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Hernandez-Armenta, C.

Publications and source records attributed to Hernandez-Armenta, C..

3 recordsLinked to original sources

Meiotic Nuclear Pore Complex Remodeling Provides Key Insights into Nuclear Basket Organization

Nuclear pore complexes (NPCs) are large proteinaceous assemblies that mediate nuclear compartmentalization. NPCs undergo largescale structural rearrangements during mitosis in metazoans and some fungi. However, our understanding of NPC remodeling beyond mitosis remains limited. Using time-lapse fluorescence microscopy, we discovered that NPCs undergo two mechanistically-separable remodeling events during budding yeast meiosis whereby parts or all of the nuclear basket transiently dissociate from the NPC core during meiosis I and II, respectively. Meiosis I detachment, observed for Nup60 and Nup2, is driven by Polo kinase-mediated phosphorylation of Nup60 at its interface with the Y-complex. Subsequent reattachment of Nup60-Nup2 to the NPC core is mediated by a lipid-binding amphipathic helix in Nup60. Preventing Nup60-Nup2 reattachment causes misorganization of the entire nuclear basket in gametes. Strikingly, meiotic nuclear basket remodeling also occurs in the distantly related fission yeast, Schizosaccharomyces pombe. Our study reveals a conserved and developmentally programmed aspect of NPC plasticity, providing key mechanistic insights into nuclear basket organization. SUMMARYKing and Wettstein et al. reveal that nuclear pore complexes undergo two distinct remodeling events during budding yeast meiosis: partial and full nuclear basket detachment. By dissecting the regulation of these events, the study provides mechanistic insights into NPC organization.

cell biology↗

A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

An outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

systems biology↗

Benchmarking substrate-based kinase activity inference using phosphoproteomic data

MotivationPhosphoproteomic experiments are increasingly used to study the changes in signalling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies.\n\nResultsWe curated public phosphoproteomic experiments to identify a gold standard dataset containing a total of 184 kinase-condition pairs where regulation is expected to occur. A list of kinase substrates was compiled and used to estimate changes in kinase activities using the following methods: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and a multiple linear regression model (MLR). We also tested weighted variants of the Z-test, and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the number of known substrates and the type of evidence (in vivo, in vitro or in silico) supporting these influence the predictions.\n\nConclusionsMost models performed well with the Z-test and the GSEA performing best as determined by the area under the ROc curve (Mean AUC=0.722). Weighting kinase targets by the kinase target sequence preference improves the results only marginally. However, the number of known substrates and the evidence supporting the interactions has a strong effect on the predictions.

bioinformatics↗