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1,321 results for “HIV-1”
DRM-labeled HIV-1 protease sequence dataset
<p>An HIV-1 protease dataset with labeled DRMs derived from the Stanford HIV Drug Resistance Database<sup>1,2</sup> is provided. It is in <em>fasta</em> format with major protease drug resistance mutations (as defined by Wensing et al.<sup>3</sup>) provided in the sequence name section (following the ">" symbol) as a comma-separated list. The dataset was used in the following paper: "Ahmed A., de Souza D. R., Link R. W., Nonnemacher M. R., Wigdahl B., Dampier W. Design of a SHERLOCK-based low resource screening assay for HIV-1 drug resistance, in preparation, 2021. "</p>
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>
X-ray structures of HIV-1 protease
<p>As of March 1, 2023, there were 233 X-ray structures of HIV-1 protease available in the RCSB PDB database (<a href="https://www.rcsb.org/">https://www.rcsb.org/</a>). Out of these structures, 219 had ligands bound in the active site while 14 did not have any ligands. To prepare the structures for analysis, we removed water, ions, and solvent molecules, extracted the ligands from the receptors, and aligned all the structures. Each HIV-1 protease structure is identified by its PDB ID (<pdbid>.pdb) while the corresponding ligand structures are named as ligs_<pdbdid>.pdb.</p>
Data and analysis scripts associated with the paper 'Long-term experimental evolution of HIV-1 reveals effects of environment and mutational history''
<p><em>Eva Bons, Christine Leemann, Karin J. Metzner, Roland R. Regoes</em></p> <p>This repository contains all the data and analysis scripts associated with the paper 'Long-term experimental evolution of HIV-1 reveals effects of environment and mutational history'</p> <p>See the readme after unpacking the .zip for a description of the files</p>
Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies
<p>Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies.</p>
Data for the "Discovery of Dehydroamino Acid Residues in the Capsid and Matrix Structural Proteins of HIV-1"
<p>Bottom-up mass spectrometry-based proteomic analysis (trypsin) was performed on four biological replicates of HIV-1 virions. These virions were isolated from HEK293T cells transfected with a HIV-1 proviral plasmid derived from the pNL4-3 molecular clone, rendered biosafe due to inactivating point mutations in both the env and vpr reading frames. There are 8 total spectra, 4 are from unlabeled aliquots of sample, and 4 are from aliquots of sample treated with glutathione to label dehydroamino acids (Spectra can be accessed on MassIVE (MSV000088220). All data was analyzed using MetaMorpheus version 0.0.319 (https://github.com/smith-chem-wisc/MetaMorpheus). Provided here are the results of this analysis.</p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (1/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>Cneg</td> <td>RNASEQ-AVF1</td> <td>RNASEQ-AVF1_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF1_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF2</td> <td>RNASEQ-AVF2_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF2_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF3</td> <td>RNASEQ-AVF3_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF3_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF4</td> <td>RNASEQ-AVF4_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF4_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF5</td> <td>RNASEQ-AVF5_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF5_S5_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA A</td> <td>RNASEQ-AVF6</td> <td>RNASEQ-AVF6_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF6_S6_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA B</td> <td>RNASEQ-AVF7</td> <td>RNASEQ-AVF7_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF7_S7_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA C</td> <td>RNASEQ-AVF8</td> <td>RNASEQ-AVF8_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF8_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (2/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>TDP-43 siRNA D</td> <td>RNASEQ-AVF9</td> <td>RNASEQ-AVF9_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF9_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF10</td> <td>RNASEQ-AVF10_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF10_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF11</td> <td>RNASEQ-AVF11_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF11_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF12</td> <td>RNASEQ-AVF12_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF12_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF13</td> <td>RNASEQ-AVF13_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF13_S5_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF14</td> <td>RNASEQ-AVF14_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF14_S6_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos B+C</td> <td>RNASEQ-AVF15</td> <td>RNASEQ-AVF15_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF15_S7_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos A+B+C</td> <td>RNASEQ-AVF16</td> <td>RNASEQ-AVF16_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF16_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
Data presented in Multi-trial analysis of HIV-1 envelope gp41-reactive antibodies among global recipients of candidate HIV-1 vaccines.
<p>This folder contains datasets analyzed in the manuscript:</p> <p>Multi-trial analysis of HIV-1 envelope gp41-reactive antibodies among global recipients of candidate HIV-1 vaccines.</p> <p>Frontiers in Immunology<br> Sec. Vaccines and Molecular Therapeutics<br> doi: 10.3389/fimmu.2022.983313</p>
Raw data accompanying the manuscript "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse". The data files are 3D image stacks of a custom-built wide field deconvolution fluorescence microscope (.tif) and super-resolution structured illumination microscopy data (.dv) of Jurkat T cells transferring HIV-1 virus particles to previously uninfected primary T cells.</p>
Antiviral activity of HIV-1 integrase strand-transfer inhibitors against mutants with integrase resistance-associated mutations and their frequency in treatment-naïve individuals
<p>The development of resistance to human immunodeficiency virus 1 (HIV-1) integrase strand-transfer inhibitors (INSTI) has been documented; however, knowledge of the impact of pre-existing integrase (IN) mutations on INSTI resistance (INSTI-R) is still evolving. The frequency of HIV-1 IN mutations in 2177 treatment-naïve subjects was investigated, along with the INSTI susceptibility of site-directed mutant viruses containing major and minor INSTI-R mutations. Total 6 of 39 minor INSTI-R mutations (M50I, S119P/G/T/R, and E157Q) were found in >1% of IN-treatment-naïve subjects with no impact on INSTI susceptibility. When each combined with major INSTI-R mutation, M50I, S119P, and E157Q led to decreased susceptibility to elvitegravir but remained sensitive to dolutegravir and bictegravir.</p>
HIV-1 genome and annotation
<p>HIV-1 genome and annotation datasets for use with Galaxy Training Materials (<a href="https://training.galaxyproject.org/">https://training.galaxyproject.org</a>). This repository contains three datasets:</p> <ol> <li>hbx2.fa - genomic sequence of HIV-1 derived from GenBank entry K03455.1 </li> <li>hxb2.bed - coordinates of genomic features and drug resistance mutations </li> <li>hxb2.dr.bed - a subset of the annotation data containing drug resistance mutations only.</li> </ol> <p>Coordinates of drug resistance mutations are derived from Los Alamos National Lab HIV <a href="https://www.hiv.lanl.gov/content/sequence/HIV/MAP/hxb2.xls">database data</a>.</p>
HIV-1 control in vivo is related to the number but not the fraction of infected cells with viral unspliced RNA
<p>In the absence of antiretroviral therapy (ART), a subset of individuals, termed HIV controllers, have levels of plasma viremia that are orders of magnitude lower than non-controllers who are at higher risk for HIV disease progression. In addition to having fewer infected cells resulting in fewer cells with HIV RNA, it is possible that lower levels of plasma viremia in controllers is due to a lower fraction of the infected cells having HIV-1 unspliced RNA (HIV usRNA) compared with non-controllers. To directly test this possibility, we used sensitive and quantitative single cell sequencing methods to compare the fraction of infected cells that contain one or more copies of HIV usRNA in peripheral blood mononuclear cells (PBMC) obtained from controllers and non-controllers. The fraction of infected cells containing HIV usRNA did not differ between the two groups. Rather, the levels of viremia were strongly associated with the total number of infected cells that had HIV usRNA, as reported by others, with controllers having 34-fold fewer infected cells per million PBMC. These results reveal for the first time that viremic control is not associated with a lower fraction of proviruses expressing HIV usRNA, unlike what is reported for elite controllers, but is only related to having fewer infected cells overall, maybe reflecting greater immune clearance of infected cells. Our findings show that proviral silencing is not a key mechanism for viremic control and will help to refine strategies towards achieving HIV remission without ART.</p>
Dataset for deep-learning in-situ classification of HIV-1 virion morphology
<p>This dataset contains TEM micrographs for HIV-1 virion samples intended for classification and detection as follows:</p> <ol> <li> <p>HIV-1_virion_classification_backbone_dataset.zip : Contains 1806 .tif images of isolated HIV-1 virions extracted and augmented from TEM micrographs. The images are divided into training (1443 images) and validation (363 images) sets and each of these is divided into eccentric, mature, immature labeled folders:</p> <ul> <li> <p>HIV-1_virion_classification_backbone_dataset/</p> <ul> <li> <p>train/</p> <ul> <li> <p>eccentric/</p> </li> <li> <p>immature/</p> </li> <li> <p>mature/</p> </li> </ul> </li> <li> <p>val/</p> <ul> <li> <p>eccentric/</p> </li> <li> <p>immature/</p> </li> <li> <p>mature/</p> </li> </ul> </li> </ul> </li> </ul> </li> <li> <p>HIV-1_rcnn_dataset_full.zip : Contains 59 .tif TEM micrographs of HIV-1 samples as well as a matching .csv file recording the attributes of each viral instance and coordinates of the rectangular region that contains it:</p> </li> </ol> <ul> <li> <p>region_data_<image_id>.csv:</p> <ul> <li> <p>filename: Name of the image this csv refers to. (Ex: 0131001.png)</p> </li> <li> <p>file_size: Size (bytes) of the image this csv refers to. (Ex: 11755590)</p> </li> <li> <p>file_attributes: Specific attributes of the micrograph. (Ex: None)</p> </li> <li> <p>region_count: Number of viral instances detected in the micrograph. (Ex: 39)</p> </li> <li> <p>region_id: ID of a specific viral region. (Ex: 1)</p> </li> <li> <p>region_shape_attributes: Coordinates of the bounding box of <region_id> that contains a virion. (Ex: {"name":"rect","x":1022,"y":357,"width":225,"height":228})</p> </li> <li> <p>region_attributes: Classification of the virion enclosed in this region (eccentric/mature/immature). (Ex: {"particle_class":"mature"})</p> </li> </ul> </li> </ul> <p>The images are divided into training (46 images) and validation (13 images) sets and each of these contains folder for each micrograph:</p> <ul> <li> <p>HIV-1_rcnn_dataset_full/</p> <ul> <li> <p>train/</p> <ul> <li> <p>0131001/</p> <ul> <li> <p>0131001.png</p> </li> <li> <p>region_data_0131001.csv</p> </li> </ul> </li> <li> <p>0131004/</p> <ul> <li> <p>0131004.png</p> </li> <li> <p>region_data_0131004.csv</p> </li> </ul> </li> <li> <p>…</p> </li> </ul> </li> <li> <p>val/</p> <ul> <li> <p>0131002/</p> <ul> <li> <p>0131002.png</p> </li> <li> <p>region_data_0131002.csv</p> </li> </ul> </li> <li> <p>0131003/</p> <ul> <li> <p>0131003.png</p> </li> <li> <p>region_data_0131003.csv</p> </li> </ul> </li> <li> <p>…</p> </li> </ul> </li> </ul> </li> </ul> <p>The first dataset (HIV-1_virion_classification_backbone_dataset.zip) is intended for viral classification algorithms while the second dataset (HIV-1_rcnn_dataset_full) is intended for detection and segmentation algorithms (for example RCNN).</p> <p>Applications of this dataset as well as source code can be found at <a href="https://github.com/Perilla-lab/TEMNet">https://github.com/Perilla-lab/TEMNet</a> .</p>
Supplementary Data for "Convergent evolution as an indicator for selection during acute HIV-1 infection"
<p><strong>Supplementary Data 1. Position of all identified mutations in the <em>env</em> gene. </strong>This file contains detailed information about the identity of the observed mutations. It provides the position in the HXB2 genome, the amino acid change they cause in the different genetic backgrounds and the number of HIV-1 subtypes (out of a total of 170) the mutations occurs in.</p> <p><strong>Supplementary Data 2. Position of all identified mutations in the <em>rev</em> exon part of the <em>env </em>gene. </strong>Same as Supplementary Data 1, except that only mutations and amino acid substitutions in the <em>rev</em> exon 2 are shown.</p> <p><strong>Supplementary Program.</strong> With this program one can redo the analyses and simulations of the manuscript.</p>
dataset for bioRxiv preprint titled 'Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease'
<p>This data supports the Figures in the preprint titled</p> <p><strong>Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease</strong></p> <p><strong>working abstract</strong></p> <p>The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS). During the progression of the disease, some individual patients acquire -drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the ‘flap region’ of the HIV-1 protease, which is a critical dynamic region involved in non-specific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and polypeptide binding of the main protease. We use a multi-agent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs (SIV and FIV). We also investigate changes in dynamics due to common drug-resistant mutations in many patients. We find that a key functional site in the flap region, a solvent-exposed isoleucine (ILE50) and surrounding sites that control flap dynamics is often targeted by drug-resistance mutations, likely leading to malfunctional molecular dynamics affecting the overall flexibility of the flap region. We conclude that better long term patient outcomes may be achieved by designing drugs that target protease regions which are less dependent upon single sites with large functional binding effects.</p>
DATASET: Replication-competent HIV-1 in human alveolar macrophages and monocytes despite nucleotide pools with elevated dUTP
<p>Complete data set to support content of the study: Cui, et al (2022) <strong>Replication-competent HIV-1 in human alveolar macrophages and monocytes despite nucleotide pools with elevated dUTP</strong></p>
Safety and Efficacy of Romidepsin and the Therapeutic Vaccine Vacc-4x for Reduction of the Latent HIV-1 Reservoir
ClinicalTrials.gov study NCT02092116. IPD Sharing: NO. Countries: 1. Publications: 2.
The Pharmacokinetics, Safety, and Tolerability of Abacavir/Dolutegravir/Lamivudine Dispersible and Immediate Release Tablets in HIV-1-Infected Children Less Than 12 Years of Age
ClinicalTrials.gov study NCT03760458. IPD Sharing: YES. Countries: 4. Publications: 2.
Re-boosting of HIV-1 Infected Subjects With Vacc-4x
ClinicalTrials.gov study NCT01712256. IPD Sharing: NO. Countries: 5. Publications: 5.
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