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opencc-by-4.0Mar 2020View details →

Data for tutorial of RNA interactome data analysis

Data required for galaxy training tutorial of RNA interactome data analysis

opencc-zeroSep 2020View details →

A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions

Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ~380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands, and binding partners for 'orphan' receptors. New PPIs include proteins expressed on multiple cell types, and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance, and may offer new therapeutic drug targets.

opencc-by-4.0Mar 2024View details →

Datasets generated for the manuscript: The basement membrane regulates the cellular localization and the cytoplasmic interactome of Yes-Associated Protein (YAP) in mammary epithelial cells

File proteinGroups-CoIP-Yap1: Dataset of co-Immunoprecipitation followed of Yes-associated protein (YAP) followed by proteomics to identify YAP interactants. File Gene_set_file_YAP: Gene sets used for gene set enrichment analysis. Files enrichr_x: output of the EnrichR tool

opencc-by-4.0Sep 2024View details →

Cross-species interactome analysis uncovers a conserved selective autophagy mechanism for protein quality control in plants

This is all the source data associated with the manuscript which has the same title with this dataset. The abstract and the authors of the manuscripts are below: Cross-species interactome analysis uncovers a conserved selective autophagy mechanism for protein quality control in plants   Víctor Sánchez de Medina Hernández1,2*, Marintia Mayola Nava García1,2*, Marion Clavel1,3, Ranjith K. Papareddy1, Veselin I. Andreev1, Varsha Mathur1, Azadeh Mohseni1,4, Marta García-León1, Peng Gao1, Juan Carlos de la Concepción1, Lorenzo Picchianti1, Nenad Grujic1, Roksolana Kobylinska1, Alibek Abdrakhmanov1,2, Héloïse Duvergé1, Gaurav Anand5, Nils Leibrock1,4, Anita Bianchi1, Margot Raffeiner6, Timothy Scott Crawford7, Luca Argirò1, Mateusz Matuszkiewicz1,8, Cheuk-Ling Wun1, Jakob Valdbjørn Kanne9, Anton Meinhart10, Elisabeth Roitinger1, Isabel Bäurle7, Byung Ho Kang11, Morten Petersen9, Suayib Üstün6, Yogesh Kulathu5, Tim Clausen10, Silvia Ramundo1, Yasin Dagdas1 1 Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria. 2 Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, A-1030, Vienna, Austria. 3 Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany. 4 Department of Applied Genetics and Cell Biology, Institute of Molecular Plant Biology, BOKU University, Vienna, Austria. 5 MRC Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, UK. 6 Faculty of Biology & Biotechnology, Ruhr-University of Bochum, 44780 Bochum, Germany. 7 Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany. 8 Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences, Warsaw, Poland. 9 Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark. 10 Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria. 11 School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.   *These authors contributed equally to this work   Correspondence: Yasin Dagdas, <a href="mailto:yasin.dagdas@gmi.oeaw.ac.at">yasin.dagdas@gmi.oeaw.ac.at</a>   Abstract Selective autophagy is a fundamental protein quality control pathway that safeguards proteostasis by degrading damaged or surplus cellular components, particularly under stress. This process is orchestrated by selective autophagy receptors (SARs) that direct specific cargo for degradation. While significant strides have been made in understanding the molecular framework of selective autophagy, the diversity of SAR repertoires across species remain largely unexplored. Through a comparative interactome analysis across five model organisms, we identified a suite of conserved and lineage-specific SAR candidates. Among these, we validated CESAR as a conserved SAR critical for proteostasis under proteotoxic stress. CESAR specifically facilitates the degradation of hydrophobic, ubiquitinated protein aggregates and is indispensable for heat stress tolerance. Our study offers a rich resource for SAR discovery and positions CESAR as a pivotal regulator of proteostasis, with broad implications for improving stress resilience in plants.

opencc-by-4.0Oct 2022View details →

Dynamic interactome of the MHC I peptide loading complex in human dendritic cells - Source Ib

Source data underlying MS data set (Fig.1). Datafile comprises MaxQuant output files.

publicJul 2024View details →

ZAP affects Zika virus RNA interactome - Table S1-ChIRP-MS data

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publicMar 2021View details →

Neuronal dystroglycan interactome

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publicJul 2024View details →

ZAP affects Zika virus RNA interactome- supplemental datasets 1 and 2

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publicSep 2020View details →

A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions

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opencc-zeroDec 2014View details →

Data from: Auxotrophy and intra-population complementary in the 'interactome' of a cultivated freshwater model community

Microorganisms are usually studied either in highly complex natural communities or in isolation as monoclonal model populations that we manage to grow in the laboratory. Here, we uncover the biology of some of the most common and yet-uncultured bacteria in freshwater environments using a mixed culture from Lake Grosse Fuchskuhle. From a single shotgun metagenome of a freshwater mixed culture of low complexity, we recovered four high-quality metagenome-assembled genomes (MAGs) for metabolic reconstruction. This analysis revealed the metabolic interconnectedness and niche partitioning of these naturally dominant bacteria. In particular, vitamin- and amino acid biosynthetic pathways were distributed unequally with a member of Crenarchaeota most likely being the sole producer of vitamin B12 in the mixed culture. Using coverage-based partitioning of the genes recovered from a single MAG intrapopulation metabolic complementarity was revealed pointing to 'social' interactions for the common good of populations dominating freshwater plankton. As such, our MAGs highlight the power of mixed cultures to extract naturally occurring 'interactomes' and to overcome our inability to isolate and grow the microbes dominating in nature.

opencc-zeroJul 2019View details →

Data from: Proximity labeling reveals novel interactomes in live Drosophila tissue

Gametogenesis is dependent on intercellular communication facilitated by stable intercellular bridges connecting developing germ cells. During Drosophila oogenesis, intercellular bridges (referred to as ring canals) have a dynamic actin cytoskeleton that drives their expansion to a diameter of 10μm. While multiple proteins have been identified as components of ring canals (RCs), we lack a basic understanding of how RC proteins interact together to form and regulate the RC cytoskeleton. We optimized a procedure for proximity-dependent biotinylation in live tissue using the APEX enzyme to interrogate the RC interactome. APEX was fused to four different RC components (RC-APEX baits) and 55 unique high-confidence preys were identified. The RC-APEX baits produced almost entirely distinct interactomes that included both known RC proteins as well as uncharacterized proteins. The proximity ligation assay was used to validate close-proximity interactions between the RC-APEX baits and their respective preys. Further, an RNAi screen revealed functional roles for several high-confidence prey genes in RC biology. These findings highlight the utility of enzyme-catalyzed proximity labeling for protein interactome analysis in live tissue and expand our understanding of RC biology.

opencc-zeroDec 2013View details →

Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

opencc-zeroDec 2011View details →

Data from: Looking at cerebellar malformations through text-mined interactomes of mice and humans

We have generated and made publicly available two very large networks of molecular interactions: 49,493 mouse-specific and 52,518 human-specific interactions. These networks were generated through automated analysis of 368,331 full-text research articles and 8,039,972 article abstracts from the PubMed database, using the GeneWays system. Our networks cover a wide spectrum of molecular interactions, such as bind, phosphorylate, glycosylate, and activate; 207 of these interaction types occur more than 1,000 times in our unfiltered, multi-species data set. Because mouse and human genes are linked through an orthological relationship, human and mouse networks are amenable to straightforward, joint computational analysis. Using our newly generated networks and known associations between mouse genes and cerebellar malformation phenotypes, we predicted a number of new associations between genes and five cerebellar phenotypes (small cerebellum, absent cerebellum, cerebellar degeneration, abnormal foliation, and abnormal vermis). Using a battery of statistical tests, we showed that genes that are associated with cerebellar phenotypes tend to form compact network clusters. Further, we observed that cerebellar malformation phenotypes tend to be associated with highly connected genes. This tendency was stronger for developmental phenotypes and weaker for cerebellar degeneration.

zenodo28/100

Interactome Analysis: miRNA-gene networks from Austrolebias charrua exposed to Roundup (Cytoscape files .cys)

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opencc-by-4.0Mar 2024View details →

zenodo28/100

Data for "Proteome-wide prediction of mode of inheritance and molecular mechanism underlying genetic diseases using structural interactomics"

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opencc-by-4.0Oct 2024View details →

zenodo28/100

Multiscale Interactome Data -- Revised

Original GitHub Repository: https://github.com/snap-stanford/multiscale-interactome Forked GitHub Repository: https://github.com/callahantiff/multiscale-interactome/tree/development     This repository stores a revised version of the original data that was used in the publication titled: <a href="https://www.biorxiv.org/content/10.1101/2020.04.30.069690v3">Identification of disease treatment mechanisms through the multiscale interactome</a>. As described in the original GitHub's Readme, the original data can be downloaded directly from: <a href="http://snap.stanford.edu/multiscale-interactome/data/data.tar.gz">http://snap.stanford.edu/multiscale-interactome/data/data.tar.gz</a>.    Description of Original Data Drug-Protein (n=8,568): <ul> <li>Source(s): <ul> <li><a href="https://go.drugbank.com/">DrugBank</a> (<code>v5.1.1; 2018</code>; <code>drugbank_approved_target_uniprot_links.csv</code>)</li> <li><a href="https://clue.io/repurposing">Drug Repurposing Hub</a> (<code>September 2018</code>)</li> </ul> </li> <li>Processing: map Uniprot to Entrez gene using <a href="https://www.genenames.org/">HUGO</a> (October 2018) and drug ids to DrugBank ids</li> <li>Filtering: Filter proteins to only keep those that appear in the Protein-Protein edge set.</li> </ul> Disease-Protein (n=25,212): <ul> <li>Source(s): <a href="https://www.disgenet.org/">DisGeNet</a> (<code>March 2018</code>)</li> <li>Filtering: only keep only expert curated gene-disease associations. (1) exclude disease-gene relationships that are inferred, based on orthology, animal models, or literature mining; (2) remove therapeutic disease-gene associations; and (3) remove disease-gene relationships that do not appear in the Protein-Protein edge set.</li> </ul> Protein-Protein (n=387,626): <ul> <li>Source(s): <ul> <li><a href="https://thebiogrid.org/">BioGRID</a> (<code>v3.5.178</code>; <code>November 2019</code>; <code>BIOGRID-ORGANISM-Homo_sapiens-3.5.178.tab</code>)</li> <li><a href="https://dip.doe-mbi.ucla.edu/dip/Main.cgi">Database of Interacting Proteins</a> (<code>February 2017</code>; <code>Hsapi20170205.txt</code>). Include all experimental methods</li> <li><a href="http://www.interactome-atlas.org/">Human Reference Protein Interactome Mapping Project</a>. Four networks derived from high-throughput yeast two hybrid assays.</li> <li>Menche 2015 (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Abstract&list_uids=25700523">PMID:25700523</a>). Compiles different types of physical protein-protein interactions.</li> </ul> </li> <li>Processing: Map protein ids to Entrez gene ids using HUGO (sources 1-2 only)</li> <li>Filtering: only human proteins with physical interactions and direct experimental evidence (no genetic/indirect)</li> </ul> Protein-Biological Process (n=34,777): Source(s): <a href="http://geneontology.org/">Gene Ontology</a> (human; <code>February 2018</code>) <ul> <li>Processing: use master ids provided by GOATOOLS (<code>v0.8.4</code>)</li> <li>Filtering: only allow: EXP, IDA, IMP, IGI, HTP, HDA, HMP, HGI. Exclude any protein-biological functions inferred from: physical interactions, gene expression patterns, phylogenetically inferred annotations or computational analyses, automatic annotations (i.e., based on author statements, curator inference, electronic annotation), and those with no biological data</li> </ul> Biological Process-Biological Process (n=22,545): <ul> <li>Source(s): <a href="http://geneontology.org/">Gene Ontology</a> (human; <code>February 2018</code>) + Gene Ontology Plus (human version; <code>July 2020</code>)</li> <li>Filtering: Allow following relationship types: regulates, positively regulates, negatively regulates, part of, is a. Only consider BPs associated with at least one drug target or disease protein (directly or through children)</li> </ul>     ⚠️ Updates to Original Implementation ⚠️ Modifications to Original Data and Code <ul> <li>Ensured every entry had a valid identifier and label</li> <li>Reconciled duplicate gene entries (i.e., gene identifiers that had been merged)</li> <li>Changed genes are listed in: <code>resources/data/updated_gene_identifiers.xlsx</code></li> </ul>

opencc-by-4.0Apr 2023View details →

ClinicalTrials.gov28/100

Neuro-immune Interactome in Parkinson's Disease

ClinicalTrials.gov study NCT07026929. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →