Dataset Information

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.

ORGANISM(S): Mus musculus

PROVIDER: GSE41607 | GEO | 2013/10/18

SECONDARY ACCESSION(S): PRJNA177680

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description: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

Project description:Currently, micro RNAs (miRNAs) constitute a promising models for cell-to-cell communication since they are transferred between cells to execute essential roles in many processes. Their structure and size, in addition to various transport mechanisms, allow them to remain stable within various biological fluids, surviving in extremely adverse conditions, including low pH, boiling, and freezing. The presence of miRNAs in reproductive fluids, such as follicular, uterine and seminal fluid, indicate potential roles in the reproductive system. These extracellular miRNAs can be transported by lipoproteins (both HDL and LDL) or other proteins, including Argonaute2 (AGO2) and nucleophosmin1 (NPM1). Another transport system is mediated by extracellular vesicles (EVs), such as apoptotic bodies, microvesicles (MVs) and/or exosome-like vesicles. EVs protect miRNAs from degradation and contribute to their stability within biological fluids. Furthermore, EVs can transport a wide range of components packaged in a selective way. For instance, Squadrito et al. (2014), used macrophages and endothelial cells to demonstrate that the sorting of miRNAs into EVs for heterotrophic cell communication is altered by both, the presence of target transcripts and the self-presence of the respectively miRNA. In addition, the signature of miRNAs found in the exosomes significantly differed for those detected in the parent cells. To date, mechanisms controlling the specific loading of miRNAs into exosomes remain unclear. Indeed, several mechanisms may govern exosome sorting of specific subsets of miRNAs. MiRNA sorting appears to be influenced by different pathways and molecules in different cell types and tissues, and miRNAs contain well defined motifs (i.e., EXOmotifs), that direct the miRNA allocation into exosomes before delivery into recipient cells. A recent study showed that this RNA sequence can be recognized by the sumoylated form of the heterogeneous ribonucleoprotein A2B1 (hnRNPA2B1). Moreover, a terminal 30 nucleotide addition in miRNAs affects their selective sorting in B cells. Another hypothesis suggests that RNAs are selectively sorted depending on the differential affinity of RNA motifs towards the raft-like region of the cytoplasmic surface of microvesicular body (MVB) limiting membranes. Current data indicate that cells can communicate with each other through the transfer of miRNA-loaded exosomes. For example, monocyte-derived exosomes deliver miR-150 to endothelial cells and enhance endothelial cell migration by reducing c-myb expression. The miRNA content of exosomes plays a critical role in such cell-to-cell communication and determines the fate of recipient cells. Thus, exosomes derived from the bone marrow mesenchymal stromal cells of myeloma patients promote tumor growth depending on the content of miR-15a in exosomes. Our group has described a novel cell-to-cell communication mechanism involving the delivery of endometrial miRNAs from the maternal endometrium to the trophectoderm cells of preimplantation embryos. Specifically, in B6C3 derived mouse embryos, we found EV-associated and free miR-30d to cause overexpression of genes involved in embryonic adhesion processes, including Itb3, Itga7 and Cdh5. Furthermore, supplementing murine embryos with miR-30d significantly improved embryo adhesion, suggesting that external miRNAs may have a functional role as transcriptomic modifiers of preimplantation embryos. Based on profiling of miRNAs in endometrial fluid, maternally-derived miRNAs are present within EVs in the uterine microenvironment. The internalization of maternally-derived exosomes has been visualized, but the mechanism by which miR-30d becomes incorporated into exosomes remains unknown. The present study aimed to elucidate the underlying mechanism of hsa-miR-30d transfer from human endometrial epithelial cells (hEECs) to the interior of exosomes and eventually to early-stage blastocysts, using a mir-30d knockout murine mode.

Dataset Information

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

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets