DNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories


Autoři: Sheila Lutz aff001;  Christian Brion aff001;  Margaret Kliebhan aff001;  Frank W. Albert aff001
Působiště autorů: Department of Genetics, Cell Biology, & Development, University of Minnesota, Minneapolis, MN, United States of America aff001
Vyšlo v časopise: DNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories. PLoS Genet 15(11): e32767. doi:10.1371/journal.pgen.1008375
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008375

Souhrn

DNA variants that alter gene expression contribute to variation in many phenotypic traits. In particular, trans-acting variants, which are often located on different chromosomes from the genes they affect, are an important source of heritable gene expression variation. However, our knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering recombinant alleles and quantifying the effects of these alleles on the expression of a green fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of each variant on the expression of multiple genes by RNA-sequencing. The three variants differed in their molecular mechanism, the type of genes they reside in, and their distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect was strongly dependent on the concentration of glucose in the culture medium. A noncoding variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic interaction, and affected cellular lipid metabolism. These results demonstrate that the molecular basis of trans-regulatory variation is diverse, highlighting the challenges in predicting which natural genetic variants affect gene expression.

Klíčová slova:

Alleles – Gene expression – Genetic loci – Glucose – Guide RNA – Saccharomyces cerevisiae – Yeast


Zdroje

1. Albert FW, Kruglyak L. The role of regulatory variation in complex traits and disease. Nature Reviews Genetics. 2015;16: 197–212. doi: 10.1038/nrg3891 25707927

2. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic Localization of Common Disease-Associated Variation in Regulatory DNA. Science. 2012;337: 1190–1195. doi: 10.1126/science.1222794 22955828

3. Liu H, Luo X, Niu L, Xiao Y, Chen L, Liu J, et al. Distant eQTLs and Non-coding Sequences Play Critical Roles in Regulating Gene Expression and Quantitative Trait Variation in Maize. Molecular Plant. 2017;10: 414–426. doi: 10.1016/j.molp.2016.06.016 27381443

4. Wallace JG, Bradbury PJ, Zhang N, Gibon Y, Stitt M, Buckler ES. Association Mapping across Numerous Traits Reveals Patterns of Functional Variation in Maize. PLOS Genetics. 2014;10: e1004845. doi: 10.1371/journal.pgen.1004845 25474422

5. Wittkopp PJ, Kalay G. Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nature Reviews Genetics. 2012;13: 59–69.

6. Kita R, Venkataram S, Zhou Y, Fraser HB. High-resolution mapping of cis-regulatory variation in budding yeast. Proceedings of the National Academy of Sciences. 2017;114: E10736–E10744.

7. Tewhey R, Kotliar D, Park DS, Liu B, Winnicki S, Reilly SK, et al. Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay. Cell. 2016;165: 1519–1529. doi: 10.1016/j.cell.2016.04.027 27259153

8. Arensbergen J van, Pagie L, FitzPatrick VD, Haas M de, Baltissen MP, Comoglio F, et al. High-throughput identification of human SNPs affecting regulatory element activity. Nat Genet. 2019;51: 1160–1169. doi: 10.1038/s41588-019-0455-2 31253979

9. Vockley CM, Guo C, Majoros WH, Nodzenski M, Scholtens DM, Hayes MG, et al. Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort. Genome Research. 2015;25: 1206–1214. doi: 10.1101/gr.190090.115 26084464

10. Chang J, Zhou Y, Hu X, Lam L, Henry C, Green EM, et al. The Molecular Mechanism of a Cis-Regulatory Adaptation in Yeast. PLoS Genetics. 2013;9: e1003813. doi: 10.1371/journal.pgen.1003813 24068973

11. Liu X, Li YI, Pritchard JK. Trans Effects on Gene Expression Can Drive Omnigenic Inheritance. Cell. 2019;177: 1022–1034.e6. doi: 10.1016/j.cell.2019.04.014 31051098

12. Albert FW, Bloom JS, Siegel J, Day L, Kruglyak L. Genetics of trans-regulatory variation in gene expression. eLife. 2018;7: e35471. doi: 10.7554/eLife.35471 30014850

13. Price AL, Patterson N, Hancks DC, Myers S, Reich D, Cheung VG, et al. Effects of cis and trans Genetic Ancestry on Gene Expression in African Americans. PLOS Genetics. 2008;4: e1000294. doi: 10.1371/journal.pgen.1000294 19057673

14. Price AL, Helgason A, Thorleifsson G, McCarroll SA, Kong A, Stefansson K. Single-tissue and cross-tissue heritability of gene expression via identity-by-descent in related or unrelated individuals. PLoS Genetics. 2011;7: e1001317. doi: 10.1371/journal.pgen.1001317 21383966

15. Grundberg E, Grundberg E, Small KS, Small KS, Hedman \AAsa K, Hedman \AAsa K, et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nature Genetics. 2012;44: 1084–1089. doi: 10.1038/ng.2394 22941192

16. Wright FA, Sullivan PF, Brooks AI, Zou F, Sun W, Xia K, et al. Heritability and genomics of gene expression in peripheral blood. Nature Genetics. 2014;46: 430–437. doi: 10.1038/ng.2951 24728292

17. 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

18. Brem RB, Yvert G, Clinton R, Kruglyak L. Genetic Dissection of Transcriptional Regulation in Budding Yeast. Science. 2002;296: 752–755. doi: 10.1126/science.1069516 11923494

19. Brem RB, Kruglyak L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proceedings of the National Academy of Sciences. 2005;102: 1572–1577.

20. Smith EN, Kruglyak L. Gene–Environment Interaction in Yeast Gene Expression. PLoS Biology. 2008;6: e83. doi: 10.1371/journal.pbio.0060083 18416601

21. Albert FW, Muzzey D, Weissman JS, Kruglyak L. Genetic Influences on Translation in Yeast. PLoS Genetics. 2014;10: e1004692. doi: 10.1371/journal.pgen.1004692 25340754

22. Albert FW, Treusch S, Shockley AH, Bloom JS, Kruglyak L. Genetics of single-cell protein abundance variation in large yeast populations. Nature. 2014;506: 494–497. doi: 10.1038/nature12904 24402228

23. Sudarsanam P, Cohen BA. Single Nucleotide Variants in Transcription Factors Associate More Tightly with Phenotype than with Gene Expression. PLoS Genetics. 2014;10: e1004325. doi: 10.1371/journal.pgen.1004325 24784239

24. Lewis JA, Broman AT, Will J, Gasch AP. Genetic Architecture of Ethanol-Responsive Transcriptome Variation in Saccharomyces cerevisiae Strains. Genetics. 2014;198: 369–382. doi: 10.1534/genetics.114.167429 24970865

25. Fraser HB, Moses AM, Schadt EE. Evidence for widespread adaptive evolution of gene expression in budding yeast. Proceedings of the National Academy of Sciences. 2010;107: 2977–2982.

26. Skelly DA, Merrihew GE, Merrihew GE, Riffle M, Riffle M, Connelly CF, et al. Integrative phenomics reveals insight into the structure of phenotypic diversity in budding yeast. Genome Research. 2013;23: 1496–1504. doi: 10.1101/gr.155762.113 23720455

27. Metzger BPH, Yuan DC, Gruber JD, Duveau F, Wittkopp PJ. Selection on noise constrains variation in a eukaryotic promoter. Nature. 2015;521: 344–347. doi: 10.1038/nature14244 25778704

28. Gagneur J, Stegle O, Zhu C, Jakob P, Tekkedil MM, Aiyar RS, et al. Genotype-Environment Interactions Reveal Causal Pathways That Mediate Genetic Effects on Phenotype. PLoS Genetics. 2013;9: e1003803. doi: 10.1371/journal.pgen.1003803 24068968

29. Parts L, Liu Y-C, Tekkedil MM, Steinmetz LM, Caudy AA, Fraser AG, et al. Heritability and genetic basis of protein level variation in an outbred population. Genome Research. 2014;24: 1363–1370. doi: 10.1101/gr.170506.113 24823668

30. Yvert G, Brem RB, Whittle J, Akey JM, Foss E, Smith EN, et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nature Genetics. 2003;35: 57–64.

31. Fehrmann S, Bottin-Duplus H, Leonidou A, Mollereau E, Barthelaix A, Wei W, et al. Natural sequence variants of yeast environmental sensors confer cell-to-cell expression variability. Molecular Systems Biology. 2013;9: 695–695. doi: 10.1038/msb.2013.53 24104478

32. Brem RB, Storey JD, Whittle J, Kruglyak L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature. 2005;436: 701–703. doi: 10.1038/nature03865 16079846

33. Ronald J, Brem RB, Whittle J, Kruglyak L. Local Regulatory Variation in Saccharomyces cerevisiae. PLoS Genetics. 2005;1: e25. doi: 10.1371/journal.pgen.0010025 16121257

34. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kruglyak L, et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genetics. 2008;40: 854–861. doi: 10.1038/ng.167 18552845

35. Thompson DA, Cubillos FA. Natural gene expression variation studies in yeast. Yeast. 2017;34: 3–17. doi: 10.1002/yea.3210 27668700

36. Foss EJ, Radulovic D, Shaffer SA, Ruderfer DM, Ruderfer DM, Bedalov A, et al. Genetic basis of proteome variation in yeast. Nature Genetics. 2007;39: 1369–1375. doi: 10.1038/ng.2007.22 17952072

37. Foss EJ, Radulovic D, Shaffer SA, Goodlett DR, Kruglyak L, Bedalov A. Genetic Variation Shapes Protein Networks Mainly through Non-transcriptional Mechanisms. PLoS Biology. 2011;9: e1001144. doi: 10.1371/journal.pbio.1001144 21909241

38. Brown KM, Landry CR, Hartl DL, Cavalieri D. Cascading transcriptional effects of a naturally occurring frameshift mutation in Saccharomyces cerevisiae. Molecular Ecology. 2008;17: 2985–2997. doi: 10.1111/j.1365-294X.2008.03765.x 18422925

39. Kim HS, Huh J, Fay JC. Dissecting the pleiotropic consequences of a quantitative trait nucleotide. FEMS Yeast Res. 2009;9: 713–722. doi: 10.1111/j.1567-1364.2009.00516.x 19456872

40. Brion C, Ambroset C, Sanchez I, Legras J-L, Blondin B. Differential adaptation to multi-stressed conditions of wine fermentation revealed by variations in yeast regulatory networks. BMC Genomics. 2013;14: 681. doi: 10.1186/1471-2164-14-681 24094006

41. Lewis JA, Gasch AP. Natural Variation in the Yeast Glucose-Signaling Network Reveals a New Role for the Mig3p Transcription Factor. G3—Genes|Genomes|Genetics. 2012;2: 1607–1612.

42. Storici F, Resnick MA. The Delitto Perfetto Approach to In Vivo Site‐Directed Mutagenesis and Chromosome Rearrangements with Synthetic Oligonucleotides in Yeast. Methods in Enzymology. Academic Press; 2006. pp. 329–345. doi: 10.1016/S0076-6879(05)09019-1 16793410

43. Alexander WG, Doering DT, Hittinger CT. High-Efficiency Genome Editing and Allele Replacement in Prototrophic and Wild Strains of Saccharomyces. Genetics. 2014;198: 859–866. doi: 10.1534/genetics.114.170118 25209147

44. DiCarlo JE, Norville JE, Mali P, Rios X, Aach J, Church GM. Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems. Nucleic Acids Research. 2013;41: 4336–4343. doi: 10.1093/nar/gkt135 23460208

45. Laughery MF, Hunter T, Brown A, Hoopes J, Ostbye T, Shumaker T, et al. New vectors for simple and streamlined CRISPR–Cas9 genome editing in Saccharomyces cerevisiae. Yeast. 2015;32: 711–720. doi: 10.1002/yea.3098 26305040

46. Akhmetov A, Laurent JM, Gollihar J, Gardner EC, Garge RK, Ellington AD, et al. Single-step Precision Genome Editing in Yeast Using CRISPR-Cas9. Bio-protocol. 2018;8: e2765. doi: 10.21769/BioProtoc.2765 29770349

47. Wach A, Brachat A, Pöhlmann R, Philippsen P. New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae. Yeast. 1994;10: 1793–1808. doi: 10.1002/yea.320101310 7747518

48. Wach A, Brachat A, Alberti‐Segui C, Rebischung C, Philippsen P. Heterologous HIS3 Marker and GFP Reporter Modules for PCR-Targeting in Saccharomyces cerevisiae. Yeast. 1997;13: 1065–1075. doi: 10.1002/(SICI)1097-0061(19970915)13:11<1065::AID-YEA159>3.0.CO;2-K 9290211

49. Goldstein AL, McCusker JH. Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast. 1999;15: 1541–1553. doi: 10.1002/(SICI)1097-0061(199910)15:14<1541::AID-YEA476>3.0.CO;2-K 10514571

50. Longtine MS, Iii AM, Demarini DJ, Shah NG, Wach A, Brachat A, et al. Additional modules for versatile and economical PCR-based gene deletion and modification in Saccharomyces cerevisiae. Yeast. 1998;14: 953–961. doi: 10.1002/(SICI)1097-0061(199807)14:10<953::AID-YEA293>3.0.CO;2-U 9717241

51. Ozcan S, Dover J, Rosenwald AG, Wölfl S, Johnston M. Two glucose transporters in Saccharomyces cerevisiae are glucose sensors that generate a signal for induction of gene expression. PNAS. 1996;93: 12428–12432. doi: 10.1073/pnas.93.22.12428 8901598

52. Özcan S, Johnston M. Function and Regulation of Yeast Hexose Transporters. Microbiol Mol Biol Rev. 1999;63: 554–569. 10477308

53. Choi Y, Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 2015;31: 2745–2747. doi: 10.1093/bioinformatics/btv195 25851949

54. Scharff-Poulsen P, Moriya H, Johnston M. Genetic Analysis of Signal Generation by the Rgt2 Glucose Sensor of Saccharomyces cerevisiae. G3 (Bethesda). 2018;8: 2685–2696. doi: 10.1534/g3.118.200338 29954842

55. Luo Y, Karpichev IV, Kohanski RA, Small GM. Purification, identification, and properties of a Saccharomyces cerevisiae oleate-activated upstream activating sequence-binding protein that is involved in the activation of POX1. J Biol Chem. 1996;271: 12068–12075. doi: 10.1074/jbc.271.20.12068 8662598

56. Rottensteiner H, Kal AJ, Hamilton B, Ruis H, Tabak HF. A heterodimer of the Zn2Cys6 transcription factors Pip2p and Oaf1p controls induction of genes encoding peroxisomal proteins in Saccharomyces cerevisiae. Eur J Biochem. 1997;247: 776–783. doi: 10.1111/j.1432-1033.1997.00776.x 9288897

57. Karpichev IV, Luo Y, Marians RC, Small GM. A complex containing two transcription factors regulates peroxisome proliferation and the coordinate induction of beta-oxidation enzymes in Saccharomyces cerevisiae. Mol Cell Biol. 1997;17: 69–80. doi: 10.1128/mcb.17.1.69 8972187

58. Litvin O, Causton HC, Chen BJ, Pe’er D. Modularity and interactions in the genetics of gene expression. Proceedings of the National Academy of Sciences. 2009;106: 6441–6446.

59. Phelps C, Gburcik V, Suslova E, Dudek P, Forafonov F, Bot N, et al. Fungi and animals may share a common ancestor to nuclear receptors. Proc Natl Acad Sci U S A. 2006;103: 7077–7081. doi: 10.1073/pnas.0510080103 16636289

60. Stukey JE, McDonough VM, Martin CE. Isolation and characterization of OLE1, a gene affecting fatty acid desaturation from Saccharomyces cerevisiae. J Biol Chem. 1989;264: 16537–16544. 2674136

61. Stukey JE, McDonough VM, Martin CE. The OLE1 gene of Saccharomyces cerevisiae encodes the delta 9 fatty acid desaturase and can be functionally replaced by the rat stearoyl-CoA desaturase gene. J Biol Chem. 1990;265: 20144–20149. 1978720

62. Goldar MM, Nishie T, Ishikura Y, Fukuda T, Takegawa K, Kawamukai M. Functional conservation between fission yeast moc1/sds23 and its two orthologs, budding yeast SDS23 and SDS24, and phenotypic differences in their disruptants. Biosci Biotechnol Biochem. 2005;69: 1422–1426. doi: 10.1271/bbb.69.1422 16041152

63. Choi JY, Stukey J, Hwang SY, Martin CE. Regulatory elements that control transcription activation and unsaturated fatty acid-mediated repression of the Saccharomyces cerevisiae OLE1 gene. J Biol Chem. 1996;271: 3581–3589. doi: 10.1074/jbc.271.7.3581 8631965

64. McIsaac RS, Oakes BL, Wang X, Dummit KA, Botstein D, Noyes MB. Synthetic gene expression perturbation systems with rapid, tunable, single-gene specificity in yeast. Nucleic Acids Research. 2013;41: e57–e57. doi: 10.1093/nar/gks1313 23275543

65. Bergenholm D, Liu G, Holland P, Nielsen J. Reconstruction of a Global Transcriptional Regulatory Network for Control of Lipid Metabolism in Yeast by Using Chromatin Immunoprecipitation with Lambda Exonuclease Digestion. mSystems. 2018;3: e00215–17. doi: 10.1128/mSystems.00215-17 30073202

66. Bloom JS, Kotenko I, Sadhu MJ, Treusch S, Albert FW, Kruglyak L. Genetic interactions contribute less than additive effects to quantitative trait variation in yeast. Nature Communications. 2015;6: 8712. doi: 10.1038/ncomms9712 26537231

67. Peter J, Chiara MD, Friedrich A, Yue J-X, Pflieger D, Bergström A, et al. Genome evolution across 1,011 Saccharomyces cerevisiae isolates. Nature. 2018;556: 339–344. doi: 10.1038/s41586-018-0030-5 29643504

68. Wang Q-M, Liu W-Q, Liti G, Wang S-A, Bai F-Y. Surprisingly diverged populations of Saccharomyces cerevisiae in natural environments remote from human activity. Molecular Ecology. 2012;21: 5404–5417. doi: 10.1111/j.1365-294X.2012.05732.x 22913817

69. Duan S-F, Han P-J, Wang Q-M, Liu W-Q, Shi J-Y, Li K, et al. The origin and adaptive evolution of domesticated populations of yeast from Far East Asia. Nat Commun. 2018;9: 1–13. doi: 10.1038/s41467-017-02088-w 29317637

70. Breslow DK, Cameron DM, Collins SR, Schuldiner M, Stewart-Ornstein J, Newman HW, et al. A comprehensive strategy enabling high-resolution functional analysis of the yeast genome. Nature Methods. 2008;5: 711–718. doi: 10.1038/nmeth.1234 18622397

71. Lee JT, Coradini ALV, Shen A, Ehrenreich IM. Layers of Cryptic Genetic Variation Underlie a Yeast Complex Trait. Genetics. 2019;211: 1469–1482. doi: 10.1534/genetics.119.301907 30787041

72. Holt S, Kankipati H, De Graeve S, Van Zeebroeck G, Foulquié-Moreno MR, Lindgreen S, et al. Major sulfonate transporter Soa1 in Saccharomyces cerevisiae and considerable substrate diversity in its fungal family. Nat Commun. 2017;8. doi: 10.1038/ncomms14247 28165463

73. Maurer MJ, Sutardja L, Pinel D, Bauer S, Muehlbauer AL, Ames TD, et al. Quantitative Trait Loci (QTL)-Guided Metabolic Engineering of a Complex Trait. ACS Synth Biol. 2017;6: 566–581. doi: 10.1021/acssynbio.6b00264 27936603

74. Trindade B de C, Holt S, Souffriau B, Lopes RB, Foulquié-Moreno MR, Thevelein JM. Identification of Novel Alleles Conferring Superior Production of Rose Flavor Phenylethyl Acetate Using Polygenic Analysis in Yeast. MBio. 2017;8: e01173–17. doi: 10.1128/mBio.01173-17 29114020

75. Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, Andre B, et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science. 1999;285: 901–906. doi: 10.1126/science.285.5429.901 10436161

76. Huh W-K, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, et al. Global analysis of protein localization in budding yeast. Nature. 2003;425: 686–691. doi: 10.1038/nature02026 14562095

77. Steinmetz LM, Sinha H, Richards DR, Spiegelman JI, Oefner PJ, McCusker JH, et al. Dissecting the architecture of a quantitative trait locus in yeast. Nature. 2002;416: 326–330. doi: 10.1038/416326a 11907579

78. Sinha H, David L, Pascon RC, Clauder-Münster S, Krishnakumar S, Nguyen M, et al. Sequential Elimination of Major-Effect Contributors Identifies Additional Quantitative Trait Loci Conditioning High-Temperature Growth in Yeast. Genetics. 2008;180: 1661–1670. doi: 10.1534/genetics.108.092932 18780730

79. Fay JC. The molecular basis of phenotypic variation in yeast. Current opinion in genetics & development. 2013;23: 672–677.

80. GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550: 204–213. doi: 10.1038/nature24277 29022597

81. Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nature Genetics. 2013;45: 1238–1243. doi: 10.1038/ng.2756 24013639

82. Small KS, Todorcevic M, Civelek M, Moustafa JSE-S, Wang X, Simon MM, et al. Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition. Nature Genetics. 2018;50: 572–+. doi: 10.1038/s41588-018-0088-x 29632379

83. Heinig M, Petretto E, Wallace C, Bottolo L, Rotival M, Lu H, et al. A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature. 2010;467: 460–464. doi: 10.1038/nature09386 20827270

84. Yao C, Joehanes R, Johnson AD, Huan T, Liu C, Freedman JE, et al. Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits. The American Journal of Human Genetics. 2017;100: 571–580. doi: 10.1016/j.ajhg.2017.02.003 28285768

85. Yang F, Wang J, Consortium TGte, Pierce BL, Chen LS, Aguet F, et al. Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis. Genome Res. 2017;27: 1859–1871. doi: 10.1101/gr.216754.116 29021290

86. Shan N, Wang Z, Hou L. Identification of trans-eQTLs using mediation analysis with multiple mediators. BMC Bioinformatics. 2019;20: 126. doi: 10.1186/s12859-019-2651-6 30925861

87. Pierce BL, Tong L, Chen LS, Rahaman R, Argos M, Jasmine F, et al. Mediation Analysis Demonstrates That Trans-eQTLs Are Often Explained by Cis-Mediation: A Genome-Wide Analysis among 1,800 South Asians. PLoS Genetics. 2014;10.

88. Bryois J, Buil A, Evans DM, Kemp JP, Montgomery SB, Conrad DF, et al. Cis and Trans Effects of Human Genomic Variants on Gene Expression. PLoS Genetics. 2014;10: e1004461. doi: 10.1371/journal.pgen.1004461 25010687

89. Wentzell AM, Rowe HC, Hansen BG, Ticconi C, Halkier BA, Kliebenstein DJ. Linking Metabolic QTLs with Network and cis-eQTLs Controlling Biosynthetic Pathways. PLOS Genetics. 2007;3: e162. doi: 10.1371/journal.pgen.0030162 17941713

90. 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. Nature Genetics. 2012;44: 502–510. doi: 10.1038/ng.2205 22446964

91. Degreif D, de Rond T, Bertl A, Keasling JD, Budin I. Lipid engineering reveals regulatory roles for membrane fluidity in yeast flocculation and oxygen-limited growth. Metabolic Engineering. 2017;41: 46–56. doi: 10.1016/j.ymben.2017.03.002 28323063

92. Li P, Fu X, Zhang L, Li S. CRISPR/Cas-based screening of a gene activation library in Saccharomyces cerevisiae identifies a crucial role of OLE1 in thermotolerance. Microbial Biotechnology. 2018;0: 1–10. doi: 10.1111/1751-7915.13333 30394685

93. Fang Z, Chen Z, Wang S, Shi P, Shen Y, Zhang Y, et al. Overexpression of OLE1 Enhances Cytoplasmic Membrane Stability and Confers Resistance to Cadmium in Saccharomyces cerevisiae. Appl Environ Microbiol. 2017;83: e02319–16. doi: 10.1128/AEM.02319-16 27793829

94. Hoppe T, Matuschewski K, Rape M, Schlenker S, Ulrich HD, Jentsch S. Activation of a Membrane-Bound Transcription Factor by Regulated Ubiquitin/Proteasome-Dependent Processing. Cell. 2000;102: 577–586. doi: 10.1016/s0092-8674(00)00080-5 11007476

95. Zhang S, Burkett TJ, Yamashita I, Garfinkel DJ. Genetic redundancy between SPT23 and MGA2: regulators of Ty-induced mutations and Ty1 transcription in Saccharomyces cerevisiae. Molecular and Cellular Biology. 1997;17: 4718–4729. doi: 10.1128/mcb.17.8.4718 9234728

96. Covino R, Ballweg S, Stordeur C, Michaelis JB, Puth K, Wernig F, et al. A Eukaryotic Sensor for Membrane Lipid Saturation. Mol Cell. 2016;63: 49–59. doi: 10.1016/j.molcel.2016.05.015 27320200

97. Jesch SA, Liu P, Zhao X, Wells MT, Henry SA. Multiple Endoplasmic Reticulum-to-Nucleus Signaling Pathways Coordinate Phospholipid Metabolism with Gene Expression by Distinct Mechanisms. J Biol Chem. 2006;281: 24070–24083. doi: 10.1074/jbc.M604541200 16777852

98. Duveau F, Toubiana W, Wittkopp PJ. Fitness Effects of Cis-Regulatory Variants in the Saccharomyces cerevisiae TDH3 Promoter. Molecular biology and evolution. 2017;34: 2908–2912. doi: 10.1093/molbev/msx224 28961929

99. Rest JS, Morales CM, Waldron JB, Opulente DA, Fisher J, Moon S, et al. Nonlinear Fitness Consequences of Variation in Expression Level of a Eukaryotic Gene. Molecular biology and evolution. 2013;30: 448–456. doi: 10.1093/molbev/mss248 23104081

100. Keren L, Hausser J, Lotan-Pompan M, Vainberg Slutskin I, Alisar H, Kaminski S, et al. Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness. Cell. 2016;166: 1282–1294.e18. doi: 10.1016/j.cell.2016.07.024 27545349

101. Schaefke B, Emerson JJ, Wang TY, Lu MYJ, Hsieh LC, Li WH. Inheritance of Gene Expression Level and Selective Constraints on Trans- and Cis-Regulatory Changes in Yeast. Molecular biology and evolution. 2013.

102. Emerson JJ, Hsieh L-C, Sung H-M, Wang T-Y, Huang C-J, Lu HH-S, et al. Natural selection on cis and trans regulation in yeasts. Genome Research. 2010;20: 826–836. doi: 10.1101/gr.101576.109 20445163

103. Shendure J, Balasubramanian S, Church GM, Gilbert W, Rogers J, Schloss JA, et al. DNA sequencing at 40: past, present and future. Nature. 2017;550: 345–353. doi: 10.1038/nature24286 29019985

104. Wagih O, Galardini M, Busby BP, Memon D, Typas A, Beltrao P. A resource of variant effect predictions of single nucleotide variants in model organisms. Molecular Systems Biology. 2018;14: e8430. doi: 10.15252/msb.20188430 30573687

105. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics. 2014;46: 310–315. doi: 10.1038/ng.2892 24487276

106. Majithia AR, Tsuda B, Agostini M, Gnanapradeepan K, Rice R, Peloso G, et al. Prospective functional classification of all possible missense variants in PPARG. Nature Genetics. 2016;48: 1570–1575. doi: 10.1038/ng.3700 27749844

107. Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet. 2018;50: 1171–1179. doi: 10.1038/s41588-018-0160-6 30013180

108. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Current protocols in human genetics. 2013;Chapter 7: Unit7.20.

109. Findlay GM, Boyle EA, Hause RJ, Klein JC, Shendure J. Saturation editing of genomic regions by multiplex homology-directed repair. Nature. 2014;513: 120–123. doi: 10.1038/nature13695 25141179

110. Matreyek KA, Starita LM, Stephany JJ, Martin B, Chiasson MA, Gray VE, et al. Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat Genet. 2018;50: 874–882. doi: 10.1038/s41588-018-0122-z 29785012

111. Klein JC, Keith A, Rice SJ, Shepherd C, Agarwal V, Loughlin J, et al. Functional testing of thousands of osteoarthritis-associated variants for regulatory activity. Nat Commun. 2019;10: 1–9. doi: 10.1038/s41467-018-07882-8

112. Sharon E, Chen S-AA, Khosla NM, Smith JD, Pritchard JK, Fraser HB. Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing. Cell. 2018;175: 544–557.e16. doi: 10.1016/j.cell.2018.08.057 30245013

113. Roy KR, Smith JD, Vonesch SC, Lin G, Tu CS, Lederer AR, et al. Multiplexed precision genome editing with trackable genomic barcodes in yeast. Nature Biotechnology. 2018;36: 512–520. doi: 10.1038/nbt.4137 29734294

114. Sadhu MJ, Bloom JS, Day L, Siegel JJ, Kosuri S, Kruglyak L. Highly parallel genome variant engineering with CRISPR–Cas9. Nat Genet. 2018;50: 510–514. doi: 10.1038/s41588-018-0087-y 29632376

115. Garst AD, Bassalo MC, Pines G, Lynch SA, Halweg-Edwards AL, Liu R, et al. Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering. Nature Biotechnology. 2017;35: 48–55. doi: 10.1038/nbt.3718 27941803

116. Bao Z, HamediRad M, Xue P, Xiao H, Tasan I, Chao R, et al. Genome-scale engineering of Saccharomyces cerevisiae with single-nucleotide precision. Nature Biotechnology. 2018;36: 505–508. doi: 10.1038/nbt.4132 29734295

117. Guo X, Chavez A, Tung A, Chan Y, Kaas C, Yin Y, et al. High-throughput creation and functional profiling of DNA sequence variant libraries using CRISPR–Cas9 in yeast. Nature Biotechnology. 2018;36: 540–546. doi: 10.1038/nbt.4147 29786095

118. Starita LM, Ahituv N, Dunham MJ, Kitzman JO, Roth FP, Seelig G, et al. Variant Interpretation: Functional Assays to the Rescue. The American Journal of Human Genetics. 2017;101: 315–325. doi: 10.1016/j.ajhg.2017.07.014 28886340

119. Sheff MA, Thorn KS. Optimized cassettes for fluorescent protein tagging in Saccharomyces cerevisiae. Yeast. 2004;21: 661–670. doi: 10.1002/yea.1130 15197731

120. Sikorski RS, Hieter P. A system of shuttle vectors and yeast host strains designed for efficient manipulation of DNA in Saccharomyces cerevisiae. Genetics. 1989;122: 19–27. 2659436

121. Malcova I, Farkasovsky M, Senohrabkova L, Vasicova P, Hasek J. New integrative modules for multicolor-protein labeling and live-cell imaging in Saccharomyces cerevisiae. FEMS Yeast Res. 2016;16. doi: 10.1093/femsyr/fow027 26994102

122. Horton RM, Hunt HD, Ho SN, Pullen JK, Pease LR. Engineering hybrid genes without the use of restriction enzymes: gene splicing by overlap extension. Gene. 1989;77: 61–68. doi: 10.1016/0378-1119(89)90359-4 2744488

123. Gietz RD, Schiestl RH. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nature Protocols. 2007;2: 31–34. doi: 10.1038/nprot.2007.13 17401334

124. Persson X-MT, Błachnio-Zabielska AU, Jensen MD. Rapid measurement of plasma free fatty acid concentration and isotopic enrichment using LC/MS. J Lipid Res. 2010;51: 2761–2765. doi: 10.1194/jlr.M008011 20526002

125. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2018;35: 526–528.

126. Liti G, Carter DM, Moses AM, Warringer J, Parts L, James SA, et al. Population genomics of domestic and wild yeasts. Nature. 2009;458: 337–341. doi: 10.1038/nature07743 19212322

127. Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York; 2016. Available: https://ggplot2.tidyverse.org

128. Sprouffske K, Wagner A. Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinformatics. 2016;17: 172. doi: 10.1186/s12859-016-1016-7 27094401

129. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67: 1–48. doi: 10.18637/jss.v067.i01

130. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

131. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology. 2016;34: 525–527. doi: 10.1038/nbt.3519 27043002

132. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46: D754–D761. doi: 10.1093/nar/gkx1098 29155950

133. Engel SR, Dietrich FS, Fisk DG, Binkley G, Balakrishnan R, Costanzo MC, et al. The Reference Genome Sequence of Saccharomyces cerevisiae: Then and Now. G3: Genes, Genomes, Genetics. 2014;4: 389–398. doi: 10.1534/g3.113.008995 24374639

134. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey of best practices for RNA-seq data analysis. Genome Biology. 2016;17: 13. doi: 10.1186/s13059-016-0881-8 26813401

135. Andrews S, others. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics, Babraham Institute, Cambridge, United Kingdom; 2010.

136. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28: 2184–2185. doi: 10.1093/bioinformatics/bts356 22743226

137. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15: 550. doi: 10.1186/s13059-014-0550-8 25516281

138. Leek JT, Storey JD. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genetics. 2007;3: e161.

139. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological). 1995; 289–300.

140. Cherry JM, Hong EL, Amundsen C, Balakrishnan R, Binkley G, Chan ET, et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Research. 2012;40: D700–D705. doi: 10.1093/nar/gkr1029 22110037

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