bioRxiv Science⌕ Search

Biology subjects

Khare, A.

Publications and source records attributed to Khare, A..

2 recordsLinked to original sources

Systematic identification of molecular mediators underlying sensing of Staphylococcus aureus by Pseudomonas aeruginosa

Bacteria typically exist in dynamic, multispecies communities where polymicrobial interactions influence fitness. Elucidating the molecular mechanisms underlying these interactions is critical for understanding and modulating bacterial behavior in natural environments. While bacterial responses to foreign species are frequently characterized at the molecular and phenotypic level, the exogenous molecules that elicit these responses are understudied. Here we outline a systematic strategy based on transcriptomics combined with genetic and biochemical screens of promoter-reporters to identify the molecules from one species that are sensed by another. We utilized this method to study interactions between the pathogens Pseudomonas aeruginosa and Staphylococcus aureus that are frequently found in co-infections. We discovered that P. aeruginosa senses diverse staphylococcal exoproducts including the metallophore staphylopine, intermediate metabolites citrate and acetoin, and multiple molecules that modulate its iron starvation response. Further, we show that staphylopine inhibits biofilm formation and that P. aeruginosa can utilize citrate and acetoin for growth, revealing that these interactions have both antagonistic and beneficial effects. Our screening approach thus identified multiple S. aureus secreted molecules that are sensed by P. aeruginosa and affect its physiology, demonstrating the efficacy of this approach, and yielding new insight into the molecular basis of interactions between these two species.

microbiology↗

A literature-derived knowledge graph augments the interpretation of single cell RNA-seq datasets

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have rapidly advanced in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p < 6.15x10-76, r = 0.24; cohens D = 2.6). Building on this, we developed an augmented annotation algorithm that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 185 clusters in 13 datasets from human blood, pancreas, lung, liver, kidney, retina, and placenta. With the optimized settings, the true cellular identity matched the top prediction in 66% of tested clusters and was present among the top five predictions for 94% of clusters. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of established cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.

genomics↗