Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Fun Sea Factory

Una piastra quadrata incisa da netti solchi d'acqua si popola di forme pure come desunte da un'architettura industriale. Un'industria del divertimento, galleggiante con residenze ricavate da silos, cilindri, cubi, piante centrali, ristoranti, discoteche, spazi comuni. Gli impianti sono a vista su una lunga fettuccia che ci spinge verso il lato est del Porto di Napol

PHD1 regulates p53‐mediated colorectal cancer chemoresistance

Abstract Overcoming resistance to chemotherapy is a major challenge in colorectal cancer (CRC) treatment, especially since the underlying molecular mechanisms remain unclear. We show that silencing of the prolyl hydroxylase domain protein PHD1, but not PHD2 or PHD3, prevents p53 activation upon chemotherapy in different CRC cell lines, thereby inhibiting DNA repair and favoring cell death. Mechanistically, PHD1 activity reinforces p53 binding to p38α kinase in a hydroxylation‐dependent manner. Following p53–p38α interaction and chemotherapeutic damage, p53 can be phosphorylated at serine 15 and thus activated. Active p53 allows nucleotide excision repair by interacting with the DNA helicase XPB, thereby protecting from chemotherapy‐induced apoptosis. In accord with this observation, PHD1 knockdown greatly sensitizes CRC to 5‐FU in mice. We propose that PHD1 is part of the resistance machinery in CRC, supporting rational drug design of PHD1‐specific inhibitors and their use in combination with chemotherapy