PRIMPOL-Mediated Adaptive Response Suppresses Replication Fork Reversal in BRCA-Deficient Cells.

Acute treatment with replication-stalling chemotherapeutics causes reversal of replication forks. BRCA proteins protect reversed forks from nucleolytic degradation, and their loss leads to chemosensitivity. Here, we show that fork degradation is no longer detectable in BRCA1-deficient cancer cells exposed to multiple cisplatin doses, mimicking a clinical treatment regimen. This effect depends on increased expression and chromatin loading of PRIMPOL and is regulated by ATR activity. Electron microscopy and single-molecule DNA fiber analyses reveal that PRIMPOL rescues fork degradation by reinitiating DNA synthesis past DNA lesions. PRIMPOL repriming leads to accumulation of ssDNA gaps while suppressing fork reversal. We propose that cells adapt to repeated cisplatin doses by activating PRIMPOL repriming under conditions that would otherwise promote pathological reversed fork degradation. This effect is generalizable to other conditions of impaired fork reversal (e.g., SMARCAL1 loss or PARP inhibition) and suggests a new strategy to modulate cisplatin chemosensitivity by targeting the PRIMPOL pathway.

Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome.

In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms. As a consequence of predicting protein function across all organisms, function prediction tools generally fail to use all of the types of data available for any specific organism, including protein and transcript expression information. Additionally, the release of Alphafold predictions for all Uniprot proteins provides a novel opportunity for leveraging structural information. We constructed a bespoke machine learning model to predict the function of recalcitrant proteins of unknown function in Pseudomonas putida based on these sources of data, which annotated 1079 terms to 213 proteins. Among the predicted functions supplied by the model, we found evidence for a significant overrepresentation of nitrogen metabolism and macromolecule processing proteins. These findings were corroborated by manual analyses of selected proteins which identified, among others, a functionally unannotated operon that likely encodes a branch of the shikimate pathway.