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468 results for “HLA”
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
<p>This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data:</p> <ul> <li>adaptscore_acute.csv <ul> <li>A csv file that contains the estimated adaptation scores for the acute data set with HLA I model.</li> </ul> </li> <li>adaptscore_leftout.csv <ul> <li>A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training.csv <ul> <li>A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training_hla1_without_clin.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation)</li> </ul> </li> <li>adaptscore_training_seed2.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed</li> </ul> </li> </ul>
Dataset for: All-atom simulations reveal the intricacies of signal transduction upon binding of HLA-E ligand to the transmembrane inhibitory CD94/NKG2A receptor
<p>This dataset contains relevant structures, input and other files that are associated with our article "<em>All-atom simulations reveal the intricacies of signal transduction upon binding of HLA-E ligand to the transmembrane inhibitory CD94/NKG2A receptor", available at https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c00249</em></p>
Mass spectrometry output SILAC labelled (F/Y) biological replicate 1 - anti-HLA-A29 antibody DK1G8
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 wildetype versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications with anti-HLA-A29 antibody DK1G8. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
data HLA-3D-Diff nov. 2024
<p>Data used in the <strong>HLA-3D-Diff visualisation interface </strong>project (see https://gitlab.inria.fr/capsid.public_codes/hla-3d-diff_public).</p>
precomputed_data_HLA-EpiCheck
<p>This file contains precomputed data used in the HLA-Epicheck project (see https://gitlab.inria.fr/capsid.public_codes/hla-epicheck). This data can be re-used to run the notebook 'dataset_gen_al_radius_15.ipynb'.</p> <p>Data is organized by locus and antigen. In each antigen folder, four types of data can be found :</p> <ul> <li> <p>patchs : prepatches computed with the script compute_prepatches.tcl. Each file contains the prepatches for a given residue and patch radius (see file name). The format used in each file is as follows: each line corresponds to a prepatch and contains two colon-separated entries. The first one corresponds to a space-separated list of the residues that compose the prepatch and the second one corresponds to the PDB frame from which the prepatch was extracted.</p> </li> <li> <p>PDBs : PDB files used for computing the prepatches and SASA data.</p> </li> <li> <p>SASAs_out : SASA values computed for each PDB frame (i.e. one SASA file per frame). The format used in each file is as follows: each line corresponds to an AA and contains the AA number (numbering starts at 0) and the corresponding SASA value.</p> </li> <li> <p>RSASA_median.txt : median RSASA computed for each AA along the trajectory. The format used in each file is as follows: each line corresponds to an AA and contains the AA number (numbering starts at 0) and the corresponding RSASA value.</p> </li> </ul>
Raw data for the article: Human Amnion Epithelial Cells Impair T Cell Proliferation: The Role of HLA-G and HLA-E Molecules
<p>The immunoprivilege status characteristic of human amnion epithelial cells (hAECs) has been recently highlighted in the context of xenogenic transplantation. However, the mechanism(s) involved in such regulatory functions have been so far only partially been clarified. Here, we have analyzed the expression of HLA-Ib molecules in isolated hAEC obtained from full term placentae. Moreover, we asked whether these molecules are involved in the immunoregulatory functions of hAEC. Human amnion-derived cells expressed surface HLA-G and HLA-F at high levels, whereas the commonly expressed HLA-E molecule has been measured at a very low level or null on freshly isolated cells. HLA-Ib molecules can be expressed as membrane-bound and soluble forms, and in all hAEC batches analyzed we measured high levels of sHLA-G and sHLA-E when hAEC were maintained in culture, and such a release was time-dependent. Moreover, HLA-G was present in extracellular vesicles (EVs) released by hAEC. hAEC suppressed T cell proliferation in vitro at different hAEC:T cell ratios, as previously reported. Moreover, inhibition of T cell proliferation was partially reverted by pretreating hAEC with anti-HLA-G, anti-HLA-E and anti-β2 microglobulin, thus suggesting that HLA-G and -E molecules are involved in hAEC-mediated suppression of T cell proliferation. Finally, either large-size EV (lsEV) or small-size EV (ssEV) derived from hAEC significantly modulated T-cell proliferation. In conclusion, we have here characterized one of the mechanism(s) underlying immunomodulatory functions of hAEC, related to the expression and release of HLA-Ib molecules.</p>
A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3' UTR region, denoting expression levels, are associated with Atopic Dermatitis
<p>The publication "A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3’ UTR region, denoting expression levels, are associated with Atopic Dermatitis" contains analysis from two different cohorts: Genetics in Atopic Dermatitis (GAD), which is the main dataset, and Pediatric Eczema Elective Registry (PEER), which is the replication cohort. Included herein are the HLA Class II genotypes for both the GAD and PEER cohorts at 2-field resolution, which forms the basis for the analysis included in the publication. (DOI: 10.3389/fgene.2023.1004138)</p>
OSE2101 Versus Chemotherapy in HLA-A2 Positive Patients With Advanced NSCLC After Immune Checkpoint Inhibitor Failure
ClinicalTrials.gov study NCT02654587. IPD Sharing: NO. Countries: 10. Publications: 1.
Mass spectrometry output SILAC labelled (F/Y) biological replicate 2 - anti-HLA-A29 antibody DK1G8
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 WT versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications with anti-HLA-A29 antibody DK1G8. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
Mass spectrometry output SILAC labelled (F/Y) biological replicate 2 - anti-HLA-ABC antibody W6/32
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 WT versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications from HLA-A29-negative (DK1G8-negatively selected) fractions with anti-HLA-ABC antibody W6/32. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
HLA Class II specificity assessed by high-density peptide microarray interactions
<p>The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery; something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I (MHC-I) prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC-I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Since MHC-II restricted CD4+ T cells control and orchestrate most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data is key to the solution of this problem; a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse and representative data. Here, we have used recombinant HLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, <em>in casu</em> containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated. Collectively, with a cost per peptide reduced to a few cents combined with the flexibility of recombinant HLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.</p>
HLA and KIR allele genotyping for HPRC-frz2 haplotype assemblies
<p>HLA/KIR annotation for available long reads assemblies, constructed by the pipeline : https://github.com/YingZhou001/Immuannot</p> <p>IPD-KIR version: V2.13.0, IPD-IMGT/HLA version: V3.59.0</p> <p>472 haploid assemblies included</p>
Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation
<p>Summary statistics for "Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation" </p><p>Files are organized in the following directory structure:</p><p><strong>G2G/</strong> - Summary statistics of G2G associations (SNPs, HLA, and gene-level analysis). </p><p><strong>HLA/ </strong>- Peptide binding prediction results</p><p><strong>preS1_haplotypes/ - </strong>Resolved intra-host haplotypes of the preS1 binding region. </p><p><strong>DnDs/</strong> - Calculation of intra-host positive selection, within the preS1 binding region. </p><p> </p>
Inverse influence of HLA-DR7, DR12 and DR13 on the pathogenesis of Der p 1-induced allergic rhinitis
<p>Supplementary tables and figures from the yet to be published study entitled "Inverse influence of HLA-DR7, DR12 and DR13 on the pathogenesis of Der p 1-induced allergic rhinitis".</p>
A protective HLA extended haplotype outweighs the major COVID-19 risk factor inherited from Neanderthals in the Sardinian population
<p>Sardinia has one of the lowest incidences of hospitalization and related mortality in Europe. In this dataset we reported 358 patients with COVID-19, in which we evaluated the frequency of the Neanderthal risk locus variant on chromosome 3 (rs35044562), considered to be a major risk factor for a severe SARS-CoV-2 disease course.</p>
Consensus nucleotide sequences for env and gag for paper: Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models
<p>This is the consensus sequence repository to the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> It contains the 10% consensus nucleotide sequences of the env and gag (only p24) protein of HIV-1 used for the training and leftout data set. The NGS sequences are available under BioProject ID PRJNA810303 and the corresponding BioSample Accession IDs are SAMN26241863:26242168 and SAMN28728524:SAMN28728529</p> <ul> <li>env_leftout.fasta <ul> <li>A fasta file that contains the consensus nucleotide sequences for the env protein for the leftout data set</li> </ul> </li> <li>env_nt_274.fasta <ul> <li>A fasta file that contains the consensus nucleotide sequences for the env protein for the training data set</li> </ul> </li> <li>gag_leftout.fasta <ul> <li>A fasta file that contains the consensus nucleotide sequences for the gag protein for the leftout data set</li> </ul> </li> <li>gag_nt_274.fasta <ul> <li>A fasta file that contains the consensus nucleotide sequences for the gag protein for the training data set</li> </ul> </li> </ul>
Gene and protein sequence features augment HLA class I ligand predictions
<p>Dataset and analyses supporting the manuscript "Gene and protein sequence features augment HLA class I ligand predictions".</p> <p>The "peptides" files contain the mass-spec detected peptides obtained from HLA ligandomics performed on the indicated tumor lines. </p> <p>The "protein data" files contain the RNAseq data (TPM) and Ribosome profiling data (ribosome occupancy) per protein, for each tumor line. </p> <p>The "source data" zip archive contains the source data underlying the figures of the manuscript.</p> <p>The "HLA ligandome analyses" zip archive contains the R scripts used for all data analysis in the manuscript, including all data and output files. These analyses can also be found at https://github.com/kasbress/HLA_Ligandome_Analyses/</p> <p> </p> <p> </p>
Screen of A6 TCR against a library of HLA-A*02:01 MHC-I peptides from the human exome
<p>T2 cells expressing a library of off targets (derived from A6 and B7 binding motifs in Hausmann 1999) are co-cultured with A6, DMF5 or 1G4 expressing T cells (from a non-A2 donor) and minigenes from surviving cells are amplified.</p>
Screen of Pr20 TCR mimic antibody against a library of HLA-A*02:01 MHC-I peptides
<p>Minigene sequencing of T2 cells sorted for high and low binding to the TCR mimic antibody "Pr20."</p>
Screen of A6 and B7 TCR against a library of HLA-A*02:01 MHC-I peptides from the human exome
<p>T2 cells expressing a library of off targets (derived from A6 and B7 binding motifs in Hausmann 1999) are co-cultured with A6, B7. DMF5 or 1G4 expressing T cells (from a non-A2 donor) and minigenes from surviving cells are amplified.</p>
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