Back

Global gene expression analysis of Streptococcus agalactiae at exponential growth phase

Silvestre, I.; Borges, V.; Duarte, S.; Nunes, A.; Sobral, R.; Vieira, L.; Gomes, J. P.; Borrego, M. J.

2020-11-14 genomics
10.1101/2020.11.13.381939 bioRxiv
Show abstract

Streptococcus agalactiae is a leading cause of neonatal infections and an increasing cause of infections in adults with underlying diseases. One of the first S. agalactiae isolates to be subjected to whole genome sequencing was NEM316, a strain responsible for a fatal case of septicemia that has been widely used as reference strain for in vitro assays. Whole transcriptome analyses may provide an essential contribute to the understanding of the molecular mechanisms responsible for bacteria adaptation and pathogenicity, still, so far, very few studies were dedicated to the analysis of global gene expression of S. agalactiae. Here, we applied RNA-sequencing to perform a comparative overview of the global gene expression levels of the S. agalactiae reference strain NEM316 at the exponential growth phase. Genes were ranked by expression level and grouped by functional category and 46% of the top-100 expressed genes encode proteins involved in "Translation, ribosomal structure and biogenesis". Among the group of highly expressed genes were also represented genes with no assigned functional category. Although this result warrants further investigation, most of them might be implicated in stress response. As very little is known about the molecular mechanisms behind the release of DNases in vitro and in vivo, we also performed preliminary assays to understand whether direct DNA exposure affects the gene expression of strain NEM316 at the exponential growth phase. No differentially expressed genes were detected, which indicates that follow-up studies are needed to disclose the complex molecular pathways (and stimuli) triggering the release of DNases. In general, we provide data on the global expression levels of NEM316 at exponential growth phase that may contribute to better understand S. agalactiae adaptation and virulence.

Matching journals

The top 7 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.