Biology subjects

Matchado, M. S.

Publications and source records attributed to Matchado, M. S..

4 recordsLinked to original sources

bioRxiv2026

A microbiota-derived protease links phage susceptibility to host epithelial responses

Bacteriophages are major ecological drivers of gut microbial ecology, yet whether bacterial mechanisms that determine phage susceptibility have consequences for the mammalian host remains poorly understood. Here, we identify dipeptidyl peptidase 11 (Dpp11a), the predominant active serine protease of the prevalent gut commensal Phocaeicola vulgatus, as an unexpected bacterial defence factor. Dpp11a protects against environmental proteases and confers resistance to bacteriophage infection. Metatranscriptomic analyses further reveal increased expression of both dpp11a and P. vulgatus-associated phage transcripts in ulcerative colitis stool samples, indicating that both components of this interaction are transcriptionally active in disease-associated human microbiomes. Using the microfluidic gut-on-a-chip co-culture model HuMiX, we show that the absence of Dpp11 is accompanied by altered epithelial tight-junction remodelling during phage-bacterial infection. Together, our findings reveal that the consequences of bacterial phage defence can extend beyond phage-bacterium interactions to the mammalian epithelium.

microbiology↗

bioRxiv2026

Gut microbiota within-host evolution enforces colonization resistance against enteric infection

Limited resource availability in the gut promotes competitive interactions between bacteria, which drive adaptive within-host evolution (1-3). While adaptive evolution of bacterial communities has been increasingly studied in the recent years (4-7), its functional implications for host physiology remain unknown. Here, we show that within-host evolution of the human commensal Enterococcus faecalis boosts colonization resistance to enteric Salmonella enterica serovar Typhimurium (S. Typhimurium) infection. During gut colonization, E. faecalis evolves the ability to metabolize fructoselysine, an abundant Amadori rearrangement product generated by thermal food processing. The depletion of this diet-derived nutrient prevents S. Typhimurium colonization by restricting an essential resource. This protective mechanism was conserved across independent mouse colonies and arises via diverse evolutionary trajectories, including nucleotide polymorphisms, gene amplifications, and a horizontal gene transfer event. Additionally, analysis of E. faecalis isolates from human infants revealed that adaptation to fructoselysine availability occurs in a diet-dependent manner. Isolates from infants fed with fructoselysine-rich formula were able to utilize fructoselysine, whereas those from infants fed with fructoselysine-poor breast milk were not. Conclusively, our results identify an inherent microbiome-driven self-healing mechanism, wherein bacterial evolution restores colonization resistance against enteric pathogens through evolved nutrient depletion. Understanding these evolutionary dynamics will inform microbiome-targeted approaches to prevent and treat infectious diseases by harnessing adaptive bacterial metabolism.

microbiology↗

bioRxiv2023

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type 2 diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

bioinformatics↗

bioRxiv2021

Namco: A microbiome explorer

16S rRNA gene profiling is currently the most widely used technique in microbiome research and allows for studying microbial diversity, taxonomic profiling, phylogenetics, functional and network analysis. While a plethora of tools have been developed for the analysis of 16S rRNA gene data, only a few platforms offer a user-friendly interface and none comprehensively covers the whole analysis pipeline from raw data processing down to complex analysis. We introduce Namco, an R shiny application that offers a streamlined interface and serves as a one-stop solution for microbiome analysis. We demonstrate Namcos capabilities by studying the association between a rich fibre diet and the gut microbiota composition. Namco helped to prove the hypothesis that butyrate-producing bacteria are prompted by fibre-enriched intervention. Namco provides a broad range of features from raw data processing and basic statistics down to machine learning and network analysis, thus covering complex data analysis tasks that are not comprehensively covered elsewhere. Namco is freely available at https://exbio.wzw.tum.de/Namco/. Impact statementAmplicon sequencing is a key technology of microbiome research and has yielded many insights into the complexity and diversity of microbiota. To fully leverage these data, a wide range of tools have been developed for raw data processing, normalization, statistical analysis and visualization. These tools are mostly available as R packages but cannot be easily linked in an automated pipeline due to the heterogeneous characteristics of microbiome data. Instead, user-friendly tools for explorative analysis are needed to give biomedical researchers without experience in scripting languages the possibility to fully exploit their data. Several tools for microbiome data analysis have been proposed in recent years which cover a broad range of functionality but few offer a user-friendly and beginner-friendly interface while covering the entire value whole value chain from raw data processing down to complex analysis. With Namco(https://exbio.wzw.tum.de/namco/), we present a beginner-friendly one-stop solution for microbiome analysis that covers upstream analyses like raw data processing, taxonomic binning and downstream analyses like basic statistics, machine learning and network analysis, among other features.

bioinformatics↗