Simonovsky, E. | bioRxiv Science

bioRxiv2023

A virally encoded high resolution screen of cytomegalovirus host dependencies

Genetic screens have transformed our ability to interrogate cellular factor requirements in infection, yet current approaches are limited in their sensitivity, biased towards early stages of infection and provide only simplistic phenotypic information which is often based on infected cell survival. Here, by engineering human cytomegalovirus to express sgRNA libraries directly from the viral genome, we developed a sensitive, versatile, viral centric approach that allows profiling of different stages along viral infection in a pooled format. Using this approach, which we termed VECOS (Virus Encoded CRISPR-based direct readOut Screening system), we identified hundreds of novel host dependency and restriction factors and quantified their direct effects on viral genome replication, viral particle secretion and infectiousness of secreted particles, providing a multi-dimensional perspective on viral-host interactions. These high resolution measurements reveal that perturbations that alter late stages in HCMV life cycle mostly regulate HCMV particle quality rather than quantity, defining correct virion assembly as a critical stage that is heavily reliant on viral-host interactions. Overall, VECOS facilitates systematic high resolution dissection of human proteins role along the infection cycle, providing a roadmap for in-depth dissection of host-herpesvirus interactions.

microbiology↗

bioRxiv2021

A tissue-aware machine learning framework enhances the mechanistic understanding and genetic diagnosis of Mendelian and rare diseases

Genetic studies of Mendelian and rare diseases face the critical challenges of identifying pathogenic gene variants and their modes-of-action. Previous efforts rarely utilized the tissue-selective manifestation of these diseases for their elucidation. Here we introduce an interpretable machine learning (ML) platform that utilizes heterogeneous and large-scale tissue-aware datasets of human genes, and rigorously, concurrently and quantitatively assesses hundreds of candidate mechanisms per disease. The resulting tissue-aware ML platform is applicable in gene-specific, tissue-specific, or patient-specific modes. Application of the platform to selected Mendelian disease genes pinpointed mechanisms that lead to tissue-specific disease manifestation. When applied jointly to diseases that manifest in the same tissue, the models revealed common known and previously underappreciated factors that underlie tissue-selective disease manifestation. Lastly, we harnessed our ML platform toward genetic diagnosis of tissue-selective rare diseases. Patient-specific models of candidate disease-causing genes from 50 patients successfully prioritized the pathogenic gene in 86% of the cases, implying that the tissue-selectivity of rare diseases aids in filtering out unlikely candidate genes. Thus, interpretable tissue-aware ML models can boost mechanistic understanding and genetic diagnosis of tissue-selective heritable diseases. A webserver supporting gene prioritization is available at https://netbio.bgu.ac.il/trace/.

genetics↗