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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. Taxon-specific primary metabolic pathways are identified using profile hidden Markov models.

Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host–viral (host–plasmid) associations using Hi-C data. Metagenome sequencing can now distinguish closely related microbes using long reads and haplotype phasing.

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. 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.

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.

The global spread of vancomycin-resistant Enterococcus faecium (VRE), which commonly occurs in hospital environments, has become a major public health concern. To facilitate genomic surveillance and tracking the transmission of VRE, ClonalTracker was designed. This tool assesses the clonal relatedness between two VRE isolates given the respective assembled genomes by analyzing the van operon, the respective transposon type and the whole genome similarity. ClonalTracker has been validated using two previously analyzed publicly available datasets and I showcase its applicability on a yet unprocessed third dataset. While the method agrees with previously published results, it is able to provide more resolution at the clustering level even in the absence of plasmid information and using as reference the minimal version of the vancomycin resistance transposon. Within this third dataset composed of 323 vanB VRE isolates, ClonalTracker found that clonal expansion is the most common dissemination mode. All in all, this tool provides new bioinformatic means to uncover dissemination patterns and elucidate links between vancomycin-resistance isolates and can be broadly accessible via its webserver hosted at www.clonaltracker.nl (as of January 2024). The local version of this tool is also available at: https://github.com/victoriapascal/clonaltrackerCompeting Interest StatementThe authors have declared no competing interest.

The human body is colonized by trillions of microorganisms including bacteria, fungi and viruses that inhabit our cavities and surfaces, such as the skin, the oral cavity, the respiratory and the gastrointestinal tract. Each body site is characterized by specific microbial communities, also referred to as microbiota, that altogether weigh around 2 kg and can be regarded as another organ. The microbiota include a diverse range of microbes, predominantly bacteria, which help us to digest food, synthesize vitamins, develop the immune system and prevent pathogen infections, among other functions. However, imbalances in these microbial communities have also been linked to some diseases, such as colon cancer, inflammatory bowel disease, cardiovascular disease, obesity and diabetes. Therefore, studying the composition and function of the human microbiota can help understanding these diseases better from a molecular point of view.Bacteria outnumber human cells by a ratio of 10 to 1, and the bacterial gene repertoire vastly exceeds the human one. The genomic diversity of the microbiota enables them to respond to different stimuli by excreting small molecules that play a relevant role in microbe-microbe and microbe-host interactions. Most of the enzymatic pathways producing these molecules are encoded in Metabolic Gene Clusters (MGCs) and belong to specialized primary metabolism. Ultimately, the circulation of these molecules can result in different phenotypes, beneficial or detrimental for the host. In order to better understand how microbes influence health, it is therefore important to profile their metabolic potential and elucidate the molecular mechanisms behind these phenotypes.Several bioinformatic tools have been designed to predict the metabolic potential of these bacteria, such as antiSMASH. This is the gold-standard tool to predict and analyse Biosynthetic Gene Clusters (BGCs) that encode secondary metabolites. Running antiSMASH on a large collection of genomes generates invaluable information, but it is difficult to fully exploit using current bioinformatic tools. For this reason, a method to store and organise the massive amounts of data is presented in Chapter 2: the antiSMASH database version 2, a platform that allows the user to interactively perform cross-genome searches from pre-computed antiSMASH runs. Besides antiSMASH, there are other tools to predict microbial metabolism that rather focus on more generic primary metabolism. As no tool existed to predict specialized primary MGCs, in Chapter 3 we combined different computational methods to investigate how large the hidden diversity of primary metabolic gene clusters is in gut microbiota, using a case study on flavoenzyme-associated pathways. We revealed a large collection of putative MGCs capable of transforming a wide range of substrates including saccharides, peptides and lipids. These results evinced that many MGCs with relevant implications for the host remain to be characterized. Moreover, it served as a proof-of-concept that targeting coding genes (Fe-S flavoenzyme coding genes in this case) in a relevant genomic context is a useful approach to mine genomes for specialized primary MGCs. From this analysis, we learned that Fe-S flavoenzymes might play a more important role in anaerobic metabolism than previously anticipated and that specialized primary metabolism is strain-specific. Motivated by these findings and by the lack of a method to mine bacterial genomes for specialized primary metabolic MGCs, we designed a new tool to functionally profile the human gut microbiome, called gutSMASH (Chapter 4).

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 6,200 full bacterial genomes and 18,576 bacterial draft genomes and is available at https://antismash-db.secondarymetabolites.org/

The gut contains an enormous diversity of simple as well as complex molecules from highly diverse food sources as well as host-secreted molecules. This presents a large metabolic opportunity for the gut microbiota, but little is known on 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 7-dehydroxylation pathway that allows gut bacteria to transform cholic acid (CA) into deoxycholic acid (DCA), 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 use 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 >1,000 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.

Abstract 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 systematically profile primary metabolic genes from the gut microbiome, identifying 19,885 gene clusters in 4,240 high-quality microbial genomes. We find 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 subjects from a Dutch population-based cohort shows that the level of 14 microbiome-derived metabolites in plasma is almost completely uncorrelated with the metagenomic abundance of the corresponding biosynthetic genes, revealing 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. Competing Interest Statement The authors have declared no competing interest.

Abstract 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 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 between different conditions. To illustrate its usefulness, we analyzed 47 metagenomic samples from healthy and caries-associated human oral microbiome samples and identified 58 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 to 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 datasets that can help 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 datasets. The current study presents a new software tool called BiG-MAP, which allows assessing gene cluster abundance and expression in microbiome samples using metagenomic and metatranscriptomic data. In this manuscript, we describe the tool and its functionalities, and how it has been validated using a mock community. Finally, using an oral microbiome dataset, we show how it can be used to generate hypotheses regarding the functional roles of gene clusters in mediating host phenotypes. Competing Interest Statement The authors have declared no competing interest.