Defining Bacterial RNA-RNA Interactomes Using CLASH.

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial pathogen accounting for high mortality rates among infected patients. Transcriptomic regulation by small RNAs (sRNAs) has been shown to regulate networks promoting antibiotic resistance and virulence in S. aureus. Yet, the biological role of most sRNAs during MRSA host infection remains unknown. To fill this gap, in collaboration with the lab of Jai Tree, we performed comprehensive RNA-RNA interactome analyses in MRSA using CLASH under conditions that mimic the host environment. Here we present a detailed version of this optimized CLASH (cross-linking, ligation, and sequencing of hybrids) protocol we recently developed, which has been tailored to explore the RNA interactome in S. aureus as well as other Gram-positive bacteria. Alongside, we introduce a compilation of helpful Python functions for analyzing folding energies of putative RNA-RNA interactions and streamlining sRNA and mRNA seed discovery in CLASH data. In the accompanying computational demonstration, we aim to establish a standardized strategy to evaluate the likelihood that observed chimeras arise from true RNA-RNA interactions.

pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine-learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine-learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that although UV-cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.