The gutSMASH web server: automated identification of primary metabolic gene clusters from the gut microbiota.

Anaerobic bacteria from the human microbiome produce a wide array of molecules at high concentrations that can directly or indirectly affect the host. The production of these molecules, mostly derived from their primary metabolism, is frequently encoded in metabolic gene clusters (MGCs). However, despite the importance of microbiome-derived primary metabolites, no tool existed to predict the gene clusters responsible for their production. For this reason, we recently introduced gutSMASH. gutSMASH can predict 41 different known pathways, including MGCs involved in bioenergetics, but also putative ones that are candidates for novel pathway discovery. To make the tool more user-friendly and accessible, we here present the gutSMASH web server, hosted at https://gutsmash.bioinformatics.nl/. The user can either input the GenBank assembly accession or upload a genome file in FASTA or GenBank format. Optionally, the user can enable additional analyses to obtain further insights into the predicted MGCs. An interactive HTML output (viewable online or downloadable for offline use) provides a user-friendly way to browse functional gene annotations and sequence comparisons with reference gene clusters as well as gene clusters predicted in other genomes. Thus, this web server provides the community with a streamlined and user-friendly interface to analyze the metabolic potential of gut microbiomes.

MIBiG 2.0: a repository for biosynthetic gene clusters of known function.

Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/.

The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters.

Natural products originating from microorganisms are frequently used in antimicrobial and anticancer drugs, pesticides, herbicides or fungicides. In the last years, the increasing availability of microbial genome data has made it possible to access the wealth of biosynthetic clusters responsible for the production of these compounds by genome mining. antiSMASH is one of the most popular tools in this field. The antiSMASH database provides pre-computed antiSMASH results for many publicly available microbial genomes and allows for advanced cross-genome searches. The current version 2 of the antiSMASH database contains annotations for 6200 full bacterial genomes and 18,576 bacterial draft genomes and is available at https://antismash-db.secondarymetabolites.org/.

gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota.

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here we introduce the gutSMASH algorithm for identification of primary metabolic gene clusters, and we used it to systematically profile gut microbiome metabolism, identifying 19,890 gene clusters in 4,240 high-quality microbial genomes. We found marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 individuals from a Dutch population-based cohort shows that the level of microbiome-derived metabolites in plasma and feces is almost completely uncorrelated with the metagenomic abundance of corresponding metabolic genes, indicating a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome.

BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes.

Microbial gene clusters encoding the biosynthesis of primary and secondary metabolites play key roles in shaping microbial ecosystems and driving microbiome-associated phenotypes. Although effective approaches exist to evaluate the metabolic potential of such bacteria through identification of these metabolic gene clusters in their genomes, no automated pipelines exist to profile the abundance and expression levels of such gene clusters in microbiome samples to generate hypotheses about their functional roles, and to find associations with phenotypes of interest. Here, we describe BiG-MAP, a bioinformatic tool to profile abundance and expression levels of gene clusters across metagenomic and metatranscriptomic data and evaluate their differential abundance and expression under different conditions. To illustrate its usefulness, we analyzed 96 metagenomic samples from healthy and caries-associated human oral microbiome samples and identified 252 gene clusters, including unreported ones, that were significantly more abundant in either phenotype. Among them, we found the muc operon, a gene cluster known to be associated with tooth decay. Additionally, we found a putative reuterin biosynthetic gene cluster from a Streptococcus strain to be enriched but not exclusively found in healthy samples; metabolomic data from the same samples showed masses with fragmentation patterns consistent with (poly)acrolein, which is known to spontaneously form from the products of the reuterin pathway and has been previously shown to inhibit pathogenic Streptococcus mutans strains. Thus, we show how BiG-MAP can be used to generate new hypotheses on potential drivers of microbiome-associated phenotypes and prioritize the experimental characterization of relevant gene clusters that may mediate them. IMPORTANCE Microbes play an increasingly recognized role in determining host-associated phenotypes by producing small molecules that interact with other microorganisms or host cells. The production of these molecules is often encoded in syntenic genomic regions, also known as gene clusters. With the increasing numbers of (multi)omics data sets that can help in understanding complex ecosystems at a much deeper level, there is a need to create tools that can automate the process of analyzing these gene clusters across omics data sets. This report presents a new software tool called BiG-MAP, which allows assessing gene cluster abundance and expression in microbiome samples using metagenomic and metatranscriptomic data. Here, we describe the tool and its functionalities, as well as its validation using a mock community. Finally, using an oral microbiome data set, we show how it can be used to generate hypotheses regarding the functional roles of gene clusters in mediating host phenotypes.

Computational genomic discovery of diverse gene clusters harbouring Fe-S flavoenzymes in anaerobic gut microbiota.

The gut contains an enormous diversity of simple as well as complex molecules from highly diverse food sources, together with host-secreted molecules. This presents a large metabolic opportunity for the gut microbiota, but little is known about how gut microbes are able to catabolize this large chemical diversity. Recently, Fe-S flavoenzymes were found to be key in the transformation of bile acids, catalysing the key step in the 7α-dehydroxylation pathway that allows gut bacteria to transform cholic acid into deoxycholic acid, an exclusively microbe-derived molecule with major implications for human health. While this enzyme family has also been implicated in a limited number of other catalytic transformations, little is known about the extent to which it is of more global importance in gut microbial metabolism. Here, we perform a large-scale computational genomic analysis to show that this enzyme superfamily has undergone a remarkable expansion in Clostridiales, and occurs throughout a diverse array of >1000 different families of putative metabolic gene clusters. Analysis of the enzyme content of these gene clusters suggests that they encode pathways with a wide range of predicted substrate classes, including saccharides, amino acids/peptides and lipids. Altogether, these results indicate a potentially important role of this protein superfamily in the human gut, and our dataset provides significant opportunities for the discovery of novel pathways that may have significant effects on human health.

Early Postoperative Endoscopic Recurrence in Crohn's Disease Is Characterised by Distinct Microbiota Recolonisation.

BACKGROUND AND AIMS: Intestinal microbiota dysbiosis is implicated in Crohn's disease [CD] and may play an important role in triggering postoperative disease recurrence [POR]. We prospectively studied faecal and mucosal microbial recolonisation following ileocaecal resection to identify the predictive value of recurrence-related microbiota. METHODS: Mucosal and/or faecal samples from 121 CD patients undergoing ileocaecal resection were collected at predefined time points before and after surgery. Ileal biopsies were collected from 39 healthy controls. POR was defined by a Rutgeerts score ≥i2b. The microbiota was evaluated by 16S rRNA sequencing. Prediction analysis was performed using C5.0 and Random Forest algorithms. RESULTS: The mucosa-associated microbiota in CD patients was characterised by a depletion of butyrate-producing species (false discovery rate [FDR] <0.01) and enrichment of Proteobacteria [FDR = 0.009] and Akkermansia spp. [FDR = 0.02]. Following resection, a mucosal enrichment of Lachnospiraceae [FDR <0.001] was seen in all patients but in POR patients, also Fusobacteriaceae [FDR <0.001] increased compared with baseline. Patients without POR showed a decrease of Streptococcaceae [FDR = 0.003] and Actinomycineae [FDR = 0.06]. The mucosa-associated microbiota profile had good discriminative power to predict POR, and was superior to clinical risk factors. At Month 6, patients experiencing POR had a higher abundance of taxa belonging to Negativicutes [FDR = 0.04] and Fusobacteria [FDR = 0.04] compared with patients without POR. CONCLUSIONS: Microbiota recolonisation after ileocaecal resection is different between recurrence and non-recurrence patients, with Fusobacteria as the most prominent player driving early POR. These bacteria involved in the early recolonisation and POR represent a promising therapeutic strategy in the prevention of disease recurrence.