#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Temporal Expression Profiling Identifies Pathways Mediating Effect of Causal Variant on Phenotype


The causal path from a genetic variant to a complex phenotype such as disease progression is often not known. Studying gene expression variation is one approach to identify the mediating genes, however, it is difficult to distinguish causative from correlative genes. This becomes a challenge especially when studying developmental and physiological traits, since they involve dynamic processes contributing to the variation and only single static expression profiling is performed. As a proof of concept, we addressed this challenge here in yeast, by studying genome-wide gene expression in the presence of the causative polymorphism of MKT1 as the sole genetic variant, during the time phase when it contributes to sporulation efficiency variation. Our analysis during early sporulation identified mitochondrial retrograde signaling and nitrogen starvation as novel regulators, acting additively to regulate sporulation efficiency. Furthermore, we showed that PUF3, a known interactor of MKT1 had an independent role in sporulation. Our results highlight the role of differential mitochondrial signaling for efficient meiosis, providing insights into the factors regulating infertility. In addition, our study has implications for characterizing the molecular effects of causal genetic variants on dynamic biological processes during development and disease progression.


Vyšlo v časopise: Temporal Expression Profiling Identifies Pathways Mediating Effect of Causal Variant on Phenotype. PLoS Genet 11(6): e32767. doi:10.1371/journal.pgen.1005195
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005195

Souhrn

The causal path from a genetic variant to a complex phenotype such as disease progression is often not known. Studying gene expression variation is one approach to identify the mediating genes, however, it is difficult to distinguish causative from correlative genes. This becomes a challenge especially when studying developmental and physiological traits, since they involve dynamic processes contributing to the variation and only single static expression profiling is performed. As a proof of concept, we addressed this challenge here in yeast, by studying genome-wide gene expression in the presence of the causative polymorphism of MKT1 as the sole genetic variant, during the time phase when it contributes to sporulation efficiency variation. Our analysis during early sporulation identified mitochondrial retrograde signaling and nitrogen starvation as novel regulators, acting additively to regulate sporulation efficiency. Furthermore, we showed that PUF3, a known interactor of MKT1 had an independent role in sporulation. Our results highlight the role of differential mitochondrial signaling for efficient meiosis, providing insights into the factors regulating infertility. In addition, our study has implications for characterizing the molecular effects of causal genetic variants on dynamic biological processes during development and disease progression.


Zdroje

1. Burga A, Lehner B. Predicting phenotypic variation from genotypes, phenotypes and a combination of the two. Curr Opin Biotechnol. 2013; 24: 803–809. doi: 10.1016/j.copbio.2013.03.004 23540420

2. Lehner B. Genotype to phenotype: lessons from model organisms for human genetics. Nat Rev Genet. 2013; 14: 168–178. doi: 10.1038/nrg3404 23358379

3. Fay JC The molecular basis of phenotypic variation in yeast. Curr Opin Genet Dev. 2013; 23: 672–677. doi: 10.1016/j.gde.2013.10.005 24269094

4. Carter H, Hofree M, Ideker T. Genotype to phenotype via network analysis. Curr Opin Genet Dev. 2013; 23: 611–621. doi: 10.1016/j.gde.2013.10.003 24238873

5. van der Sijde MR, Ng A, Fu J. Systems genetics: From GWAS to disease pathways. Biochim Biophys Acta. 2014; 1842: 1903–1909. doi: 10.1016/j.bbadis.2014.04.025 24798234

6. Battle A, Montgomery SB Determining causality and consequence of expression quantitative trait loci. Hum Genet. 2014; 133: 727–735. doi: 10.1007/s00439-014-1446-0 24770875

7. Smith EN, Kruglyak L Gene-environment interaction in yeast gene expression. PLoS Biol. 2008; 6: e83. doi: 10.1371/journal.pbio.0060083 18416601

8. Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M. Mapping complex disease traits with global gene expression. Nat Rev Genet. 2009; 10: 184–194. doi: 10.1038/nrg2537 19223927

9. 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 Genet. 2013; 9: e1003803. doi: 10.1371/journal.pgen.1003803 24068968

10. Bar-Joseph Z, Gitter A, Simon I. Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet. 2012; 13: 552–564. doi: 10.1038/nrg3244 22805708

11. Gerrits A, Li Y, Tesson BM, Bystrykh LV, Weersing E, Ausema A, Dontje B, et al. Expression quantitative trait loci are highly sensitive to cellular differentiation state. PLoS Genet. 2009; 5: e1000692. doi: 10.1371/journal.pgen.1000692 19834560

12. Montgomery SB, Dermitzakis ET. From expression QTLs to personalized transcriptomics. Nat Rev Genet. 2011; 12: 277–282. doi: 10.1038/nrg2969 21386863

13. Francesconi M, Lehner B. The effects of genetic variation on gene expression dynamics during development. Mol Syst Biol. 2013; 505: 208–211.

14. 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

15. Deutschbauer AM, Davis RW. Quantitative trait loci mapped to single-nucleotide resolution in yeast. Nat Genet. 2005; 37: 1333–1340. 16273108

16. Lee S- I, Dudley AM, Drubin D, Silver PA, Krogan NJ, Pe'er D, et al. Learning a prior on regulatory potential from eQTL data. PLoS Genet. 2009; 5: e1000358. doi: 10.1371/journal.pgen.1000358 19180192

17. 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. 11907579

18. Sinha H, Nicholson BP, Steinmetz LM, McCusker JH. Complex genetic interactions in a quantitative trait locus. PLoS Genet. 2006; 2: e13. 16462944

19. Demogines A, Smith E, Kruglyak L, Alani E. Identification and dissection of a complex DNA repair sensitivity phenotype in Baker's yeast. PLoS Genet. 2008; 4: e1000123. doi: 10.1371/journal.pgen.1000123 18617998

20. Dimitrov LN, Brem RB, Kruglyak L, Gottschling DE. Polymorphisms in multiple genes contribute to the spontaneous mitochondrial genome instability of Saccharomyces cerevisiae S288c strains. Genetics. 2009; 183: 365–383. doi: 10.1534/genetics.109.104497 19581448

21. Swinnen S, Schaerlaekens K, Pais T, Claesen J, Hubmann G, Yang Y, et al. Identification of novel causative genes determining the complex trait of high ethanol tolerance in yeast using pooled-segregant whole-genome sequence analysis. Genome Res. 2012; 22: 975–984. doi: 10.1101/gr.131698.111 22399573

22. 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

23. van Werven FJ, Amon A. Regulation of entry into gametogenesis. Philos Trans R Soc B Biol Sci. 2011; 366: 3521–3531. doi: 10.1098/rstb.2011.0081 22084379

24. Neiman AM. Sporulation in the budding yeast Saccharomyces cerevisiae. Genetics. 2011; 189: 737–765. doi: 10.1534/genetics.111.127126 22084423

25. Deutschbauer AM, Williams RM, Chu AM, Davis RW. Parallel phenotypic analysis of sporulation and postgermination growth in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A. 2002; 99: 15530–15535. 12432101

26. Lorenz K, Cohen BA. Causal variation in yeast sporulation tends to reside in a pathway bottleneck. PLoS Genet. 2014; 10: e1004634. doi: 10.1371/journal.pgen.1004634 25211152

27. Honigberg SM. Signal pathway integration in the switch from the mitotic cell cycle to meiosis in yeast. J Cell Sci. 2003; 116: 2137–2147. 12730290

28. Kassir Y, Granot D, Simchen G. IME1, a positive regulator gene of meiosis in S. cerevisiae. Cell. 1988; 52: 853–862. 3280136

29. Tsuchiya D, Yang Y, Lacefield S. Positive feedback of NDT80 expression ensures irreversible meiotic commitment in budding yeast. PLoS Genet. 2014; 10: e1004398. doi: 10.1371/journal.pgen.1004398 24901499

30. Mitchell AP. Control of meiotic gene expression in Saccharomyces cerevisiae. Microbiol Rev. 1994; 58: 56–70. 8177171

31. Sudarsanam P, Cohen BA. Single nucleotide variants in transcription factors associate more tightly with phenotype than with gene expression. PLoS Genet. 2014; 10: e1004325. doi: 10.1371/journal.pgen.1004325 24784239

32. Neiman AM. Ascospore formation in the yeast Saccharomyces cerevisiae. Microbiol Mol Biol Rev. 2005; 69: 565–584. 16339736

33. Liu Z, Butow RA. Mitochondrial retrograde signaling. Ann Rev Genet. 2006; 40: 159–185. 16771627

34. Broach JR. Nutritional control of growth and development in yeast. Genetics. 2012; 192: 73–105. doi: 10.1534/genetics.111.135731 22964838

35. Ray D, Ye P. Characterization of the metabolic requirements in yeast meiosis. PLoS ONE. 2013; 8: e63707. doi: 10.1371/journal.pone.0063707 23675502

36. Liao X, Butow RA. RTG1 and RTG2: two yeast genes required for a novel path of communication from mitochondria to the nucleus. Cell. 1993; 72: 61–71. 8422683

37. Scott S, Abul-Hamd AT, Cooper TG. Roles of the Dal82p domains in allophanate/oxalurate-dependent gene expression in Saccharomyces cerevisiae. J Biol Chem. 2000; 275: 30886–30893. 10906145

38. Jia Y, Rothermel B, Thornton J, Butow RA. A basic helix-loop-helix-leucine zipper transcription complex in yeast functions in a signaling pathway from mitochondria to the nucleus. Mol Cell Biol. 1997; 17: 1110–1117. 9032238

39. Rabitsch KP, Tóth A, Gálová M, Schleiffer A, Schaffner G, Aigner E, et al. A screen for genes required for meiosis and spore formation based on whole-genome expression. Curr Biol. 2001; 11: 1001–1009. 11470404

40. Primig M, Williams RM, Winzeler EA, Tevzadze GG, Conway AR, Hwang SY, et al. The core meiotic transcriptome in budding yeasts. Nat Genet. 2000; 26: 415–423. 11101837

41. Sekito T, Thornton J, Butow RA. Mitochondria-to-nuclear signaling is regulated by the subcellular localization of the transcription factors Rtg1p and Rtg3p. Mol Biol Cell. 2000; 11: 2103–2115. 10848632

42. Jambhekar A, Amon A. Control of meiosis by respiration. Curr Biol. 2008; 18: 969–975. doi: 10.1016/j.cub.2008.05.047 18595705

43. Fazlollahi M, Lee E, Muroff I, Lu XJ, Gomez-Alcala P, Causton HC, et al. Harnessing natural sequence variation to dissect post-transcriptional regulatory networks in yeast. G3 (Bethesda). 2014; 4: 1539–1553. doi: 10.1534/g3.114.012039 24938291

44. Gerber AP, Herschlag D, Brown PO. Extensive association of functionally and cytotopically related mRNAs with Puf family RNA-binding proteins in yeast. PLoS Biol. 2004; 2: e79. 15024427

45. Civelek M, Lusis AJ. Systems genetics approaches to understand complex traits. Nat Rev Genet. 2014; 15: 34–48. doi: 10.1038/nrg3575 24296534

46. Westra HJ, Franke L. From genome to function by studying eQTLs. Biochim Biophys Acta. 2014; 1842: 1896–1902. doi: 10.1016/j.bbadis.2014.04.024 24798236

47. Saint Pierre A, Génin E How important are rare variants in common disease? Brief Funct Genomics. 2014; 13: 353–361. doi: 10.1093/bfgp/elu025 25005607

48. Barbour JA, Turner N. Mitochondrial stress signaling promotes cellular adaptations. Int J Cell Biol. 2014; 2014: 1–12.

49. Chen XJ, Wang X, Kaufman BA, Butow RA. Aconitase couples metabolic regulation to mitochondrial DNA maintenance. Science. 2005; 307: 714–717. 15692048

50. May-Panloup P, Chrétien MF, Jacques C, Vasseur C, Malthièry Y, Reynier P. Low oocyte mitochondrial DNA content in ovarian insufficiency. Hum Reprod. 2005; 20: 593–597. 15608038

51. Starovoytova AN, Sorokin MI, Sokolov SS, Severin FF, Knorre DA. Mitochondrial signaling in Saccharomyces cerevisiae pseudohyphae formation induced by butanol. FEMS Yeast Res. 2013; 13: 367–374. doi: 10.1111/1567-1364.12039 23448552

52. 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

53. Enyenihi AH, Saunders WS. Large-scale functional genomic analysis of sporulation and meiosis in Saccharomyces cerevisiae. Genetics. 2003; 163: 47–54. 12586695

54. Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, et al. The transcriptional program of sporulation in budding yeast. Science. 1998; 282: 699–705. 9784122

55. Mortimer RK, Johnston JR. Genealogy of principal strains of the yeast genetic stock center. Genetics. 1986; 113: 35–43. 3519363

56. Anderson JB, Funt J, Thompson DA, Prabhu S, Socha A, Sirjusingh C, et al. Determinants of divergent adaptation and Dobzhansky-Muller interaction in experimental yeast populations. Curr Biol. 2010; 20: 1383–1388. doi: 10.1016/j.cub.2010.06.022 20637622

57. Warringer J, Zörgö E, Cubillos FA, Zia A, Gjuvsland A, Simpson JT, et al. Trait variation in yeast is defined by population history. PLoS Genet. 2011; 7: e1002111. doi: 10.1371/journal.pgen.1002111 21698134

58. 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. Nat Genet. 2008; 40: 854–861. doi: 10.1038/ng.167 18552845

59. Saint-Georges Y, Garcia M, Delaveau T, Jourdren L, Le Crom S, Lemoine S, et al. Yeast mitochondrial biogenesis: a role for the PUF RNA-binding protein Puf3p in mRNA localization. PLoS ONE. 2008; 3: e2293. doi: 10.1371/journal.pone.0002293 18523582

60. Devaux F, Lelandais G, Garcia M, Goussard S, Jacq C. Posttranscriptional control of mitochondrial biogenesis: spatio-temporal regulation of the protein import process. FEBS Letters. 2010; 584: 4273–4279. doi: 10.1016/j.febslet.2010.09.030 20875412

61. Tadauchi T, Inada T, Matsumoto K, Irie K. Posttranscriptional regulation of HO expression by the Mkt1-Pbp1 complex. Mol Cell Biol. 2004; 24: 3670–3681. 15082763

62. Singh A, Minia I, Droll D, Fadda A, Clayton C, Erben E. Trypanosome MKT1 and the RNA-binding protein ZC3H11: interactions and potential roles in post-transcriptional regulatory networks. Nucleic Acids Res. 2014; 42: 4652–4668. doi: 10.1093/nar/gkt1416 24470144

63. Kedersha N, Anderson P. Mammalian stress granules and processing bodies. Methods Enzymol. 2007; 431: 61–81 17923231

64. Swetloff A, Conne B, Huarte J, Pitetti JL, Nef S, Vassalli JD. Dcp1-bodies in mouse oocytes. Mol Biol Cell. 2009; 20: 4951–4961. doi: 10.1091/mbc.E09-02-0123 19812249

65. Buchan JR, Muhlrad D, Parker R. P-bodies promote stress granule assembly in Saccharomyces cerevisiae. J Cell Biol. 2008; 183: 441–455. doi: 10.1083/jcb.200807043 18981231

66. Goldstein AL, McCusker JH. Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast. 1999; 15: 1541–1553. 10514571

67. Gietz RD, Woods RA. Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method. Methods Enzymol. 2002; 350: 87–96. 12073338

68. Tomar P, Bhatia A, Ramdas S, Diao L, Bhanot G, Sinha H. Sporulation genes associated with sporulation efficiency in natural isolates of yeast. PLoS ONE. 2013; 8: e69765. doi: 10.1371/journal.pone.0069765 23874994

69. Lardenois A, Liu Y, Walther T, Chalmel F, Evrard B, Granovskaia M, et al. Execution of the meiotic noncoding RNA expression program and the onset of gametogenesis in yeast require the conserved exosome subunit Rrp6. Proc Natl Acad Sci U S A. 2011; 108: 1058–1063. doi: 10.1073/pnas.1016459108 21149693

70. Eastwood MD, Cheung SWT, Lee KY, Moffat J, Meneghini MD Developmentally programmed nuclear destruction during yeast gametogenesis. Dev Cell. 2012; 23: 35–44. doi: 10.1016/j.devcel.2012.05.005 22727375

71. Bellí G, Garí E, Aldea M, Herrero E. Functional analysis of yeast essential genes using a promoter-substitution cassette and the tetracycline-regulatable dual expression system. Yeast. 1998; 14: 1127–1138. 9778798

72. Xu Z, Wei W, Gagneur J, Clauder-Münster S, Smolik M, Huber W, et al. Antisense expression increases gene expression variability and locus interdependency. Mol Syst Biol. 2011; 7: 1–10.

73. Huber W, von Heydebreck A, Sültmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002; 18 Suppl 1: S96–S104. 12169536

74. Loader C. Locfit: Local regression, likelihood and density estimation. R package version 1.5. 2007.

75. Leek JT, Monsen E, Dabney AR, Storey JD. EDGE: extraction and analysis of differential gene expression. Bioinformatics. 2006; 22: 507–508. 16357033

76. Teixeira MC, Monteiro PT, Guerreiro JF, Gonçalves JP, Mira NP, dos Santos SC, et al. The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic Acids Res. 2014; 42: D161–D166. doi: 10.1093/nar/gkt1015 24170807

77. Magni P, Ferrazzi F, Sacchi L, Bellazzi R. TimeClust: a clustering tool for gene expression time series. Bioinformatics. 2008; 24: 430–432. 18065427

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2015 Číslo 6
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#