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      Integrated ordination of miRNA and mRNA expression profiles

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          Abstract

          Background

          Several studies have investigated miRNA and mRNA co-expression to identify regulatory networks at the transcriptional level. A typical finding of these studies is the presence of both negative and positive miRNA-mRNA correlations. Negative correlations are consistent with the expected, faster degradation of target mRNAs, whereas positive correlations denote the existence of feed-forward regulations mediated by transcription factors. Both mechanisms have been characterized at the molecular level, although comprehensive methods to represent miRNA-mRNA correlations are lacking. At present, genome-wide studies are able to assess the expression of more than 1000 mature miRNAs and more than 35,000 well-characterized human genes. Even if studies are generally restricted to a small subset of genes differentially expressed in specific diseases or experimental conditions, the number of potential correlations remains very high, and needs robust multivariate methods to be conveniently summarized by a small set of data.

          Results

          Nonparametric Kendall correlations were calculated between miRNAs and mRNAs differentially expressed in livers of patients with acute liver failure (ALF) using normal livers as controls. Spurious correlations due to the histopathological composition of samples were removed by partial correlations. Correlations were then transformed into distances and processed by multidimensional scaling (MDS) to map the miRNA and mRNA relationships. These showed: (a) a prominent displacement of miRNA and mRNA clusters in ALF livers, as compared to control livers, indicative of gene expression dysregulation; (b) a clustering of mRNAs consistent with their functional annotations [CYP450, transcription factors, complement, proliferation, HLA class II, monocytes/macrophages, T cells, T-NK cells and B cells], as well as a clustering of miRNAs with the same seed sequence; and (c) a tendency of miRNAs and mRNAs to populate distinct regions of the MDS plot. MDS also allowed to visualize the network of miRNA-mRNA target pairs.

          Conclusions

          Different features of miRNA and mRNA relationships can be represented as thematic maps within the framework of MDS obtained from pairwise correlations. The symmetric distribution of positive and negative correlations between miRNA and mRNA expression suggests that miRNAs are involved in a complex bidirectional molecular network, including, but not limited to, the inhibitory regulation of miRNA targets.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-015-1971-9) contains supplementary material, which is available to authorized users.

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          Most cited references 32

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight?

              MicroRNAs constitute a large family of small, approximately 21-nucleotide-long, non-coding RNAs that have emerged as key post-transcriptional regulators of gene expression in metazoans and plants. In mammals, microRNAs are predicted to control the activity of approximately 30% of all protein-coding genes, and have been shown to participate in the regulation of almost every cellular process investigated so far. By base pairing to mRNAs, microRNAs mediate translational repression or mRNA degradation. This Review summarizes the current understanding of the mechanistic aspects of microRNA-induced repression of translation and discusses some of the controversies regarding different modes of microRNA function.
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                Author and article information

                Affiliations
                [ ]Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
                [ ]Liver Transplantation Center, Brotzu Hospital, Cagliari, Italy
                [ ]Hepatic Pathogenesis Section, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
                Contributors
                gdiaz@unica.it
                faustozamboni@aob.it
                ashleytice@gmail.com
                pfarci@niaid.nih.gov
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                12 October 2015
                12 October 2015
                2015
                : 16
                4603994 1971 10.1186/s12864-015-1971-9
                © Diaz et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                Methodology Article
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                © The Author(s) 2015

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