Using brain cell-type-specific protein interactomes to interpret genetic data in schizophrenia

Hsu, Yu-Han H.; Nacu, Eugen; Liu, Ruize; Kim, April; Tsafou, Kalliopi; Petrossian, Natalie; Crotty, William J.; Suh, Jung Min; Pintacuda, Greta; Riseman, Jackson; Martín, Jacqueline M.; Małolepsza, Edyta; Li, Taibo; Singh, Tarjinder; Ge, Tian; Egri, Shawn B.; Tanenbaum, Benjamin; Stanclift, Caroline R.; Apffel, Annie; Carr, Steven A.; Schenone, Monica; Jaffe, Jacob D.; Fornelos, Nadine; Huang, Hailiang; Eggan, Kevin; Lage, Kasper

doi:10.1101/2021.10.07.21264568

Cited by 5 publications

(8 citation statements)

References 82 publications

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“…Although we did not observe an analogous enrichment for common variants of ASDs in the network, this could be due to power limitations of the ASD GWAS. In a parallel study, 71 we generated an iN-derived PPI network for SCZ-associated genes prioritized from well-powered GWAS data, showed that the network is enriched for common variant risk of SCZ, and used it to complement fine-mapping and expression quantitative trait loci (eQTL) co-localization approaches to prioritize additional genes from GWAS loci. Performing similar analyses on the ASD PPI network when larger ASD GWAS become available may reveal whether rare and common variants of ASDs converge onto shared pathways represented in the network, as we have already observed for the transcriptional circuit regulated by IGF2BP1-3.…”

Section: Discussionmentioning

confidence: 99%

“…Our data not only empower hypothesis generation to study the mechanisms of individual ASD-associated genes but also suggest convergent pathways that may inform therapeutic intervention or clinical stratification strategies. More generally, the approach described in this study and the parallel SCZ study 71 can be expanded to generate larger PPI networks across brain cell types and neurodevelopmental stages to uncover novel pathways underlying psychiatric disorders.…”

Section: Discussionmentioning

confidence: 99%

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Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders

Pintacuda¹,

Hsu²,

Tsafou³

et al. 2023

Cell Genomics

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders

Pintacuda¹,

Hsu²,

Tsafou³

et al. 2023

Cell Genomics

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“…Recently, Pintacuda et al(51) and Hsu et al(52) demonstrated that mapping experimental, cell type-specific protein-protein interaction networks for GWAS gene products has great potential for understanding complex disease pathobiology. Their experiments were conducted in brain cells, mapping connections between (among others) GWAS genes associated with neurological disorders and found that the interactomes were enriched for genes related to the pathogenesis of the respective diseases.…”

Section: Discussionmentioning

confidence: 99%

HHIP protein interactions in lung cells provide insight into COPD pathogenesis

Deritei,

Inuzuka,

Castaldi

et al. 2024

Preprint

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Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP, a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP, its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2, and FBLN5. These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.

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“…pioneer/settler models show TFBSs are often >50 bp apart in REs) PPIs (Martin et al 2023). By weighing known direct/indirect PPIs in the context of TG regulation, NetREm helps characterize existing PPINs at a cell-type level (Johnson et al 2021;Murtaza et al 2022;Hsu et al 2022). It also helps address the link prediction problem, flagging undiscovered PPIs for follow-ups (Singh and Vig 2017).…”

Section: Discussionmentioning

confidence: 99%

NetREm Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

Khullar,

Huang,

Ramesh

et al. 2023

Preprint

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Transcription factor (TF) coordination plays a key role in gene regulation such as protein-protein interactions (PPIs) and DNA co-bindings. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF coordination and gene regulation of various cell types remains unclear. To address this, we have developed a novel computational approach, Network Regression Embedding (NetREm), to reveal cell-type TF-TF coordination activities for gene regulation. NetREm leverages network-constrained regularization using prior interaction knowledge (e.g., protein, chromatin, TF binding) to analyze single-cell gene expression data. We test NetREm by simulation data and apply it to analyze various cell types in both central and peripheral nerve systems (PNS) such as neuronal, glial and Schwann cells as well as in Alzheimer’s disease (AD). Our findings uncover cell-type coordinating TFs and identify new TF-target gene candidate links. We also validate our top predictions using Cut&Run and knockout loss-of-function expression data in rat and mouse models and compare our results with additional functional genomic data including expression quantitative trait loci (eQTL) and Genome-Wide Association Studies (GWAS) to link genetic variants to TF coordination. NetREm is open-source available athttps://github.com/SaniyaKhullar/NetREm.

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Using brain cell-type-specific protein interactomes to interpret genetic data in schizophrenia

Cited by 5 publications

References 82 publications

Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders

Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders

HHIP protein interactions in lung cells provide insight into COPD pathogenesis

NetREm Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

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