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Musser, J. M.

Publications and source records attributed to Musser, J. M..

22 records · Page 2Linked to original sources
bioRxiv2019

Genetic heterogeneity of the Spy1336/R28 - Spy1337 Virulence Axis in Streptococcus pyogenes and Effect on Gene Transcript Levels and Pathogenesis

Streptococcus pyogenes is a strict human pathogen responsible for more than 700 million infections annually worldwide. Strains of serotype M28 S. pyogenes are typically among the five more abundant types causing invasive infections and pharyngitis in adults and children. Type M28 strains also have an unusual propensity to cause puerperal sepsis and neonatal disease. We recently discovered that a one-nucleotide indel in an intergenic homopolymeric tract located between genes Spy1336/R28 and Spy1337 altered virulence in a mouse model of infection. In the present study, we analyzed size variation in this homopolymeric tract and determined the extent of heterogeneity in the number of tandemly-repeated 79-amino acid domains in the coding region of Spy1336/R28 in large samples of strains recovered from humans with invasive infections. Both repeat sequence elements are highly polymorphic in natural populations of M28 strains. Variation in the homopolymeric tract results in (i) changes in transcript levels of Spy1336/R28 and Spy1337 in vitro, (ii) differences in virulence in a mouse model of necrotizing myositis, and (iii) global transcriptome changes as shown by RNAseq analysis of isogenic mutant strains. Variation in the number of tandem repeats in the coding sequence of Spy1336/R28 is responsible for size variation of R28 protein in natural populations. Isogenic mutant strains in which genes encoding R28 or transcriptional regulator Spy1337 are inactivated are significantly less virulent in a nonhuman primate model of necrotizing myositis. Our findings provide impetus for additional studies addressing the role of R28 and Spy1337 variation in pathogen-host interactions.

microbiology↗
bioRxiv2018

Subdivision of ancestral scale genetic program underlies origin of feathers and avian scutate scales

Birds and other reptiles possess a diversity of feather and scale-like skin appendages. Feathers are commonly assumed to have originated from ancestral scales in theropod dinosaurs. However, most birds also have scaled feet, indicating birds evolved the capacity to grow both ancestral and derived morphologies. This suggests a more complex evolutionary history than a simple linear transition between feathers and scales. We set out to investigate the evolution of feathers via the comparison of transcriptomes assembled from diverse skin appendages in chicken, emu, and alligator. Our data reveal that feathers and the overlapping scutate scales of birds share more similar gene expression to each other, and to two types of alligator scales, than they do to the tuberculate reticulate scales on bird footpads. Accordingly, we propose a history of skin appendage diversification, in which feathers and bird scutate scales arose from ancestral archosaur body scales, whereas reticulate scales arose earlier in tetrapod evolution. We also show that many \"feather-specific genes\" are also expressed in alligator scales. In-situ hybridization results in feather buds suggest that these genes represent ancestral scale genes that acquired novel roles in feather morphogenesis and were repressed in bird scales. Our findings suggest that the differential reuse, in feathers, and suppression, in bird scales, of genes ancestrally expressed in archosaur scales has been a key factor in the origin of feathers - and may represent an important mechanism for the origin of evolutionary novelties.

evolutionary biology↗
bioRxiv2017

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to apidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within {+/-}1 two-fold dilution factor, is 92%. Individual accuracies are[≥]90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

bioinformatics↗