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7,350 results for “HIV”
Deep-sequencing of viral genomes from treatment-naive HIV-infected persons shows positive association between intrahost genetic diversity and viral load
<p><strong><span>Background:</span></strong><span> Infection with human immunodeficiency virus type 1 (HIV) typically results from transmission of a small and genetically uniform viral population. Following transmission, the virus population becomes more diverse because of recombination and acquired mutations through genetic drift and selection. Viral intrahost genetic diversity remains a major obstacle to the cure of HIV; however, there is a disagreement whether intrahost viral genetic diversification associates positively or negatively with disease progression and progression markers. Viral load is a key progression marker and understanding its relationship to viral intrahost genetic diversity could help design future strategies for HIV monitoring and treatment.</span></p> <p><span><strong>Methods:</strong> </span><span>We analyzed deep-sequenced viral genomes from 2,650 treatment-naive HIV-infected persons to measure the intrahost genetic diversity of 2,447 genomic codon positions as calculated by Shannon entropy. We tested for associations between viral load (VL) and amino acid (AA) entropy accounting for sex, age, race, duration of infection, and HIV population structure.</span></p> <p><strong><span>Results:</span></strong><span><strong> </strong>We confirmed that the intrahost genetic diversity is highest in the <em>env</em> gene. Furthermore, we showed that mean Shannon entropy is significantly associated with VL, especially in infections of >24 months duration. We identified 16 significant associations between VL (p-value<2.0x10<sup>-5</sup>) and Shannon entropy at AA positions which in our association analysis explained 13% of the variance in VL.</span></p> <p><strong><span>Conclusions: </span></strong><span>Our results elucidate that viral intrahost genetic diversity is associated with VL and could be used as a better disease progression marker than HIV consensus sequence variants, especially in infections of longer duration. We emphasize that viral intrahost diversity should be considered when studying viral genomes and infection outcomes.</span></p>
Molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants
<p>This data set accompanies the publication by S. Conti, E. Lau, and V. Ovchinnikov entitled "On the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations", to be published in the MDPI journal Antibodies. It contains molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants, as described in the paper. The format of the trajectory files is CHARMM-compatible dcd. The files can be visualized with the program Visual Molecular Dynamics (VMD) (see paper by Humphrey et al. 1996, J. Molec. Graphics). The accompanying file "view" is a tcl-based script for VMD that can be executed in the Linux environment using: "vmd -e view", which will display the trajectory of the mutant specified by editing the first noncomment line of the script.<br> </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>
FIV model for COVID-19 in People with HIV
<div> <p>Baseline characteristic data for animal groups</p> <p> </p> <p> </p> </div> <div>Output tables from viral_variant_caller (https://github.com/stenglein-lab/viral_variant_caller) and SNPGenie (https://github.com/chasewnelson/SNPGenie) pipeline.</div> <div> </div> <div> </div> <div>Next-generation sequencing reads are available from the NCBI SRA under Bioproject<a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fbioproject%2FPRJNA1129543&data=05%7C02%7Cshoroq.shatnawi%40okstate.edu%7C0ccb2c0f42b847d049c808dc9acaa999%7C2a69c91de8494e34a230cdf8b27e1964%7C0%7C0%7C638555443277564291%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=MDrv0ysURQ6YvOBdyX7kSeTcLp7Hh50BKU6rE%2FvXpfw%3D&reserved=0" rel="noopener noreferrer"> </a>PRJNA1129543: <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fbioproject%2FPRJNA1129543&data=05%7C02%7Cshoroq.shatnawi%40okstate.edu%7C0ccb2c0f42b847d049c808dc9acaa999%7C2a69c91de8494e34a230cdf8b27e1964%7C0%7C0%7C638555443277573779%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=k8T6f22oMhX66jaQSDKyOzK9khFTyROZNa7JlLdG%2BNY%3D&reserved=0" rel="noopener noreferrer">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1129543</a></div>
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>
Data From: Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping
<p>This is the data set from all figures and tables from the manuscript "Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping", which is posted to the ChemRxiv preprint server (10.26434/chemrxiv-2024-5bqm7) and currently in consideration for peer-reviewed publication elsewhere.</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>
ProdgerLab-StarDist-HIV Target Cell Training Set
<p>40 annotated immunofluorescence microscopy images (600 microns x 600 microns) of foreskin tissue stained for CD3/CD4/CCR5/Nuclei. These images were used to train StarDist models used for the identification of HIV Target Cells in foreskin tissue section scans. </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>
Fig. 1 in Infection with Toxoplasma gondii can promote chronic liver diseases in HIV-infected individuals
Fig. 1. Frequency of different liver pathologies in deceased patients from cohorts seropositive and seronegative to Toxoplasma gondii (Nicolle et Manceaux, 1908). * – difference between the indices for the entire cohort of seropositive patients and for the group of deceased patients from this cohort (p <0.05); ^ – difference between the indices for the entire cohort of seronegative patients and for the group of deceased patients from this cohort (p <0.05)
More Options for Children and Adolescents (MOCHA): Oral and Long-Acting Injectable Cabotegravir and Rilpivirine in HIV-Infected Children and Adolescents
ClinicalTrials.gov study NCT03497676. IPD Sharing: YES. Countries: 5. Publications: 6.
Immunogenicity Study of Vacc-4x Versus Placebo in Patients Infected With HIV
ClinicalTrials.gov study NCT00659789. IPD Sharing: NO. Countries: 5. Publications: 1.
Task-shifted Adaptation of the WHO-PEN Intervention to Address Cardio-metabolic Complications in People Living With HIV in Zambia
ClinicalTrials.gov study NCT05005130. IPD Sharing: YES. Countries: 1. Publications: 0.
Simulation Training for Labor and Delivery Providers to Address HIV Stigma During Childbirth in Tanzania
ClinicalTrials.gov study NCT05271903. IPD Sharing: NO. Countries: 1. Publications: 3.
Combination Partner HIV Testing Strategies for HIV-positive and HIV-negative Pregnant Women
ClinicalTrials.gov study NCT04124536. IPD Sharing: YES. Countries: 1. Publications: 1.
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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.
Annotated Behaviour and Observability Dataset (ABODe)
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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.
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.
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.