Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Gene expression data in Mouse Lung Epithelial cells MLE-12 transfected with inhibitors and precursors for miR-30a, miR-30d, miR-23b, miR-125 and miR-337, miR-466a, miR466d, miR-476c, respectively


ABSTRACT: The regulation of gene expression in cells, including by microRNAs (miRNAs), is a dynamic process. Current methods for identifying microRNA targets by combining sequence, miRNA and mRNA expression data do not adequately utilize the temporal information and thus miss important miRNAs and their targets. We developed a new method, mirDREM, that uses probabilistic modeling to reconstruct dynamic regulatory networks which explain how temporal gene expression is jointly regulated by microRNAs and transcription factors (TFs). We used mirDREM to study the regulation of postnatal lung development in mice. The reconstructed network for this process identified several known miRNAs and TFs and provided novel predictions about additional miRNAs and the specific developmental phases they regulate. Microarray data of Mouse Lung Epithelial cells MLE-12 after transfection with inhibitors for miR-30a, miR-30d, miR-23b and miR-125 and with precursors for miR-337, miR-466a, miR466d and miR-476c. The results provide a general insight into the gene expression profile which was modulated by the inhibition or overexpression of these microRNAs. We experimentally validated several predictions and show that miR-30d, miR-30a, and miR-467c are new regulators of proliferation in lung cells. Our analysis establishes new links between identified miRNAs and lung diseases, supporting recent evidence that such diseases may represent reversal of lung differentiation. We first analyzed the endogenous expression of miR-30a, miR-30d, miR-23b, miR-125, miR-337, miR-466a, miR466d and miR-476c in MLE-12 cells. Then, we transfected the MLE-12 cells with inhibitors ( miR-30a, miR-30d, miR-23b and miR-125) for the highly expressed and precursos for those that were almost undetectable (miR-337, miR-466a, miR466d and miR-476c). RNA level of the 8 microRNAs was verify by qRT-PCR in order to validate the transfection efficiency. Finally, 0.5ug of total RNA was used to performe the gene expression microarrays for each condition

ORGANISM(S): Mus musculus

SUBMITTER: christian lino cardenas 

PROVIDER: E-GEOD-41607 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Reconstructing dynamic microRNA-regulated interaction networks.

Schulz Marcel H MH   Pandit Kusum V KV   Lino Cardenas Christian L CL   Ambalavanan Namasivayam N   Kaminski Naftali N   Bar-Joseph Ziv Z  

Proceedings of the National Academy of Sciences of the United States of America 20130828 39


The regulation of gene expression in cells, including by microRNAs (miRNAs), is a dynamic process. Current methods for identifying miRNA targets by combining sequence and miRNA and mRNA expression data do not adequately use the temporal information and thus miss important miRNAs and their targets. We developed the MIRna Dynamic Regulatory Events Miner (mirDREM), a probabilistic modeling method that uses input-output hidden Markov models to reconstruct dynamic regulatory networks that explain how  ...[more]

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