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Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function. Maps of protein-protein interactions (PPIs) help identify new components of pathways, complexes, and processes. In this work, state-of-the-art methods are used to identify binary Drosophila PPIs, generating broadly useful physical and data resources. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms of coronavirus disease 2019 (COVID-19) disease severity to efficiently design therapies for emerging virus variants remains an urgent challenge of the ongoing pandemic. Infection and immune reactions are mediated by direct contacts between viral molecules and the host proteome, and the vast majority of these virus–host contacts (the 'contactome') have not been identified. Here, we present a systematic contactome map of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with the human host encompassing more than 200 binary virus–host and intraviral protein–protein interactions. We find that host proteins genetically associated with comorbidities of severe illness and long COVID are enriched in SARS-CoV-2 targeted network communities. Evaluating contactome-derived hypotheses, we demonstrate that viral NSP14 activates nuclear factor κB (NF-κB)-dependent transcription, even in the presence of cytokine signaling. Moreover, for several tested host proteins, genetic knock-down substantially reduces viral replication. Additionally, we show for USP25 that this effect is phenocopied by the small-molecule inhibitor AZ1. Our results connect viral proteins to human genetic architecture for COVID-19 severity and offer potential therapeutic targets. A SARS-CoV-2-human protein contactome map uncovers an inhibitor of viral replication. [ABSTRACT FROM AUTHOR]

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships1,2. Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes. A human binary protein interactome map that includes around 53,000 protein–protein interactions involving more than 8,000 proteins provides a reference for the study of human cellular function in health and disease. [ABSTRACT FROM AUTHOR]

Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other's partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome. Computational protein-protein interaction (PPI) prediction has the potential to complement experimental efforts to map interactomes. Here, the authors show that proteins tend to interact if one is similar to the other's partners and that PPI prediction based on this principle is highly accurate. [ABSTRACT FROM AUTHOR]

RE-1 silencing transcription factor (REST) is a transcriptional repressor that regulates gene expression by binding to repressor element 1. However, despite its critical function in physiology, little is known about its interaction proteins. Here we identified 204 REST-interacting proteins using affinity purification and mass spectrometry. The interactome included proteins associated with mRNA processing/splicing, chromatin organization, and transcription. The interactions of these REST-interacting proteins, which included TRIM28, were confirmed by co-immunoprecipitation and immunocytochemistry, respectively. Gene Ontology (GO) analysis revealed that neuronal differentiation-related GO terms were enriched among target genes that were co-regulated by REST and TRIM28, while the level of CTNND2 was increased by the knockdown of REST and TRIM28. Consistently, the level of CTNND2 increased while those of REST and TRIM28 decreased during neuronal differentiation in the primary neurons, suggesting that CTNND2 expression may be co-regulated by both. Furthermore, neurite outgrowth was increased by depletion of REST or TRIM28, implying that reduction of both REST and TRIM28 could promote neuronal differentiation via induction of CTNND2 expression. In conclusion, our study of REST reveals novel interacting proteins which could be a valuable resource for investigating unidentified functions of REST and also suggested functional links between REST and TRIM28 during neuronal development. [ABSTRACT FROM AUTHOR]

Gut microbes might influence host metabolic homeostasis and contribute to the pathogenesis of type 2 diabetes (T2D), which is characterized by insulin resistance. Bacteria-derived extracellular vesicles (EVs) have been suggested to be important in the pathogenesis of diseases once believed to be non-infectious. Here, we hypothesize that gut microbe-derived EVs are important in the pathogenesis of T2D. In vivo administration of stool EVs from high fat diet (HFD)-fed mice induced insulin resistance and glucose intolerance compared to regular diet (RD)-fed mice. Metagenomic profiling of stool EVs by 16S ribosomal DNA sequencing revealed an increased amount of EVs derived from Pseudomonas panacis (phylum Proteobacteria) in HFD mice compared to RD mice. Interestingly, P. panacis EVs blocked the insulin signaling pathway in both skeletal muscle and adipose tissue. Moreover, isolated P. panacis EVs induced typical diabetic phenotypes, such as glucose intolerance after glucose administration or systemic insulin injection. Thus, gut microbe-derived EVs might be key players in the development of insulin resistance and impairment of glucose metabolism promoted by HFD. [ABSTRACT FROM AUTHOR]