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1,985 results for “Antigen”
Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans
<p><strong>Abstract: </strong><br>SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.</p> <p><strong>More Information:</strong></p> <ul> <li><strong>Preprint:</strong> <a href="https://www.researchsquare.com/article/rs-3304466/v1">Research Square.</a></li> <li><strong>Sample information</strong>: data_inventory.csv file.</li> <li><strong>Code</strong> code_github_repo.zip or at the <a href="https://github.com/ncborcherding/COVID_TCR">original github repo</a></li> <li><strong>Interactive Portal</strong>: <a href="https://cellpilot.emed.wustl.edu/">CellPilot</a></li> </ul>
Human Stromal Antigen 1 (STAG1) Cohesin Subunit SA-1; A Target Enabling Package
<p>Loss of function mutations in the cohesin subunit gene <a href="https://www.ncbi.nlm.nih.gov/gene/10735">STAG2</a> are common in a variety of cancers (1). These cells become dependent on the paralogous cohesin subunit <a href="https://www.ncbi.nlm.nih.gov/gene/10274">STAG1</a> (2-4). Mutants of STAG1 that disrupt the binding to the cohesin subunit <a href="https://www.ncbi.nlm.nih.gov/gene/5885">RAD21</a> cannot complement the loss of STAG2. This TEP examines the druggability of STAG1 as a synthetic lethal strategy to treat <em>stag2<sup>-</sup></em> cancers. The TEP includes crystal structures of two domains of STAG1, alone and in complex with Rad21-rderived peptides. We performed screens of a fragment library and identified small molecules bound to pockets in the two domains of STAG1. We also developed assays for binding of RAD21 peptides to STAG1, which can be used to screen for molecules that disrupt binding.</p>
CEPHIA Evaluation Panel data for Maxim and Sedia Limiting Antigen Avidity assays
<p>CEPHIA Evaluation Panel dataset containing final ODn values for each assay. The first file contains results on all standard panel samples, and the second contains results on blinded control samples tested in replicate.</p>
P2PXML Dataset: Deep Geometric Framework to Predict Antibody-Antigen Binding Affinity
<p>In drug development, the efficacy of an antibody depends on how the antibody interacts with the target antigen. The strength of these interactions indicates how successful an antibody is in neutralizing an antigen. Therefore, the strength, measured by “binding affinity”, is a critical aspect of antibody engineering. In theory, the higher the binding affinity, the higher the chances are that the antibody is successful against the target antigen. Currently, techniques such as molecular docking and molecular dynamics are utilized in quantifying the binding affinity. However, owing to the computational complexity of the aforementioned techniques, running simulations for large antibodies/antigens remains a daunting task. Despite the commendable improvements in deep learning-based binding affinity prediction, such approaches are highly dependent on the quality of the antibody-antigen structures and they tend to overlook the importance of capturing the evolutionary details of proteins upon mutation. Further, most of the existing datasets for the task only include antibody-antigen pairs related to one antigen variant and, thus, are not suitable for developing comprehensive data-driven approaches. To circumvent the said complexities, we first curate the largest and most generalized datasets for antibody-antigen binding affinity prediction, consisting of both protein sequences and structures. Subsequently, we propose a deep geometric neural network comprising a structure-based model and a sequence-based model that considers both atomistic and evolutionary details when predicting the binding affinity. The proposed framework exhibited a 10% improvement in mean absolute error compared to the state-of-the-art models while showing a strong correlation between the predictions and target values. We release the datasets and code publicly https://drug-discovery-entc.github.io/p2pxml/ to support the development of antibody-antigen binding affinity prediction frameworks for the benefit of science and society. </p>
Supplementary dataset to the publication "Ultraviolet C inactivation of Coxiella burnetii for production of a structurally preserved whole cell vaccine antigen"
<p>The dataset supplements the journal article "Ultraviolet C inactivation of <em>Coxiella burnetii </em>for production of a structurally preserved whole cell vaccine antigen" published by Katja Mertens-Scholz, Amira A. Moawad, Elisabeth M. Liebler-Tenorio, Andrea Helming, Jennifer Andrack, Peter Miethe, Heinrich Neubauer, Mathias W. Pletz and Ina-Gabriele Richter in the journal BMC Microbiology (https://doi.org/10.1186/s12866-024-03246-z). The file "NMII 100µW" contains all data regarding inactivation of <em>C. burnetii</em> Nine Mile phase II with 100µW in a time dependent manner. The file "NMI 100 and 250µW" contains all data regarding inactivation of <em>C. burnetii</em> Nine Mile phase I with 100µW and 250µW in a time dependent manner. The file "surviving fraction" contains all data regarding inactivation of <em>C. burnetii </em>Nine Mile phase I and II after UVC treatment. The file "serology" contains all data obtained from ELISA experiments. The file "diameter" contains all data regarding the bacterial diameter after UVC treatment.</p>
Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho
<p>These are pseudo-anonymised data from the MISTRAL study: "Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho". The data dictionary explains the data available in the dataset. Between December 2020 and September 2021, 2131 individuals with either COVID symptoms or contact with a COVID-positive case were included from two hospitals in Lesotho and had a valid PCR results.</p>
Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data
<p>Dataset with Zambia data belonging to the publication: Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data</p>
Spatial integration of transcription and splicing in a dedicated compartment sustains monogenic antigen expression in African trypanosomes
<p>This repository contains the data for the manuscript <a href="https://doi.org/10.1038/s41564-020-00833-4">https://doi.org/10.1038/s41564-020-00833-4</a>.</p> <p>The HiC analysis pipeline can be found at <a href="https://github.com/bgbrink/PRJEB35632">https://github.com/bgbrink/PRJEB35632</a>.</p> <p><strong>Abstract</strong></p> <p>Highly selective gene expression is a key requirement for antigenic variation in several pathogens, allowing evasion of host immune responses and maintenance of persistent infections. African trypanosomes — parasites that cause lethal diseases in humans and livestock — employ an antigenic variation mechanism that involves monogenic antigen expression from a pool of >2,600 antigen-coding genes. In other eukaryotes, the expression of individual genes can be enhanced by mechanisms involving the juxtaposition of otherwise distal chromosomal loci in the three-dimensional nuclear space. However, trypanosomes lack classical enhancer sequences or regulated transcription initiation. In this context, it has remained unclear how genome architecture contributes to monogenic transcription elongation and transcript processing. Here, we show that the single expressed antigen-coding gene displays a specific inter-chromosomal interaction with a major messenger RNA splicing locus. Chromosome conformation capture (Hi-C) revealed a dynamic reconfiguration of this inter-chromosomal interaction upon activation of another antigen. Super-resolution microscopy showed the interaction to be heritable and splicing dependent. We found a specific association of the two genomic loci with the antigen exclusion complex, whereby VSG exclusion 1 (VEX1) occupied the splicing locus and VEX2 occupied the antigen-coding locus. Following VEX2 depletion, loss of monogenic antigen expres- sion was accompanied by increased interactions between previously silent antigen genes and the splicing locus. Our results reveal a mechanism to ensure monogenic expression, where antigen transcription and messenger RNA splicing occur in a specific nuclear compartment. These findings suggest a new means of post-transcriptional gene regulation.</p>
Cross-reactive CD8+ T cell responses to tumor-associated antigens (TAAs) and homologous microbiota-derived antigens (MoAs)
<p><strong><span>Background: </span></strong><span>We have recently shown extensive sequence and conformational homology between tumor-associated antigens (TAAs) and antigens derived from microorganisms (MoAs). The present study aimed to assess the breadth of T-cell recognition specific to MoAs and the corresponding TAAs in healthy subjects (HS) and patients with cancer (CP).</span></p> <p><strong><span>Method: </span></strong><span>A library of >100 peptide-MHC (pMHC) combinations was used to generate DNA-barcode labelled multimers. Homologous peptides were selected from the Cancer Antigenic Peptide Database, as well as Bacteroidetes/Firmicutes-derived peptides. They were incubated with CD8+ T cells from the peripheral blood of HLA-A*02:01 healthy individuals (n=10) and cancer patients (n=16). T cell recognition was identified using tetramer-staining analysis. Cytotoxicity assay was performed using as target cells TAP-deficient T2 cells loaded with MoA or the paired TuA.</span></p> <p><strong><span>Results: </span></strong><span>A total of 66 unique pMHC recognized by CD8+ T cells across all groups were identified. Of these, 21 epitopes from microbiota were identified as novel immunological targets. Reactivity against selected TAAs was observed for both HS and CP. pMHC tetramer staining confirmed CD8+ T cell populations cross-reacting with CTA SSX2 and paired microbiota epitopes. Moreover, PBMCs activated with the MoA where shown to release IFNγ as well as to exert cytotoxic activity against cells presenting the paired TuA.</span></p> <p><strong><span>Conclusions: </span></strong><span>Several predicted microbiota-derived MoAs are recognized by T cells in HS and CP. Reactivity against TAAs was observed also in HS, primed by the homologous bacterial antigens. CD8+ T cells cross-reacting with MAGE-A1 and paired microbiota epitopes were identified in three subjects. Therefore, the microbiota can elicit an extensive repertoire of natural memory T cells to TAAs, possibly able to control tumor growth (“natural anti-cancer vaccination”). In addition, non-self MoAs can be included in preventive/therapeutic off-the-shelf cancer vaccines with more potent anti-tumor efficacy than those based on TAAs.</span></p>
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
Antigen Delivery Controlled by an On-Demand Photorelease
<p><span>To eliminate infected and cancerous cells, antigen processing and presentation play a pivotal role through the recognition of antigenic peptides displayed on Major Histocompatibility Complex class I (MHC I) molecules. Here, we developed a photostimulated antigen release system that enables the temporal inception of antigen flux. Simple and effective photocaging of the human immunodeficiency virus (HIV)-Nef73-derived epitope, a representative high-affinity MHC I ligand, was provided by steric hindrance to block the recognition by the transporter associated with antigen processing (TAP) in the peptide loading complex (PLC). In response to light, a heteronomous release of antigens and subsequent translocation in various scenarios is demonstrated, including a TAP-related ATP-binding cassette (ABC) transporter reconstituted in liposomes and the native PLC in the endoplasmic reticulum (ER) membrane of human cells. The photochemically induced ‘<em>burst</em>’ of antigens opens new prospects for a mechanistic analysis of the antigen translocation machinery and will help to provide insights into antigen processing pathways via an on-demand, subcellular pulse-chase release of antigens.</span></p>
MOLECULAR MIMICRY BETWEEN TUMOR ASSOCIATED ANTIGENS AND MICROBIOTA-DERIVED EPITOPES.
<p><strong>Background: </strong>The gut microbiota profile is unique for each individual and are composed by different bacteria species according to individual birth-to-infant transitions. In the last years, the local and systemic effects of microbiota on cancer onset, progression and response to treatments, such as immunotherapies, have been extensively described. Here we offer a new perspective, proposing a role for the microbiota based on the molecular mimicry of tumor-associated antigens by microbiome-associated antigens.</p> <p><strong>Methods:</strong> In the present study we looked for homology between published TAAs and non-self microbiota-derived epitopes. Blast search for sequence homology was combined with extensive bioinformatics analyses.</p> <p><strong>Results:</strong> Several evidences for homology between TAAs and microbiota-derived antigens have been found. Strikingly, three cases of 100% homology between the paired sequences has been identified. The predicted average affinity to HLA molecules of microbiota-derived antigens is very high (<100nM). The structural conformation of the microbiota-derived epitopes is, in general, highly similar to the corresponding TAA. In some cases, it is identical and contact areas with both HLA and TCR chains are indistinguishable. Moreover, the spatial conformation of TCR-facing residues can be identical in paired TAA and microbiota-derived epitopes, with exactly the same values of planar as well as dihedral angles.</p> <p><strong>Conclusions:</strong> The data reported in the present study show for the first time the high homology in the linear sequence as well as in structure and conformation between TAAs and peptides derived from microbiota species of the Firmicutes and the Bacteroidetes phyla, which together account for 90% of gut microbiota. Cross-reacting CD8<sup>+</sup> T cell responses are very likely induced. Therefore, the anti-microbiota T cell memory may turn out to be an anti-cancer T cell memory, able to control the growth of cancer developed during the lifetime if the expressed TAA is similar to the microbiota epitope. This may ultimately represent a relevant selective advantage for cancer patients and may lead to a novel preventive anti-cancer vaccine strategy.</p>
A copro-antigen ELISA for the detection of ascarid infections in chickens
<p>Dataset for the infection experiments to evaluate the performance of copro-antigen ELISA to assess nematode infections. Chickens were divided into three groups. One group was kept as uninfected control, and the birds of the other two groups were either experimentally infected with 100 or 1,000 embryonated/infective eggs of either <em>Ascaridia galli</em> or <em>Heterakis gallinarum</em></p>
Fig. 1 in Pre-treatment of canine plasma with heat, rather than acid, efficiently enhances Dirofilaria immitiS antigen detection
Fig. 1 Detection of Dirofilaria immitis antigen by ELISA for twofold-diluted plasma samples with and without treatment. A spectrophotometric OD reading at a wavelength of 415 nm was obtained for each sample using the microplate reader SpectraMax® i Series-Spectramax Id3. Filled black circle, untreated sera. Open blue circle, ICD-1 protocol. Filled blue circle, ICD-2 protocol. Open red circle, ICD-3 protocol. Filled red circle, ICD-4 protocol. The error was represented as mean ± 95% confidence interval
Fig. 1 in Identification and characterization of Rhipicephalus (Boophilus) microplus candidate protective antigens for the control of cattle tick infestations
Fig. 1 Antibody response in vaccinated cattle. Bovine serum antibody titers to recombinant antigens were determined by ELISA in cattle vaccinated with ubiquitin, subolesin, Bm86, and adjuvant/ saline control. Antibody titers in immunized cattle were expressed as the OD450 nm value for the highest serum dilution (1:1,000) and compared between vaccinated and control cattle using an ANOVA test (*P<0.05). The time of vaccination shots (arrows) and tick infestation are indicated
Figure 5 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography
Figure 5. The intensity values of NIE (a), SsIR (b), and NIE-SsIR (c) ICT kits were evaluated using an in-house strip reader (red line indicates cut-off intensity value). Groups I, II, and III represented healthy controls, proven strongyloidiasis, and other parasitic infections, respectively. Any value above the cut-off value (red horizontal lines) is negative. Receiver operator characteristic (ROC) area analyses of the NIE, SsIR, and NIE-SsIR ICT kits to compare accuracy of all kits with gold standard methods (d).
Figure 3 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography
Figure 3. The purified NIE (a) and SsIR (b) fusion-tagged proteins visualized following electrophoresis through 12% and 10% SDS–PAGE, respectively. The gels were stained with Coomassie Brilliant Blue. Representative dot ELISAs (c) using NIE (1–2), SsIR (3–4), and mixed NIE and SsIR (5–6) proteins as the antigens probed with pooled positive (1, 3, 5) and negative (2, 4, 6) sera. M indicates molecular mass maker.
Figure 1 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography
Figure 1. Flow diagram of study design for the NIE, SsIR, and NIE-SsIR ICT kits. The SsIR ICT kit procedure was performed as previously described by Boonroumkaew et al. [4].
Figure 2 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography
Figure 2. The parts of an ICT kit (left) and reference colour card (right) (a). An ICT kit showing a positive result (b) with a band at both control (C) and test (T) lines. An ICT kit showing a negative result (c) with a band only at the C line. The intensity value of the colour image is specified by the red, green, and blue parameters as separate integers from 0 to 255 with the 8-bit representation of a pixel in the image. The intensity values were plotted for C and T lines of the positive (d) and negative (e) ICT kits. The intensity values (d and e) were related to colour band intensity of C and T lines in the strips. S indicates sample hole.
Figure 4 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography
Figure 4. Representative results of the NIE, SsIR, and NIE-SsIR ICT kits, Positive, positive pooled serum samples; Negative, negative pooled serum samples; Hc, healthy control; Ss, proven strongyloidiasis; Gl, giardiasis; Eh, amoebiasis; Bh, blastocystosis; Ov, opisthorchiasis; Fg, fascioliasis; Ph, paragonimiasis; Tn, taeniasis; Cc, cysticercosis; Se, sparganosis; Hw, hookworm infections; Al, ascariasis; Tt, trichuriasis; Ts, trichinellosis; Ac, angiostrongyliasis; Gs, gnathostomiasis; Cp, capillariasis. The intensity cut-off levels for a positive result of the NIE, SsIR, and NIE-SsIR ICT kits were 1, 1, and>0.5, respectively by the naked eye and <164, <164, and <167, respectively by the in-house strip reader. The "+" and "—" symbols indicated positive and negative results.
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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)
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.
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.