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There has been a striking generational increase in life-threatening food allergies in Westernized societies1,2. One hypothesis to explain this rising prevalence is that twenty-first century lifestyle practices, including misuse of antibiotics, dietary changes, and higher rates of Caesarean birth and formula feeding have altered intestinal bacterial communities; early-life alterations may be particularly detrimental3,4. To better understand how commensal bacteria regulate food allergy in humans, we colonized germ-free mice with feces from healthy or cow's milk allergic (CMA) infants5. We found that germ-free mice colonized with bacteria from healthy, but not CMA, infants were protected against anaphylactic responses to a cow's milk allergen. Differences in bacterial composition separated the healthy and CMA populations in both the human donors and the colonized mice. Healthy and CMA colonized mice also exhibited unique transcriptome signatures in the ileal epithelium. Correlation of ileal bacteria with genes upregulated in the ileum of healthy or CMA colonized mice identified a clostridial species, Anaerostipes caccae, that protected against an allergic response to food. Our findings demonstrate that intestinal bacteria are critical for regulating allergic responses to dietary antigens and suggest that interventions that modulate bacterial communities may be therapeutically relevant for food allergy.
Pubmed ID: 30643289
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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 mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023.Software package for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data, but also supporting analysis of other types of data. QIMME analyzes and transforms raw sequencing data generated on Illumina or other platforms to publication quality graphics and statistics.
View all literature mentionsSoftware package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsA program to merge paired end Illumina reads that are overlapping into a single longer read.
View all literature mentionsA chimeric sequence detection utility, compatible with near-full length Sanger sequences and shorter 454-FLX sequences (~500 bp).
View all literature mentionsQuality control software that perform checks on raw sequence data coming from high throughput sequencing pipelines. This software also provides a modular set of analyses which can give a quick impression of the quality of the data prior to further analysis.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Algorithm for high-dimensional biomarker discovery and explanation that identifies genes, pathways, or taxa characterizing the differences between two or more biological conditions. The algorithm identifies features that are statistically different among biological classes, then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior. Statistical significance and biological relevance are emphasized.
View all literature mentionslaboratory mouse with name C3H/HeN from MGI.
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