Biology subjects

Medema, M.

Publications and source records attributed to Medema, M..

3 recordsLinked to original sources

bioRxiv2023

CAGECAT: The CompArative GEne Cluster Analysis Toolbox for rapid search and visualisation of homologous gene clusters

BackgroundCo-localized sets of genes that encode specialized functions are common across microbial genomes and occur in genomes of larger eukaryotes as well. Important examples include Biosynthetic Gene Clusters (BGCs) that produce specialized metabolites with medicinal, agricultural, and industrial value (e.g. antimicrobials). Comparative analysis of BGCs can aid in the discovery of novel metabolites by highlighting distribution and identifying variants in public genomes. Unfortunately, gene-cluster-level homology detection remains inaccessible, time-consuming and difficult to interpret. ResultsThe comparative gene cluster analysis toolbox (CAGECAT) is a rapid and user-friendly platform to mitigate difficulties in comparative analysis of whole gene clusters. The software provides homology searches and downstream analyses without the need for command-line or programming expertise. By leveraging remote BLAST databases, which always provide up-to-date results, CAGECAT can yield relevant matches that aid in the comparison, taxonomic distribution, or evolution of an unknown query. The service is extensible and interoperable and implements the cblaster and clinker pipelines to perform homology search, filtering, gene neighbourhood estimation, and dynamic visualisation of resulting variant BGCs. With the visualisation module, publication-quality figures can be customized directly from a web-browser, which greatly accelerates their interpretation via informative overlays to identify conserved genes in a BGC query. ConclusionOverall, CAGECAT is an extensible software that can be interfaced via a standard web-browser for whole region homology searches and comparison on continually updated genomes from NCBI. The public web server and installable docker image are open source and freely available without registration at: https://cagecat.bioinformatics.nl

bioinformatics↗

bioRxiv2021

Do size and shape matter? Exploring the interactions and the metabolome of the soil isolate Hylemonella gracilis

Microbial community analysis of aquatic environments showed that an important component of its microbial diversity consists of bacteria with cell sizes of ~0.1 m. Such small bacteria can show genomic reductions and metabolic dependencies with other bacteria. However, so far no study investigated if such bacteria exist in terrestrial environments like e.g. soil. Here, we isolated soil bacteria that passed through a 0.1 m filter, by applying a novel isolation and culturing approach. The complete genome of one of the isolates was sequenced and the bacterium was identified as Hylemonella gracilis. A set of co-culture assays with phylogenetically distant soil bacteria with different cell and genome sizes was performed. The co-culture assays revealed that H. gracilis grows better when interacting with other soil bacteria like Paenibacillus sp. AD87 and Serratia plymuthica. Transcriptomics and metabolomics showed that H. gracilis was able to change gene expression, behavior, and biochemistry of the interacting bacteria without direct cell-cell contact. Our study indicates that bacteria are present in the soil that can pass through a 0.1 m filter. These bacteria may have been overlooked in previous research on soil microbial communities. Such small bacteria, exemplified here by H. gracilis, are able to induce transcriptional and metabolomic changes in other bacteria upon their interactions in soil. In vitro, the studied interspecific interactions allowed utilization of growth substrates that could not be utilized by monocultures, suggesting that biochemical interactions between substantially different sized soil bacteria may contribute to the symbiosis of soil bacterial communities. ImportanceAnalysis of aquatic microbial communities revealed that parts of its diversity consist of bacteria with cell sizes of ~0.1 m. Such bacteria can show genomic reductions and metabolic dependencies with other bacteria. So far, no study investigated if such bacteria exist in terrestrial environments e.g. soil. By applying a novel isolation method, we show that such bacteria also exist in soil. The isolated bacteria was identified as Hylemonella gracilis. Co-culture assays with phylogenetically different soil bacteria revealed that H. gracilis grows better when co-cultured with other soil bacteria. Transcriptomics and metabolomics showed that H. gracilis was able to change gene expression, behavior, and biochemistry of the interacting bacteria without direct contact. Our study revealed that bacteria are present in soil that can pass through 0.1 m filters. Such bacteria may have been overlooked in previous research on soil microbial communities and may contribute to the symbiosis of soil bacterial communities.

microbiology↗

bioRxiv2021

Mining metagenomes for natural product biosynthetic gene clusters: unlocking new potential with ultrafast techniques

Microorganisms produce an immense variety of natural products through the expression of Biosynthetic Gene Clusters (BGCs): physically clustered genes that encode the enzymes of a specialized metabolic pathway. These natural products cover a wide range of chemical classes (e.g., aminoglycosides, lantibiotics, nonribosomal peptides, oligosaccharides, polyketides, terpenes) that are highly valuable for industrial and medical applications1. Metagenomics, as a culture-independent approach, has greatly enhanced our ability to survey the functional potential of microorganisms and is growing in popularity for the mining of BGCs. However, to effectively exploit metagenomic data to this end, it will be crucial to more efficiently identify these genomic elements in highly complex and ever-increasing volumes of data2. Here, we address this challenge by developing the ultrafast Biosynthetic Gene cluster MEtagenomic eXploration toolbox (BiG-MEx). BiG-MEx rapidly identifies a broad range of BGC protein domains, assess their diversity and novelty, and predicts the abundance profile of natural product BGC classes in metagenomic data. We show the advantages of BiG-MEx compared to standard BGC-mining approaches, and use it to explore the BGC domain and class composition of samples in the TARA Oceans3 and Human Microbiome Project datasets4. In these analyses, we demonstrate BiG-MExs applicability to study the distribution, diversity, and ecological roles of BGCs in metagenomic data, and guide the exploration of natural products with clinical applications.

bioinformatics↗