MiRImpact as a Methodological Tool for the Analysis of MicroRNA at the Level of Molecular Pathways

  • Anton A. BuzdinEmail author
  • Nikolay M. Borisov
Reference work entry


Intracellular molecular pathways (IMPs) involve multiple gene products implicated in certain biological functions. The best-known IMPs are metabolic pathways, signaling pathways, DNA repair pathways, and cytoskeleton reorganization pathways. The pathway-level of analysis in molecular biology provides a number of advantages compared to the analysis of single genes. First of all, IMPs are more stable biomarkers. This can be explained by the fact that most frequently several or even many individual gene products are involved in a single elementary biological process. For example, the members of RAF family or regulatory protein kinases can be all involved in the same biological process of signal transduction, by acting in an interchangeable way as the MAP kinase kinase kinases downstream to the RAS proteins. The RAS family, in turn, consists of many proteins that may exert basically the same functions, and so on. A variation in the expression of a single family member is hard to interpret, whereas the pathway level of analysis enables obtaining an integral figure for all the nodes and family members. Secondly, the pathway level of data analysis makes it possible to significantly reduce the experimental error of measuring gene expression. This allows to reduce or even eliminate the batch effects and to compare the data obtained using different experimental platforms. Several analytic approaches have been published to digest the mRNA or proteomic data at the level of IMPs, but an approach crosslinking the changes in microRNA (miR) profiles with the activation of molecular pathways was missing. Recently, we proposed a bioinformatic method termed MiRImpact, which enables to link the high-throughput miR expression data with the estimated outcome on the regulation of molecular pathways. MiRImpact was used to establish interactomic signatures for hundreds of molecular pathways, specific to stem cell differentiation, cancer progression, and cytomegalovirus infection. Of note, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation for building more objective models of intracellular molecular processes.


Systems biology Bioinformatics Intracellular molecular pathways Gene expression Transcriptomics Proteomics Epigenetics MicroRNA miR Cancer Biomarkers Stem cell differentiation 

List of Abbreviations


Cells after infection


Bladder cancer


Cells highly sensitive to HCMV infection


Intracellular molecular pathway


Cells low sensitive to HCMV infection


Pathway activation strength, calculated using microRNA expression data


Next-generation sequencing


Pathway activation strength, calculated using mRNA or protein expression data


Pathway activation strength, calculated using ribosome profiling gene expression data


Cells without infection


  1. Afsari B, Geman D, Fertig EJ (2014) Learning dysregulated pathways in cancers from differential variability analysis. Cancer Informat 13:61–67Google Scholar
  2. Aliper A, Belikov AV et al (2016) In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state. Aging 8:2127–2152CrossRefGoogle Scholar
  3. Aliper AM, Frieden-Korovkina VP et al (2014) Interactome analysis of myeloid-derived suppressor cells in murine models of colon and breast cancer. Oncotarget 5:11345–11353CrossRefGoogle Scholar
  4. Artcibasova AV, Korzinkin MB et al (2016) MiRImpact, a new bioinformatic method using complete microRNA expression profiles to assess their overall influence on the activity of intracellular molecular pathways. Cell Cycle 5:689–698CrossRefGoogle Scholar
  5. Bauer-Mehren A, Furlong LI, Sanz F (2009) Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol Syst Biol 5:290CrossRefGoogle Scholar
  6. Blagosklonny MV (2011) The power of chemotherapeutic engineering: arresting cell cycle and suppressing senescence to protect from mitotic inhibitors. Cell Cycle 10:2295–2298CrossRefGoogle Scholar
  7. Blagosklonny MV (2013) MTOR-driven quasi-programmed aging as a disposable soma theory: blind watchmaker vs. intelligent designer. Cell Cycle 12:1842–1847CrossRefGoogle Scholar
  8. Bolstad BM, Irizarry RA et al (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193CrossRefGoogle Scholar
  9. Borisov N, Aksamitiene E et al (2009) Systems-level interactions between insulin-EGF networks amplify mitogenic signaling. Mol Syst Biol 5:256CrossRefGoogle Scholar
  10. Borisov NM, Chistopolsky AS et al (2008) Domain-oriented reduction of rule-based network models. IET Syst Biol 2:342–351CrossRefGoogle Scholar
  11. Branzei D, Foiani M (2008) Regulation of DNA repair throughout the cell cycle. Nat Rev Mol Cell Biol 9:297–308CrossRefGoogle Scholar
  12. Buzdin AA, Zhavoronkov AA et al (2014a) Oncofinder, a new method for the analysis of intracellular signaling pathway activation using transcriptomic data. Front Genet 5:55CrossRefGoogle Scholar
  13. Buzdin AA, Zhavoronkov AA et al (2014b) The Oncofinder algorithm for minimizing the errors introduced by the high-throughput methods of transcriptome analysis. Front Mol Biosci 1:8CrossRefGoogle Scholar
  14. Buzdin AA, Artcibasova AV et al (2016) Early stage of cytomegalovirus infection suppresses host microRNA expression regulation in human fibroblasts. Cell Cycle 15:3378–3389CrossRefGoogle Scholar
  15. Conzelmann H, Saez-Rodriguez J et al (2006) A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks. BMC Bioinformatics 7:34CrossRefGoogle Scholar
  16. Croft D, Mundo AF et al (2014) The Reactome pathway knowledgebase. Nucleic Acids Res 42:D472–D477CrossRefGoogle Scholar
  17. Daniels BC, Chen YL et al (2008) Sloppiness, robustness, and evolvability in systems biology. Curr Opin Biotechnol 19:389–395CrossRefGoogle Scholar
  18. Demidenko ZN, Blagosklonny MV (2011) The purpose of the HIF-1/PHD feedback loop: to limit mTOR-induced HIF-1α. Cell Cycle 10:1557–1562CrossRefGoogle Scholar
  19. Disanza A, Frittoli E et al (2009) Endocytosis and spatial restriction of cell signaling. Mol Oncol 3:280–296CrossRefGoogle Scholar
  20. Elkon R, Vesterman R et al (2008) SPIKE- a database, visualization and analysis tool of cellular signaling pathways. BMC Bioinformatics 9:110CrossRefGoogle Scholar
  21. Filteau M, Diss G, Torres-Quiroz F, Dube AK, Schraffl A, Bachmann VA, Gagnon-Arsenault I et al (2015) Systematic identification of signal integration by protein kinase A. Proc Natl Acad Sci 112(14):4501–4506. Scholar
  22. Griesinger AM, Birks DK et al (2013) Characterization of distinct immunophenotypes across pediatric brain tumor types. J Immunol 191(9):4880–4888CrossRefGoogle Scholar
  23. Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70CrossRefGoogle Scholar
  24. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674CrossRefGoogle Scholar
  25. Ho JWK, Stefani M et al (2008) Differential variability analysis of gene expression and its application to human diseases. Bioinformatics 24:i390–i398CrossRefGoogle Scholar
  26. Hsu SD, Tseng YT et al (2014) miRTarBase update 2014: an information resource for experimentally validated miRNA-TARGET INTERACTIONS. Nucleic Acids Res 42:D78–D85CrossRefGoogle Scholar
  27. Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8:e1002375CrossRefGoogle Scholar
  28. Kholodenko BN, Demin OV et al (1999) Quantification of short term signaling by the epidermal growth factor receptor. J Biol Chem 274:30169–30181CrossRefGoogle Scholar
  29. Kholodenko BN, Kiyatkin A et al (2002) Untangling the wires: a strategy to trace functional interactions in signaling and gene networks. PNAS 99:12841–12846CrossRefGoogle Scholar
  30. Kiyatkin A, Aksamitiene A et al (2006) Scaffolding protein Grb2-associated binder 1 sustains epidermal growth factor-induced mitogenic and survival signaling by multiple positive feedback loops. J Biol Chem 281:19925–19938CrossRefGoogle Scholar
  31. Kulesh DA, Clive DR et al (1987) Identification of interferon-modulated proliferation-related cDNA sequences. PNAS 84:8453–8457CrossRefGoogle Scholar
  32. Kuzmina NB, Nikolay MM (2011) Handling complex rule-based models of mitogenic cell signaling (on the example of ERK activation upon EGF stimulation). Int Proc Chem Biol Environ Eng 5:76–82Google Scholar
  33. Lebedev TD, Spirin PV et al (2015) Receptor tyrosine kinase KIT may regulate expression of genes involved in spontaneous regression of neuroblastoma. Mol Biol 49:1052–1055Google Scholar
  34. Lezhnina K, Kovalchuk O et al (2014) Novel robust biomarkers for human bladder cancer based on activation of intracellular signaling pathways. Oncotarget 5:9022–9032CrossRefGoogle Scholar
  35. Love ML, Huber W, Anders A (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550CrossRefGoogle Scholar
  36. Makarev E, Cantor C et al (2014) Pathway activation profiling reveals new insights into age-related macular degeneration and provides avenues for therapeutic interventions. Aging 6:1064–1075CrossRefGoogle Scholar
  37. Makarev E, Izumchenko E et al (2016) Common pathway signature in lung and liver fibrosis. Cell Cycle 15:1667–1673CrossRefGoogle Scholar
  38. Malumbres M, Barbacid M (2009) Cell cycle, CDKs and cancer: a changing paradigm. Nat Rev Cancer 9:153–166CrossRefGoogle Scholar
  39. Marshall CJ (1995) Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation. Cell 80:179–185CrossRefGoogle Scholar
  40. Mathivanan S, Periaswamy B et al (2006) An evaluation of human protein-protein interaction data in the public domain. BMC Bioinformatics 7(Suppl 5):S19CrossRefGoogle Scholar
  41. Mitrea C, Taghavi Z et al (2013) Methods and approaches in the topology-based analysis of biological pathways. Front Physiol 4:278CrossRefGoogle Scholar
  42. Nakaya A, Katayama T et al (2013) KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters. Nucleic Acids Res 41:D353–DS57CrossRefGoogle Scholar
  43. Nikitin A, Egorov S et al (2003) Pathway Studio – the analysis and navigation of molecular networks. Bioinformatics 19:2155–2157CrossRefGoogle Scholar
  44. Olsvik O, Wahlberg J et al (1993) Use of automated sequencing of polymerase chain reaction-generated amplicons to identify three types of cholera toxin subunit B in Vibrio cholerae O1 strains. J Clin Microbiol 31:22–25PubMedPubMedCentralGoogle Scholar
  45. Ozerov IV, Lezhnina LV et al (2016) In silico pathway activation network decomposition analysis (iPANDA) as a method for biomarker development. Nat Commun 7:13427CrossRefGoogle Scholar
  46. Ram DR, Ilyukha V et al (2016) Balance between short and long isoforms of cFLIP regulates FAS-mediated apoptosis in vivo. PNAS 113:1606–1611CrossRefGoogle Scholar
  47. Shepelin D, Korzinkin M et al (2016) Molecular pathway activation features linked with transition from normal skin to primary and metastatic melanomas in human. Oncotarget 7:656–670CrossRefGoogle Scholar
  48. Sonnenschein C, Soto AM (2013) The aging of the 2000 and 2011 Hallmarks of Cancer reviews: a critique. J Biosci 38(3):651–663CrossRefGoogle Scholar
  49. Spirin PV, Lebedev TD et al (2014) Silencing AML1-ETO gene expression leads to simultaneous activation of both pro-apoptotic and proliferation signaling. Leukemia 28:2222–2228CrossRefGoogle Scholar
  50. Tian L, Greenberg SA et al (2005) Discovering statistically significant pathways in expression profiling studies. PNAS 102:13544–13549CrossRefGoogle Scholar
  51. UniProt Consortium (2011) Ongoing and future developments at the universal protein resource. Nucleic Acids Res 39:D214–DD19CrossRefGoogle Scholar
  52. Vergoulis T, Vlachos IS et al (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40:D222–D229CrossRefGoogle Scholar
  53. Vermeulen K, van Bockstaele DR, Berneman ZN (2003) The cell cycle: a review of regulation, deregulation and therapeutic targets in cancer. Cell Prolif 36:131–149CrossRefGoogle Scholar
  54. Vivar JC, Pemu P et al (2013) Redundancy control in pathway databases (ReCiPa): an application for improving gene-set enrichment analysis in omics studies and ‘big data’ biology. Omics J Integr Biol 17:414–422CrossRefGoogle Scholar
  55. Zeeberg BR, Feng W et al (2003) GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 4:R28CrossRefGoogle Scholar
  56. Zhang J, Li J, Deng HW (2009) Identifying gene interaction enrichment for gene expression data. PLoS One 4:e8064CrossRefGoogle Scholar
  57. Zhavoronkov A, Buzdin AA et al (2014) Signaling pathway cloud regulation for in silico screening and ranking of the potential geroprotective drugs. Front Genet 5:49CrossRefGoogle Scholar
  58. Zhu Q, Izumchenko E et al (2015) Pathway activation strength is a novel independent prognostic biomarker for cetuximab sensitivity in colorectal cancer patients. Hum Genome Var 2:15009CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and TechnologiesNational Research Centre “Kurchatov Institute”MoscowRussia
  2. 2.OmicsWay CorporationWalnutUSA
  3. 3.Group for Genomic Regulation of Cell Signaling SystemsShemyakin-Ovchinnikov Institute of Bioorganic ChemistryMoscowRussia
  4. 4.Department of Personalized MedicineFirst Oncology Research and Advisory CenterMoscowRussia

Personalised recommendations