10 research outputs found
The high prevalence of Clostridioides difficile among nursing home elders associates with a dysbiotic microbiome
Get PDFClostridioides difficile disproportionally affects the elderly living in nursing homes (NHs). Our objective was to explore the prevalence of C. difficile in NH elders, over time and to determine whether the microbiome or other clinical factors are associated with C. difficile colonization. We collected serial stool samples from NH residents. C. difficile prevalence was determined by quantitative polymerase-chain reaction detection of Toxin genes tcdA and tcdB; microbiome composition was determined by shotgun metagenomic sequencing. We used mixed-effect random forest modeling machine to determine bacterial taxa whose abundance is associated with C. difficile prevalence while controlling for clinical covariates including demographics, medications, and past medical history. We enrolled 167 NH elders who contributed 506 stool samples. Of the 123 elders providing multiple samples, 30 (24.4%) elders yielded multiple samples in which C. difficile was detected and 78 (46.7%) had at least one C. difficile positive sample. Elders with C. difficile positive samples were characterized by increased abundances of pathogenic or inflammatory-associated bacterial taxa and by lower abundances of taxa with anti-inflammatory or symbiotic properties. Proton pump inhibitor (PPI) use is associated with lower prevalence of C. difficile (Odds Ratio 0.46; 95%CI, 0.22-0.99) and the abundance of bacterial species with known beneficial effects was higher in PPI users and markedly lower in elders with high C. difficile prevalence.C. difficile is prevalent among NH elders and a dysbiotic gut microbiome associates with C. difficile colonization status. Manipulating the gut microbiome may prove to be a key strategy in the reduction of C. difficile in the NH
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Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states
Get PDFThe complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.00
