrition is well established for many terrestrial animals, while its importance for fish and particularly Atlantic salmon is unclear. Here, we present the Salmon Microbial Genome Atlas (SMGA) originating from wild and farmed fish both in freshwater and seawater, and consisting of 211 high-quality bacterial genomes, recovered by cultivation (n=131) and gut metagenomics (n=80). Bacterial genomes were taxonomically assigned into 14 different orders, including 28 distinctive genera and 31 potentially novel species. Benchmarking the SMGA, we functionally characterized key populations in the salmon gut that were detected in vivo. This included the ability to degrade diet-derived fibers and release vitamins and other exo-metabolites with known beneficial effects, which were validated by in vitro cultivation and untargeted metabolomics. Together, the SMGA enables high resolution functional insight into salmon gut microbiota with relevance for salmon nutrition and health.

microbiology↗

Forecasting of a complex microbial community using meta-omics

Microbial communities are complex assemblages whose dynamics are shaped by abiotic and biotic factors. A major challenge concerns correctly forecasting the community behaviour in the future. In this context, communities in biological wastewater treatment plants (BWWTPs) represent excellent model systems, because forecasting them is required to ultimately control and operate the plants in a sustainable manner. Here, we forecast the microbial community from the water-air interface of the anaerobic tank of a BWWTP via longitudinal meta-omics (metagenomics, metatranscriptomics and metaproteomics) data covering 14 months at weekly intervals. We extracted all the available time-dependent information, summarised it in 17 temporal signals (explaining 91.1% of the temporal variance) and linked them over time to rebuild the sequence of ecological phenomena behind the community dynamics. We forecasted the signals over the following five years and tested the predictions with 21 extra samples. We were able to correctly forecast five signals accounting for 22.5% of the time-dependent information in the system and generate mechanistic predictions on the ecological events in the community (e.g. a predation cycle involving bacteria, viruses and amoebas). Through the forecasting of the 17 signals and the environmental variables readings we reconstructed the gene abundance and expression for the following 5 years, showing a nearly perfect trend prediction (coefficient of determination [≥] 0.97) for the first 2 years. The study demonstrates the maturity of microbial ecology to forecast composition and gene expression of open microbial ecosystems using year-spanning interactions between community cycles and environmental parameters.

microbiology↗

The Food Additive Xanthan Gum Drives Adaptation of the Human Gut Microbiota

The diets of industrialized countries reflect the increasing use of processed foods, often with the introduction of novel food additives. Xanthan gum is a complex polysaccharide with unique rheological properties that have established its use as a widespread stabilizer and thickening agent1. However, little is known about its direct interaction with the gut microbiota, which plays a central role in digestion of other, chemically-distinct dietary fiber polysaccharides. Here, we show that the ability to digest xanthan gum is surprisingly common in industrialized human gut microbiomes and appears to be contingent on the activity of a single bacterium that is a member of an uncultured bacterial genus in the family Ruminococcaceae. We used a combination of enrichment culture, multi-omics, and recombinant enzyme studies to identify and characterize a complete pathway in this uncultured bacterium for the degradation of xanthan gum. Our data reveal that this keystone degrader cleaves the xanthan gum backbone with a novel glycoside hydrolase family 5 (GH5) enzyme before processing the released oligosaccharides using additional enzymes. Surprisingly, some individuals harbor a Bacteroides species that is capable of consuming oligosaccharide products generated by the keystone Ruminococcaceae or a purified form of the GH5 enzyme. This Bacteroides symbiont is equipped with its own distinct enzymatic pathway to cross-feed on xanthan gum breakdown products, which still harbor the native linkage complexity in xanthan gum, but it cannot directly degrade the high molecular weight polymer. Thus, the introduction of a common food additive into the human diet in the past 50 years has promoted the establishment of a food chain involving at least two members of different phyla of gut bacteria.

microbiology↗