RESUMEN
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.
Asunto(s)
Biología Computacional , Aprendizaje Automático , Proteoma , Proteómica , Proteínas de Unión al ARN , ARN , Proteoma/metabolismo , Humanos , Proteínas de Unión al ARN/metabolismo , Proteínas de Unión al ARN/química , ARN/metabolismo , ARN/química , Sitios de Unión , Proteómica/métodos , Biología Computacional/métodos , Unión Proteica , Programas Informáticos , Bases de Datos de ProteínasRESUMEN
Background: The cGAS/STING pathway, part of the innate immune response to foreign DNA, can be activated by cell's own DNA arising from the processing of the genome, including the degradation of nascent DNA at arrested replication forks, which can be upregulated in cancer cells. Recent evidence raises a possibility that the cGAS/STING pathway may also modulate the very processes that trigger it, e.g., DNA damage repair or processing of stalled forks. Methods: We manipulated STING levels in human cells by depleting or re-expressing it, and assessed the effects of STING on replication using microfluidics-assisted replication track analysis, or maRTA, a DNA fiber assay, as well as immuno-precipitation of nascent DNA, or iPOND. We also assessed STING subcellular distribution and its ability to activate. Results: Depletion of STING suppressed and its re-expression in STING-deficient cancer cells upregulated the degradation of nascent DNA at arrested replication forks. Replication fork arrest was accompanied by the STING pathway activation, and a STING mutant that does not activate the pathway failed to upregulate nascent DNA degradation. cGAS was required for STING's effect on degradation, but this requirement could be bypassed by treating cells with a STING agonist. Cells expressing inactive STING had a reduced level of RPA on parental and nascent DNA of arrested forks and a reduced CHK1 activation compared to cells with the wild type STING. STING also affected unperturbed fork progression in a subset of cell lines. STING fractionated to the nuclear fractions enriched for structural components of chromatin and nuclear envelope, and furthermore, it associated with the chromatin of arrested replication forks as well as post-replicative chromatin. Conclusion: Our data highlight STING as a determinant of stalled replication fork integrity, thus revealing a novel connection between the replication stress and innate immune responses.