Open access

Introductory Chapter: A Brief Overview of Transcriptional and Post-transcriptional Regulation

Written By

Kais Ghedira

Published: 10 October 2018

DOI: 10.5772/intechopen.79753

From the Edited Volume

Transcriptional and Post-transcriptional Regulation

Edited by Kais Ghedira

Chapter metrics overview

1,518 Chapter Downloads

View Full Metrics

1. Prologue

The regulation of gene expression is the process by which expression of genes is controlled (induced or repressed) at the cell level in a particular time under a particular condition. It is a fundamental process to diverse other biological processes that occur within the cell including cell development and differentiation, the response and the adaptation to environmental stresses. Gene regulation has classically been viewed as the interaction between proteins to regulatory elements located at the vicinity of the transcription start site within promoters. However, gene regulation is a more complex process that involves additional layers of control including chromatin remodeling, nucleosome positioning, histone modifications, DNA-binding regulatory proteins such as transcription factors and noncoding RNA [1, 2, 3]. Such process requires structural and chemical changes to the genetic material, binding of proteins to specific DNA elements to regulate transcription, or mechanisms that modulate translation of mRNA.

Indeed, gene expression is controlled at multiple cellular levels consisting in the chromatin level through chromatin modification and remodeling, the mRNA level (transcriptional and posttranscriptional regulation) and protein level (translation regulation and posttranslational degradation).

This introductory chapter will give a brief overview on the transcriptional and posttranscriptional regulation, list the main database resources that can be used for transcriptional and/or posttranscriptional regulation data and finally list the main tools allowing to predict TF and miRNA gene targets.


2. Transcriptional regulation

Regulation at the transcriptional level involves proteins called transcription factors (TFs) that recognize and bind specifically to regulatory elements within the promoter regions to control the expression of a downstream gene. These TFs regulate target genes—by turning them on and off—in order to make sure that they are transcribed into mRNA within the cell at the right time and in the right amount. TFs are classified into three large families of DNA-binding domains that include:

  1. Basic helix-loop-helix (bHLH) proteins found in organisms from yeast to humans and function in critical developmental processes controlling embryonic development, particularly in neurogenesis, myogenesis, heart development, and hematopoiesis [4, 5].

  2. The TFs with basic leucine zipper domains [6].

  3. TFs with the helix-turn-helix (HTH) domains that are involved in a wide range of functions beyond transcription regulation, including DNA repair and replication, RNA metabolism, and protein-protein interactions in diverse signaling contexts [7, 8]. This group also includes homeobox (zinc finger, HOX-like, TALE, POU, etc.) and homeodomain protein products.

High-throughput techniques including ChIP-on-chip/ChIP-seq and enhanced yeast one-hybrid have been widely employed to uncover protein-DNA interactions [9, 10] and represent convenient methods to identify and characterize the repertoire of regulatory elements that can be targeted by a protein of interest or transcription factors that can bind a DNA sequence of interest [11], respectively. Thanks to the ENCODE (Encyclopedia of DNA Elements) project aiming to build a comprehensive parts list of functional elements in the human genome including regulatory elements that control cells, such regulatory data were made available for the scientific community (; [12] and led to largely improve our understanding of gene regulation.

In addition to the ENCODE project, several regulatory databases have been developed for including multiple animals/plants/microorganisms regulation data. Table 1 lists the most widely used transcriptional regulation database with a brief description, reference to original publication and current accessible website URL.

DatabaseAcronymWebsite linkDescriptionReferences
TRANSFACTRANSFAC® is a maintained and curated database of eukaryotic transcription factors, their genomic binding sites, and DNA-binding profiles.[13]
Transcription Regulatory Regions databaseTRRD is a unique information resource, accumulating information on the structural and functional organization of transcription regulatory regions of eukaryotic genes.[14]
Ensembl RegulationEnsembl Regulation Regulation provides resources used for studying gene expression and its regulation in human and mouse, with a focus on the transcriptional and posttranscriptional mechanisms.[15]
Regulatory Network Repository of Transcription Factor and microRNA Mediated Gene RegulationsRegNetwork is developed based on 25 databases that provide the regulatory relationship information, annotation, and other necessary information in order to derive the regulatory relationships.[16]
Transcriptional Regulatory Element DatabaseTRED provides good training datasets for further genome-wide cis-regulatory element prediction, assist detailed functional studies, and facilitate to decipher the gene regulatory networks.[17]
Transcriptional Regulatory Relationships Unraveled by Sentence Based Text miningTRRUST database provides information of mode of regulation (activation or repression).[18]
Open Regulatory Annotation databaseORegAnno Open Regulatory Annotation database (ORegAnno) is a resource for curated regulatory annotation.[19]
PRODORICPRODORIC2http://www.prodoric2.deThe PRODORIC2 database hosts one of the largest collections of DNA-binding sites for prokaryotic transcription factors.[20]
Gene Transcription Regulation DatabaseGTRD most complete collection of uniformly processed ChIP-seq data to identify transcription factor binding sites for human and mouse.[21]
Transcription factor prediction databaseDBD is a database of predicted transcription factors in completely sequenced genomes.[22]

Table 1.

Eukaryotic and prokaryotic regulation databases.

Acronyms in bold letters denote curated databases.


3. Post-transcriptional regulation

A very large part of the human genome constitutes noncoding elements classified as small noncoding RNAs (sncRNAs) and long noncoding RNAs (lncRNAs). These noncoding components are receiving increased attention from researchers due to their predicted important role in posttranscriptional regulation. Small ncRNAs class includes small interfering RNAs (siRNAs), microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs), endogenous small interfering RNAs (endo-siRNAs or esiRNAs), promoter associate RNAs (pRNAs), small nucleolar RNAs (snoRNAs), and sno-derived RNAs, while lncRNAs includes linc RNA, NAT, eRNA, circ RNA, ceRNAs, PROMPTS. Both lncRNAs and sncRNAs have been identified at regulatory elements [23, 24]. Among these noncoding elements, microRNAs have been the most widely investigated since their discovery in the early 1990s, underscoring their importance in posttranscriptional gene regulation [25]. These later act as posttranscriptional regulators of their messenger RNA (mRNA) targets via mRNA degradation and/or translational repression [26]. It has been widely evidenced that miRNA-mediated downregulation is a one-way process leading to the repression of translation and/or target mRNA degradation [27, 28, 29, 30]; however, recent studies have shown that miRNAs are able to upregulate gene expression in specific cell types and conditions with distinct transcripts and proteins [31].

Pulling down microRNA-induced silencing complexes (miRISCs) immunoprecipitation method allows researchers to collect information on microRNAs and their mRNA targets in vivo. Such information has been collected and stored in several public databases. Table 2 contains the most widely used posttranscriptional regulation database with a brief description, reference to original publication and current functional website URL.

DatabaseAcronymWebsite linkDescriptionReferences
The microRNA databasemiRBase miRBase database is a searchable database of published miRNA sequences and annotation.[32]
The experimentally validated microRNA-target interactions databasemiRTarBase has accumulated miRNA-target interactions (MTIs), which are collected by manually surveying pertinent literature.[33]
miRDBmiRDB is an online database for miRNA target prediction and functional annotations[34]
miRNAMapmiRNAMap online resource that stores information related to the known miRNAs in metazoan.[35]
Vir-MirVir-Mir predicted viral miRNA candidate hairpins[36]
Virus miRNA TargetViTA collects virus data from miRBase and ICTV, VirGne, VBRC, etc. and provide effective annotations, including human miRNA expression, virus-infected tissues, annotation of virus, and comparisons.[37]
miRecordsmiRecords is a resource for animal miRNA-target interactions.[38]
microRNA Data Integration PortalmirDIP several million human microRNA-target predictions, which were collected across 30 different resources.[39]

Table 2.

Eukaryotic and prokaryotic posttranscriptional regulation databases.

Acronyms in bold letters denote curated databases.


4. The interplay between TFs and miRNAs

Transcription factors (TFs) and microRNAs (miRNAs) are key regulators of gene expression. Several studies have shown that abnormal miRNA and/or TF expression can be critical for cell survival and development through targeting critical genes in the cellular system. In the last decade, several bioinformatic studies have been performed to elucidate transcriptional and posttranscriptional (mostly miRNA-mediated) regulatory interactions. Besides experimental techniques (ChIP-Seq, ChIP-ChIP, yeast two-hybrid, miRISCs), computational tools have been developed to predict the TF-gene target and/or miRNA-target interactions. Table 3 lists some bioinformatic tools used to predict transcriptional and posttranscriptional regulation. Using such tools and/or through the integration of data collected from public databases (Tables 1 and 2), researchers were able to generate regulatory networks aiming to understand mechanisms involved in some phenotypes and/or diseases. Recent studies focused on the study of mixed miRNA/TF feed-forward regulatory loops (FFLs) through genome-wide transcriptional and posttranscriptional regulatory network integration to decipher the complex and interlinked cascade of events related to several diseases [46, 47, 48]. Such approaches provide the scientific community with the ability to investigate the interplay between TFs and miRNAs in a given system.

Tool/Web toolWebsite linkDescriptionReferences
TF-target prediction
TargetFinder a web-based resource for finding genes that show a similar expression pattern to a group of user-selected genes.[40]
BART: Binding analysis for regulation of transcription novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6000 existing ChIP-seq datasets for over 400 factors in human or mouse.[41]
MATCH is a weight matrix-based program for predicting transcription factor binding sites (TFBS) in DNA sequences.[42]
MiRNA-target prediction
RNAhybrid is a tool for finding the minimum free energy hybridization of a long and a short RNA.[43]
TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA.[44]
miRWalk the biggest available collection of predicted and experimentally verified miRNA-target interactions with various novel and unique features.[45]

Table 3.

TF and miRNA target prediction tools.


5. Conclusion

During these last years, transcriptional and posttranscriptional regulation constituted the most important layers of gene regulation. However, a recent study by Barna group [49] has upset our understanding of gene regulation. Indeed, while researchers have believed for decades that ribosomes are identical showing no preference for translating RNA molecules into proteins, it appears that these later exhibit a preference for translating certain types of genes. One type of ribosome, for example, prefers to translate genes involved in cellular differentiation, while another specializes in genes that carry out essential metabolic duties. This study is uncovering a new layer of gene expression regulation that will have broad implications for basic science and human disease.


  1. 1. Venters BJ, Pugh BF. How eukaryotic genes are transcribed. Critical Reviews in Biochemistry and Molecular Biology. 2009;44:117-141
  2. 2. Mercer TR, Dinger ME, Mattick JS. Long non-coding RNAs: Insights into functions. Nature Reviews. Genetics. 2009;10:155-159
  3. 3. Goodrich JA, Kugel JF. Non-coding-RNA regulators of RNA polymerase II transcription. Nature Reviews. Molecular Cell Biology. 2006;7:612-616
  4. 4. Littlewood TD, Evan GI. Transcription factors 2: Helix-loop-helix. Protein Profile. 1995;2(6):621-702. PMID 7553065
  5. 5. Jones S. An overview of the basic helix-loop-helix proteins. Genome Biology. 2004;5(6):226. Epub 2004 May 28. Review. PubMed PMID: 15186484; PubMed Central PMCID: PMC463060
  6. 6. Vinson C, Myakishev M, Acharya A, Mir AA, Moll JR, Bonovich M. Classification of human B-ZIP proteins based on dimerization properties. Molecular and Cellular Biology. 2002;22(18):6321-6335. DOI: 10.1128/MCB.22.18.6321-6335.2002. PMC 135624. PMID 12192032
  7. 7. Wintjens R, Rooman M. Structural classification of HTH DNA-binding domains and protein-DNA interaction modes. Journal of Molecular Biology. 1996;262(2):294-313. DOI: 10.1006/jmbi.1996.0514. PMID 8831795
  8. 8. Aravind L, Anantharaman V, Balaji S, Babu MM, Iyer LM. The many faces of the helix-turn-helix domain: Transcription regulation and beyond. FEMS Microbiology Reviews. 2005;29(2):231-262. Review. PubMed PMID: 15808743
  9. 9. Johnson DS, Mortazavi A, Myers RM, Wold B. Genome-wide mapping of in vivo protein-DNA interactions. Science. 2007;316:1497-1502
  10. 10. Reece-Hoyes JS, Barutcu AR, McCord RP, Jeong JS, Jiang L, MacWilliams A, Yang X, Salehi-Ashtiani K, Hill DE, Blackshaw S, Zhu H, Dekker J, Walhout AJM. Yeast one-hybrid assays for gene-centered human gene regulatory network mapping. Nature Methods. 2011;8:1050-1052
  11. 11. Reece-Hoyes JS, Diallo A, Lajoie B, Kent A, Shrestha S, Kadreppa S, Pesyna C, Dekker J, Myers CL, Walhout AJ. Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping. Nature Methods. 2011;8(12):1059-1064. DOI: 10.1038/nmeth.1748. PubMed PMID: 22037705; PubMed Central PMCID: PMC3235803
  12. 12. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57-74. DOI: 10.1038/nature11247. PubMed PMID: 22955616; PubMed Central PMCID: PMC3439153
  13. 13. Wingender E. The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief Bioinform. 2008 Jul;9(4):326-332. DOI: 10.1093/bib/bbn016. Epub 2008 Apr 24. PubMed PMID: 18436575
  14. 14. Kolchanov NA, Ignatieva EV, Ananko EA, Podkolodnaya OA, Stepanenko IL, Merkulova TI, Pozdnyakov MA, Podkolodny NL, Naumochkin AN, Romashchenko AG. Transcription regulatory regions database (TRRD): Its status in 2002. Nucleic Acids Research. 2002;30(1):312-317
  15. 15. Zerbino DR, Johnson N, Juetteman T, Sheppard D, Wilder SP, Lavidas I, Nuhn M, Perry E, Raffaillac-Desfosses Q, Sobral D, Keefe D, Gräf S, Ahmed I, Kinsella R, Pritchard B, Brent S, Amode R, Parker A, Trevanion S, Birney E, Dunham I, Flicek P. Ensembl regulation resources. Database (Oxford). 2016 Feb 17;2016. pii: bav119. DOI: 10.1093/database/bav119. Print 2016. PubMed PMID: 26888907; PubMed Central PMCID: PMC4756621
  16. 16. Liu ZP, Wu C, Miao H, Wu H. RegNetwork: An integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database (Oxford). 2015 Sep 30;2015. pii: bav095. DOI: 10.1093/database/bav095. Print 2015. PubMed PMID: 26424082; PubMed Central PMCID: PMC4589691
  17. 17. Jiang C, Xuan Z, Zhao F, Zhang MQ. TRED: A transcriptional regulatory element database, new entries and other development. Nucleic Acids Research. 2007;35(Database issue):D137-D140. PubMed PMID: 17202159; PubMed Central PMCID: PMC1899102
  18. 18. TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Research; 26 Oct, 2017
  19. 19. Lesurf R, Cotto KC, Wang G, Griffith M, Kasaian K, Jones SJM, Montgomery SB, Griffith OL, The Open Regulatory Annotation Consortium. ORegAnno 3.0: A community-driven resource for curated regulatory annotation. Nucleic Acids Research. 2016;44(D1):D126-D132. DOI: 10.1093/nar/gkv1203
  20. 20. Münch R, Hiller K, Barg H, Heldt D, Linz S, Wingender E, Jahn D. PRODORIC: Prokaryotic database of gene regulation. Nucleic Acids Research. 2003;31:266-269
  21. 21. Yevshin IS, Sharipov RN, Valeev TF, Kel AE, Kolpakov FA. GTRD: A database of transcription factor binding sites identified by ChIP-seq experiments. Nucleic Acids Research. 2017;45(D1):D61-D67
  22. 22. Wilson D, Charoensawan V, Kummerfeld SK, Teichmann SA. DBD––Taxonomically broad transcription factor predictions: New content and functionality. Nucleic Acids Research. 2008;36(suppl_1):D88-D92. DOI: 10.1093/nar/gkm964
  23. 23. Lewis BP, Shih I-H, Jones-Rhoades MW, Bartel DP, Burge CB. Prediction of mammalian microRNA targets. Cell. 2003;115(7):787-798
  24. 24. Brennecke J, Stark A, Russell RB, Cohen SM. Principles of microRNA-target recognition. PLoS Biology. 2005;3(3):e85
  25. 25. Valinezhad Orang A, Safaralizadeh R, Kazemzadeh-Bavili M. Mechanisms of miRNA-mediated gene regulation from common downregulation to mRNA-specific upregulation. International Journal of Genomics. 2014;2014:970607. DOI: 10.1155/2014/970607. Epub 2014 Aug 10. Review. PubMed PMID: 25180174; PubMed Central PMCID: PMC4142390
  26. 26. Catalanotto C, Cogoni C, Zardo G. MicroRNA in control of gene expression: An overview of nuclear functions. International Journal of Molecular Sciences. 2016;17(10):pii: E1712. Review. PubMed PMID: 27754357; PubMed Central PMCID: PMC5085744
  27. 27. Zeng Y, Yi R, Cullen BR. MicroRNAs and small interfering RNAs can inhibit mRNA expression by similar mechanisms. Proceedings of the National Academy of Sciences of the United States of America. 2003;100(17):9779-9784. Epub 2003 Aug 5. PubMed PMID: 12902540; PubMed Central PMCID: PMC187842
  28. 28. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000;403(6772):901-906. PubMed PMID: 10706289
  29. 29. Llave C, Xie Z, Kasschau KD, Carrington JC. Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science. 2002;297(5589):2053-2056. PubMed PMID: 12242443
  30. 30. Lee RC, Feinbaum RL, Ambros V. The C. Elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75(5):843-854. PubMed PMID: 8252621
  31. 31. Place RF, Li LC, Pookot D, Noonan EJ, Dahiya R. MicroRNA-373 induces expression of genes with complementary promoter sequences. Proceedings of the National Academy of Sciences of the United States of America. 2008;105(5):1608-1613. DOI: 10.1073/pnas.0707594105. Epub 2008 Jan 28. Erratum in: Proc Natl Acad Sci U S A. 2018 Mar 19;:. PubMed PMID: 18227514; PubMed Central PMCID: PMC2234192
  32. 32. Kozomara A, Griffiths-Jones S. miRBase: Annotating high confidence microRNAs using deep sequencing data. Narrative. 2014;42:D68-D73
  33. 33. Chou C-H, Shrestha S, Yang C-D, Chang N-W, Lin Y-L, Liao K-W, Huang W-C, Sun T-H, Tu S-J, Lee W-H, Chiew M-Y, Tai C-S, Wei T-Y, Tsai T-R, Huang H-T, Wang C-Y, Wu H-Y, Ho S-Y, Chen P-R, Chuang C-H, Hsieh P-J, Wu Y-S, Chen W-L, Li M-J, Wu Y-C, Huang X-Y, Ng FL, Buddhakosai W, Huang P-C, Lan K-C, Huang C-Y, Weng S-L, Cheng Y-N, Liang C, Hsu W-L, Huang H-D. miRTarBase update 2018: A resource for experimentally validated microRNA-target interactions. Nucleic Acids Research. 2018;46(D1):D296-D302. DOI: 10.1093/nar/gkx1067
  34. 34. Wong N, Wang X. miRDB: An online resource for microRNA target prediction and functional annotations. Nucleic Acids Research. 2015;43(D1):D146-D152
  35. 35. Hsu SD, Chu CH, Tsou AP, Chen SJ, Chen HC, Hsu PW, Wong YH, Chen YH, Chen GH, Huang HD. miRNAMap 2.0: Genomic maps of microRNAs in metazoan genomes. Nucleic Acids Research. 2008;36(Database issue):D165-D169
  36. 36. Li SC, Shiau CK, Lin WC. Vir-Mir db: Prediction of viral microRNA candidate hairpins. Nucleic Acids Res. 2008 Jan;36(Database issue):D184-D189. Epub 2007 Aug 15. PubMed PMID: 17702763; PubMed Central PMCID: PMC2238904
  37. 37. Hsu PW, Lin LZ, Hsu SD, Hsu JB, Huang HD. ViTa: Prediction of host microRNAs targets on viruses. Nucleic Acids Research. 2007;35(Database issue):D381-D385
  38. 38. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: An integrated resource for microRNA-target interactions. Nucleic Acids Research. 2009;37:D105-D110
  39. 39. Tokar T, Pastrello C, Rossos AEM, Abovsky M, Hauschild AC, Tsay M, Lu R, Jurisica I. mirDIP 4.1-integrative database of human microRNA target predictions. Nucleic Acids Research. 2018;46(D1):D360-D370. DOI: 10.1093/nar/gkx1144. PubMed PMID: 29194489; PubMed Central PMCID: PMC5753284
  40. 40. Szymon M. Kiełbasa, Nils Blüthgen, Michael Fähling, Ralf Mrowka; A resource for systematic discovery of transcription factor target genes, Nucleic Acids Research, 38, suppl 2, 2010, W233–W238, 10.1093/nar/gkq374
  41. 41. Wang Z, Civelek M, Miller CL, Sheffield NC, Guertin MJ, Zang C. BART: A transcription factor prediction tool with query gene sets or epigenomic profiles. Bioinformatics. 2018 Mar 28. DOI: 10.1093/bioinformatics/bty194. [Epub ahead of print] PubMed PMID: 29608647
  42. 42. Kel AE, Goessling E, Reuter I, Cheremushkin E, Kel-Margoulis OV, Wingender E. Match(TM): A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Research. 2003;31:3576-3579
  43. 43. Marc R, Steffen P, Hoechsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes RNA. RNA. 2004
  44. 44. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015 Aug 12;4. DOI: 10.7554/eLife.05005. PubMed PMID: 26267216; PubMed Central PMCID: PMC4532895
  45. 45. Dweep H et al. miRWalk—Database: Prediction of possible miRNA binding sites by ‘walking’ the genes of 3 genomes. Journal of Biomedical Informatics. 2011;44:839-837
  46. 46. Friard O, Re A, Taverna D, De Bortoli M, Corá D. CircuitsDB: A database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse. BMC Bioinformatics. 2010;11:435. DOI: 10.1186/1471-2105-11-435. PubMed PMID: 20731828; PubMed Central PMCID: PMC2936401
  47. 47. Wang H, Luo J, Liu C, Niu H, Wang J, Liu Q, Zhao Z, Xu H, Ding Y, Sun J, Zhang Q. Investigating microRNA and transcription factor co-regulatory networks in colorectal cancer. BMC Bioinformatics. 2017;18(1):388. DOI: 10.1186/s12859-017-1796-4. PubMed PMID: 28865443; PubMed Central PMCID: PMC5581471
  48. 48. Nampoothiri SS, Fayaz SM, Rajanikant GK. A novel five-node feed-forward loop unravels miRNA-gene-TF regulatory relationships in ischemic stroke. Molecular Neurobiology. 2018. DOI: 10.1007/s12035-018-0963-6. [Epub ahead of print] PubMed PMID: 29524052
  49. 49. Simsek D, Tiu GC, Flynn RA, Byeon GW, Leppek K, Xu AF, Chang HY, Barna M. The mammalian ribo-interactome reveals ribosome functional diversity and heterogeneity. Cell. 2017;169(6):1051-1065.e18. DOI: 10.1016/j.cell.2017.05.022. PubMed PMID: 28575669; PubMed Central PMCID: PMC5548193

Written By

Kais Ghedira

Published: 10 October 2018