Transcriptional Profiling and Co-expression Integration for the Filtering of Relevant Bacterial sRNA–mRNA Interactions: Application to Staphylococcus aureus Biofilm

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ID: 332385
2026
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Abstract
Abstract Small regulatory ribonucleic acids coordinate bacterial gene expression and contribute to biofilm development, virulence, and stress adaptation. We characterized these regulators across six Staphylococcus aureus strains by integrating newly generated transcriptome data from strain Bmb9393 with public datasets from five additional strains. Sequence homology and covariance models supported annotation of candidate loci, whose expression and genomic context were characterized under biofilm and planktonic conditions. Differential expression and weighted gene co-expression analysis identified conserved and strain-specific patterns, including ten small regulatory transcripts consistently upregulated and four consistently downregulated in biofilm. We combined predictions from four interaction tools with differential-expression consensus and co-expression evidence in a sequential prioritization framework. Under calibrated thresholds, this framework reduced 1,731,060 candidate regulatory interactions to 1,290 prioritized pairs. Comparisons across three threshold schemes showed nested target retention but substantial changes in the selected regulatory partners. The 31 literature-curated interactions used for threshold calibration did not constitute independent validation, and none survived all downstream filters. The open-source graphical application integrates documented processed inputs and exports intermediate results, supporting reproducible prioritization. The resulting candidates provide hypotheses for experimental investigation of biofilm-associated regulation.
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Authors Carolina Albuquerque Massena Ribeiro, Guadalupe del Rosario Quispe Saji, Maiana de Oliveira Cerqueira e Costa, Alice Slotfeldt Viana, Mariana Fernandes Carvalho, Agnes Marie Sá Figueiredo, Edgardo Galán-Vásquez, J Eduardo Martínez-Hernández, Marisa Fabiana Nicolas
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag309
URL
https://doi.org/10.1093/bioadv/vbag309
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