Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels
Autoři:
Michael D. Morgan aff001; Etienne Patin aff003; Bernd Jagla aff004; Milena Hasan aff004; Lluís Quintana-Murci aff003; John C. Marioni aff001
Působiště autorů:
Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
aff001; Cancer Research UK–Cambridge Institute, Robinson Way, Cambridge, United Kingdom
aff002; Human Evolutionary Genetics Unit, Institut Pasteur, CNRS UMR2000, Paris, France
aff003; Cytometry and Biomarkers UTechS, Institut Pasteur, Paris, France
aff004; Hub Bioinformatique et Biostatisque, Départment de Biologie Computationalle—USR 3756 CNRS, Institut Pasteur, Paris, France
aff005; Human Genomics and Evolution, Collège de France, Paris, France
aff006; EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
aff007
Vyšlo v časopise:
Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels. PLoS Genet 16(3): e32767. doi:10.1371/journal.pgen.1008686
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008686
Souhrn
Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans.
Klíčová slova:
Flow cytometry – Gene expression – Gene regulation – Genetic polymorphism – Human genetics – Immune system proteins – Phenotypes – Protein expression
Zdroje
1. Ozbudak EM, Thattai M, Kurtser I, Grossman AD, van Oudenaarden A. Regulation of noise in the expression of a single gene. Nat Genet. 2002;31: 69–73. doi: 10.1038/ng869 11967532
2. Swain PS, Elowitz MB, Siggia ED. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci. 2002;99: 12795–12800. doi: 10.1073/pnas.162041399 12237400
3. Zopf CJ, Quinn K, Zeidman J, Maheshri N. Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression. Kondev J, editor. PLoS Comput Biol. 2013;9: e1003161. doi: 10.1371/journal.pcbi.1003161 23935476
4. Kiviet DJ, Nghe P, Walker N, Boulineau S, Sunderlikova V, Tans SJ. Stochasticity of metabolism and growth at the single-cell level. Nature. 2014;514: 376–379. doi: 10.1038/nature13582 25186725
5. Fang M, Xie H, Dougan SK, Ploegh H, van Oudenaarden A. Stochastic Cytokine Expression Induces Mixed T Helper Cell States. Bhandoola A, editor. PLoS Biol. 2013;11: e1001618. doi: 10.1371/journal.pbio.1001618 23935453
6. Elowitz MB. Stochastic Gene Expression in a Single Cell. Science. 2002;297: 1183–1186. doi: 10.1126/science.1070919 12183631
7. Sanchez A, Golding I. Genetic Determinants and Cellular Constraints in Noisy Gene Expression. Science. 2013;342: 1188–1193. doi: 10.1126/science.1242975 24311680
8. Eling N, Morgan MD, Marioni JC. Challenges in measuring and understanding biological noise. Nat Rev Genet. 2019 [cited 22 May 2019]. doi: 10.1038/s41576-019-0130-6 31114032
9. Charlebois DA, Abdennur N, Kaern M. Gene Expression Noise Facilitates Adaptation and Drug Resistance Independently of Mutation. Phys Rev Lett. 2011;107. doi: 10.1103/PhysRevLett.107.218101 22181928
10. Shaffer SM, Dunagin MC, Torborg SR, Torre EA, Emert B, Krepler C, et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature. 2017;546: 431–435. doi: 10.1038/nature22794 28607484
11. Duveau F, Hodgins-Davis A, Metzger BP, Yang B, Tryban S, Walker EA, et al. Fitness effects of altering gene expression noise in Saccharomyces cerevisiae. eLife. 2018;7. doi: 10.7554/eLife.37272 30124429
12. Schultz D, Wolynes PG, Jacob EB, Onuchic JN. Deciding fate in adverse times: Sporulation and competence in Bacillus subtilis. Proc Natl Acad Sci. 2009;106: 21027–21034. doi: 10.1073/pnas.0912185106 19995980
13. Antebi YE, Reich-Zeliger S, Hart Y, Mayo A, Eizenberg I, Rimer J, et al. Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates. Bhandoola A, editor. PLoS Biol. 2013;11: e1001616. doi: 10.1371/journal.pbio.1001616 23935451
14. Metzger BPH, Yuan DC, Gruber JD, Duveau F, Wittkopp PJ. Selection on noise constrains variation in a eukaryotic promoter. Nature. 2015;521: 344. doi: 10.1038/nature14244 25778704
15. Sharon E, van Dijk D, Kalma Y, Keren L, Manor O, Yakhini Z, et al. Probing the effect of promoters on noise in gene expression using thousands of designed sequences. Genome Res. 2014;24: 1698–1706. doi: 10.1101/gr.168773.113 25030889
16. Morgan MD, Marioni JC. CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness. Genome Biol. 2018;19. doi: 10.1186/s13059-018-1461-x 29945659
17. Faure AJ, Schmiedel JM, Lehner B. Systematic Analysis of the Determinants of Gene Expression Noise in Embryonic Stem Cells. Cell Syst. 2017 [cited 9 Nov 2017]. doi: 10.1016/j.cels.2017.10.003 29102610
18. Hornung G, Bar-Ziv R, Rosin D, Tokuriki N, Tawfik DS, Oren M, et al. Noise-mean relationship in mutated promoters. Genome Res. 2012;22: 2409–2417. doi: 10.1101/gr.139378.112 22820945
19. Larsson AJM, Johnsson P, Hagemann-Jensen M, Hartmanis L, Faridani OR, Reinius B, et al. Genomic encoding of transcriptional burst kinetics. Nature. 2019;565: 251–254. doi: 10.1038/s41586-018-0836-1 30602787
20. Bartman CR, Hamagami N, Keller CA, Giardine B, Hardison RC, Blobel GA, et al. Transcriptional Burst Initiation and Polymerase Pause Release Are Key Control Points of Transcriptional Regulation. Mol Cell. 2019;73: 519–532.e4. doi: 10.1016/j.molcel.2018.11.004 30554946
21. Battich N, Stoeger T, Pelkmans L. Control of Transcript Variability in Single Mammalian Cells. Cell. 2015;163: 1596–1610. doi: 10.1016/j.cell.2015.11.018 26687353
22. Torre EA, Arai E, Bayatpour S, Beck LE, Emert BL, Shaffer SM, et al. Genetic screening for single-cell variability modulators driving therapy resistance. bioRxiv. 2019 [cited 22 May 2019]. doi: 10.1101/638809
23. Boyle EA, Li YI, Pritchard JK. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. 2017;169: 1177–1186. doi: 10.1016/j.cell.2017.05.038 28622505
24. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101: 5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856
25. Roederer M, Quaye L, Mangino M, Beddall MH, Mahnke Y, Chattopadhyay P, et al. The Genetic Architecture of the Human Immune System: A Bioresource for Autoimmunity and Disease Pathogenesis. Cell. 2015;161: 387–403. doi: 10.1016/j.cell.2015.02.046 25772697
26. Patin E, Bergstedt J, Rouilly V, Libri V, Urrutia A, Alanio C, et al. Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors. Nat Immunol. 2018;19: 302–314. doi: 10.1038/s41590-018-0049-7 29476184
27. Bar-Even A, Paulsson J, Maheshri N, Carmi M, O’Shea E, Pilpel Y, et al. Noise in protein expression scales with natural protein abundance. Nat Genet. 2006;38: 636–643. doi: 10.1038/ng1807 16715097
28. Kempe H, Schwabe A, Crémazy F, Verschure PJ, Bruggeman FJ. The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise. Matera AG, editor. Mol Biol Cell. 2015;26: 797–804. doi: 10.1091/mbc.E14-08-1296 25518937
29. Tanouchi Y, Pai A, Park H, Huang S, Stamatov R, Buchler NE, et al. A noisy linear map underlies oscillations in cell size and gene expression in bacteria. Nature. 2015;523: 357–360. doi: 10.1038/nature14562 26040722
30. Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, et al. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol. 2013;31: 748–752. doi: 10.1038/nbt.2642 23873083
31. Ansel J, Bottin H, Rodriguez-Beltran C, Damon C, Nagarajan M, Fehrmann S, et al. Cell-to-Cell Stochastic Variation in Gene Expression Is a Complex Genetic Trait. PLOS Genet. 2008;4: e1000049. doi: 10.1371/journal.pgen.1000049 18404214
32. Jimenez-Gomez JM, Corwin JA, Joseph B, Maloof JN, Kliebenstein DJ. Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise. Gibson G, editor. PLoS Genet. 2011;7: e1002295. doi: 10.1371/journal.pgen.1002295 21980300
33. Lu Y, Biancotto A, Cheung F, Remmers E, Shah N, McCoy JP, et al. Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations. Immunity. 2016;45: 1162–1175. doi: 10.1016/j.immuni.2016.10.025 27851916
34. Bahar R, Hartmann CH, Rodriguez KA, Denny AD, Busuttil RA, Dollé MET, et al. Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature. 2006;441: 1011–1014. doi: 10.1038/nature04844 16791200
35. Martinez-Jimenez CP, Eling N, Chen H-C, Vallejos CA, Kolodziejczyk AA, Connor F, et al. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science. 2017;355: 1433–1436. doi: 10.1126/science.aah4115 28360329
36. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42: 348–354. doi: 10.1038/ng.548 20208533
37. Casale FP, Rakitsch B, Lippert C, Stegle O. Efficient set tests for the genetic analysis of correlated traits. Nat Methods. 2015;12: 755–758. doi: 10.1038/nmeth.3439 26076425
38. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4. doi: 10.1186/s13742-015-0047-8 25722852
39. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81: 559–575. doi: 10.1086/519795 17701901
40. The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Grundberg E, Small KS, Hedman ÅK, Nica AC, Buil A, et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet. 2012;44: 1084–1089. doi: 10.1038/ng.2394 22941192
41. Consortium GTEx. Genetic effects on gene expression across human tissues. Nature. 2017;550: 204–213. doi: 10.1038/nature24277 29022597
42. Deutsch S, Lyle R, Dermitzakis ET, Attar H, Subrahmanyan L, Gehrig C, et al. Gene expression variation and expression quantitative trait mapping of human chromosome 21 genes. Hum Mol Genet. 2005;14: 3741–3749. doi: 10.1093/hmg/ddi404 16251198
43. Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, et al. Genome-wide associations of gene expression variation in humans. PLoS Genet. 2005;1: e78. doi: 10.1371/journal.pgen.0010078 16362079
44. Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, et al. Population genomics of human gene expression. Nat Genet. 2007;39: 1217–1224. doi: 10.1038/ng2142 17873874
45. Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, Attar-Cohen H, et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science. 2009;325: 1246–1250. doi: 10.1126/science.1174148 19644074
46. Zhernakova DV, Deelen P, Vermaat M, van Iterson M, van Galen M, Arindrarto W, et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet. 2017;49: 139–145. doi: 10.1038/ng.3737 27918533
47. Schmiedel BJ, Singh D, Madrigal A, Valdovino-Gonzalez AG, White BM, Zapardiel-Gonzalo J, et al. Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression. Cell. 2018;175: 1701–1715.e16. doi: 10.1016/j.cell.2018.10.022 30449622
48. Kasela S, Kisand K, Tserel L, Kaleviste E, Remm A, Fischer K, et al. Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells. Lappalainen T, editor. PLOS Genet. 2017;13: e1006643. doi: 10.1371/journal.pgen.1006643 28248954
49. Ishigaki K, Kochi Y, Suzuki A, Tsuchida Y, Tsuchiya H, Sumitomo S, et al. Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis. Nat Genet. 2017;49: 1120–1125. doi: 10.1038/ng.3885 28553958
50. Chen L, Ge B, Casale FP, Vasquez L, Kwan T, Garrido-Martín D, et al. Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells. Cell. 2016;167: 1398–1414.e24. doi: 10.1016/j.cell.2016.10.026 27863251
51. Fairfax BP, Makino S, Radhakrishnan J, Plant K, Leslie S, Dilthey A, et al. Genetics of gene expression in primary immune cells identifies cell type–specific master regulators and roles of HLA alleles. Nat Genet. 2012;44: 502. doi: 10.1038/ng.2205 22446964
52. Allaire PD, Marat AL, Dall’Armi C, Di Paolo G, McPherson PS, Ritter B. The Connecdenn DENN Domain: A GEF for Rab35 Mediating Cargo-Specific Exit from Early Endosomes. Mol Cell. 2010;37: 370–382. doi: 10.1016/j.molcel.2009.12.037 20159556
53. Dietrich J, Hou X, Wegener A-MK, Pedersen LØ, Ødum N, Geisler C. Molecular Characterization of the Di-leucine-based Internalization Motif of the T Cell Receptor. J Biol Chem. 1996;271: 11441–11448. doi: 10.1074/jbc.271.19.11441 8626701
54. Dietrich J, Hou X, Wegener AM, Geisler C. CD3 gamma contains a phosphoserine-dependent di-leucine motif involved in down-regulation of the T cell receptor. EMBO J. 1994;13: 2156–2166. doi: 10.1002/j.1460-2075.1994.tb06492.x 8187769
55. Luton F, Buferne M, Legendre V, Chauvet E, Boyer C, Schmitt-Verhulst AM. Role of CD3gamma and CD3delta cytoplasmic domains in cytolytic T lymphocyte functions and TCR/CD3 down-modulation. J Immunol Baltim Md 1950. 1997;158: 4162–4170.
56. Borroto A, Lama J, Niedergang F, Dautry-Varsat A, Alarcón B, Alcover A. The CD3 epsilon subunit of the TCR contains endocytosis signals. J Immunol Baltim Md 1950. 1999;163: 25–31.
57. Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, et al. Heritability and Tissue Specificity of Expression Quantitative Trait Loci. PLoS Genet. 2006;2: e172. doi: 10.1371/journal.pgen.0020172 17054398
58. Gibson G, Weir B. The quantitative genetics of transcription. Trends Genet. 2005;21: 616–623. doi: 10.1016/j.tig.2005.08.010 16154229
59. Foss EJ, Radulovic D, Shaffer SA, Goodlett DR, Kruglyak L, Bedalov A. Genetic Variation Shapes Protein Networks Mainly through Non-transcriptional Mechanisms. Eisen MB, editor. PLoS Biol. 2011;9: e1001144. doi: 10.1371/journal.pbio.1001144 21909241
60. Sarkar AK, Tung P-Y, Blischak JD, Burnett JE, Li YI, Stephens M, et al. Discovery and characterization of variance QTLs in human induced pluripotent stem cells. Cotsapas C, editor. PLOS Genet. 2019;15: e1008045. doi: 10.1371/journal.pgen.1008045 31002671
61. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14: 865–868. doi: 10.1038/nmeth.4380 28759029
62. Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics. 2009;10. doi: 10.1186/1471-2105-10-106 19358741
63. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2017. Available: https://www.R-project.org
64. Howie BN, Donnelly P, Marchini J. A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies. Schork NJ, editor. PLoS Genet. 2009;5: e1000529. doi: 10.1371/journal.pgen.1000529 19543373
65. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015;526: 68–74. doi: 10.1038/nature15393 26432245
66. The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010;467: 1061–1073. doi: 10.1038/nature09534 20981092
67. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: A Tool for Genome-wide Complex Trait Analysis. Am J Hum Genet. 2011;88: 76–82. doi: 10.1016/j.ajhg.2010.11.011 21167468
68. Bates TC, Maes H, Neale MC. umx: Twin and Path-Based Structural Equation Modeling in R. Twin Res Hum Genet. 2019;22: 27–41. doi: 10.1017/thg.2019.2 30944056
69. Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinforma Oxf Engl. 2016;32: 1479–1485. doi: 10.1093/bioinformatics/btv722 26708335
70. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci. 2003;100: 9440–9445. doi: 10.1073/pnas.1530509100 12883005
71. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27: 1133–1163. doi: 10.1002/sim.3034 17886233
72. Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007;16: 309–330. doi: 10.1177/0962280206077743 17715159
73. Pierce BL, Burgess S. Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators. Am J Epidemiol. 2013;178: 1177–1184. doi: 10.1093/aje/kwt084 23863760
74. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46: 1734–1739. doi: 10.1093/ije/dyx034 28398548
75. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44: 512–525. doi: 10.1093/ije/dyv080 26050253
76. Bowden J, Hemani G, Davey Smith G. Invited Commentary: Detecting Individual and Global Horizontal Pleiotropy in Mendelian Randomization—A Job for the Humble Heterogeneity Statistic? Am J Epidemiol. 2018 [cited 29 Jul 2019]. doi: 10.1093/aje/kwy185 30188969
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