Predicting molecular mechanisms of hereditary diseases by using their tissue‐selective manifestation

Simonovsky, Eyal; Sharon, Moran; Ziv, Maya; Mauer, Omry; Hekselman, Idan; Jubran, Juman; Vinogradov, Ekaterina; Argov, Chanan M; Basha, Omer; Kerber, Lior; Yogev, Yuval; Segrè, Ayellet V.; Im, Hae Kyung; Birk, Ohad S.; Rokach, Lior; Yeger-Lotem, Esti

doi:10.15252/msb.202211407

Cited by 8 publications

References 80 publications

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Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

Argov,

Shneyour,

Jubran

et al. 2024

Mol Syst Biol

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Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted “Tissue Risk Assessment of Causality by Expression for variants” (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants’ mode of action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, the interpretation of all tissue models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Collectively, these results show that tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases.

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Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

Argov,

Shneyour,

Jubran

et al. 2024

Mol Syst Biol

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SPIDER: constructing cell-type-specific protein–protein interaction networks

Kupershmidt,

Kasif,

Sharan

2024

Bioinformatics Advances

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Motivation Protein–protein interactions (PPIs) play essential roles in the buildup of cellular machinery and provide the skeleton for cellular signaling. However, these biochemical roles are context dependent and interactions may change across cell type, time, and space. In contrast, PPI detection assays are run in a single condition that may not even be an endogenous condition of the organism, resulting in static networks that do not reflect full cellular complexity. Thus, there is a need for computational methods to predict cell-type-specific interactions. Results Here we present SPIDER (Supervised Protein Interaction DEtectoR), a graph attention-based model for predicting cell-type-specific PPI networks. In contrast to previous attempts at this problem, which were unsupervised in nature, our model’s training is guided by experimentally measured cell-type-specific networks, enhancing its performance. We evaluate our method using experimental data of cell-type-specific networks from both humans and mice, and show that it outperforms current approaches by a large margin. We further demonstrate the ability of our method to generalize the predictions to datasets of tissues lacking prior PPI experimental data. We leverage the networks predicted by the model to facilitate the identification of tissue-specific disease genes. Availability and implementation Our code and data are available at https://github.com/Kuper994/SPIDER.

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ProAct: quantifying the differential activity of biological processes in tissues, cells, and user-defined contexts

Sharon,

Gruber,

Argov

et al. 2023

Nucleic Acids Research

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The distinct functions and phenotypes of human tissues and cells derive from the activity of biological processes that varies in a context-dependent manner. Here, we present the Process Activity (ProAct) webserver that estimates the preferential activity of biological processes in tissues, cells, and other contexts. Users can upload a differential gene expression matrix measured across contexts or cells, or use a built-in matrix of differential gene expression in 34 human tissues. Per context, ProAct associates gene ontology (GO) biological processes with estimated preferential activity scores, which are inferred from the input matrix. ProAct visualizes these scores across processes, contexts, and process-associated genes. ProAct also offers potential cell-type annotations for cell subsets, by inferring them from the preferential activity of 2001 cell-type-specific processes. Thus, ProAct output can highlight the distinct functions of tissues and cell types in various contexts, and can enhance cell-type annotation efforts. The ProAct webserver is available at https://netbio.bgu.ac.il/ProAct/.

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Predicting molecular mechanisms of hereditary diseases by using their tissue‐selective manifestation

Cited by 8 publications

References 80 publications

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

SPIDER: constructing cell-type-specific protein–protein interaction networks

ProAct: quantifying the differential activity of biological processes in tissues, cells, and user-defined contexts

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