Weimer, B. C. | Search bioRxiv

bioRxiv2019

Salmonella Enhances Osteogenic Differentiation in Adipose-Derived Mesenchymal Stem Cells

The potential of mesenchymal stem cells (MSCs) for tissue repair and regeneration has garnered great attention. While MSC interaction with microbes at sites of tissue damage and inflammation is likely, especially in the gut, the consequences of bacterial association have yet to be elucidated. This study investigated the effect of Salmonella enterica ssp enterica serotype Typhimurium on MSC trilineage differentiation path and mechanism. Through examination of key markers of differentiation, immunomodulatory regulators, and inflammatory cytokines, we demonstrated that Salmonella altered osteogenic and chondrogenic differentiation pathways in human and goat adipose-derived MSCs. Gene expression profiles defined signaling pathway alterations in response to Salmonella association not observed in epithelial cells. We uncovered significant differential expression (P < 0.05) of genes associated with anti-apoptotic and pro-proliferative responses in MSCs during Salmonella challenge. These observations led us to conclude that bacteria, specifically Salmonella, induce pathways that influence functional differentiation trajectories in MSCs, thus implicating substantial microbial influence on MSC physiology and immune activity.

microbiology↗

bioRxiv2019

Biological machine learning combined with bacterial population genomics reveals common and rare allelic variants of genes to cause disease

Highly dimensional data generated from bacterial whole genome sequencing is providing unprecedented scale of information that requires appropriate statistical frameworks of analysis to infer biological function from bacterial genomic populations. Application of genome wide association study (GWAS) methods is an emerging approach with bacterial population genomics that yields a list of genes associated with a phenotype with an undefined importance among the candidates in the list. Here, we validate the combination of GWAS, machine learning, and pathogenic bacterial population genomics as a novel scheme to identify SNPs and rank allelic variants to determine associations for accurate estimation of disease phenotype. This approach parsed a dataset of 1.2 million SNPs that resulted in a ranked importance of associated alleles of Campylobacter jejuni porA using multiple spatial locations over a 30-year period. We validated this approach using previously proven laboratory experimental alleles from an in vivo guinea pig abortion model. This approach, termed BioML, defined intestinal and extraintestinal groups that have differential allelic variants that cause abortion. Divergent variants containing indels that defeated gene callers were rescued using biological context and knowledge that resulted in defining rare and divergent variants that were maintained in the population over two continents and 30 years. This study defines the capability of machine learning coupled to GWAS and population genomics to simultaneously identify and rank alleles to define their role in abortion, and more broadly infectious disease.

genomics↗