Biology subjects

Basha, O.

Publications and source records attributed to Basha, O..

4 recordsLinked to original sources

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↗

bioRxiv2020

The landscape of molecular chaperones across human tissues reveals a layered architecture of core and variable chaperones

The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, the organization of the chaperone system across physiological human tissues has received little attention. Here, we used human tissue RNA-sequencing profiles to analyze the expression and organization of chaperones across 29 main tissues. We found that relative to protein-coding genes, chaperones were significantly more ubiquitously and highly expressed across all tissues. Nevertheless, differential expression analysis revealed that most chaperones were up- or down-regulated in certain tissues, suggesting that they have tissue-specific roles. In agreement, chaperones that were upregulated in skeletal muscle were highly enriched in mouse myoblasts and in nematodes muscle tissue, and overlapped significantly with chaperones that are causal for muscle diseases. We also identified a distinct subset of chaperones that formed a uniformly-expressed, cross-family core group conducting basic cellular functions that was significantly more essential for cell survival. Altogether, this suggests a layered architecture of chaperones across tissues that is composed of shared core elements that are complemented by variable elements which give rise to tissue-specific functions and sensitivities, thereby contributing to the tissue-specificity of protein misfolding diseases. Significance StatementProtein misfolding diseases, such as neurodegenerative disorders and myopathies, are often manifested in a specific tissue or even a specific cell type. Enigmatically, however, they are typically caused by mutations in widely expressed proteins. Here we focused on chaperones, the main and basic components of the protein-folding machinery of cells. Computational analyses of large scale tissue transcriptomes unveils that the chaperone system is composed of core essential elements that are uniformly expressed across tissues, and of variable elements that are differentially expressed in a tissue-specific manner. This organization allows each tissue to fit the quality control system to its specific requirements and illuminates the mechanisms that underlie a tissues susceptibility to protein-misfolding diseases.

systems biology↗

bioRxiv2019

Differential network analysis of human tissue interactomes highlights tissue-selective processes and genetic disorder genes

MotivationDifferential network analysis, designed to highlight interaction changes between conditions, is an important paradigm in network biology. However, network analysis methods have been typically designed to compare between few conditions, were rarely applied to protein interaction networks (interactomes). Moreover, large-scale benchmarks for their evaluation have been lacking.\n\nResultsHere, we assess five network analysis methods by applying them to 34 human tissues interactomes. For this, we created a manually-curated benchmark of 6,499 tissue-specific, gene ontology biological processes, and analyzed the ability of each method to expose these tissue-process associations. The four differential network analysis methods outperformed the non-differential, expression-based method (AUCs of 0.82-0.9 versus 0.69, respectively). We then created another benchmark, of 1,527 tissue-specific disease cases, and analyzed the ability of differential network analysis methods to highlight additional disease-related genes. Compared to a non-differential subnetworks surrounding a known disease-causing gene, the extremely-differential subnetwork (top 1%) was significantly enriched for additional disease-causing genes in 18.6% of the cases (p[≤]10e-3). In 5/10 tissues tested, including Muscle, nerve and heart tissues (p = 2.54E-05, 2.71E-04, 3.63E-19), such enrichments were highly significant.\n\nSummaryAltogether, our study demonstrates that differential network analysis of human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. Moreover, it offers expansive manually-curated datasets of tissue-selective processes and diseases that could serve for benchmark and for analyses in many other studies.\n\nContactestiyl@bgu.ac.il

bioinformatics↗

bioRxiv2019

A reference map of the human protein interactome

Global insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human \"all-by-all\" binary reference interactome map, or \"HuRI\". With ~53,000 high-quality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.

systems biology↗