Biology subjects

Augustijn, H. E.

Publications and source records attributed to Augustijn, H. E..

9 recordsLinked to original sources

bioRxiv2026

A global regulatory atlas of Streptomyces reveals conserved and diversified transcriptional networks across actinomycetes

Transcriptional regulatory networks determine how bacteria integrate environmental signals with growth, metabolism and stress adaptation. Actinomycetes encode exceptionally large repertoires of transcription factors (TFs) coordinating morphological development, environmental adaptation and specialized metabolism, yet their regulatory networks remain poorly defined. Here, we apply DNA affinity purification sequencing (DAP-seq) to 789 predicted TFs of Streptomyces coelicolor, generating genome-wide binding maps for 393 regulators and expanding the experimentally supported regulome from [~]8% to [~]50%. Integration with ChIP-seq reveals pleiotropic regulators and hierarchical network architecture linking primary metabolism, development and biosynthetic gene clusters (BGCs). Multiplexed DAP-seq (multiDAP) across 16 additional actinomycetes uncovered deeply conserved regulatory circuits alongside widespread divergence in TF-target interactions. Together, these data establish a regulatory atlas for Streptomyces and related actinomycetes, enabling researchers to explore control of genes, pathways, BGCs and conserved network modules. This provides a foundation for predictive analysis and engineering of complex bacterial phenotypes in biotechnology and medicine.

microbiology↗

bioRxiv2026

A previously unappreciated class of metal-dependent bile salt hydrolases from the human gut microbiome

Bile salt hydrolases (BSHs) are gut microbial enzymes that catalyze the deconjugation of glycine-or taurine-conjugated bile acids (BAs), a key step in shaping the BA pool in the human gastrointestinal tract and modulating host-gut microbiome interactions.1-3 All known BSHs are members of the N-terminal nucleophile (Ntn) hydrolase superfamily and share a conserved architecture and mechanism involving a nucleophilic active site cysteine.4,5 This knowledge has guided predictions and study of BSH activity in the gut microbiome6,7 as well as the development of BSH inhibitors8. Here, we report the discovery and characterization of a previously unknown BSH from the human gut bacterium Bilophila wadsworthia that belongs to the metal-dependent amidohydrolase superfamily and exhibits robust and specific activity toward taurine-conjugated bile salts. We show this secreted enzyme, metalloBSH, utilizes a metallocofactor for BA deconjugation, a mechanism distinct from that of canonical Ntn-type BSHs. MetalloBSHs are conserved in B. wadsworthia and present in many other Desulfovibrionaceae found in vertebrate gut microbiomes. Analysis of multi-omic datasets indicates metalloBSHs are expressed in vivo and correlate with BA metabolism. Overall, our findings reshape our understanding of BSH activity in the gut microbiome and highlight the promise of activity guided discovery in revealing previously overlooked gut microbial enzymes.

biochemistry↗

bioRxiv2025

plantiSMASH 2.0: improvements to detection, annotation, and prioritization of plant biosynthetic gene clusters

Plants produce bioactive compounds as part of their specialized metabolism, with applications in medicine, agriculture, and nutrition. The biosynthesis of a growing number of these specialized metabolites has been found to be encoded in biosynthetic gene clusters (BGCs), creating increasing demand for genome mining tools to automate their detection. plantiSMASH enables the identification of putative plant BGCs through a rule-based approach, available via both command-line and web interfaces. Here, we present plantiSMASH 2.0 (https://plantismash.bioinformatics.nl/), a major update that expands and improves the original framework with revised and additional BGC detection rules (now supporting 12 BGC types), substrate prediction for selected enzyme families, and regulatory analysis through transcription factor binding site detection. The updated plantiSMASH 2.0 database includes 30,423 putative BGCs across 430 genomes. Together, these improvements make plantiSMASH 2.0 a powerful and comprehensive platform for the detection and characterization of plant biosynthetic pathways, supporting and accelerating research in plant specialized metabolism and plant natural product discovery.

bioinformatics↗

bioRxiv2024

Prediction of gene cluster function based on transcriptional regulatory networks uncovers a novel locus required for desferrioxamine B biosynthesis

Bacteria produce a plethora of natural products that are in clinical, agricultural and biotechnological use. Genome mining revealed millions of biosynthetic gene clusters (BGCs) that encode their biosynthesis, and the major challenge is to predict the bioactivities of the molecules these BGCs specify, and how to elicit their expression. Here, we present an innovative strategy whereby we harness the power of regulatory networks combined with global gene expression patterns to predict BGC functions. Studying the regulon of iron master regulator DmdR1 in Streptomyces coelicolor combined with co-expression data and large-scale comparative genome analysis identified the novel desJGH gene cluster. Mutational and metabolomics analysis showed that desJGH is required for biosynthesis of the clinical drug desferrioxamine B. DesJGH thereby dictate the balance between the structurally distinct desferrioxamines B and E. We propose regulation-based genome mining as a promising approach to functionally prioritize BGCs to accelerate the discovery of novel bioactive molecules.

genomics↗

bioRxiv2024

LogoMotif: a comprehensive database of transcription factor binding site profiles in Actinobacteria

Actinobacteria undergo a complex multicellular life cycle and produce a wide range of specialized metabolites, including the majority of the antibiotics. These biological processes are controlled by intricate regulatory pathways, and to better understand how they are controlled we need to augment our insights into the transcription factor binding sites. Here, we present LogoMotif (https://logomotif.bioinformatics.nl), an open-source database for characterized and predicted transcription factor binding sites in Actinobacteria, along with their cognate position weight matrices and hidden Markov models. Genome-wide predictions of binding site locations in Streptomyces model organisms are supplied and visualized in interactive regulatory networks. In the web interface, users can freely access, download and investigate the underlying data. With this curated collection of actinobacterial regulatory interactions, LogoMotif serves as a basis for binding site predictions, thus providing users with clues on how to elicit the expression of genes of interest and guide genome mining efforts. HighlightsO_LIActinobacterial regulatory networks are key for compound discovery, including antibiotics. C_LIO_LIContains [~]400 validated and [~]12,100 predicted interactions, presented in interactive networks. C_LIO_LIServes as foundation for regulatory predictions in the gene cluster detection tool, antiSMASH. C_LIO_LILogoMotifs data and algorithms provide knowledge on expression and functional inference of genes. C_LIO_LILogoMotif aids in the discovery of novel chemistry within Actinobacteria and beyond. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/582527v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@cfb7cdorg.highwire.dtl.DTLVardef@12c017borg.highwire.dtl.DTLVardef@155096org.highwire.dtl.DTLVardef@15dcbcc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

FERMO: a Dashboard for Streamlined Rationalized Prioritization of Molecular Features from Mass Spectrometry Data

Small molecules shape phenotypic variation by modulating biological processes, yet linking specific molecules to observed traits remains challenging due to the complexity of biological samples. Liquid chromatography-tandem mass spectrometry routinely detects hundreds of molecules per sample, and computational tools aid in the selection of the biologically relevant subset by organizing, annotating, and integrating orthogonal data. Existing tools typically focus on facilitating data-driven exploration to support manual interpretation, rather than more objective, data-driven prioritization and hypothesis-generation. Here, we introduce FERMO, a free online dashboard interface for prioritization of molecular features and samples associated with phenotypes of interest. FERMOs modular framework automates data processing, annotation, and integration of standardized phenotypic and other metadata. FERMO supports both exploratory and targeted analysis through efficient interactive visualization, reproducible prioritization, and data filtering. We demonstrate FERMOs utility in benchmarking studies prioritizing bioactive compounds from complex biological matrices. FERMO is freely available at https://fermo.bioinformatics.nl/.

bioinformatics↗

bioRxiv2022

The faecal metabolome and its determinants in inflammatory bowel disease

ObjectiveInflammatory bowel disease (IBD) is a multifactorial immune-mediated inflammatory disease of the intestine, comprising Crohns disease and ulcerative colitis. By characterising metabolites in faeces, combined with faecal metagenomics, host genetics and clinical characteristics, we aimed to unravel metabolic alterations in IBD. DesignWe measured 1,684 different faecal metabolites and 8 short-chain and branched-chain fatty acids in stool samples of 424 IBD patients and 255 non-IBD controls. Regression analyses were used to compare concentrations of metabolites between cases and controls and determine the relationship between metabolites and each participants lifestyle, clinical characteristics and gut microbiota composition. Moreover, genome-wide association analysis was conducted on faecal metabolite levels. ResultsWe identified over 300 molecules that were differentially abundant in the faeces of patients with IBD. The ratio between a sphingolipid and L-urobilin could discriminate between IBD and non-IBD samples (AUC = 0.85). We found changes in the bile acid pool in patients with dysbiotic microbial communities and a strong association between faecal metabolome and gut microbiota. For example, the abundance of Ruminococcus gnavus was positively associated with tryptamine levels. In addition, we found 158 associations between metabolites and dietary patterns, and polymorphisms near NAT2 strongly associated with coffee metabolism. ConclusionIn this large-scale analysis, we identified alterations in the metabolome of patients with IBD that are independent of commonly overlooked confounders such as diet and surgical history. Considering the influence of the microbiome on faecal metabolites, our results pave the way for future interventions targeting intestinal inflammation.

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↗

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↗