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266 results for “Interactome”

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zenodo44/100

Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia

This repository contains data described in this study: Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia. Ennis S et. al., iScience, 2023. DOI: <a href="https://doi.org/10.1016/j.isci.2023.106943">10.1016/j.isci.2023.106943</a>.   Contents: <ul> <li>bone_marrow.h5ad  - AnnData file with the integrated dataset</li> <li>ref_model_final.tar.gz - A zipped folder containing the scVI model for the integrated dataset</li> <li>Supplemental_material.tar.gz - A zipped folder containing the supplemental figures and tables from the publication</li> </ul>

opencc-by-4.0Jun 2023View details →

zenodo44/100

The proximity interactome of the peach-potato aphid (Myzus persicae) cathepsin B in Arabidopsis thaliana

Introduction In agriculture, the peach-potato aphid Myzus persicae (Sulzer) has one of the broadest host ranges among insects and cause devastating crop losses worldwide (CABI, 2022). They are highly adaptable, displaying a wide range of plastic responses to environmental cues, including the ability to develop as either winged or wingless forms and to reproduce through either asexual or sexual means (Brisson, 2010; Ogawa and Miura, 2014; Grantham and Brisson, 2018). Remarkably, M. persicae differentially regulate the transcription of certain gene clusters to facilitate colonization of diverse plant species (Mathers et al., 2017; Chen et al., 2020). Among these gene clusters are members of the cysteine protease family, cathepsin B (CathB). Host responsive CathB genes are organized in tandemly repeated clusters in the M. persicae genome and belong to a recently expanded clade in phylogeny (Mathers et al., 2017). They are upregulated when aphids feed on Arabidopsis thaliana and Brassica rapa and knock down of their expression using RNA interference reduces aphid reproduction on A. thaliana (Chen et al., 2020). Intriguingly, peptides corresponding to CathB proteins are detected in M. persicae oral secretion (OS), indicating that at least some CathB proteins are directly delivered into plant cells during aphid feeding (Guo et al., 2020; Liu et al., 2024). Among M. persicae CathB proteins, CathB6 is most highly expressed in aphids on A. thaliana (Chen et al., 2020) and most abundant in M. persicae OS (Liu et al., 2024). To identify the potential plant targets of M. persicae CathB, we optimized the TurboID-based proximity labelling and MS (PL-MS) protocol (Fig. 1). As a first step, we generated stable transgenic A. thaliana lines producing GFP or CathB6 as C-terminal TurboID-3×FLAG fusions (GFP-TurboID or CathB6-TurboID). Seedlings of these plants were treated with biotin followed by affinity capture with streptavidin beads (Fig. 2A). Enrichment of biotinylated proteins was confirmed by western blotting (Fig. 2B), followed by nanoLC-MS/MS analyses. Principal component analysis (PCA) of the MS data showed that the three CathB6-TurboID samples were grouped together, separately from three GFP-TurboID samples (Fig. 2C). Furthermore, MA plot confirmed that the CathB6-TurboID and GFP-TurboID samples are distinct (Fig. 2D). From the complete dataset, 267 A. thaliana proteins exhibited statistically significant enrichment (p-value < 0.05) of more than 2-fold and were consistently identified in at least two replicates of the CathB6-TurboID samples compared to the GFP-TurboID controls (Fig. 2E, Table 1). This compares to 223 proteins in the GFP-TurboID samples versus CathB6-TurboID samples (Fig. 2E, Table 1). Additionally, we identified 20 unique peptides corresponding to CathB6 in the CathB6-TurboID samples and 19 unique peptides corresponding to GFP in the GFP-TurboID samples (Table 1). These data suggest that this PL-MS protocol worked dnd identified genuine interactors of CathB6. Together, this dataset identifies 267 potential plant interactors of aphid CathB6, which may contribute to CathB6 modulation of A. thaliana plant for colonization. Further mechanistic studies should be done to characterize if these potential interactors are involved and how the relevant pathways are affected after CathB delivery through aphid feeding.   Materials and Methods Plasmid construction For the construction of plasmids producing CathB6-TurboID-3×FLAG, the coding sequences corresponding to the catalytic domain (without signal peptide and prodomain regions) of CathB6 (Arg61-Asn338) and TurboID-3×FLAG were separately amplified. Then, the two fragments were connected using overlap PCR (Nelson and Fitch, 2011). After cloning of the sequence corresponding to the CathB6-TurboID-3×FLAG fragment into the pJET vector and sequencing, CathB6-TurboID-3×FLAG was amplified with primers containing attB extensions and cloned into the pDONOR207 vector, followed by the ligation to Gateway destination vector pB7WG2 containing a 35S promoter. Similar cloning methods were used for construction of GFP-TurboID-3×FLAG. Plant transformation The constructed plasmids were introduced into Agrobacterium tumefaciens strain GV3101, and the cultures were grown on plates at 28 °C for 24–48 hrs. Then, positive colonies were identified via PCR using plasmids extracted from overnight liquid cultures and gene-specific primers. Positive colonies were grown at 28 °C in liquid cultures and transformed into A. thaliana Col-0 plants using the floral dipping method (Bechtold, 1993). Transgenic seeds were harvested and selected on Murashige and Skoog (MS) medium supplemented with 20 μg/mL phosphinothricin (BASTA) and screened for ratio of 3:1 alive/dead segregation. After screening for two or three generations, transgenic plants were deemed to harbor single homozygous transgenes and were used for proximity labeling once germinated seeds achieved a 100% survival rate. Proximity labelling Seeds of A. thaliana plants stably expressing GFP-TurboID-3×FLAG or CathB6-TurboID-3×FLAG were sowed on ½ MS plates containing 1.0% sucrose and 0.3% phytagel and placed under long-day condition (16 h light/8 h dark) at 22 °C. After 10 days, 2.5 g seedlings were collected and submerged in 50 µM biotin solution for 4 hrs at RT. Afterwards, seedlings were rinsed with ice-cold MilliQ water for 5 times. After removing excess liquid with paper towel, seedings were ground with pestle, mortar and nitrogen to a fine powder. Protein extraction was performed in 5 mL of extraction buffer [150 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 10% Glycerol, 10 mM DTT, 0.4% Nonidet-40, 0.1% (w/v) Deoxycholic acid, 2% (w/v) PVPP, 1 tablet of cOmplete protease Inhibitor cocktail (Roche, Catalog number 10697498001)] and incubation on a rotor wheel at 4 °C for 30 min, followed by centrifugation of the tubes at 5000 g for 15 min to remove the cell debris. The upper soluble fraction was then run through the Zeba Spin Desalting Column (Thermo Fisher Scientific, Catalog number 89893) to remove excess biotin from the lysates. Fifty (50) µL of desalted lystate was used as input for western blot analysis, while the rest of the desalted lysate was incubated with High Capacity Streptavidin Agarose Resin (Thermo Fisher Scientific, Catalog number 20361) on a rotor wheel at 4 °C overnight. The next day, Streptavidin beads were sequentially washed once in 1 mL Buffer 1 (2% SDS in water), once in 1 mL Buffer 2 [150 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 10% Glycerol, 0.1% (w/v) Deoxycholic acid (w/v), 1% Triton X-100], once in buffer 3 [10 mM Tris-HCl (pH 7.4), 250 mM LiCl, 1 mM EDTA, 0.1% (w/v) Deoxycholic acid, 1% (v/v) NP40], twice in Buffer 4 [50mM Tris-HCl (pH 7.5)], and six times in Buffer 5 (50mM ammonium bicarbonate, pH 8.0). Finally, the streptavidin beads were resuspended in 200 µL of 50 mM ammonium bicarbonate. For quality control of the TurboID immunoprecipitation, 10% (20 µL) of the suspension was taken out for Western blot analysis, and the remaining bead suspension was flash-frozen in liquid nitrogen and stored at -80 °C and submitted to nano LC-MS/MS analysis. For western blot analysis, 20 µL of suspended streptavidin beads in washing buffer 5 were added to 10 µL of 4× LDS Sample Loading Buffer, 10 mM DTT and 2 mM biotin, and boiled for 10 min. Samples were loaded onto 12% SDS-PAGE gels (Invitrogen) and transferred to 0.22 μm PVDF membranes using the Bio-Rad mini-PROTEAN Electrophoresis system. Membranes were hybridized with Streptavidin-HRP. NanoLC-MS/MS  Biotinylated proteins enriched with streptavidin beads were processed with trypsin via on bead digestion. The beads were washed in water and resuspended in of 1.5% sodium deoxycholate (SDC; Merck) in 0.2 M EPPS-buffer (Merck) to 50% bead slurry vol/vol, pH 8.5 and vortexed under heating. Cysteine residues were reduced with dithiothreitol, alkylated with iodoacetamide, and the proteins digested with trypsin in the SDC buffer according to standard procedures for 8 hrs. The beads were then pelleted by centrifugation and the supernatant was collected for SDC precipitation by adding trifluoroacetic acid (TFA) to a final concentration of 0.2%. The clear supernatant was subjected to C18 SPE using home-made stage tips with C18 Reprosil_pur 120, 5 µm (Dr. Maisch GmbH, Germany). Aliquots were analyzed by nano LC-MS/MS on an Orbitrap Eclipse™ Tribrid™ mass spectrometer equipped with a FAIMS Pro Duo interface coupled to an UltiMate® 3000 RSLC nano LC system (Thermo Fisher Scientific, Hemel Hempstead, UK). The samples were loaded onto a trap cartridge (PepMap™ Neo Trap Cartridge, C18, 5um, 0.3x5mm, Thermo) with 0.1% TFA at 15 µl min-1 for 3 min. The trap column was then switched in-line with the analytical column (Aurora Frontier TS, 60 cm nanoflow UHPLC column, ID 75 µm, reversed phase C18, 1.7 µm, 120 Å; IonOpticks, Fitzroy, Australia) for separation at 55°C using the following gradient of solvents: A (water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid) at a flow rate of 0.26 µl min-1 : 0-3 min 1% B (parallel to trapping); 3-10 min increase B (curve 4) to 8%; 10-102 min linear increase B to 48; followed by a ramp to 99% B and re-equilibration to 0% B. Total runtime was 140 min. Mass spectrometry data were acquired between 10 and 110 min with the FAIMS device set to three compensation voltages (-35V, -50V, -65V) at standard resolution for 1.0 s each with the following MS settings in positive ion mode: OT resolution 120K, profile mode, mass range m/z 300-1600, normalized AGC target 100%, max inject time 50 ms; MS2 in IT Turbo mode: quadrupole isolation window 1 Da, charge states 2-5, threshold 1e4, HCD CE = 30, AGC target standard, max. injection time dynamic, dynamic exclusion 1 count for 15 s with mass tolerance of ±10 ppm, one charge state per precursor only. The mass spectrometry raw data were processed and quantified in Proteome Discoverer 3.1 (PD3.1) (Thermo) using the search engine CHIMERYS (MSAID, Munich, Germany); all mentioned tools of the following workflow are nodes of the proprietary Proteome Discoverer (PD) software. The A. thaliana protein sequence database (TAIR10, 35,386 entries, from 14/12/2010), the two sequences of the used TurboID constructs, and the MaxQuant contaminants database (240812, 246 entries) were imported into PD adding a reversed sequence database for decoy searches. The database search was performed using the search engine CHIMERYS (MSAID, Munich, Germany). The processing workflow started with spectrum recalibration, Minora Feature Detection with min. trace length 5, S/N 2.5, PSM confidence high, and Top N Peak Filter with 20 peaks per 100 Da. For CHIMERYS, the inferys_3.0.0_fragmentation prediction model with FDR targets 0.01 (strict) and 0.05 (relaxed), a fragment tolerance of 0.3 Da, enzyme trypsin with 2 missed cleavages, variable modification oxidation (M), fixed modification carbamidomethyl (C) were used. The consensus workflow in the PD3.1 software was used to evaluate the peptide identifications and to measure the abundances of the peptides based on the LC-peak intensities. For chromatographic alignment and feature mapping, a retention time tolerance of 2 min, a mass tolerance of 1 ppm, and an S/N threshold of 5 were used. For quantification, three replicates per condition were measured. In PD3.1, the following parameters were used for ratio calculation: normalization on total peptide abundances, protein abundance-based ratio calculation using the Top3 most abundant peptides, missing values imputation by low abundance resampling, hypothesis testing by t-test (background based), adjusted p-value calculation by BH-method.  The results were exported into a Microsoft Excel table including data for protein abundances, ratios, p-values, number of peptides, protein coverage, the CHIMERYS identification score and other important values.   Data availability statement The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier with the dataset identifier PXD057789 and 10.6019/PXD057789.   Acknowledgements  This research was funded by UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) grants to SAH (BB/V008544/1 and BB/R009481/1). Additional Support is provided by the BBSRC Institute Strategy Programmes (BBS/E/J/000PR9797 and BBS/E/JI/230001B) awarded to the JIC. The JIC is grant-aided by the John Innes Foundation.   Conflicts of Interest  The authors declare that no conflicts of interest exist.   Legends of figures and tables Figure 1. Principle of CathB6-TurboID based proximity labelling with MS (PL-MS). The TurboID biotin ligase (TurboID) is fused to C-terminus of CathB6. Exogenous addition of biotin (yellow stars) biotinylates proteins in the proximity of CathB6-TurboID fusion protein, whereas distal proteins are not biotinylated. The biotinylated proteins are captured by incubating total proteins extracts with streptavidin beads. Peptides derived from biotinylated proteins, most of which are in the proximity of CathB6-TurboID, are detected by nanoLC-MS. Fig. 2. Sample preparation and quantification for CathB6-TurboID interactome in A. thaliana. (A) Sample preparation working flow for TurboID-based proximity labeling. GFP-TurboID and CathB6-TurboID seedlings were treated with 50 µM biotin for 4 hrs at room temperature. (B) Visualization on western blots of biotinylated proteins detected after desalting step (input) and 12 wash steps of Streptavidin beads (Streptavidin IP) as per workflow shown in (A). (C) Principal component analysis (PCA) of three replicates of GFP-TurboID and CathB6-TurboID samples. (D) MA plot of three replicates of GFP-TurboID and CathB6-TurboID samples. (E) Venn diagrams showing the overlap of proteins identified in three biological replicates of GFP-TurboID (left) and CathB6-TurboID (right) upon a fold-change of CathB6-TurboID/GFP-TurboID > 2, n = 267.  Table 1. Full list of proteins detected from CathB6-TurboID PL-MS.     References Bechtold, N. (1993). In planta Agrobacterium-mediated gene transfer by infiltration of adult Arabidopsis thaliana plants. CR Acad. Sci. Paris, Life Sci. 316, 1194-1199. Brisson, J.A. (2010). Aphid wing dimorphisms: linking environmental and genetic control of trait variation. Philos Trans R Soc Lond B Biol Sci 365, 605-616. CABI, C.f.A.a.B.I. (2022). Myzus persicae (green peach aphid). CABI Compendium. Chen, Y., Singh, A., Kaithakottil, G.G., Mathers, T.C., Gravino, M., Mugford, S.T., van Oosterhout, C., Swarbreck, D., and Hogenhout, S.A. (2020). An aphid RNA transcript migrates systemically within plants and is a virulence factor. Proc Natl Acad Sci U S A 117, 12763-12771. Grantham, M.E., and Brisson, J.A. (2018). Extensive Differential Splicing Underlies Phenotypically Plastic Aphid Morphs. Mol Biol Evol 35, 1934-1946. Guo, H., Zhang, Y., Tong, J., Ge, P., Wang, Q., Zhao, Z., Zhu-Salzman, K., Hogenhout, S.A., Ge, F., and Sun, Y. (2020). An Aphid-Secreted Salivary Protease Activates Plant Defense in Phloem. Curr Biol 30, 4826-4836.e4827. Liu, Q., Goldberg, J.K., Mugford, S.T., Saalbach, G., Martins, C., Singh, A., Kaithakotti, G.G., Swarbreck, D., and Hogenhout, S.A. (2024). The salivary proteome of the green peach aphid/peach-potato aphid (Myzus persicae) (Sulzer, 1776) (Hemiptera, Aphididae) (Zenodo). Mathers, T.C., Chen, Y., Kaithakottil, G., Legeai, F., Mugford, S.T., Baa-Puyoulet, P., Bretaudeau, A., Clavijo, B., Colella, S., Collin, O., Dalmay, T., Derrien, T., Feng, H., Gabaldón, T., Jordan, A., Julca, I., Kettles, G.J., Kowitwanich, K., Lavenier, D., Lenzi, P., Lopez-Gomollon, S., Loska, D., Mapleson, D., Maumus, F., Moxon, S., Price, D.R., Sugio, A., van Munster, M., Uzest, M., Waite, D., Jander, G., Tagu, D., Wilson, A.C., van Oosterhout, C., Swarbreck, D., and Hogenhout, S.A.(2017). Rapid transcriptional plasticity of duplicated gene clusters enables a clonally reproducing aphid to colonise diverse plant species. Genome Biol 18, 27. Nelson, M.D., and Fitch, D.H. (2011). Overlap extension PCR: an efficient method for transgene construction. Methods Mol Biol 772, 459-470. Ogawa, K., and Miura, T. (2014). Aphid polyphenisms: trans-generational developmental regulation through viviparity. Front Physiol 5, 1.

opencc-by-4.0Nov 2024View details →

zenodo44/100

Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"

Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields. Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample. Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.

opencc-by-4.0Jul 2021View details →

zenodo44/100

Literature Curated PPIs from "Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay"

This dataset includes the literature curated protein-protein interactions supporting the manuscript titled 'Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay'.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Raw NanoLuc and NanoBiT Data from "Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay"

This dataset includes raw NanoLuc and NanoBiT data supporting the manuscript titled 'Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay'.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Analyzed NanoBiT Data from "Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay"

This dataset includes analyzed NanoBiT data supporting the manuscript titled 'Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay'.

opencc-by-4.0Oct 2024View details →

zenodo40/100

Towards a reproducible interactome: semantic-based detection of redundancies to unify protein-protein interaction databases

Protein-protein interactions (PPIs) play an ubiquitous and fundamental role in all biological processes. Information on PPIs described in the literature is annotated and made available by several protein-interaction databases. Because most databases have their own curation rules and priorities, they often annotate overlapping sets of publications, which leads to redundancies. We developed a semantic-based approach which enables to accurately detect redundancies within PPI datasets from multiple databases. We applied this approach to assemble a "reproducible interactome", with PPIs supported by at least two methods or publications.

opencc-by-4.0Oct 2021View details →

zenodo40/100

Source data for analysis of super-enhancer interactomes v1

Super-enhancer interactomes from single-cells link clustering and transcription Derek Le, Antonina Hafner, Sadhana Gaddam, Kevin Wang, Alistair Boettiger This Zenodo repository contains data files associated with our analysis. Software for processing the data is available in the associated github repository:  https://github.com/BoettigerLab/SEclustering-2024 Additional information can be found in the associated manuscript, currently in preparation -- once it is posted on BioRxiv, it will be linked here.  This deposition currently includes 1) SuperEnhancerLoci.xlsx - a master data table linking the genomic sequence barcode data from the corrected tables (described below) to the corresponding super-enhancer and their genomic coordinates in mm10. 2) Corrected_Data_Tables_by_FOV.zip -- contains drift corrected and chromatically corrected x,y,z coordinates and cellular barcode data to track cell type and coordinate barcode data to identify genomic sequences.  3) Processed_Seq_Data.zip -- re-processed sequencing based data used in this study. 4) FOF-CT_Spot_tables.zip -- draft versions of the 4DN FOF-CT formatted data-standard spot tables.  See data format description here: https://fish-omics-format.readthedocs.io/en/latest/ 5) Probe_Sequences.zip -- fasta files, bed files, and codebook tables for the RNA and DNA probe sequences used in this study.

opencc-by-4.0Apr 2024View details →

zenodo40/100

The solute carrier superfamily interactome

Solute carrier (SLC) transporters form a protein superfamily that enables transmembrane transport of a wide range of substrates including nutrients, vitamins, ions and drugs. There are about 450 different SLCs, residing in cellular and a variety of subcellular membranes, and playing a vital role in cellular homeostasis and metabolic regulation. Loss-of-function of an unusually high proportion of SLC transporters is genetically associated with a plethora of human diseases, making them a rapidly emerging but challenging drug target class. Knowledge of the protein environment of transporters may elucidate the molecular basis for their functional integration with metabolic and cellular pathways and help conceive pharmacological interventions based on modulating proteostatic regulations, including functional restoration. We aimed at obtaining a global survey of the SLC protein interaction landscape by using a robust, standardized, one-step interaction proteomics protocol. We mapped the protein-protein interactions of 396 SLCs, covering thousands of novel relationships, to be assessed individually in the future using transporter-specific assays. Network and clustering analysis allowed identification of proteins likely to affect the proteostatic regulation of transporters. We employed a streamlined functional assessment process based on RNA interference (RNAi)-genetic perturbation of cells and measurement of protein stability and localization, including some transport assays, to positively validate the functional role of over 50 new interactions. As an example, we detail the role of a SLC16A6 phospho-degron in the recruitment of a SKP1-CUL1-F-box protein E3 ligase, and the contributions of PDZ-domain proteins LIN7C and MPP1 to the subcellular localization of SLC43A2. Overall, our work provides insights into the complex molecular network of membrane transporters while offering the largest experimental mass spectrometry-derived membrane protein data set to date as a resource for the scientific community.

opencc-by-4.0Sep 2024View details →

zenodo36/100

Systematic reanalysis of co-fractionation mass spectrometry data: predicted interactomes

This upload contains predicted interactomes for 27 species or clades: 17 individual organisms with at least three published CF-MS experiments, and 9 phylogenetic groupings of those organisms.  The following individual organisms are represented: <ul> <li>Arabidopsis thaliana</li> <li>Brassica oleracea</li> <li>Caenorhabditis elegans</li> <li>Chaetomium thermophilum</li> <li>Chlamydomonas reinhardtii</li> <li>Dictyostelium discoideum</li> <li>Drosophila melanogaster</li> <li>Nematostella vectensis</li> <li>Homo sapiens</li> <li>Mus musculus</li> <li>Oryza sativa</li> <li>Plasmodium berghei</li> <li>Plasmodium falciparum</li> <li>Plasmodium knowlesi</li> <li>Strongylocentrotus purpuratus</li> <li>Triticum aestivum</li> <li>Trypanosoma brucei</li> <li>Xenopus laevis</li> </ul> The following clades are also represented: <ul> <li>BOP clade</li> <li>Deuterostomia</li> <li>Eucharontoglires</li> <li>Eukaryota</li> <li>Mesangiospermae</li> <li>Opiskothonta</li> <li>Plasmodium</li> <li>Tetrapoda</li> <li>Viridaplantae</li> </ul> The interactomes are provided in two forms. Files in the 'All interactions' directory include the complete classifier scores for every possible protein pair (sorted in descending order). Files in the '50% precision' directory include only those interactions identified above 50% precision, for convenience.  Proteins were mapped to orthogroups using eggNOG. Maps from eggNOG orthogroups to UniProt accessions are available from https://github.com/skinnider/CF-MS-analysis/tree/master/data/resources/eggNOG. The phylogenetic tree used to group species into clades is also available from https://github.com/skinnider/CF-MS-analysis/tree/master/data/resources/TimeTree/species.nwk. The third and final directory, 'Human', contains the consensus CF-MS human interactome, in which proteins were merged across 46 human experiments by their gene names, rather than based on eggNOG orthogroups. The directory contains both complete classifier scores (file 'classifier-scores.tsv.gz') and the consensus CF-MS interactome, at 50% precision (file 'CF-MS-interactome.tsv').

opencc-by-4.0Nov 2020View details →

zenodo36/100

"Global Functional Genomics Reveals GRK5 as a Therapeutic Target for Cystic Fibrosis", CFTR interactomes

A tidy selection of published CFTR interactomes (or CFTR-related omics datasets) Collects data from the original online sources, converts gene/protein identifiers into a standard tidy format and updates the identifiers. The Uniprot ID is taken as reference and all other identifiers are genereated from them. This causes some datasets to end up with less genes/proteins than reported. Datasets included <ul> <li>Botelho (2021) - CFTR traffic regulators <ul> <li>Botelho et al (2022), submitted</li> </ul> </li> <li>Pankow (2015) - CFTR interactome <ul> <li>Pankow et al (2015) deltaF508 CFTR interactome remodelling promotes rescue of cystic fibrosis. Nature. 528, 510--516. <a href="https://doi.org/10.1038/nature15729">https://doi.org/10.1038/nature15729</a></li> </ul> </li> <li>Canato (2018) - CFTR interactome <ul> <li>Canato et al (2018) Proteomic interaction profiling reveals KIFC1 as a factor involved in early targeting of F508del-CFTR to degradation. Cell Mol Life Sci. 75(24):4495-4509. <a href="https://doi.org/10.1007/s00018-018-2896-7">https://doi.org/10.1007/s00018-018-2896-7</a></li> </ul> </li> <li>Santos (2019) - CFTR interactome <ul> <li>Santos et al (2019) Folding Status Is Determinant over Traffic-Competence in Defining CFTR Interactors in the Endoplasmic Reticulum. Cells. 8(4):353. <a href="https://doi.org/10.3390/cells8040353">https://doi.org/10.3390/cells8040353</a></li> </ul> </li> <li>Hutt (2018) - CFTR interactome <ul> <li>Hutt et al (2018) A Proteomic Variant Approach (ProVarA) for Personalized Medicine of Inherited and Somatic Disease. J Mol Biol. 430: 2951-2973. <a href="https://doi.org/10.1016/j.jmb.2018.06.017">https://doi.org/10.1016/j.jmb.2018.06.017</a></li> </ul> </li> <li>Rauniyar (2014) - CFTR proteome <ul> <li>Rauniyar et al (2014) Quantitative Proteomic Profiling Reveals Differentially Regulated Proteins in Cystic Fibrosis Cells. J Proteome Res. 13(11): 4668-4675. <a href="https://doi.org/10.1021/pr500370g">https://doi.org/10.1021/pr500370g</a></li> </ul> </li> <li>Almaça (2013) - ENaC regulome <ul> <li>Almaça et al (2013) High-content siRNA screen reveals global ENaC regulators and potential cystic fibrosis therapy targets. Cell. 154(6):1390-400. <a href="https://doi.org/10.1016/j.cell.2013.08.045">https://doi.org/10.1016/j.cell.2013.08.045</a></li> </ul> </li> <li>Tomati (2018) - CFTR regulome <ul> <li>Tomati et al (2018) High-throughput screening identifies FAU protein as a regulator of mutant cystic fibrosis transmembrane conductance regulator channel. J Biol Chem. 293(4):1203-1217. <a href="https://doi.org/10.1074/jbc.m117.816595">https://doi.org/10.1074/jbc.m117.816595</a></li> </ul> </li> <li>Simpson (2012) - Secretome <ul> <li>Simpson et al (2012) Genome-wide RNAi screening identifies human proteins with a regulatory function in the early secretory pathway. Nature Cell Biology. 14, 764-774. <a href="https://doi.org/10.1038/ncb2510">https://doi.org/10.1038/ncb2510</a></li> </ul> </li> <li>Wang (2006) - CFTR interactome <ul> <li>Wang et al (2006) Hsp90 Cochaperone Aha1 Downregulation Rescues Misfolding of CFTR in Cystic Fibrosis. Cell. 127(4):803-815. <a href="https://doi.org/10.1016/j.cell.2006.09.043">https://doi.org/10.1016/j.cell.2006.09.043</a></li> </ul> </li> <li>Reilly (2017) - CFTR interactome <ul> <li>Reilly et al (2017) Targeting the PI3K/Akt/mTOR signalling pathway in Cystic Fibrosis. Sci Rep. 9;7(1):7642. <a href="https://doi.org/10.1038/s41598-017-06588-z">https://doi.org/10.1038/s41598-017-06588-z</a></li> </ul> </li> <li>Gilchrist (2006) - Secretome <ul> <li>Gilchrist et al (2006) Quantitative Proteomics Analysis of the Secretory Pathway. Cell. 127(6):1265-1281. <a href="https://doi.org/10.1016/j.cell.2006.10.036">https://doi.org/10.1016/j.cell.2006.10.036</a></li> </ul> </li> <li>Pankow (2019) - CFTR interactome <ul> <li>Pankow et al (2019) A posttranslational modification code for CFTR maturation is altered in cystic fibrosis. Science Signaling. 12(562):eaan7984. <a href="https://doi.org/10.1126/scisignal.aan7984">https://doi.org/10.1126/scisignal.aan7984</a></li> </ul> </li> <li>Dang (2020) - CF lung disease modifier genes <ul> <li>Dang et al (2020) Mining GWAS and eQTL data for CF lung disease modifiers by gene expression imputation. PLoS One. 15(11):e0239189. <a href="https://doi.org/10.1371/journal.pone.0239189">https://doi.org/10.1371/journal.pone.0239189</a></li> </ul> </li> <li>Hodos (2020) - CF genomic meta-analysis <ul> <li>Hodos et al (2020) Integrative genomic meta-analysis reveals novel molecular insights into cystic fibrosis and deltaF508-CFTR rescue. Sci Rep 10(1):20553. <a href="http://dx.doi.org/10.1038/s41598-020-76347-0">http://dx.doi.org/10.1038/s41598-020-76347-0</a></li> </ul> </li> </ul>

opencc-by-4.0Jun 2022View details →

zenodo36/100

Multi-complex integrative structure determination of the human HDAC1/2 interactome

Histone deacetylases (HDACs) 1 and 2 are Class I HDACs that are members of several transcriptional regulatory complexes including coREST, MIER, NuRD, and SIN3. The interaction of each HDAC within the complex is undefined. Our studies utilize Affinity Purification and Crosslinking Mass Spectrometry to define HDAC1 and 2 interactions. We determined the structures of NuRD, SIN3A, and coREST by integrative structural modeling. Lastly, we characterize MHAP1, previously known as C16orf87, define it as a novel member of the MIER HDAC complex, and also obtain a structural model for an HDAC1:MIER1: MHAP1 complex.

opencc-by-4.0Apr 2024View details →

zenodo36/100

Mitochondrial DNA and RNA interactomes

Protocol for isolation of mitochondrial DNA and RNA interacting proteins and MS data results.

opencc-by-4.0Nov 2024View details →

zenodo36/100

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

Source data underlying MS data set (SFig.2). Datafile comprises MS raw files + MaxQuant output files.

opencc-by-4.0Oct 2022View details →

zenodo36/100

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

Source data underlying Raji cell data set (SFig.3). Datafile comprises MS raw data + MaxQuant output files.

opencc-by-4.0Oct 2022View details →

zenodo36/100

Spatial metatranscriptomics resolves host-bacteria-fungi interactomes, Source Data

Source Data for a publication: Spatial metatranscriptomics resolves host-bacteria-fungi interactomes.  Includes the data sets to generate the results.  Contains five different experiment types: - Pst  bacterial infiltration experiment - Enrichment experiment with different array types - Comparison between SmT vs. Amp-seq - Outdoor-grown leaf experiments - Sterile leaf experiment For each of the experiments are included (if generated, see the README file): - Gene count matrices - Microbial taxa count matrices - Bright field images - Alignment files (Spot files) - Putative microbial reads and related probe information - Data for enrichment analysis - Fluorescent images and corresponding fluorescent values

opencc-by-4.0Nov 2023View details →

zenodo36/100

CFdb: interactomes

This upload contains predicted interactomes for 23 species derived from meta-analysis of co-fractionation mass spectrometry (CF-MS) data. The following individual organisms are represented:<ul><li>Arabidopsis thaliana</li><li>Brassica oleracea</li><li>Caenorhabditis elegans</li><li>Chaetomium thermophilum</li><li>Chlamydomonas reinhardtii</li><li>Dictyostelium discoideum</li><li>Drosophila melanogaster</li><li>Glycine max</li><li>Homo sapiens</li><li>Mus musculus</li><li>Nematostella vectensis</li><li>Oryza sativa</li><li>Plasmodium berghei</li><li>Plasmodium falciparum</li><li>Plasmodium knowlesi</li><li>Saccharomyces cerevisiae</li><li>Strongylocentrotus purpuratus</li><li>Triticum aestivum</li><li>Trypanosoma brucei</li><li>Xenopus laevis</li><li>Escherichia coli</li><li>Anabaena sp. PCC 7120</li><li>Synechocystis sp. PCC 6803</li></ul>Network inference was performed by training a random forest classifier on known complexes (from CORUM or EcoCyc, for eukaryotes and prokaryotes respectively) to predict interacting protein pairs in cross-validation. Files include the complete classifier scores for every possible protein pair, sorted in descending order. The resulting networks can then be thresholded at an arbitrary precision.

opencc-by-4.0Jun 2023View details →

dryad36/100

Data from: BioID2-based tau interactome reveals novel and known protein interactions associated with multiple cellular pathways

Open the record for dataset details and reuse information.

publicSep 2025View details →

dryad36/100

Data from: Identification of druggable targets from the interactome of the Androgen Receptor and Serum Response Factor pathways in prostate cancer

Open the record for dataset details and reuse information.

publicNov 2024View details →

dryad36/100

Data from: Resolving the three-dimensional interactome of human accelerated regions during human and chimpanzee neurodevelopment

Open the record for dataset details and reuse information.

publicMar 2025View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record