Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy

BackgroundmicroRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs.ResultsWe present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future.ConclusionsThis paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets show that the proposed method uncovers many known miRNA targets as well as new potentially promising miRNA-mRNA interactions. These interactions could not be achieved by the normal Bayesian network structure learning.

[1]  David Maxwell Chickering,et al.  Learning Bayesian Networks is , 1994 .

[2]  Jan Krützfeldt,et al.  Strategies to determine the biological function of microRNAs , 2006, Nature Genetics.

[3]  Jason H. Moore,et al.  Characterization of microRNA expression levels and their biological correlates in human cancer cell lines. , 2007, Cancer research.

[4]  Chunxiang Zhang,et al.  MicroRNomics: a newly emerging approach for disease biology. , 2008, Physiological genomics.

[5]  S. Cohen,et al.  microRNA functions. , 2007, Annual review of cell and developmental biology.

[6]  K. Gunsalus,et al.  Combinatorial microRNA target predictions , 2005, Nature Genetics.

[7]  G. Goodall,et al.  The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1 , 2008, Nature Cell Biology.

[8]  Dirk Husmeier,et al.  Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks. , 2007, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

[9]  Janet Rossant,et al.  Annual review of cell and developmental biology. , 2007 .

[10]  C. Burge,et al.  Prediction of Mammalian MicroRNA Targets , 2003, Cell.

[11]  Tongbin Li,et al.  miRecords: an integrated resource for microRNA–target interactions , 2008, Nucleic Acids Res..

[12]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[13]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[14]  Ligang Wu,et al.  MicroRNAs direct rapid deadenylation of mRNA. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Luis M. de Campos,et al.  Bayesian network learning algorithms using structural restrictions , 2007, Int. J. Approx. Reason..

[16]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[17]  Joel Michalski,et al.  MicroRNA-146a modulates human bronchial epithelial cell survival in response to the cytokine-induced apoptosis. , 2009, Biochemical and biophysical research communications.

[18]  Giovanni De Micheli,et al.  Prediction of regulatory modules comprising microRNAs and target genes , 2005, ECCB/JBI.

[19]  A. Pasquinelli,et al.  Regulation by let-7 and lin-4 miRNAs Results in Target mRNA Degradation , 2005, Cell.

[20]  Jens Timmer,et al.  Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge , 2007, BMC Systems Biology.

[21]  Dong-Guk Shin,et al.  Reverse Engineering of Gene Regulatory Network by Integration of Prior Global Gene Regulatory Information , 2008, 2008 IEEE International Conference on Bioinformatics and Biomedicine.

[22]  J. Steitz,et al.  Switching from Repression to Activation: MicroRNAs Can Up-Regulate Translation , 2007, Science.

[23]  K. Kimura,et al.  Hypoxia promotes fibrogenesis in vivo via HIF-1 stimulation of epithelial-to-mesenchymal transition. , 2007, The Journal of clinical investigation.

[24]  Lin He,et al.  MicroRNAs: small RNAs with a big role in gene regulation , 2004, Nature Reviews Genetics.

[25]  V. Ambros.  The functions of animal microRNAs , 2004, Nature.

[26]  John Quackenbush,et al.  Seeded Bayesian Networks: Constructing genetic networks from microarray data , 2008, BMC Systems Biology.

[27]  Tu Bao Ho,et al.  Finding microRNA regulatory modules in human genome using rule induction , 2008, BMC Bioinformatics.

[28]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[29]  Martin Reczko,et al.  The database of experimentally supported targets: a functional update of TarBase , 2008, Nucleic Acids Res..

[30]  P. Savagner,et al.  Leaving the neighborhood: molecular mechanisms involved during epithelial‐mesenchymal transition , 2001, BioEssays : news and reviews in molecular, cellular and developmental biology.

[31]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[32]  W. Filipowicz,et al.  Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? , 2008, Nature Reviews Genetics.

[33]  D. Bartel,et al.  The impact of microRNAs on protein output , 2008, Nature.

[34]  G CastellanoJavier,et al.  Bayesian network learning algorithms using structural restrictions , 2007 .

[35]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[36]  Sun-Mi Park,et al.  The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2. , 2008, Genes & development.

[37]  Yvonne Tay,et al.  MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation , 2008, Nature.

[38]  Anthony C. Davison,et al.  Bootstrap Methods and Their Application , 1998 .

[39]  Brendan J. Frey,et al.  Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data , 2006, RECOMB.

[40]  L. Lim,et al.  MicroRNA targeting specificity in mammals: determinants beyond seed pairing. , 2007, Molecular cell.

[41]  Jiuyong Li,et al.  Discovery of functional miRNA-mRNA regulatory modules with computational methods , 2009, J. Biomed. Informatics.

[42]  J. Castle,et al.  Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs , 2005, Nature.

[43]  Phillip D Zamore,et al.  Beginning to understand microRNA function , 2007, Cell Research.

[44]  V. Ambros,et al.  The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 , 1993, Cell.

[45]  R. Lavker,et al.  MicroRNA-184 antagonizes microRNA-205 to maintain SHIP2 levels in epithelia , 2008, Proceedings of the National Academy of Sciences.

[46]  K. Zatloukal,et al.  miR‐29a suppresses tristetraprolin, which is a regulator of epithelial polarity and metastasis , 2009, EMBO reports.

[47]  Byoung-Tak Zhang,et al.  BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm045 Data and text mining Discovery of microRNA–mRNA modules via population-based probabilistic learning , 2007 .

[48]  Stijn van Dongen,et al.  miRBase: tools for microRNA genomics , 2007, Nucleic Acids Res..

[49]  M. Korpal,et al.  The miR-200 Family Inhibits Epithelial-Mesenchymal Transition and Cancer Cell Migration by Direct Targeting of E-cadherin Transcriptional Repressors ZEB1 and ZEB2* , 2008, Journal of Biological Chemistry.

[50]  Raghu Kalluri,et al.  The epithelial–mesenchymal transition: new insights in signaling, development, and disease , 2006, The Journal of cell biology.

[51]  R. Place,et al.  MicroRNA-373 induces expression of genes with complementary promoter sequences , 2008, Proceedings of the National Academy of Sciences.