Data from: Dynamics of the CD9 interactome during bacterial infection of epithelial cells by proximity labelling proteomics
Data files
Oct 21, 2025 version files 41.52 KB
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CellVis_Enrichment_240_mins.csv
14.10 KB
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CellVis_Enrichment_30_mins.csv
9.36 KB
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CellVis_Enrichment_60_mins.csv
12.84 KB
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README.md
5.22 KB
Abstract
Bacterial species utilise different receptors at the cell membrane to adhere to cells. Previously, we demonstrated that interference with CD9, a human tetraspanin, reduces adherence of multiple species of bacteria to cells. CD9 is not a receptor but organises numerous commandeered host proteins at the cell membrane; however, the full interactome has not yet been delineated. Using a CD9 proximity labelling model, a first for CD9, we observed a diverse interactome, with 710 enriched proteins in uninfected cells. Proximal proteins were associated with various cellular processes, including extracellular matrix (ECM)–receptor interactions and tight junctions. Several known bacterial receptors were also detected, including CD44, CD46, and CD147. The interactome was dynamic during infection with two distinct bacterial species, Neisseria meningitidis and Staphylococcus aureus. In total, 12 human proteins were enriched during meningococcal infection, compared to one during staphylococcal infection, demonstrating different host factor requirements during CD9-mediated bacterial adherence. CD44 or CD147 knockdown reduced staphylococcal and meningococcal adherence, respectively, but not vice versa. However, in combination with CD9 interference, no additive effects were observed, demonstrating association of these proteins during infection. We have developed a tool that measures changes within the CD9 interactome, demonstrated CD9 as a universal organiser of bacterial ‘adhesion platforms’, and shown efficacy of a disrupting CD9-derived peptide.
Dataset DOI: 10.5061/dryad.m905qfvfc
Description of the data and file structure
Data S2. Cell compartment and cellular pathway analysis of identified proteins. Data files contain analysis of significantly enriched proteins identified by mass spectrometry at 30, 60 and 240 minutes. Data files supplied by SubcellulaRVis demonstrates the cellular compartment analysis of the enriched proteins. Data files supplied by WebGestalt provide the KEGG pathway analysis of the significantly enriched proteins.
Files and variables
File: CellVis_Enrichment_30_mins.csv
Description: Cellular compartment analysis from SubcellulaRVis after 30 mins.
File: CellVis_Enrichment_60_mins.csv
Description: Cellular compartment analysis from SubcellulaRVis after 60 mins.
File: CellVis_Enrichment_240_mins.csv
Description: Cellular compartment analysis from SubcellulaRVis after 240 mins.
- Compartment: The cellular localization category based on Gene Ontology (GO) Cellular Component terms.
- p: p value associated with the enrichment of genes within the GO annotated pathway
- FDR: False discovery rate associated with genes enriched within the GO annotated pathway
- Significant: Whether the compartment passes the statistical significance threshold
- n: The number of genes from input list that are associated with that cellular compartment
- Genes: The list of genes from dataset that map to that compartment
File: Supplementary_Data_2.zip (hosted on Zenodo)
Description: Files are organised in to analysis of datasets from 30, 60 and 240 minutes.
Each directory contains:
i) a .html file containing the KEGG pathway analysis from WebGestalt
ii) various .txt and .png files which are used to build the .html file
Description of .txt files and .png files within zipped directories:
enriched_geneset_wsc_topsets_wg_result.txt* - describes the top GO sets observed through a weighted set cover analysis of the dataset.
enrichment_results_wg_result.txt* - describes the top GO sets observed with no redundancy reduction.
Column 1 - geneSet - GO annotated pathway, hsa*
Column 2 - description - description of GO annotated pathway
Column 3 - link - hyperlink to the GO annotated pathway
Column 4 - size - number of genes associated with the GO annotated pathway
Column 5 - overlap - number of genes from the analysed dataset which overlap with the GO annotated pathway
Column 6 - expect - number of genes expected to overlap with the GO annotated dataset
Column 7 - enrichmentRatio - ratio of expected enriched genes. The number of overlapping genes divided by the number of expected genes
Column 8 - pValue - p value associated with the enrichment of genes within the GO annotated pathway
Column 9 - FDR - False discovery rate associated with genes enriched within the GO annotated pathway
Column 10 - overlapId - Entrez Gene ID associated with enriched genes from the analysed dataset associated with the GO annotated pathway
Column 11 - userId - Gene names associated with the enriched genes from the analysed dataset associated with the GO annotated pathway
goslim_summary_wg_result.png* - demonstrates bar charts produced by GO Slim analysis showing Biological process categories, Cellular component categories and Molecular Function categories
goslim_summary_wg_result_bp.txt* - tabular form of the GO Slim analysis showing biological process analysis
Column 1 - GO biological process annotation
Column 2 - description of GO biological process
Column 3 - number of genes associated
goslim_summary_wg_result_cc.txt* - tabular form of the GO Slim analysis showing cellular compartment analysis
Column 1 - GO cellular compartment annotation
Column 2 - description of GO biological process
Column 3 - number of genes associated
goslim_summary_wg_result_mf.txt** - *tabular form of the GO Slim analysis showing molecular function analysis
Column 1 - GO molecular function annotation
Column 2 - description of GO biological process
Column 3 - number of genes associated
interestingID_mappingTable_wg_result.txt* - table showing mapped function of analysed genes
Column 1 - userId - GeneID provided by the user
Column 2 - geneSymbol - recognised gene symbol
Column 3 - geneName - full gene name
Column 4 - entrezgene - number associated with the gene in the Entrez database
Column 5 - gLink - Hyperlink associated with the gene within the NCBI database
interestingID_unmappedList_wg_result.txt* - list of unmapped genes from the analysed dataset, lists user inputted gene names not recognised in the database
Code/software
Files will require to be unzipped using any compression software. .html files can be opened with any web browser, .csv files can be opened with Microsoft Excel.
Access information
Data was analysed from the following sources:
- SubcellulaRVis (https://shiny.its.manchester.ac.uk/subcellularvis/)
- WebGestalt (https://www.webgestalt.org/)
