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12,799 results for “Immunization”
Immune-mediated hematological disease in dogs is associated with alterations of the fecal microbiota: a pilot study
<div> <h3>Background</h3> <p>The dog is the most popular companion animal and is a valuable large animal model for several human diseases. Canine immune-mediated hematological diseases, including immune-mediated hemolytic anemia (IMHA) and immune thrombocytopenia (ITP), share many features in common with autoimmune hematological diseases of humans. The gut microbiome has been linked to systemic illness, but few studies have evaluated its association with immune-mediated hematological disease. To address this knowledge gap, 16S rRNA gene sequencing was used to profile the fecal microbiota of dogs with spontaneous IMHA and ITP at presentation and following successful treatment. In total, 21 affected and 13 healthy control dogs were included in the study.</p> <h3>Results</h3> <p>IMHA/ITP is associated with remodeling of fecal microbiota, marked by decreased relative abundance of the spirochete <em>Treponema</em> spp., increased relative abundance of the pathobionts <em>Clostridium septicum</em> and <em>Escherichia coli</em>, and increased overall microbial diversity. Logistic regression analysis demonstrated that <em>Treponema</em> spp. were associated with decreased risk of IMHA/ITP (odds ratio [OR] 0.24–0.34), while Ruminococcaceae UCG-009 and Christensenellaceae R-7 group were associated with increased risk of disease (OR =&thinsp;6.84 [95% CI 2–32.74] and 8.36 [95% CI 1.85–71.88] respectively).</p> <h3>Conclusions</h3> <p>This study demonstrates an association of immune-mediated hematological diseases in dogs with fecal dysbiosis, and points to specific bacterial genera as biomarkers of disease. Microbes identified as positive or negative risk factors for IMHA/ITP represent an area for future research as potential targets for new diagnostic assays and/or therapeutic applications.</p> </div>
Mitochondrial background can explain variable costs of immune deployment
<p>Corrected data and code for Model 2a from "Mitochondrial background can explain variable costs of immune deployment"</p> <p>In late March, we found that the proportion of time a fly spent active (measured as the number of active minutes (activity bouts) divided by lifespan) had been miscalculated. Although the statistical output was similar and the highest order interaction term remained significant, our interpretation of Figures 2a - 2c changed as a result of this error. </p> <p> </p>
Overcoming nutritional immunity by engineering iron-scavenging bacteria for cancer therapy
<p>Certain bacteria demonstrate the ability to target and colonize the tumor microenvironment, a characteristic that positions them as innovative carriers for delivering various therapeutic agents in cancer therapy. Nevertheless, our understanding of how bacteria adapt their physiological condition to the tumor microenvironment remains elusive. In this work, we employed liquid chromatography-tandem mass spectrometry to examine the proteome of <em>E. coli</em> colonized in murine tumors. Compared to <em>E. coli </em>cultivated in the rich medium, we found that <em>E. coli </em>colonized in tumors notably upregulated the processes related to ferric ions, including enterobactin biosynthesis and iron homeostasis. This finding indicated that the tumor is an iron-deficient environment to <em>E. coli</em>. We also found that the colonization of <em>E. coli </em>in the tumor led to an increased expression of lipocalin 2 (LCN2), a host protein that can sequester enterobactin. We therefore engineered <em>E. coli</em> to evade the nutritional immunity provided by LCN2. By introducing the IroA cluster, the <em>E. coli</em> synthesizes the glycosylated enterobactin, which creates steric hindrance to avoid the LCN2 sequestration. The IroA-<em>E. coli</em> showed enhanced resistance to LCN2 and significantly improved the anti-tumor activity in mice. Moreover, the mice were cured by the IroA-<em>E. coli </em>treatment became resistant to the tumor re-challenge, indicating the establishment of immunological memory. Overall, our study underscores the crucial role of bacteria's ability to acquire ferric ions within the tumor microenvironment for effective cancer therapy.</p>
Oncogenic calreticulin induces TGF-β expression and Treg expansion in the bone marrow microenvironment as a mechanism of immune escape
<p>This repository contains all necessary scRNA-seq inputs to reproduce the results described in "Oncogenic calreticulin induces TGF-β expression and Treg expansion in the bone marrow microenvironment as a mechanism of immune escape" by Schmidt et al. (Cancer Research 2024). </p> <p>Content:</p> <ol> <li>"MPN_calreticulin_bm.R" --> R script containing all code</li> <li>"cells_table.RDS" --> cells table containing, cell_id, UMAP coordinates, complexity, cell type annotation and metadata</li> <li>"normalized_matrix.RDS" --> quality control filtered, log2-normalized and centered expression matrix</li> <li>"reference_signatures.RDS" --> all external signatures used for this study</li> <li>"EV2_*", "EV5_*", "MPN2_*", "MPN5_*", --> cellranger outputs</li> </ol>
Quit bugging me: Phorid fly parasitoids affect expression of an immune gene in foraging fire ant workers
<p>This data contains RT-qPCR and diet measurements for quantifying gene expression of red imported fire ant, <em>Solenopsis invicta </em>workers in the presence and absence of the parasitoid fire ant decapitating fly, <em>Pseudacteon curvatus</em>, and assessing the amount of food after exposure to <em>P. curvatus</em>, respectively. The genes investigated were the <em>Solenopsis invicta</em> <em>foraging gene</em> (<em>Sifor</em>), <em>odorant binding protein 11 </em>(<em>OBP-11</em>), <em>abaecin, defensin-2 </em>(<em>Def-2</em>),<em> cytochrome P450 4C1-like </em>(<em>CYP4C1-like</em>), and <em>hymenoptaecin </em>(<em>hym</em>)<em>. Ribosomal Protein 18</em> (<em>RPL18</em>) was used as the reference gene. These genes of <em>S. invicta</em> workers of unknown infection status in fire ant colonies exposed to decapitating flies and control colonies were observed over a 48-h period. Two diets were measured before and after in control and treatment colonies (i.e., colonies exposed to <em>P. curvatus</em>).</p>
Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalised therapy
<div> <div> <div> <p>Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, a disease with dismal overall survival. Advances in treatment are hindered by a lack of preclinical models. Here we show how a personalised organotypic 'avatar' created from resected tissue, allows spatial and temporal reporting on a complete in situ tumour microenvironment, and mirrors clinical responses. Our perfusion culture method extends tumour slice viability, maintaining stable tumour content, metabolism, stromal composition, and immune cell populations for 12 days. Using multiplexed immunofluorescence and spatial transcriptomics, we identify immune neighbourhoods and potential for immunotherapy. We employed avatars to assess the impact of a pre-clinically validated metabolic therapy and show recovery of stromal and immune phenotypes and tumour re-differentiation. To determine clinical relevance, we monitored avatar response to gemcitabine treatment and identified a patient avatar-predicable response from clinical follow-up. Thus, avatars provide valuable information for the syngeneic testing of novel therapeutics and a truly personalised therapeutic assessment platform for patients.</p> </div> </div> </div>
TGF-β neutralization attenuates tumor residency of activated T cells to enhance systemic immunity in mice
<p>Deep TCR sequencing was performed using the TCR Profiling Kit from MiLaboratories (Mouse α/β TCR RNA; Kit MiLaboratories; TMMR-001). Deep TCR sequencing was analyzed using the MiXCR software from MiLaboratories per manufacturer's recommendations. The files correspond to the TCR-beta sequences.<br>The files are named as follows:</p> <p> </p> <table> <tbody> <tr> <td>file_name</td> <td>cell type sequenced</td> <td>tissue of origin</td> <td>treatment</td> <td>mouse_id</td> </tr> <tr> <td>21BA1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>23BA2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>25BA3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>27BA4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>29BA5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>22BA1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>24BA2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>26BA3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>28BA4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>30BA5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>31CON1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>6</td> </tr> <tr> <td>33CON2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>7</td> </tr> <tr> <td>35CON3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>8</td> </tr> <tr> <td>37CON4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>9</td> </tr> <tr> <td>39CON5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>10</td> </tr> <tr> <td>32CON1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>6</td> </tr> <tr> <td>34CON2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>7</td> </tr> <tr> <td>36CON3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>8</td> </tr> <tr> <td>38CON4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>9</td> </tr> <tr> <td>40CON5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>10</td> </tr> <tr> <td>1aPDL11dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>3aPDL12dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>5aPDL13dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>7aPDL14dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>9aPDL15dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>2aPDL11SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>4aPDL12SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>6aPDL13SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>8aPDL14SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>10aPDL15SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>11aTGFB1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>13aTGFB2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>15aTGFB3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>17aTGFB4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>19aTGFB5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> <tr> <td>12aTGFB1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>14aTGFB2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>16aTGFB3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>18aTGFB4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>20aTGFB5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> </tbody> </table>
Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics
<p>Supplementary material to the article: Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer’s Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics,These data are available to researchers.</p>
Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines
<p>Online Supplementary Dataset of the manuscript "Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines"</p>
Supporting dataset for the publication 'Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity'
<p>This dataset accompanies the publication "Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity" by William S DeWitt III, Anajane Smith, Gary Schoch, John A Hansen, Frederick A Matsen IV and Philip Bradley, accepted for publication in the journal eLife. It contains data on T cell receptor occurrence patterns and repertoire features that can be processed with the software tools provided in the github repository https://github.com/phbradley/pubtcrs in order to replicate the findings of the manuscript.</p> <p> </p>
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization
<p>Data resulting from the experiments described in "Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization" (https://doi.org/10.1016/j.swevo.2018.12.010). The contents of the files is the following:</p> <ul> <li>CAMO-AIS_SWEVO.zip: All of the data below in a single file</li> <li>CAMO_Iowa.zip: Audio and Data for the orchestrations with the Iowa sound database found at http://theremin.music.uiowa.edu/MIS.html</li> <li>CAMO_Phil.zip: Audio and Data for the orchestrations with the Philharmonia sound database found at https://www.philharmonia.co.uk/explore/sound_samples</li> <li>CAMO_RWC.zip: Audio and Data for the orchestrations with the RWC sound database found at https://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html</li> <li>CAMO_SOL.zip: Audio and Data for the orchestrations with the Studio Online sound database available with Orchids http://forumnet.ircam.fr/product/orchids-en/</li> <li>Listening_Test.zip: Raw data (i.e., perceptual similarity ratings) from the listening test found at http://http://camo.inesctec.pt/. This data is anonymous (each participant is assigned a reference number) so the participants cannot be identified.</li> </ul> <p>See the README.txt file for a detailed description of the contents of each file.</p>
Supplemental Material from: Immune Profiling of Thyroid Carcinomas Suggests the Existence of Two Major Phenotypes: an ATC-like and a PDTC-like
<p>Supplemental Material from: Immune Profiling of Thyroid Carcinomas Suggests the Existence of Two Major Phenotypes: an ATC-like and a PDTC-like</p>
A machine learning model reveals expansive downregulation of ligand-receptor interactions enhancing lymphocyte infiltration in melanoma with developed resistance to Immune Checkpoint Blockade
<p>Data repository containing the data to reproduce the results and findings that are published in:</p> <p>Sahni, S., Wang, B., Wu, D. <em>et al.</em> A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. <em>Nat Commun</em> <strong>15</strong>, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4</p>
Metadata: In-ovo stimulation trains innate immunity to mitigate Campylobacter jejuni in broiler chickens
<p><span><span>Metadata of the paper investigating the physiological and genomic responses of these <em>in-ovo </em>stimulated chickens to <em>Campylobacter jejuni </em>infection. </span> </span></p>
SupplementaryData_A_helper_NLR_targets_organellar_membranes_to_trigger_immunity
<p>Supplementary data for "A_helper_NLR_targets_organellar_membranes_to_trigger_immunity" including:</p> <p>1.Phylogenetics analysis - Data S1 to 6) - Fig. 1A and S1</p> <p>2.AlphaFold3 Models - Data S7 - Fig. 1B and S2</p> <p>3.NRG1_delta14(Nb)_delta16(At) alignment - Data S8</p> <p>4.nrg1 KO plants genotyping (Data S9) - more details in Materials and Methods.</p> <p>5.HR_index_data - Data S10 - raw HR data presented in manuscript.</p> <p>6.Movie S1 - NRG1 puncta upon XopQ activation.</p>
Spleen STARmap PLUS dataset of Transient hepatic reconstitution of thymic factors enhances aged immunity
<p>Here is the STARmap PLUS dataset of Spleen section included in "<strong>Transient hepatic reconstitution of thymic factors enhances aged immunity</strong>" from Friedrich et al. </p>
Data for Marshall et al, "Microbial metabolism disrupts cytokine activity to impact host immune response"
<p>Raw data for publication "Microbial metabolism disrupts cytokine activity to impact host immune response", Marshall EKP et al. The archive is organized into folders containing raw data pertaining to each figure.</p>
Modulation of gene expression in immune related organs by in-ovo stimulation with probiotics and prophybiotics in broiler chickens
<p><span>The current study was performed to further investigate the tissue specific immune modulation and metabolic gene regulation of these <em>in-ovo</em> stimulated chickens. Accordingly, the expression of immune related genes was determined in the secondary immune organs, cecal tonsils and the spleen as well as the liver which is an important organ for both the immune system and metabolism. Additionally, the expression of the genes related to metabolism was also analyzed in the liver.</span></p>
Diversity in CRISPR-based immunity protects susceptible genotypes by restricting phage spread and evolution
Diversity in host resistance often associates with reduced pathogen spread. This may result from ecological and evolutionary processes, likely with feedback between them. Theory and experiments on bacteria-phage interactions have shown that genetic diversity of the bacterial adaptive immune system can limit phage evolution to overcome resistance. Using the CRISPR-Cas bacterial immune system and lytic phage, we engineered a host-pathogen system where each bacterial host genotype could be infected by only one phage genotype. With this model system, we explored how CRISPR diversity impacts the spread of phage when they can overcome a resistance allele, how immune diversity affects the evolution of the phage to increase its host range, and if there was feedback between these processes. We show that increasing CRISPR diversity benefits susceptible bacteria via a dilution effect, which limits the spread of the phage. We suggest that this ecological effect impacts the evolution of novel phage genotypes, which then feeds back into phage population dynamics.
No evidence for single-copy immune-gene specific signals of selection in termites
<p>Selection pressures from pathogens appear to play an important role in shaping social evolution. Social behavior, in particular brood care, is associated with pathogen pressure in wood-dwelling "lower" termites. Yet, generally pathogen pressure is predicted to be low in wood-dwelling termite species that never leave the nest except for the mating flight. In comparison, pathogen pressure is predicted to be higher in species that leave the nest to forage, and thus constantly encounter a diversity of microbes from their environment. We hypothesized that such differences in predicted pathogen pressure are also reflected by differences in the intensity of natural selection on immune genes. We tested this hypothesis in a phylogenetic framework, analyzing rates of non-synonymous and synonymous substitutions on single-copy immune genes. Therefore, we leveraged recent genomic and transcriptomic data from eight termite species, representing wood-dwelling and foraging species as well as 14 additional species spanning the winged insects (Pterygota). Our results provide no evidence for a role of pathogen pressure in selection intensity on single-copy immune genes. Instead, we found evidence for a genome-wide pattern of relaxed selection in termites.</p>
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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.