Defesa de Dissertação de Mestrado: Biological and Computational Analysis of Small RNAs in the Gene Regulation of S. aureus Biofilms

Orientadores:
Marisa Fabiana Nicolas - Laboratório Nacional de Computação Científica - LNCC
Maiana de Oliveira Cerqueira e Costa - Laboratório Nacional de Computação Científica - LNCC
Jesus Eduardo Martinez Hernandez - Universidad Mayor - UNINCOR

Banca Examinadora:
Marisa Fabiana Nicolas - Laboratório Nacional de Computação Científica - LNCC (presidente)
Luciane Prioli Ciapina - Laboratório Nacional de Computação Científica - LNCC
Gloria Regina Franco - Universidade Federal de Minas Gerais - UFMG
Diogo Antonio Tschoeke - Universidade Federal do Rio de Janeiro - UFRJ

Suplentes:
Fabio Lima Custodio - Laboratório Nacional de Computação Científica - LNCC< br> Laurival Antonio Vilas-Boas - Universidade Estadual de Londrina - UEL

Resumo:

Small regulatory RNAs (sRNAs) are central to bacterial adaptation, influencing virulence, metabolism, and biofilm formation. Staphylococcus aureus's post-transcriptional regulation is crucial for responding to environmental cues, including shifts between planktonic growth and biofilm development. This work aimed to identify sRNAs in the S. aureus BMB9393 strain comprehensively, characterize their expression under contrasting growth conditions, and predict their potential mRNA targets.To achieve this, we combined high-throughput RNA sequencing with robust computational workflows. We profiled gene expression under biofilm and planktonic conditions across multiple strains, using sequence homology and covariance models for sRNA prediction. Each identified sRNA and annotated gene feature was integrated into a unified genomic framework, enabling differential expression analysis. Subsequent filtering steps, guided by interaction energy thresholds, probabilistic predictions, redundancy reduction, and network-level insights, substantially reduced the initial set of over 1.7 million raw sRNA-mRNA interactions to a more tractable subset of high-confidence pairs. Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression (DE) approaches helped pinpoint sRNAs associated with key regulatory hubs and virulence-related genes, including those involved in biofilm formation. Notably, several identified sRNAs were predicted to target genes such as sarA and clfA, which play crucial roles in the establishment and maintenance of biofilms. Comparisons between intragenic and antisense sRNAs highlighted differences in targeting potential, with antisense sRNAs yielding more plausible regulatory interactions. This work integrates large-scale prediction, filtering, and network-based prioritization to refine the catalog of candidate sRNA-mRNA pairs. The results provide a val uable starting point for future experimental validation and functional assays, advancing our understanding of sRNA-mediated regulation in S. aureus and informing strategies to mitigate biofilm-related infections.