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      Conserved Regulation of p53 Network Dosage by MicroRNA–125b Occurs through Evolving miRNA–Target Gene Pairs

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          Abstract

          MicroRNAs regulate networks of genes to orchestrate cellular functions. MiR-125b, the vertebrate homologue of the Caenorhabditis elegans microRNA lin-4, has been implicated in the regulation of neural and hematopoietic stem cell homeostasis, analogous to how lin-4 regulates stem cells in C. elegans. Depending on the cell context, miR-125b has been proposed to regulate both apoptosis and proliferation. Because the p53 network is a central regulator of both apoptosis and proliferation, the dual roles of miR-125b raise the question of what genes in the p53 network might be regulated by miR-125b. By using a gain- and loss-of-function screen for miR-125b targets in humans, mice, and zebrafish and by validating these targets with the luciferase assay and a novel miRNA pull-down assay, we demonstrate that miR-125b directly represses 20 novel targets in the p53 network. These targets include both apoptosis regulators like Bak1, Igfbp3, Itch, Puma, Prkra, Tp53inp1, Tp53, Zac1, and also cell-cycle regulators like cyclin C, Cdc25c, Cdkn2c, Edn1, Ppp1ca, Sel1l, in the p53 network. We found that, although each miRNA–target pair was seldom conserved, miR-125b regulation of the p53 pathway is conserved at the network level. Our results lead us to propose that miR-125b buffers and fine-tunes p53 network activity by regulating the dose of both proliferative and apoptotic regulators, with implications for tissue stem cell homeostasis and oncogenesis.

          Author Summary

          MicroRNAs (miRNAs) are tiny endogenous RNAs that can regulate the expression of hundreds of genes simultaneously, thus orchestrating changes in gene networks and mediating cellular functions in both plants and animals. Although the identification of individual targets of miRNAs is of major importance, to date few studies have sought to uncover miRNA targets at the gene network level and general principles of miRNA regulation at the network level. Here we describe how miR-125b targets 20 apoptosis and proliferation genes in the p53 network. We found that, although each miRNA-target pair evolves rapidly across vertebrates, regulation of the p53 pathway by miR-125b is conserved at the network level. The structure of the miR-125b regulatory network suggests that miR-125b buffers and fine-tunes p53 network activity. This buffering feature of miR-125b has implications for our understanding of how miR-125b regulates oncogenesis and tissue stem cell homeostasis. We believe these findings on miR-125b support a new fundamental principle for how miRNAs regulate gene networks in general.

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          Most cited references43

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          A coding-independent function of gene and pseudogene mRNAs regulates tumour biology

          The canonical role of messenger RNA (mRNA) is to deliver protein-coding information to sites of protein synthesis. However, given that microRNAs bind to RNAs, we hypothesized that RNAs possess a biological role in cancer cells that relies upon their ability to compete for microRNA binding and is independent of their protein-coding function. As a paradigm for the protein-coding-independent role of RNAs, we describe the functional relationship between the mRNAs produced by the PTEN tumour suppressor gene and its pseudogene (PTENP1) and the critical consequences of this interaction. We find that PTENP1 is biologically active as determined by its ability to regulate cellular levels of PTEN, and that it can exert a growth-suppressive role. We also show that PTENP1 locus is selectively lost in human cancer. We extend our analysis to other cancer-related genes that possess pseudogenes, such as oncogenic KRAS. Further, we demonstrate that the transcripts of protein coding genes such as PTEN are also biologically active. Together, these findings attribute a novel biological role to expressed pseudogenes, as they can regulate coding gene expression, and reveal a non-coding function for mRNAs.
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            Network motifs in the transcriptional regulation network of Escherichia coli

            Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.
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              Structure and function of the feed-forward loop network motif.

              Engineered systems are often built of recurring circuit modules that carry out key functions. Transcription networks that regulate the responses of living cells were recently found to obey similar principles: they contain several biochemical wiring patterns, termed network motifs, which recur throughout the network. One of these motifs is the feed-forward loop (FFL). The FFL, a three-gene pattern, is composed of two input transcription factors, one of which regulates the other, both jointly regulating a target gene. The FFL has eight possible structural types, because each of the three interactions in the FFL can be activating or repressing. Here, we theoretically analyze the functions of these eight structural types. We find that four of the FFL types, termed incoherent FFLs, act as sign-sensitive accelerators: they speed up the response time of the target gene expression following stimulus steps in one direction (e.g., off to on) but not in the other direction (on to off). The other four types, coherent FFLs, act as sign-sensitive delays. We find that some FFL types appear in transcription network databases much more frequently than others. In some cases, the rare FFL types have reduced functionality (responding to only one of their two input stimuli), which may partially explain why they are selected against. Additional features, such as pulse generation and cooperativity, are discussed. This study defines the function of one of the most significant recurring circuit elements in transcription networks.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                September 2011
                September 2011
                15 September 2011
                : 7
                : 9
                : e1002242
                Affiliations
                [1 ]Stem Cell and Developmental Biology, Genome Institute of Singapore, Singapore, Singapore
                [2 ]Immune Disease Institute and Program in Cellular and Molecular Medicine, Children's Hospital Boston, Harvard Medical School, Boston, Massachusetts, United States of America
                [3 ]Computation and Systems Biology, Singapore–MIT Alliance, Singapore, Singapore
                [4 ]Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America
                [5 ]NUS Graduate School for Integrative Sciences and Engineering, Singapore, Singapore
                [6 ]Fish Developmental Biology, Institute of Molecular and Cell Biology, Singapore, Singapore
                [7 ]Bioinformatics Group, Singapore Immunology Network, Singapore, Singapore
                [8 ]Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
                [9 ]Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, United States of America
                [10 ]Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
                [11 ]Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America
                University of California San Francisco, United States of America
                Author notes

                Conceived and designed the experiments: MTNL NS-C HFL BL. Performed the experiments: MTNL NS-C SLK LC JT CT. Analyzed the data: MTNL NS-C. Contributed reagents/materials/analysis tools: VK EO AL HY. Wrote the paper: NS-C MTNL HFL BL JL.

                Article
                PGENETICS-D-11-00477
                10.1371/journal.pgen.1002242
                3174204
                21935352
                8c265206-a3bd-46a0-bdfd-ffe1fc988288
                Le T. N. et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 6 March 2011
                : 27 June 2011
                Page count
                Pages: 11
                Categories
                Research Article
                Biology
                Genetics
                Gene Expression
                RNA interference

                Genetics
                Genetics

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