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Mucha, P. J.

Publications and source records attributed to Mucha, P. J..

2 recordsLinked to original sources

Pathogenic potential in catheter associated Escherichia coli is associated with separable biofilm and virulence gene determinants

Urinary catheterization facilitates Escherichia coli colonization of the urinary tract and increases infection risk. While specific pathotypes are well-recognized for some E. coli infections, it is unclear whether strain-specific characteristics among E. coli are associated with infection risk in catheterized patients. Here we used comparative genomics and a simulated catheter biofilm model to compare strains associated with catheter-associated urinary tract infection (CAUTI) and catheter-associated asymptomatic bacteriuria (CAASB). CAUTI was associated with a phylotype B2 sub-clade dominated by the multidrug resistant ST131 lineage, while CAASB isolates were genetically more diverse. Catheter-associated biofilm formation was widespread but quantitatively variable among isolates. Network community analysis resolved distinct groups of genes associated with infection or biofilm formation, with iron acquisition-associated genes prominent throughout. Using a reporter construct and targeted mutagenesis, we detected a biofilm phenotype for the ferric citrate transport (Fec) system, the most prominent correlate of high catheter biofilm formation in these patients. In mixed cultures, catheter biofilms formed by some CAASB strains suppressed catheter colonization by ST131 CAUTI isolates. These results are consistent with a paradigm in which catheter biofilm-associated genes increase infection risk in strains with a high pathogenic potential and decrease infection risk through niche exclusion in strains with low pathogenic potential.

microbiology

FastPG: Fast clustering of millions of single cells

Current single-cell experiments can produce datasets with millions of cells. Unsupervised clustering can be used to identify cell populations in single-cell analysis but often leads to interminable computation time at this scale. This problem has previously been mitigated by subsampling cells, which greatly reduces accuracy. We built on the graph-based algorithm PhenoGraph and developed FastPG which has the same cell assignment accuracy but is on average 27x faster in our tests. FastPG also has higher cell assignment accuracy than two other fast clustering methods, FlowSOM and PARC. AvailabilityFastPG is available here: https://github.com/sararselitsky/FastPG

bioinformatics