Biology subjects

Fischbach, M. A.

Publications and source records attributed to Fischbach, M. A..

28 records · Page 2Linked to original sources

bioRxiv2021

A metabolomics pipeline enables mechanistic interrogation of the gut microbiome

Gut microbes modulate host phenotypes and are associated with numerous health effects in humans, ranging from cancer immunotherapy response to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microbes has hindered defining mechanistic connections between individual microbial strains and host phenotypes. One key way the gut microbiome influences host physiology is through the production of small molecules1-3, yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites (MDMs) in diverse sample types. We report the metabolic profiles of 178 gut microbe strains using our library of 833 metabolites. Leveraging this metabolomics resource we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover novel metabolism in Bacteroides, and employ comparative genomics-based discovery of candidate biochemical pathways. MDMs can be detected in diverse biofluids in gnotobiotic and conventional mice and traced back to corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microbe and microbe-host interactions.

microbiology↗

bioRxiv2021

A systematic analysis of metabolic pathways in 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 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.

bioinformatics↗

bioRxiv2021

Eliciting a potent antitumor immune response by expressing tumor antigens in a skin commensal

Immune modulation has become central to treating cancer. However, global immune stimulation is only effective in a subset of patients and can lead to serious complications, including colitis and type I diabetes. Newer modalities like engineered T cells and tumor vaccines are more specific, but they have shown limited efficacy in solid tumors and are difficult to scale. Bacterial strains from the human microbiome can induce antigen-specific T cells to help maintain barrier function. Here, we redirect CD8+ and CD4+ T cells elicited by the skin commensal Staphylococcus epidermidis to recognize tumor cells by expressing tumor-derived antigens in the bacterial cell. S. epidermidis expressing the model antigen ovalbumin (S. epidermidis-OVA) stimulates antigen-specific CD8+ and CD4+ T cells in vitro. The subcellular localization of the antigen skews the response: cell wall-attached OVA preferentially stimulates CD8+ T cells whereas secreted OVA predominantly induces CD4+ T cells. In a syngeneic tumor model (OVA-expressing B16 melanoma), mice colonized topically with S. epidermidis-OVA exhibit a marked reduction in subcutaneous tumor volume compared to mice colonized with S. epidermidis expressing mCherry; this effect is dependent on live bacteria and a combination of CD8+ and CD4+ T cells. S. epidermidis-OVA also reduces tumor burden when tumor cells are injected intravenously (a model of metastasis), demonstrating that the antitumor effect operates in tissues distant from the site of bacterial colonization. S. epidermidis strains expressing neoantigen peptides from the B16 tumor cell line exhibit potent antitumor efficacy without inducing an autoimmune response against melanocytes in healthy tissue. Antigen-expressing colonists are a simple but powerful strategy to elicit a targeted T cell response in the context of cancer and other diseases.

microbiology↗

bioRxiv2020

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 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. ImportanceMicrobes 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.

bioinformatics↗

bioRxiv2020

Integration of machine learning and pan-genomics expands the biosynthetic landscape of RiPP natural products

Most clinical drugs are based on microbial natural products, with compound classes including polyketides (PKS), non-ribosomal peptides (NRPS), fluoroquinones and ribosomally synthesized and post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for known classes of natural products are easy to identify in genome sequences, BGCs for new compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes such regions based on the presence of new enzymology and based on patterns of gene cluster and precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could not be found by existing programs. One of these was studied further and elucidated as a novel subfamily of lanthipeptides, designated Class V. Two previously unidentified modifying enzymes are proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel natural product families can be discovered by methods going beyond sequence similarity searches to integrate multiple pathway discovery criteria. Code and data availabilityThe source code of DecRiPPter is freely available online at https://github.com/Alexamk/decRiPPter. Results of the data analysis are available online at http://www.bioinformatics.nl/~medem005/decRiPPter_strict/index.html and http://www.bioinformatics.nl/~medem005/decRiPPter_mild/index.html (for the strict and mild filters, respectively). All training data and code used to generate these, as well as outputs of the data analyses, are available on Zenodo at doi:10.5281/zenodo.3834818.

microbiology↗

bioRxiv2020

Computational genomic discovery of diverse gene clusters harboring 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 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.

bioinformatics↗

bioRxiv2019

A metabolic pathway for bile acid dehydroxylation by the gut microbiome

The gut microbiota synthesize hundreds of molecules, many of which are known to impact host physiology. Among the most abundant metabolites are the secondary bile acids deoxycholic acid (DCA) and lithocholic acid (LCA), which accumulate at ~500 M and are known to block C. difficile growth1, promote hepatocellular carcinoma2, and modulate host metabolism via the GPCR TGR53. More broadly, DCA, LCA and their derivatives are a major component of the recirculating bile acid pool4; the size and composition of this pool are a target of therapies for primary biliary cholangitis and nonalcoholic steatohepatitis. Despite the clear impact of DCA and LCA on host physiology, incomplete knowledge of their biosynthetic genes and a lack of genetic tools in their native producer limit our ability to modulate secondary bile acid levels in the host. Here, we complete the pathway to DCA/LCA by assigning and characterizing enzymes for each of the steps in its reductive arm, revealing a strategy in which the A-B rings of the steroid core are transiently converted into an electron acceptor for two reductive steps carried out by Fe-S flavoenzymes. Using anaerobic in vitro reconstitution, we establish that a set of six enzymes is necessary and sufficient for the 8-step conversion of cholic acid to DCA. We then engineer the pathway into Clostridium sporogenes, conferring production of DCA and LCA on a non-producing commensal and demonstrating that a microbiome-derived pathway can be expressed and controlled heterologously. These data establish a complete pathway to two central components of the bile acid pool, and provide a road map for deorphaning and engineering pathways from the microbiome as a critical step toward controlling the metabolic output of the gut microbiota.

microbiology↗

bioRxiv2019

Recovery of the gut microbiota after antibiotics depends on host diet and environmental reservoirs

That antibiotics alter microbiota composition and increase infection susceptibility is well known, but their generalizable effects on the gut commensal community and dependence on environmental variables remain open questions. Here, we systematically compared antibiotic responses in gnotobiotic and conventional mice across antibiotics, microbiotas, diets, and housing status. We identify remarkable resilience, whereby a humanized microbiota recovers before drug administration ends, with transient dominance of resistant Bacteroides and taxa-asymmetric reduction in diversity. In other cases, in vitro sensitivities were not predictive of in vivo responses, underscoring the significance of host and community contexts. A fiber-deficient diet exacerbated collapse of the microbiota and delayed recovery, despite the presence of a similar core community across diets at the point of maximal disturbance. Resilience to a second ciprofloxacin treatment was observed via response reprogramming, in which species replacement after ciprofloxacin treatment established resilience to a second treatment, and also through cross housing transmission. Single-housing drastically disrupted recovery, highlighting the importance of environmental microbial reservoirs and suggesting sanitation may exacerbate the duration of antibiotic-mediated disruption. Our findings highlight the ability of the commensal microbiota to deterministically adapt to large perturbations, and the translational potential for modulating diet, sanitation, and microbiota composition during antibiotics.

microbiology↗

bioRxiv2019

Decoding commensal-host communication through genetic engineering of Staphylococcus epidermidis

Commensal skin bacteria elicit potent, antigen-specific immune responses in the skin without barrier breach or visible inflammation. While microbial modulation of immune homeostasis has profound consequences for epithelial health and inflammatory skin diseases, the mechanisms of microbe-immune crosstalk in the skin are largely unknown. A key barrier to mechanistic work has been genetic intractability of one of the most prevalent skin colonists, Staphylococcus epidermidis (S. epidermidis). Here, we develop a novel method to create a library of mutants with defined cell envelope alterations in primary human S. epidermidis isolates. By colonizing mice with these mutants, we uncover bacterial molecules involved in the induction of defined immune signatures. Notably, we show that under conditions of physiologic colonization, S. epidermidis cell envelope glycolipids are sensed by C-type lectin receptors, likely in non-myeloid cells, in conjunction with Toll-like receptors. This combinatorial signaling determines the quality of T cell responses and results in the potential for greater specificity toward commensal microbiota than previously appreciated. Additionally, the microbial molecules required for the colonization-induced immune response are dispensable for T cells responses in a model of S. epidermidis infection, but differentially modulate innate inflammatory responses. Thus, the same microbe uses distinct sets of molecules to signal to the immune system commensal versus pathogenic behavior, and differential sensing of these microbial signals depends on host context.

microbiology↗

bioRxiv2019

Cutibacterium acnes antibiotic production shapes niche competition in the human skin microbiome

The composition of the skin microbiome varies widely among individuals sampled at the same body site. A key question is which molecular factors determine strain-level variability within sub-ecosystems of the skin. We used a genomics-guided approach to identify an antibacterial biosynthetic gene cluster in Cutibacterium acnes (formerly Propionibacterium acnes) that is widely distributed across individuals and skin sites. Experimental characterization of this cluster enabled the identification of a new thiopeptide antibiotic, cutimycin. Analysis of individual human skin hair follicles showed that cutimycin is an important factor regulating colonization resistance against Staphylococcus species.\n\nOne Sentence SummaryCutimycin, a thiopeptide antibiotic produced by a widespread skin commensal, reduces Staphylococcus colonization of human follicles.

microbiology↗