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.

The role of RNA-binding proteins in mediating adaptive responses in Gram-positive bacteria.

Bacteria are constantly subjected to stressful conditions, such as antibiotic exposure, nutrient limitation and oxidative stress. For pathogenic bacteria, adapting to the host environment, escaping defence mechanisms and coping with antibiotic stress are crucial for their survival and the establishment of a successful infection. Stress adaptation relies heavily on the rate at which the organism can remodel its gene expression programme to counteract the stress. RNA-binding proteins mediating co- and post-transcriptional regulation have recently emerged as important players in regulating gene expression during adaptive responses. Most of the research on these layers of gene expression regulation has been done in Gram-negative model organisms where, thanks to a wide variety of global studies, large post-transcriptional regulatory networks have been uncovered. Unfortunately, our understanding of post-transcriptional regulation in Gram-positive bacteria is lagging behind. One possible explanation for this is that many proteins employed by Gram-negative bacteria are not well conserved in Gram-positives. And even if they are conserved, they do not always play similar roles as in Gram-negative bacteria. This raises the important question whether Gram-positive bacteria regulate gene expression in a significantly different way. The goal of this review was to discuss this in more detail by reviewing the role of well-known RNA-binding proteins in Gram-positive bacteria and by highlighting their different behaviours with respect to some of their Gram-negative counterparts. Finally, the second part of this review introduces several unusual RNA-binding proteins of Gram-positive species that we believe could also play an important role in adaptive responses.

Monitoring Protein-RNA Interaction Dynamics in vivo at High Temporal Resolution using χCRAC.

The interaction between RNA-binding proteins (RBPs) and their RNA substrates exhibits fluidity and complexity. Within its lifespan, a single RNA can be bound by many different RBPs that will regulate its production, stability, activity, and degradation. As such, much has been done to understand the dynamics that exist between these two types of molecules. A particularly important breakthrough came with the emergence of 'cross-linking and immunoprecipitation' (CLIP). This technique allowed stringent investigation into which RNAs are bound by a particular RBP. In short, the protein of interest is UV cross-linked to its RNA substrates in vivo, purified under highly stringent conditions, and then the RNAs covalently cross-linked to the protein are converted into cDNA libraries and sequenced. Since its conception, many derivative techniques have been developed in order to make CLIP amenable to particular fields of study. However, cross-linking using ultraviolet light is notoriously inefficient. This results in extended exposure times that make the temporal study of RBP-RNA interactions impossible. To overcome this issue, we recently designed and built much-improved UV irradiation and cell harvesting devices. Using these new tools, we developed a protocol for time-resolved analyses of RBP-RNA interactions in living cells at high temporal resolution: Kinetic CRoss-linking and Analysis of cDNAs (χCRAC). We recently used this technique to study the role of yeast RBPs in nutrient stress adaptation. This manuscript provides a detailed overview of the χCRAC method and presents recent results obtained with the Nrd1 RBP.