Biology subjects

Haro, F.

Publications and source records attributed to Haro, F..

4 recordsLinked to original sources

bioRxiv2024

Statistical design of a synthetic microbiome that clears a multi-drug resistant gut pathogen

Engineering functional microbiomes is challenging due to complex interactions between bacteria and their environments1-6. Using a set of 848 gut commensal strains and clearance of multi-drug resistant Klebsiella pneumoniae (Kp-MH258) as a target function, we engineered a functional 15-member synthetic microbiome--SynCom15--through a statistical approach agnostic to strain phenotype, mechanism of action, bacterial interactions, or composition of natural microbiomes. Our approach involved designing, building, and testing 96 metagenomically diverse consortia, learning a generative model using community strain presence/absence as input, and distilling model constraints through statistical inference. SynCom15 cleared Kp-MH258 across in vitro, ex vivo, and in vivo environments, matching the efficacy of a fecal microbiome transplant in a clinically relevant murine model of infection. The mechanism of suppression by SynCom15 was related to fatty acid production coupled with environmental acidification. SynCom15 also suppressed other pathogens--Clostridioides difficile, Escherichia coli, and other K. pneumoniae strains--but through different mechanisms. Sensitivity analysis revealed models trained on strain presence/absence captured the statistical structure of pathogen suppression, illustrating that community representation was key to our approach succeeding. Our framework, Constraint Distillation, could be a general and efficient strategy for building emergent complex systems, offering a path towards synthetic ecology more broadly.

synthetic biology↗

bioRxiv2023

Exposure and resistance to lantibiotics impact microbiota composition and function

The intestinal microbiota is composed of hundreds of distinct microbial species that interact with each other and their mammalian host. Antibiotic exposure dramatically impacts microbiota compositions and leads to acquisition of antibiotic-resistance genes. Lantibiotics are ribosomally synthesized and post-translationally modified peptides produced by some bacterial strains to inhibit the growth of competing bacteria. Nisin A is a lantibiotic produced by Lactococcus lactis that is commonly added to food products to reduce contamination with Gram-positive pathogens. Little is known, however, about lantibiotic-resistance of commensal bacteria inhabiting the human intestine. Herein, we demonstrate that Nisin A administration to mice alters fecal microbiome compositions and the concentration of taurine-conjugated primary bile acids. Lantibiotic Resistance System genes (LRS) are encoded by lantibiotic-producing bacterial strains but, we show, are also prevalent in microbiomes across human cohorts spanning vastly different lifestyles and 5 continents. Bacterial strains encoding LRS have enhanced in vivo fitness upon dietary exposure to Nisin A but reduced fitness in the absence of lantibiotic pressure. Differential binding of host derived, secreted IgA contributes to fitness discordance between bacterial strains encoding or lacking LRS. Although LRS are associated with mobile genetic elements, sequence comparisons of LRS encoded by distinct bacterial species suggest they have been long-term components of their respective genomes. Our study reveals the prevalence, abundance and physiologic significance of an underappreciated subset of antimicrobial resistance genes encoded by commensal bacterial species constituting the human gut microbiome, and provides insights that will guide development of microbiome augmenting strategies.

microbiology↗

bioRxiv2023

Virulence and genomic diversity among clinical isolates of ST1 (BI/NAP1/027) Clostridioides difficile

Clostridioides difficile (C. difficile), a leading cause of nosocomial infection, produces toxins that damage the colonic epithelium and results in colitis that varies from mild to fulminant. Variation in disease severity is poorly understood and has been attributed to host factors (age, immune competence and intestinal microbiome composition) and/or virulence differences between C. difficile strains, with some, such as the epidemic BI/NAP1/027 (MLST1) strain, being associated with greater virulence. We tested 23 MLST1(ST1) C. difficile clinical isolates for virulence in antibiotic-treated C57BL/6 mice. All isolates encoded a complete Tcd pathogenicity locus and achieved similar colonization densities in mice. Disease severity varied, however, with 5 isolates causing lethal infections, 16 isolates causing a range of moderate infections and 2 isolates resulting in no detectable disease. The avirulent ST1 isolates did not cause disease in highly susceptible Myd88-/- or germ-free mice. Genomic analysis of the avirulent isolates revealed a 69 base-pair deletion in the N-terminus of the cdtR gene, which encodes a response regulator for binary toxin (CDT) expression. Genetic deletion of the 69 base-pair cdtR sequence in the highly virulent ST1 R20291 C. difficile strain rendered it avirulent and reduced toxin gene transcription in cecal contents. Our study demonstrates that a natural deletion within cdtR attenuates virulence in the epidemic ST1 C. difficile strain without reducing colonization and persistence in the gut. Distinguishing strains on the basis of cdtR may enhance the specificity of diagnostic tests for C. difficile colitis.

microbiology↗

bioRxiv2021

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular phenotypes emerge from a hierarchy of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be extracted from the statistical pattern of proteome variation as measured across thousands of bacteria and that these hierarchies reflect the emergence of complex bacterial phenotypes. We describe the mathematics underlying our statistical approach and validate our results through gene-set enrichment analysis and comparison to existing experimentally-derived hierarchical databases. We demonstrate the biological utility of our unbiased hierarchical models by creating a model of motility in Pseudomonas aeruginosa and using it to discover a previously unappreciated genetic effector of twitch-based motility. Overall, our approach, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), predicts hierarchies of protein interaction networks describing emergent biological function using only the statistical pattern of bacterial proteome variation.

systems biology↗