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Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies.

ABSTRACT: Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder characterized by deficits in social interactions and communication. Protein-altering variants in many genes have been shown to contribute to ASD; however, understanding the convergence across many genes remains a challenge. We demonstrate that coexpression patterns from 993 human postmortem brains are significantly correlated with the transcriptional consequences of CRISPR perturbations in human neurons. Across 71 ASD risk genes, there was significant tissue-specific convergence implicating synaptic pathways. Tissue-specific convergence was further demonstrated across schizophrenia and atrial fibrillation risk genes. The degree of ASD convergence was significantly correlated with ASD association from rare variation and differential expression in ASD brains. Positively convergent genes showed intolerance to functional mutations and had shorter coding lengths than known risk genes even after removing association with ASD. These results indicate that convergent coexpression can identify potentially novel genes that are unlikely to be discovered by sequencing studies.

SUBMITTER: Liao C

PROVIDER: S-EPMC10112287 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies.

Liao Calwing C Moyses-Oliveira Mariana M De Esch Celine E F CEF Bhavsar Riya R Nuttle Xander X Li Aiqun A Yu Alex A Burt Nicholas D ND Erdin Serkan S Fu Jack M JM Wang Minghui M Morley Theodore T Han Lide L Dion Patrick A PA Rouleau Guy A GA Zhang Bin B Brennand Kristen J KJ Talkowski Michael E ME Ruderfer Douglas M DM

Cell genomics 20230309 4

Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder characterized by deficits in social interactions and communication. Protein-altering variants in many genes have been shown to contribute to ASD; however, understanding the convergence across many genes remains a challenge. We demonstrate that coexpression patterns from 993 human postmortem brains are significantly correlated with the transcriptional consequences of CRISPR perturbations in human neurons. Across 71 ASD risk ...[more]

PMID: 37082147

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Project description:BackgroundCancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging.ResultsWe find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes-while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~ 2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes.ConclusionsConsidering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer.

Dataset Information

Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies.

Publications

Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies.

Similar Datasets

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