Dataset Information

Micro-RNAs in regenerating lungs: an integrative systems biology analysis of murine influenza pneumonia.

ABSTRACT:

Background

Tissue regeneration in the lungs is gaining increasing interest as a potential influenza management strategy. In this study, we explored the role of microRNAs, short non-coding RNAs involved in post-transcriptional regulation, during pulmonary regeneration after influenza infection.

Results

We profiled miRNA and mRNA expression levels following lung injury and tissue regeneration using a murine influenza pneumonia model. BALB/c mice were infected with a sub-lethal dose of influenza A/PR/8(H1N1) virus, and their lungs were harvested at 7 and 15 days post-infection to evaluate the expression of ~300 miRNAs along with ~36,000 genes using microarrays. A global network was constructed between differentially expressed miRNAs and their potential target genes with particular focus on the pulmonary repair and regeneration processes to elucidate the regulatory role of miRNAs in the lung repair pathways. The miRNA arrays revealed a global down-regulation of miRNAs. TargetScan analyses also revealed specific miRNAs highly involved in targeting relevant gene functions in repair such as miR-290 and miR-505 at 7 dpi; and let-7, miR-21 and miR-30 at 15 dpi.

Conclusion

The significantly differentially regulated miRNAs are implicated in the activation or suppression of cellular proliferation and stem cell maintenance, which are required during the repair of the damaged lungs. These findings provide opportunities in the development of novel repair strategies in influenza-induced pulmonary injury.

SUBMITTER: Tan KS

PROVIDER: S-EPMC4108790 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

Micro-RNAs in regenerating lungs: an integrative systems biology analysis of murine influenza pneumonia.

Tan Kai Sen KS Choi Hyungwon H Jiang Xiaoou X Yin Lu L Seet Ju Ee JE Patzel Volker V Engelward Bevin P BP Chow Vincent T VT

BMC genomics 20140711

<h4>Background</h4>Tissue regeneration in the lungs is gaining increasing interest as a potential influenza management strategy. In this study, we explored the role of microRNAs, short non-coding RNAs involved in post-transcriptional regulation, during pulmonary regeneration after influenza infection.<h4>Results</h4>We profiled miRNA and mRNA expression levels following lung injury and tissue regeneration using a murine influenza pneumonia model. BALB/c mice were infected with a sub-lethal dose ...[more]

PMID: 25015185

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Project description:Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes.Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000 M. tuberculosis genes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information about M. tuberculosis persistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.

Dataset Information

Micro-RNAs in regenerating lungs: an integrative systems biology analysis of murine influenza pneumonia.

Background

Results

Conclusion

Publications

Micro-RNAs in regenerating lungs: an integrative systems biology analysis of murine influenza pneumonia.

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