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Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Programming

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Reverse Engineering of Regulatory Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2719))

Abstract

Transcription factors (TFs) bind to specific regions of DNA known as transcription factor binding sites (TFBSs) and modulate gene expression by interacting with the transcriptional machinery. TFBSs are typically located upstream of target genes, within a few thousand base pairs of the transcription start site. The binding of TFs to TFBSs influences the recruitment of the transcriptional machinery, thereby regulating gene transcription in a precise and specific manner. This chapter provides practical examples and case studies demonstrating the extraction of upstream gene regions from the genome, identification of TFBSs using PWMEnrich R/Bioconductor package, interpretation of results, and preparation of publication-ready figures and tables. The EOMES promoter is used as a case study for single DNA sequence analysis, revealing potential regulation by the LHX9-FOXP1 complex during embryonic development. Additionally, an example is presented on how to investigate TFBSs in the upstream regions of a group of genes, using a case study of differentially expressed genes in response to human parainfluenza virus type 1 (HPIV1) infection and interferon-beta. Key regulators identified in this context include the STAT1:STAT2 heterodimer and interferon regulatory factor family proteins. The presented protocol is designed to be accessible to individuals with basic computer literacy. Understanding the interactions between TFs and TFBSs provides insights into the complex transcriptional regulatory networks that govern gene expression, with broad implications for several fields such as developmental biology, immunology, and disease research.

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References

  1. Jacob F, Monod J (1961) Genetic regulatory mechanisms in the synthesis of proteins. J Mol Biol 3(3):318–356

    Article  Google Scholar 

  2. Crick FH (1958) On protein synthesis. Symp Soc Exp Biol 12:138–163

    Google Scholar 

  3. Dynan WS, Tjian R (1983) The promoter-specific transcription factor Sp1 binds to upstream sequences in the SV40 early promoter. Cell 35(1):79–87

    Article  Google Scholar 

  4. Nakabeppu Y, Ryder K, Nathans D (1988) DNA binding activities of three murine Jun proteins: stimulation by fos. Cell 55(5):907–915

    Article  Google Scholar 

  5. Muley VY, Pathania A (2017) Gene expression. Encyclopedia of animal cognition and behavior. Springer, Cham

    Google Scholar 

  6. Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T (2014) The FANTOM consortium and the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507(7493):462–470

    Article  Google Scholar 

  7. Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E, Volkert TL (2000) Genome-wide location and function of DNA binding proteins. Science 290(5500):2306–2309

    Article  Google Scholar 

  8. Muley VY, López-Victorio CJ, Ayala-Sumuano JT, González-Gallardo A, González-Santos L, Lozano-Flores C, Wray G, Hernández-Rosales M, Varela-Echavarría A (2020) Conserved and divergent expression dynamics during early patterning of the telencephalon in mouse and chick embryos. Prog Neurobiol 186:101735

    Article  Google Scholar 

  9. Levine M, Tjian R (2003) Transcription regulation and animal diversity. Nature 424(6945):147–151

    Article  Google Scholar 

  10. Muley VY, König R (2022) Human transcriptional gene regulatory network compiled from 14 data resources. Biochimie 193:115–125

    Article  Google Scholar 

  11. Diamond MI, Miner JN, Yoshinaga SK, Yamamoto KR (1990) Transcription factor interactions: selectors of positive or negative regulation from a single DNA element. Science 249(4974):1266–1272

    Article  Google Scholar 

  12. Djordjevic M, Sengupta AM, Shraiman BI (2003) A biophysical approach to transcription factor binding site discovery. Genome Res 13(11):2381–2390

    Article  Google Scholar 

  13. Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, Chen X, Taipale J, Hughes TR, Weirauch MT (2018) The human transcription factors. Cell 172(4):650–665

    Article  Google Scholar 

  14. Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM (2009) A census of human transcription factors: function, expression and evolution. Nat Rev Genet 10(4):252–263

    Article  Google Scholar 

  15. Stormo GD (2000) DNA binding sites: representation and discovery. Bioinformatics 16(1):16–23

    Article  Google Scholar 

  16. Mathelier A, Zhao X, Zhang AW, Parcy F, Worsley-Hunt R, Arenillas DJ, Buchman S, Chen CY, Chou A, Ienasescu H, Lim J (2014) JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res 42(D1):D142–D147

    Article  Google Scholar 

  17. Pachkov M, Balwierz PJ, Arnold P, Ozonov E, Van Nimwegen E (2012) SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates. Nucleic Acids Res 41(D1):D214–D220

    Article  Google Scholar 

  18. Pratt HE, Andrews GR, Phalke N, Huey JD, Purcaro MJ, van der Velde A, Moore JE, Weng Z (2022) Factorbook: an updated catalog of transcription factor motifs and candidate regulatory motif sites. Nucleic Acids Res 50(D1):D141–D149

    Article  Google Scholar 

  19. Stojnic R, Diez D (2015) PWMEnrich: PWM enrichment analysis. R Package Version 4(0):10–8129

    Google Scholar 

  20. Porcelli D, Fischer B, Russell S, White R (2019) Chromatin accessibility plays a key role in selective targeting of Hox proteins. Genome Biol 20:1–9

    Article  Google Scholar 

  21. ** H, Stojnic R, Adryan B, Ozdemir A, Stathopoulos A, Frasch M (2013) Genome-wide screens for in vivo Tinman binding sites identify cardiac enhancers with diverse functional architectures. PLoS Genet 9(1):e1003195

    Article  Google Scholar 

  22. Ma X, Ezer D, Navarro C, Adryan B (2015) Reliable scaling of position weight matrices for binding strength comparisons between transcription factors. BMC Bioinform 16(1):1–3

    Article  Google Scholar 

  23. Frith MC, Fu Y, Yu L, Chen JF, Hansen U, Weng Z (2004) Detection of functional DNA motifs via statistical over-representation. Nucleic Acids Res 32(4):1372–1381

    Article  Google Scholar 

  24. Kimura N, Nakashima K, Ueno M, Kiyama H, Taga T (1999) A novel mammalian T-box-containing gene, Tbr2, expressed in mouse develo** brain. Dev Brain Res 115(2):183–193

    Article  Google Scholar 

  25. Boonyaratanakornkit JB, Bartlett EJ, Amaro-Carambot E, Collins PL, Murphy BR, Schmidt AC (2009) The C proteins of human parainfluenza virus type 1 (HPIV1) control the transcription of a broad array of cellular genes that would otherwise respond to HPIV1 infection. J Virol 83(4):1892–1910

    Article  Google Scholar 

  26. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12(2):115–121

    Article  Google Scholar 

  27. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):1–6

    Article  Google Scholar 

  28. Stojnic R (2022) PWMEnrich.hsapiens.background: H. Sapiens background for PWMEnrich

    Google Scholar 

  29. Shannon P, Richards M (2022) MotifDb: an annotated collection of protein-DNA binding sequence motifs

    Google Scholar 

  30. Team TBD (2020) BSgenome.hsapiens.UCSC.hg19: Full genome sequences for homo sapiens (UCSC version hg19, based on GRCh37.p 13)

    Google Scholar 

  31. Pagès H (2023). BSgenome: software infrastructure for efficient representation of full genomes and their SNPs

    Google Scholar 

  32. Carlson M, Maintainer BP (2015) org.Hs.eg.db: genome wide annotation for human

    Google Scholar 

  33. Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan MT, Carey VJ (2013) Software for computing and annotating genomic ranges. PLoS Comput Biol 9(8):e1003118

    Article  Google Scholar 

  34. Zhang MQ (1998) Identification of human gene core promoters in silico. Genome Res 8(3):319–326

    Article  Google Scholar 

  35. Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R, Min R (2012) Architecture of the human regulatory network derived from ENCODE data. Nature 489(7414):91–100

    Article  Google Scholar 

  36. Neph S, Vierstra J, Stergachis AB, Reynolds AP, Haugen E, Vernot B, Thurman RE, John S, Sandstrom R, Johnson AK, Maurano MT (2012) An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489(7414):83–90

    Article  Google Scholar 

  37. Sanyal A, Lajoie BR, Jain G, Dekker J (2012) The long-range interaction landscape of gene promoters. Nature 489(7414):109–113

    Article  Google Scholar 

  38. Pagès H, Aboyoun P, Gentleman R, DebRoy S (2019) Biostrings: efficient manipulation of biological strings. R Package Version 2(0):10–8129

    Google Scholar 

  39. Elsen GE, Hodge RD, Bedogni F, Daza RA, Nelson BR, Shiba N, Reiner SL, Hevner RF (2013) The protomap is propagated to cortical plate neurons through an Eomes-dependent intermediate map. Proc Natl Acad Sci 110(10):4081–4086

    Article  Google Scholar 

  40. Bertuzzi S, Porter FD, Pitts A, Kumar M, Agulnick A, Wassif C, Westphal H (1999) Characterization of Lhx9, a novel LIM/homeobox gene expressed by the pioneer neurons in the mouse cerebral cortex. Mech Dev 81(1–2):193–198

    Article  Google Scholar 

  41. Gehring WJ (1992) The homeobox in perspective. Trends Biochem Sci 17(8):277–280

    Article  Google Scholar 

  42. Banerjee-Basu S, Baxevanis AD (2001) Molecular evolution of the homeodomain family of transcription factors. Nucleic Acids Res 29(15):3258–3269

    Article  Google Scholar 

  43. Trelles MP, Levy T, Lerman B, Siper P, Lozano R, Halpern D, Walker H, Zweifach J, Frank Y, Foss-Feig J, Kolevzon A (2021) Individuals with FOXP1 syndrome present with a complex neurobehavioral profile with high rates of ADHD, anxiety, repetitive behaviors, and sensory symptoms. Mol Autism 12(1):1–5

    Article  Google Scholar 

  44. Nunez BS, Geng CD, Pedersen KB, Millro-Macklin CD, Vedeckis WV (2005) Interaction between the interferon signaling pathway and the human glucocorticoid receptor gene 1A promoter. Endocrinology 146(3):1449–1457

    Article  Google Scholar 

  45. Petta I, Dejager L, Ballegeer M, Lievens S, Tavernier J, De Bosscher K, Libert C (2016) The interactome of the glucocorticoid receptor and its influence on the actions of glucocorticoids in combatting inflammatory and infectious diseases. Microbiol Mol Biol Rev 80(2):495–522

    Article  Google Scholar 

  46. Cheon H, Holvey-Bates EG, Schoggins JW, Forster S, Hertzog P, Imanaka N, Rice CM, Jackson MW, Junk DJ, Stark GR (2013) IFNβ-dependent increases in STAT1, STAT2, and IRF9 mediate resistance to viruses and DNA damage. EMBO J 32(20):2751–2763

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Independent Researcher, Hingoli, India

    Vijaykumar Yogesh Muley

  2. Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico

    Vijaykumar Yogesh Muley

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  1. Vijaykumar Yogesh Muley
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Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Jalpaiguri Govt. Engineering College, Jalpaiguri, West Bengal, India

    Sudip Mandal

1 Electronic Supplementary Material

Table S1

Genes differentially expressed by wt HPIV1 or IFNβ compared to mock infection (XLSX 11 kb)

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Muley, V.Y. (2024). Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Programming. In: Mandal, S. (eds) Reverse Engineering of Regulatory Networks. Methods in Molecular Biology, vol 2719. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3461-5_12

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  • DOI: https://doi.org/10.1007/978-1-0716-3461-5_12

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3460-8

  • Online ISBN: 978-1-0716-3461-5

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