Mapping the common gene networks that underlie related diseases

Rosenthal, Sara Brin; Wright, Sarah N.; Liu, Sophie; Churas, Christopher; Chilin‐Fuentes, Daisy R.; Chen, Chi‐Hua; Fisch, Kathleen M.; Pratt, Dexter; Kreisberg, Jason F.; Ideker, Trey

doi:10.1038/s41596-022-00797-1

Cited by 6 publications

(14 citation statements)

References 36 publications

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“…The resulting networks may contain false-positive protein interactions (e.g., proteins that interact in the reference protein interaction network but do not interact in a specific cell type) and false-negative protein interactions (e.g., proteins that interact only within a particular cell type context that may be unknown, or even undiscovered). Neverthe-less, strong performance gains of P innacle over context-free models indicate the importance of contextualized prediction and suggest a direction to enhance existing analyses on protein interaction networks [6, 16, 20, 22, 23, 52, 139, 140].…”

Section: Discussionmentioning

confidence: 99%

Contextual AI models for single-cell protein biology

Huang

Sumathipala

et al. 2023

Preprint

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Understanding protein function and discovering molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across diverse biological contexts, such as tissues and cell types, remains a significant challenge for existing algorithms. We introduce PINNACLE, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-aware protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, PINNACLE provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs. PINNACLE's contextualized representations of proteins reflect cellular and tissue organization and PINNACLE's tissue representations enable zero-shot retrieval of the tissue hierarchy. Pretrained PINNACLE protein representations can be adapted for downstream tasks: to enhance 3D structure-based protein representations (PD-1/PD-L1 and B7-1/CTLA-4) at cellular resolution and to study the genomic effects of drugs across cellular contexts. PINNACLE outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and can pinpoint cell type contexts that are more predictive of therapeutic targets than context-free models (29 out of 156 cell types in rheumatoid arthritis; 13 out of 152 cell types in inflammatory bowel diseases). PINNACLE is a network-based contextual AI model that dynamically adjusts its outputs based on biological contexts in which it operates.

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Section: Discussionmentioning

confidence: 99%

Contextual AI models for single-cell protein biology

Huang

Sumathipala

et al. 2023

Preprint

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“…A propagated network was constructed with NetColoc (0.1.7) 69 . PCNet 70 was utilized for the background human gene interaction network, which contains 18,820 nodes and 2,693,109 edges.…”

Section: Methodsmentioning

confidence: 99%

“…By using the 187 damaging MM DNM genes, a propagated network, Meningomyel-ome, was generated with NetColoc 69 with a background protein network PCNet 70 , incorporating 439 nodes and 2,447 edges. Big blue circle: damaging DNM genes, Small purple circle: propagated gene, Green border: known mouse NTD genes.…”

Section: Extended Data Figurementioning

confidence: 99%

The contribution of de novo coding mutations to meningomyelocele

Ha,

Tang,

Nisal

et al. 2024

Preprint

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Meningomyelocele (MM) is considered a genetically complex disease resulting from failure of neural tube closure (NTD). Patients display neuromotor disability and frequent hydrocephalus requiring ventricular shunting. A few proposed genes contribute to disease susceptibility, but most risk remains unexplained1. We postulated that de novo mutations (DNMs) under purifying selection contribute to MM risk2. Here we recruited a cohort of 851 MM trios requiring shunting at birth, compared with 732 control trios, and found that de novo likely gene disrupting or damaging missense mutations occur in approximately 22.3% of subjects, 28% of which are estimated to contribute to disease risk. The 187 genes with damaging DNMs collectively define networks including actin cytoskeleton and microtubule-based processes, axon guidance, and histone modification. Gene validation demonstrates partial or complete loss of function, impaired signaling and defective neural tube closure inXenopusembryos. Our results suggest DNMs make key contributions to MM risk, and highlight critical pathways required for neural tube closure in human embryogenesis.

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“…We used the Python package NetColoc (Rosenthal et al, 2023) (https://pypi.org/project/netcoloc/) for network propagation and co-localization. The sets of significant trait-associated genes from GWAS were used as "seed" genes for network propagation using a Random Walk with Restart (Vanunu et al, 2010) algorithm.…”

Section: Network Propagation and Co-localizationmentioning

confidence: 99%

“…Each set was sampled to preserve the size and degree distribution of the original input set. As previously implemented (Rosenthal et al, 2021(Rosenthal et al, , 2023Wright et al, 2023), we binned all genes in the network by degree with a minimum of 10 nodes per bin. For each gene, the NPS was calculated as a z-score comparing the observed heat at that gene after network propagation of the gene set, to the mean of the null distribution heats at that gene.…”

Section: Network Propagation and Co-localizationmentioning

confidence: 99%

Rare and Common Variants Associated with Alcohol Consumption Identify a Conserved Molecular Network

Leger,

Meredith,

Ideker

et al. 2024

Preprint

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Genome-wide association studies (GWAS) have identified hundreds of common variants associated with alcohol consumption. In contrast, rare variants have only begun to be studied for their role in alcohol consumption. No studies have examined whether common and rare variants implicate the same genes and molecular networks. To address this knowledge gap, we used publicly available alcohol consumption GWAS summary statistics (GSCAN, N=666,978) and whole exome sequencing data (Genebass, N=393,099) to identify a set of common and rare variants for alcohol consumption. Gene-based analysis of each dataset have implicated 294 (common variants) and 35 (rare variants) genes, including ethanol metabolizing genes ADH1B and ADH1C, which were identified by both analyses, and ANKRD12, GIGYF1, KIF21B, and STK31, which were identified only by rare variant analysis, but have been associated with related psychiatric traits. We then used a network colocalization procedure to propagate the common and rare gene sets onto a shared molecular network, revealing significant overlap. The shared network identified gene families that function in alcohol metabolism, including ADH, ALDH, CYP, and UGT. 74 of the genes in the network were previously implicated in comorbid psychiatric or substance use disorders, but had not previously been identified for alcohol-related behaviors, including EXOC2, EPM2A, CACNB3, and CACNG4. Differential gene expression analysis showed enrichment in the liver and several brain regions supporting the role of network genes in alcohol consumption. Thus, genes implicated by common and rare variants identify shared functions relevant to alcohol consumption, which also underlie psychiatric traits and substance use disorders that are comorbid with alcohol use.

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Mapping the common gene networks that underlie related diseases

Cited by 6 publications

References 36 publications

Contextual AI models for single-cell protein biology

Contextual AI models for single-cell protein biology

The contribution of de novo coding mutations to meningomyelocele

Rare and Common Variants Associated with Alcohol Consumption Identify a Conserved Molecular Network

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