Abstract SARS-CoV2, severe acute respiratory syndrome coronavirus 2, is frequently associated with neurological manifestations. Despite the presence of mild to severe CNS-related symptoms in a cohort of patients, there is no consensus whether the virus can infect directly brain tissue or if the symptoms in patients are a consequence of peripheral infectivity of the virus. Here, we use long-term human stem cell-derived cortical organoids to assess SARS-CoV2 infectivity of brain cells and unravel the cell-type tropism and its downstream pathological effects. Our results show consistent and reproducible low levels of SARS-CoV2 infection of astrocytes, deep projection neurons, upper callosal neurons and inhibitory neurons in 6 months human cortical organoids. Interestingly, astrocytes showed the highest infection rate among all infected cell populations that led to increased presence of reactive states. Further, transcriptomic analysis revealed overall changes in expression of genes related to cell metabolism, astrocyte activation and, inflammation and further, upregulation of cell survival pathways. Thus, local and minor infectivity of SARS-CoV2 in the brain may induce widespread adverse effects and may lead to resilience of dysregulated neurons and astrocytes within an inflammatory environment.
The Tax oncoprotein encoded by the Human T-cell leukemia virus type 1 (HTLV-1) plays a pivotal role in viral persistence and pathogenesis. HTLV-1 infected cells proliferate faster than normal lymphocytes, expand through mitotic division and accumulate genomic lesions. Here, we show that Tax associates with the minichromosome maintenance MCM2-7 helicase complex and localizes to origins of replication. Tax modulates the spatiotemporal program of origin activation and fires supplementary origins at the onset of S phase. Thereby, Tax increases the DNA replication rate, accelerates S phase progression but also generates a replicative stress characterized by the presence of genomic lesions. Mechanistically, Tax favors p300 recruitment and histone hyperacetylation at late replication domains advancing their replication timing in early S phase.
ABSTRACT Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
ABSTRACT Key steps in viral propagation, immune suppression, and pathology are mediated by direct, binary, physical interactions between viral and host proteins. To understand the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we generated an unbiased systematic map of binary interactions between viral and host proteins, complementing previous co-complex association maps by conveying more direct mechanistic understanding and potentially enabling targeted disruption of direct interactions. To this end, we deployed two parallel strategies, identifying 205 virus-host and 27 intraviral binary interactions amongst 171 host and 19 viral proteins, and confirming high quality of these interactions via a calibrated orthogonal assay. Host proteins interacting with SARS-CoV-2 proteins are enriched in various cellular processes, including immune signaling and inflammation, protein ubiquitination, and membrane trafficking. Specific subnetworks provide new hypotheses related to viral modulation of host protein homeostasis and T-cell regulation. The binary virus-host protein interactions we identified can now be prioritized as targets for therapeutic intervention. More generally, we provide a resource of systematic maps describing which SARS-CoV-2 and human proteins interact directly.
ABSTRACT Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose the concept of an “assayome” for binary protein-protein interaction (PPI) mapping as an optimal combination of assays and/or assay versions that maximizes detection of true positive interactions, while avoiding detection of random protein pairs. We engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions differing by protein expression systems and tagging configurations. The resulting N2H assayome recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our assayome concept should be applicable to systematic mapping of other biological landscapes.
