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Compared to adults, infants suffer higher rates of hospitalization, severe clinical complications, and mortality due to influenza infection. We found that γδ T cells protected neonatal mice against mortality during influenza infection. γδ T cell deficiency did not alter viral clearance or interferon-γ production. Instead, neonatal influenza infection induced the accumulation of interleukin-17A (IL-17A)-producing γδ T cells, which was associated with IL-33 production by lung epithelial cells. Neonates lacking IL-17A-expressing γδ T cells or Il33 had higher mortality upon influenza infection. γδ T cells and IL-33 promoted lung infiltration of group 2 innate lymphoid cells and regulatory T cells, resulting in increased amphiregulin secretion and tissue repair. In influenza-infected children, IL-17A, IL-33, and amphiregulin expression were correlated, and increased IL-17A levels in nasal aspirates were associated with better clinical outcomes. Our results indicate that γδ T cells are required in influenza-infected neonates to initiate protective immunity and mediate lung homeostasis.
Pubmed ID: 30170813 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
View all literature mentionsSoftware performing alignment of high-throughput RNA-seq data. Aligns RNA-seq reads to reference genome using uncompressed suffix arrays.
View all literature mentionsThis monoclonal targets CD16/32
View all literature mentionsThis monoclonal targets beta-Actin
View all literature mentionsThis monoclonal targets Stat3
View all literature mentionsThis monoclonal targets Phospho-Stat3 (Tyr705)
View all literature mentionsThis polyclonal targets Mouse IL-33
View all literature mentionsThis unknown targets Goat
View all literature mentionsThis polyclonal targets Mouse Amphiregulin Affinity Purified Ab
View all literature mentionsThis polyclonal targets Mouse Amphiregulin
View all literature mentionsThis monoclonal targets CD326
View all literature mentionsThis monoclonal targets CD31
View all literature mentionsThis monoclonal targets CD11c
View all literature mentionsThis monoclonal targets CD11b
View all literature mentionsThis monoclonal targets Ly-6G Ly-6C
View all literature mentionsThis monoclonal targets FOXP3
View all literature mentionsThis monoclonal targets IFN-gamma
View all literature mentionsThis monoclonal targets IL-17A
View all literature mentionsThis cocktail targets CD3ε, Ly-6G/Ly-6C, CD11b, CD45R/B220, TER-119
View all literature mentionsThis monoclonal targets IL-33Ralpha
View all literature mentionsThis monoclonal targets Ly-6A/E
View all literature mentionsThis monoclonal targets CD196
View all literature mentionsThis monoclonal targets CD44
View all literature mentionsThis monoclonal targets CD27
View all literature mentionsThis monoclonal targets CD25
View all literature mentionsThis monoclonal targets CD90.2
View all literature mentionsThis monoclonal targets CD3
View all literature mentionsThis monoclonal targets CD8a
View all literature mentionsThis monoclonal targets CD4
View all literature mentionsThis monoclonal targets TCR beta chain
View all literature mentionsThis monoclonal targets TCR gamma/delta
View all literature mentionsA manually curated resource of signal transduction pathways in humans. All pathways are freely available for download in BioPAX level 3.0, PSI-MI version 2.5 and SBML version 2.1 formats. The slim pathway models representing only core reactions in each pathway are available at NetSlim. All the NetPath pathway models are also submitted to WikiPathways.
View all literature mentionsSoftware package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.
View all literature mentionsBioconductor software package for Empirical analysis of Digital Gene Expression data in R. Used for differential expression analysis of RNA-seq and digital gene expression data with biological replication.
View all literature mentionsSoftware performing alignment of high-throughput RNA-seq data. Aligns RNA-seq reads to reference genome using uncompressed suffix arrays.
View all literature mentionsStatistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.
View all literature mentionsSoftware for single-cell flow cytometry analysis. Its functions include management, display, manipulation, analysis and publication of the data stream produced by flow and mass cytometers.
View all literature mentionsCell line MDCK is a Spontaneously immortalized cell line with a species of origin Canis lupus familiaris
View all literature mentionsgene symbol note: interleukin 33 mutant strain targeted mutation 1, Velocigene This is a legacy resource.
View all literature mentionsMus musculus with name B6.129P2-Tcrdtm1Mom/J from IMSR.
View all literature mentionsMus musculus with name C57BL/6J from IMSR.
View all literature mentionsCell line A-549 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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