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Biomedical subjects

James M Musser

Publications and source records attributed to James M Musser.

2 recordsLinked to original sources

Gene Contribution of Streptococcus dysgalactiae Subspecies equisimilis, an Emerging Pathogen, to Experimental Primate Necrotizing Myositis.

Streptococcus dysgalactiae subspecies equisimilis (SDSE) is an emerging human pathogen closely related to group A Streptococcus. However, its genetic requirements for survival and growth in different conditions and for causing invasive infections remain poorly understood. To address this gap, transposon-directed insertion-site sequencing was used to identify genes contributing to fitness in experimental necrotizing myositis in nonhuman primates. Using two SDSE stG62647 human clinical isolates, MGCS36044 and MGCS36089, highly saturated transposon mutant libraries were generated and analyzed following in vitro growth and in vivo infection in eight nonhuman primates. A total of 398 essential genes were identified to be shared by both strains during growth in vitro and in vivo, and 17 and 7 conditionally essential genes required only in vitro or only in vivo, respectively. Additionally, 117 and 110 genes in MGCS36044 and MGCS36089, respectively, were found to be associated with fitness during necrotizing myositis. Transposon insertions in 34 MGCS36044 genes conferred increased fitness, whereas mutation of 83 genes conferred decreased fitness. Similarly, in MGCS36089, mutations in 38 and 72 genes conferred increased or decreased fitness, respectively. Importantly, both strains shared 46 fitness-associated genes, including an enrichment of transporter genes, highlighting nutrient acquisition as a dominant requirement during infection. The results provide critical information for guiding future translational efforts to develop preventive and therapeutic strategies against human SDSE infections.

Animals

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values.

Over the last four years, each successive wave of the COVID-19 pandemic has been caused by variants with mutations that improve the transmissibility of the virus. Despite this, we still lack tools for predicting clinically important features of the virus. In this study, we show that it is possible to predict the PCR cycle threshold (Ct) values from clinical detection assays using sequence data. Ct values often correspond with patient viral load and the epidemiological trajectory of the pandemic. Using a collection of 36,335 high quality genomes, we built models from SARS-CoV-2 intrahost single nucleotide variant (iSNV) data, computing XGBoost models from the frequencies of A, T, G, C, insertions, and deletions at each position relative to the Wuhan-Hu-1 reference genome. Our best model had an R2 of 0.604 [0.593-0.616, 95% confidence interval] and a Root Mean Square Error (RMSE) of 5.247 [5.156-5.337], demonstrating modest predictive power. Overall, we show that the results are stable relative to an external holdout set of genomes selected from SRA and are robust to patient status and the detection instruments that were used. This study highlights the importance of developing modeling strategies that can be applied to publicly available genome sequence data for use in disease prevention and control.

SARS-CoV-2