Ontology highlight
ABSTRACT:
PROVIDER: PRJNA909326 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22590174.fastq.gz | Fastqsanger.gz | |||
| SRR22590175.fastq.gz | Fastqsanger.gz | |||
| SRR22590176.fastq.gz | Fastqsanger.gz | |||
| SRR22590177.fastq.gz | Fastqsanger.gz | |||
| SRR22590178.fastq.gz | Fastqsanger.gz |
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2012-08-09 | E-GEOD-21721 | biostudies-arrayexpress
Project description:We report the application of Solexa/Illumina’s RNA-seq sequencing approaches for transcriptome in a marine fish under different conditions (bacterial- and mock-challenged conditions). By obtaining over four billion bases of sequence from the cDNA, we generated 169,950 none-redundant consensus sequences, from which 44842 functional transcripts with complete or various length of encoding regions were identified. More than 52% of these transcripts could be enriched in approximately 219 known metabolic or signaling pathways, among of which 2673 transcripts were found to be associated with immune-relevant genes. Besides, about 8% of the transcripts seemed fish-specific genes that have never been described before. This study provides a framework for the application of comprehensive chromatin profiling towards characterization of diverse mammalian cell populations. Our study provided a global survey of the gene activities in host defense against bacterial infection in a non-model marine fish.
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Project description:Rationale: Lower respiratory tract infections continue to exact unacceptable worldwide mortality, often because the infecting pathogen cannot be identified. The respiratory epithelia provide protection from pneumonias through organism-specific generation of antimicrobial products, offering potential insight into the identity of infecting pathogens. Objective: This study assesses the capacity of the host gene expression response to infection to predict lower respiratory pathogens without reliance on culture data. Methods: Mice were inhalationally challenged with S. pneumoniae, P. aeruginosa, A. fumigatus or PBS prior to whole genome gene expression microarray analysis of their pulmonary parenchyma. Characteristic gene expression patterns for each condition were identified, allowing the derivation of prediction rules for each pathogen. After confirming the predictive capacity of gene expression data in blinded challenges, a computerized algorithm was devised to predict the infectious conditions of subsequent subjects. Measurements and Main Results: We observed robust, pathogen-specific gene expression patterns as early as 2 h after infection. We were able to develop a predictive model that used a limited number of specifically regulated transcripts to discriminate perfectly among infecting pathogens in the training data. Validation trials using an algorithmic decision tree revealed 94.4% diagnostic accuracy when discerning the presence of bacterial infection. The model subsequently differentiated between bacterial pathogens with 71.4% accuracy and between non-bacterial conditions with 70.0% accuracy, both far exceeding the expected diagnostic yield of standard culture-based bronchoscopy with bronchoalveolar lavage. Conclusions: These data substantiate the specificity of the pulmonary innate immune response and support the feasibility of a gene expression-based clinical tool for pneumonia diagnosis. Keywords: pathogen- and time-dependent host lung gene expression changes in pneumonia
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