Back

Gene contribution of Streptococcus dysgalactiae subspecies equisimilis, an emerging pathogen, to experimental primate necrotizing myositis

Bari, S. M. N.; Eraso, J. M.; Olsen, R. J.; Zhu, L.; Musser, J. M.

2026-02-11 microbiology
10.64898/2026.02.10.705160 bioRxiv
Show abstract

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, we used Transposon-Directed Insertion-site Sequencing (TraDIS) to identify genes contributing to fitness in experimental necrotizing myositis in non-human primates (NHPs). Using two SDSE stG62647 human clinical isolates, MGCS36044 and MGCS36089, we generated highly saturated transposon mutant libraries and analyzed them following in vitro growth and in vivo infection in eight NHPs. We identified 398 essential genes shared by both strains during growth in vitro and in vivo, and 17 and seven conditionally essential genes required only in vitro or only in vivo, respectively. Additionally, we identified 117 and 110 genes in MGCS36044 and MGCS36089, respectively, that were 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.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.