The Functional Consequences of Variation in Transcription Factor Binding
An important question in genomics is to understand how a class of proteins called “transcription factors” controls the expression level of other genes in the genome in a cell-type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and it is generally accepted that much of the binding does not strongly influence gene expression. To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then examined which genes were impacted by the reduced transcription factor level. Our results implicate some attributes that might influence what binding is functional, but they also suggest that a simple model of functional vs. non-functional binding may not suffice.
Vyšlo v časopise:
The Functional Consequences of Variation in Transcription Factor Binding. PLoS Genet 10(3): e32767. doi:10.1371/journal.pgen.1004226
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1004226
Souhrn
An important question in genomics is to understand how a class of proteins called “transcription factors” controls the expression level of other genes in the genome in a cell-type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and it is generally accepted that much of the binding does not strongly influence gene expression. To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then examined which genes were impacted by the reduced transcription factor level. Our results implicate some attributes that might influence what binding is functional, but they also suggest that a simple model of functional vs. non-functional binding may not suffice.
Zdroje
1. JolmaA, YanJ, WhitingtonT, ToivonenJ, NittaKR, et al. (2013) DNA-binding specificities of human transcription factors. Cell 152: 327–339 doi:10.1016/j.cell.2012.12.009
2. NobregaMA, OvcharenkoI, AfzalV, RubinEM (2003) Scanning human gene deserts for long-range enhancers. Science 302: 413 doi:10.1126/science.1088328
3. BernsteinBE, BirneyE, DunhamI, GreenED, GunterC, et al. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57–74 doi:10.1038/nature11247
4. Pique-RegiR, DegnerJF, PaiAA, GaffneyDJ, GiladY, et al. (2011) Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res 21: 447–455 doi:10.1101/gr.112623.110
5. SongL, CrawfordGE (2010) DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb Protoc 2010: pdb.prot5384 doi:10.1101/pdb.prot5384
6. YanJ, EngeM, WhitingtonT, DaveK, LiuJ, et al. (2013) Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. Cell 154: 801–813 doi:10.1016/j.cell.2013.07.034
7. GaffneyDJ, VeyrierasJ-B, DegnerJF, Pique-RegiR, PaiAA, et al. (2012) Dissecting the regulatory architecture of gene expression QTLs. Genome Biol 13: R7 doi:10.1186/gb-2012-13-1-r7
8. BellJT, PaiAA, PickrellJK, GaffneyDJ, Pique-RegiR, et al. (2011) DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol 12: R10 doi:10.1186/gb-2011-12-1-r10
9. DegnerJF, PaiAA, Pique-RegiR, VeyrierasJ-B, GaffneyDJ, et al. (2012) DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482: 390–394 doi:10.1038/nature10808
10. SpivakovM, AkhtarJ, KheradpourP, BealK, GirardotC, et al. (2012) Analysis of variation at transcription factor binding sites in Drosophila and humans. Genome Biol 13: R49 doi:10.1186/gb-2012-13-9-r49
11. ErnstJ, KheradpourP, MikkelsenTS, ShoreshN, WardLD, et al. (2011) Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473: 43–49 doi:10.1038/nature09906
12. GiladY, RifkinSA, PritchardJK (2008) Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet 24: 408–415 doi:10.1016/j.tig.2008.06.001
13. ChiaN-Y, ChanY-S, FengB, LuX, OrlovYL, et al. (2010) A genome-wide RNAi screen reveals determinants of human embryonic stem cell identity. Nature 468: 316–320 doi:10.1038/nature09531
14. YangA, ZhuZ, KapranovP, McKeonF, ChurchGM, et al. (2006) Relationships between p63 binding, DNA sequence, transcription activity, and biological function in human cells. Mol Cell 24: 593–602 doi:10.1016/j.molcel.2006.10.018
15. KrigSR, JinVX, BiedaMC, O'GeenH, YaswenP, et al. (2007) Identification of genes directly regulated by the oncogene ZNF217 using chromatin immunoprecipitation (ChIP)-chip assays. J Biol Chem 282: 9703–9712 doi:10.1074/jbc.M611752200
16. XuX, BiedaM, JinVX, RabinovichA, OberleyMJ, et al. (2007) A comprehensive ChIP-chip analysis of E2F1, E2F4, and E2F6 in normal and tumor cells reveals interchangeable roles of E2F family members. Genome Res 17: 1550–1561 doi:10.1101/gr.6783507
17. KawajiH, SeverinJ, LizioM, WaterhouseA, KatayamaS, et al. (2009) The FANTOM web resource: from mammalian transcriptional landscape to its dynamic regulation. Genome Biol 10: R40 doi:10.1186/gb-2009-10-4-r40
18. SuzukiH, ForrestARR, van NimwegenE, DaubCO, BalwierzPJ, et al. (2009) The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat Genet 41: 553–562 doi:10.1038/ng.375
19. ChengC, AlexanderR, MinR, LengJ, YipKY, et al. (2012) Understanding transcriptional regulation by integrative analysis of transcription factor binding data. Genome Res 22: 1658–1667 doi:10.1101/gr.136838.111
20. GersteinMB, KundajeA, HariharanM, LandtSG, YanK-K, et al. (2012) Architecture of the human regulatory network derived from ENCODE data. Nature 489: 91–100 doi:10.1038/nature11245
21. AlemánLM, DoenchJ, SharpPA (2007) Comparison of siRNA-induced off-target RNA and protein effects. RNA 13: 385–395 doi:10.1261/rna.352507
22. De CandiaP, BlekhmanR, ChabotAE, OshlackA, GiladY (2008) A combination of genomic approaches reveals the role of FOXO1a in regulating an oxidative stress response pathway. PLoS One 3: e1670 doi:10.1371/journal.pone.0001670
23. TamuraT, YanaiH, SavitskyD, TaniguchiT (2008) The IRF family transcription factors in immunity and oncogenesis. Annu Rev Immunol 26: 535–584 doi:10.1146/annurev.immunol.26.021607.090400
24. AshburnerM, BallCA, BlakeJA, BotsteinD, ButlerH, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25–29 doi:10.1038/75556
25. KannoY, LeviB-Z, TamuraT, OzatoK (2005) Immune cell-specific amplification of interferon signaling by the IRF-4/8-PU.1 complex. J Interferon Cytokine Res 25: 770–779 doi:10.1089/jir.2005.25.770
26. TsunoT, MejidoJ, ZhaoT, SchmeisserH, MorrowA, et al. (2009) IRF9 is a key factor for eliciting the antiproliferative activity of IFN-alpha. J Immunother 32: 803–816 doi:10.1097/CJI.0b013e3181ad4092
27. EberléD, HegartyB, BossardP, FerréP, FoufelleF (2004) SREBP transcription factors: master regulators of lipid homeostasis. Biochimie 86: 839–848 doi:10.1016/j.biochi.2004.09.018
28. SiepelA, BejeranoG, PedersenJS, HinrichsAS, HouM, et al. (2005) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 15: 1034–1050 doi:10.1101/gr.3715005
29. GraurD, ZhengY, PriceN, AzevedoRBR, ZufallRA, et al. (2013) On the immortality of television sets: “function” in the human genome according to the evolution-free gospel of ENCODE. Genome Biol Evol 5: 578–590 doi:10.1093/gbe/evt028
30. GitterA, SiegfriedZ, KlutsteinM, FornesO, OlivaB, et al. (2009) Backup in gene regulatory networks explains differences between binding and knockout results. Mol Syst Biol 5: 276 doi:10.1038/msb.2009.33
31. VilellaAJ, SeverinJ, Ureta-VidalA, HengL, DurbinR, et al. (2009) EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates. Genome Res 19: 327–335 doi:10.1101/gr.073585.107
32. LatchmanDS (2001) Transcription factors: bound to activate or repress. Trends Biochem Sci 26: 211–213 doi:10.1016/S0968-0004(01)01812-6
33. BoyleP, DesprésC (2010) Dual-function transcription factors and their entourage: unique and unifying themes governing two pathogenesis-related genes. Plant Signal Behav 5: 629–634.
34. HobertO, JallalB, UllrichA (1996) Interaction of Vav with ENX-1, a putative transcriptional regulator of homeobox gene expression. Mol Cell Biol 16: 3066–3073.
35. HiraiSI, RyseckRP, MechtaF, BravoR, YanivM (1989) Characterization of junD: a new member of the jun proto-oncogene family. EMBO J 8: 1433–1439.
36. FarnhamPJ (2009) Insights from genomic profiling of transcription factors. Nat Rev Genet 10: 605–616 doi:10.1038/nrg2636
37. BigginMD (2011) Animal transcription networks as highly connected, quantitative continua. Dev Cell 21: 611–626 doi:10.1016/j.devcel.2011.09.008
38. ThurmanRE, RynesE, HumbertR, VierstraJ, MauranoMT, et al. (2012) The accessible chromatin landscape of the human genome. Nature 489: 75–82 doi:10.1038/nature11232
39. WangZ, GersteinM, SnyderM (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10: 57–63 doi:10.1038/nrg2484
40. PeirsonSN (2003) Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis. Nucleic Acids Res 31: 73e–73 doi:10.1093/nar/gng073
41. LiH, DurbinR (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760 doi:10.1093/bioinformatics/btp324
42. AbecasisGR, AltshulerD, AutonA, BrooksLD, DurbinRM, et al. (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073 doi:10.1038/nature09534
43. DuP, KibbeWA, LinSM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24: 1547–1548 doi:10.1093/bioinformatics/btn224
44. GentlemanRC, CareyVJ, BatesDM, BolstadB, DettlingM, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80 doi:10.1186/gb-2004-5-10-r80
45. Gagnon-BartschJA, SpeedTP (2012) Using control genes to correct for unwanted variation in microarray data. Biostatistics 13: 539–552 doi:10.1093/biostatistics/kxr034
46. SmythGK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3: Article3 doi:10.2202/1544-6115.1027
47. BolstadB, IrizarryR, ÅstrandM, SpeedT (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19: 185–193.
48. BrettschneiderJ, CollinF, BolstadBM, SpeedTP (2008) Quality assessment for short oligonucleotide microarray data. Technometrics 50: 241–264 doi:10.1198/004017008000000334
49. StoreyJD, TibshiraniR (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci 100: 9440–9445 doi:10.1073/pnas.1530509100
50. DjebaliS, DavisCA, MerkelA, DobinA, LassmannT, et al. (2012) Landscape of transcription in human cells. Nature 489: 101–108 doi:10.1038/nature11233
51. MatysV, Kel-Margoulis OV, FrickeE, LiebichI, LandS, et al. (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 34: D108–10 doi:10.1093/nar/gkj143
52. QuinlanAR, HallIM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26: 841–842 doi:10.1093/bioinformatics/btq033
53. DaleRK, PedersenBS, QuinlanAR (2011) Pybedtools: a flexible Python library for manipulating genomic datasets and annotations. Bioinformatics 27: 3423–3424 doi:10.1093/bioinformatics/btr539
54. NephS, KuehnMS, ReynoldsAP, HaugenE, ThurmanRE, et al. (2012) BEDOPS: high-performance genomic feature operations. Bioinformatics 28: 1919–1920 doi:10.1093/bioinformatics/bts277
55. EdgarR (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30: 207–210 doi:10.1093/nar/30.1.207
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
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