Marine Biotechnology

, Volume 13, Issue 2, pp 215–231

Transcriptome Profiling of Embryonic Development Rate in Rainbow Trout Advanced Backcross Introgression Lines

  • Peng Xu
  • Lauren M. McIntyre
  • Julie Scardina
  • Paul A. Wheeler
  • Gary H. Thorgaard
  • Krista M. Nichols
Original Article

Abstract

In rainbow trout (Oncorhynchus mykiss) and other fishes, embryonic development rate is an ecologically and evolutionarily important trait that is closely associated with survival and physiological performance later in life. To identify genes differentially regulated in fast and slow-developing embryos of rainbow trout, we examined gene expression across developmental time points in rainbow trout embryos possessing alleles linked to a major quantitative trait loci (QTL) for fast versus slow embryonic development rate. Whole genome expression microarray analyses were conducted using embryos from a fourth generation backcross family, whereby each backcross generation involved the introgression of the fast-developing alleles for a major development rate QTL into a slow-developing clonal line of rainbow trout. Embryos were collected at 15, 19, and 28 days post-fertilization; sex and QTL genotype were determined using molecular markers, and cDNA from 48 embryos were used for microarray analysis. A total of 183 features were identified with significant differences between embryonic development rate genotypes. Genes associated with cell cycle growth, muscle contraction and protein synthesis were expressed significantly higher in embryos with the fast-developing allele (Clearwater) than those with the slow-developing allele (Oregon State University), which may associate with fast growth and early body mass construction in embryo development. Across time points, individuals with the fast-developing QTL allele appeared to have earlier onset of these developmental processes when compared to individuals with the slow development alleles, even as early as 15 days post-fertilization. Differentially expressed candidate genes chosen for linkage mapping were localized primarily to regions outside of the major embryonic development rate QTL, with the exception of a single gene (very low-density lipoprotein receptor precursor).

Keywords

Rainbow trout Gene expression QTL Embryonic development rate Microarray 

Supplementary material

10126_2010_9283_MOESM1_ESM.doc (54 kb)
Supplemental Table 1Primer sequences used for marker-assisted selection and qRT-PCR (DOC 54 kb)
10126_2010_9283_MOESM2_ESM.doc (56 kb)
Supplemental Table 2Primer sequences and details for genes successfully amplified for linkage mapping (DOC 56 kb)
10126_2010_9283_MOESM3_ESM.doc (44 kb)
Supplemental Figure 1Comparison of gene expression results from qRT-PCR analysis and microarray analysis. On the y-axis, the positive values indicate the genes have higher expression in CW embryos, and the negative values indicate the genes have higher expression in OSU embryos. Ten genes examined in the qRT-PCR analysis are presented as subunit of the THO complex (THO differentially expressed at 28 dpf), transforming growth factor-beta-induced protein ig-h3 precursor (TGFBI differentially expressed at 19 dpf), high choriolytic enzyme 1 precursor (HCE1 differentially expressed at 28 dpf ), myosin light chain 1, skeletal muscle isoform (MYL1differentially expressed at 19 dpf), myosin regulatory light chain 2 (MYL2 differentially expressed at 19 dpf), myosin light chain 3, skeletal muscle isoform (MYL3 differentially expressed at 19 dpf), thiosulfate sulfurtransferase KAT (KAT, differentially expressed at 15 dpf), ceruloplasmin precursor (CP differentially expressed at 19 dpf), parvalbumin beta 1 (pvalb1 differentially expressed at 15 dpf and 19 dpf), parvalbumin beta 2 (pvalb2 differentially expressed at 19 dpf). The asterisk is used to indicate the statistical significance in the qRT-PCR at the p < 0.05 level (DOC 44 kb)

References

  1. Allendorf F, Thorgaard G (1984) Tetraploidy and the evolution of salmonid fishes. In: Turner BJ (ed) Evolutionary genetics of fishes. Plenum Press, New YorkGoogle Scholar
  2. Allendorf FW, Knudsen KL, Leary RF (1983) Adaptive significance of differences in the tissue-specific expression of a phosphoglucomutase gene in rainbow trout. P Natl Acad Sci USA 80:1397–1400CrossRefGoogle Scholar
  3. Baker DA, Russell S (2009) Gene expression during Drosophila melanogaster egg development before and after reproductive diapause. BMC Genomics 10:242PubMedCrossRefGoogle Scholar
  4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 57:289–300Google Scholar
  5. Breitling R, Li Y, Tesson BM, Fu JY, Wu CL, Wiltshire T, Gerrits A, Bystrykh LV, De Haan G, Su AI, Jansen RC (2008) Genetical genomics: spotlight on QTL hotspots. PLoS Genetics 4:e1000232PubMedCrossRefGoogle Scholar
  6. Broadhurst MK, Lee RS, Hawkins S, Wheeler TT (2005) The p100 EBNA-2 coactivator: a highly conserved protein found in a range of exocrine and endocrine cells and tissues in cattle. Biochim Biophys Acta 1681:126–133PubMedGoogle Scholar
  7. Brunelli JP, Wertzler KJ, Sundin K, Thorgaard GH (2008) Y-specific sequences and polymorphisms in rainbow trout and Chinook salmon. Genome 51:739–748PubMedCrossRefGoogle Scholar
  8. Chomczynski P, Sacchi N (1987) Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem 162:156–159PubMedCrossRefGoogle Scholar
  9. Collins JH (1991) Myosin light chains and troponin C: structural and evolutionary relationships revealed by amino acid sequence comparisons. J Muscle Res Cell Motil 12:3–25PubMedCrossRefGoogle Scholar
  10. Cox L, Birnbaum S, Vandeberg J (2002) Identification of candidate genes regulating HDL cholesterol using a chromosomal region expression array. Genome Res 12:1693–1702PubMedCrossRefGoogle Scholar
  11. Derome N, Bernatchez L (2006) The transcriptomics of ecological convergence between 2 limnetic coregonine fishes (Salmonidae). Mol Biol and Evol 23:2370–2378CrossRefGoogle Scholar
  12. Derome N, Duchesne P, Bernatchez L (2006) Parallelism in gene transcription among sympatric lake whitefish (Coregonus clupeaformis Mitchill) ecotypes. Mol Ecol 15:1239–1249PubMedCrossRefGoogle Scholar
  13. Ding ST, Lilburn MS (2002) The ontogeny of fatty acid-binding protein in turkey (Meleagridis gallopavo) intestine and yolk sac membrane during embryonic and early posthatch development. Poultry Sci 81:1065–1070Google Scholar
  14. Einum S, Fleming IA (2000) Selection against late emergence and small offspring in Atlantic salmon (Salmo salar). Evolution 54:628–639PubMedGoogle Scholar
  15. Evsikov AV, De Evsikova CM (2009) Gene expression during the oocyte-to-embryo transition in mammals. Molecular Reprod Dev 76:805–818CrossRefGoogle Scholar
  16. Friedman JE, Watson JA Jr, Lam DW, Rokita SE (2006) Iodotyrosine deiodinase is the first mammalian member of the NADH oxidase/flavin reductase superfamily. J Biol Chem 281:2812–2819PubMedCrossRefGoogle Scholar
  17. Gahr SA, Vallejo RL, Weber GM, Shepherd BS, Silverstein JT, Rexroad CE 3rd (2008) Effects of short-term growth hormone treatment on liver and muscle transcriptomes in rainbow trout (Oncorhynchus mykiss). Physiol Genomics 32:380–392PubMedGoogle Scholar
  18. Haidle L, Janssen JE, Gharbi K, Moghadam HK, Ferguson MM, Danzmann RG (2008) Determination of quantitative trait loci (QTL) for early maturation in rainbow trout (Oncorhynchus mykiss). Mar Biotechnol 10:579–592PubMedCrossRefGoogle Scholar
  19. Hatze H (1973) A theory of contraction and a mathematical model of striated muscle. J Theor Biol 40:219–246PubMedCrossRefGoogle Scholar
  20. Jansen RC, Nap JP (2001) Genetical genomics: the added value from segregation. Trends Genet 17:388–391PubMedCrossRefGoogle Scholar
  21. Jarvinen AK, Hautaniemi S, Edgren H, Auvinen P, Saarela J, Kallioniemi OP, Monni O (2004) Are data from different gene expression microarray platforms comparable? Genomics 83:1164–1168PubMedCrossRefGoogle Scholar
  22. Jensen JOT, Jensen ME (1999) IncubWin: a new Windows 95/98, NT computer program for predicting embryonic stages in Pacific salmon and steelhead trout. Bull Aquacult Assoc Canada 99–94:28–30Google Scholar
  23. Kawasaki H, Nakayama S, Kretsinger RH (1998) Classification and evolution of EF-hand proteins. Biometals 11:277–295PubMedCrossRefGoogle Scholar
  24. Kroening S, Solomovitch S, Sachs M, Wullich B, Goppelt-Struebe M (2009) Regulation of connective tissue growth factor (CTGF) by hepatocyte growth factor in human tubular epithelial cells. Nephrol Dial Transplant 24:755–762PubMedCrossRefGoogle Scholar
  25. Liu H, Cheng H, Tirunagaru V, Sofer L, Burnside J (2001) A strategy to identify positional candidate genes conferring Marek’s disease resistance by integrating DNA microarrays and genetic mapping. Anim Genet 32:351–359PubMedCrossRefGoogle Scholar
  26. Mathavan S, Lee SGP, Mak A, Miller LD, Murthy KRK, Govindarajan KR, Tong Y, Wu YL, Lam SH, Yang H, Ruan YJ, Korzh V, Gong ZY, Liu ET, Lufkin T (2005) Transcriptome analysis of zebrafish embryogenesis using microarrays. Plos Genetics 1:260–276PubMedCrossRefGoogle Scholar
  27. Mizuno H, Hamada A, Shimada K, Honda H (2007) Tropomyosin as a regulator of the sliding movement of actin filaments. Biosystems 90:449–455PubMedCrossRefGoogle Scholar
  28. Murthi P, Fitzpatrick E, Borg AJ, Donath S, Brennecke SP, Kalionis B (2008) GAPDH, 18 S rRNA and YWHAZ are suitable endogenous reference genes for relative gene expression studies in placental tissues from human idiopathic fetal growth restriction. Placenta 29:798–801PubMedCrossRefGoogle Scholar
  29. Nichols KM, Broman KW, Sundin K, Young JM, Wheeler PA, Thorgaard GH (2007) Quantitative trait loci x maternal cytoplasmic environment interaction for development rate in Oncorhynchus mykiss. Genetics 175:335–347PubMedCrossRefGoogle Scholar
  30. Nichols KM, Felip A, Wheeler PA, Thorgaard GH (2008) The genetic basis of smoltification-related traits in Oncorhynchus mykiss. Genetics 179:1559–1575PubMedCrossRefGoogle Scholar
  31. Nichols KM, Young WP, Danzmann RG, Robison BD, Rexroad C, Noakes M, Phillips RB, Bentzen P, Spies I, Knudsen K, Allendorf FW, Cunningham BM, Brunelli J, Zhang H, Ristow S, Drew R, Brown KH, Wheeler PA, Thorgaard GH (2003) A consolidated linkage map for rainbow trout (Oncorhynchus mykiss). Anim Genet 34:102–115PubMedCrossRefGoogle Scholar
  32. Nolte AW, Renaut S, Bernatchez L (2009) Divergence in gene regulation at young life history stages of whitefish (Coregonus sp.) and the emergence of genomic isolation. BMC Evol Biol 9:59PubMedCrossRefGoogle Scholar
  33. O’malley KG, Sakamoto T, Danzmann RG, Ferguson MM (2003) Quantitative trait loci for spawning date and body weight in rainbow trout: testing for conserved effects across ancestrally duplicated chromosomes. J Hered 94:273–284PubMedCrossRefGoogle Scholar
  34. Olsvik PA, Softeland L, Lie KK (2008) Selection of reference genes for qRT-PCR examination of wild populations of Atlantic cod Gadus morhua. BMC Res Notes 1:47PubMedCrossRefGoogle Scholar
  35. Perry SV (1973) The control of muscular contraction. Symp Soc Exp Biol 27:531–550PubMedGoogle Scholar
  36. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29:e45PubMedCrossRefGoogle Scholar
  37. Phillips RB, Nichols KM, Dekoning JJ, Morasch MR, Keadey KA, Rexroad C, Gahr SA, Danzmann RG, Drew RE, Thorgaard GH (2006) Assignment of rainbow trout linkage groups to specific chromosomes. Genetics 174:1661–1670PubMedCrossRefGoogle Scholar
  38. Purcell MK, Nichols KM, Winton JR, Kurath G, Thorgaard GH, Wheeler P, Hansen JD, Herwig RP, Park LK (2006) Comprehensive gene expression profiling following DNA vaccination of rainbow trout against infectious hematopoietic necrosis virus. Mol Immunol 43:2089–2106PubMedCrossRefGoogle Scholar
  39. Raible F, Brand M (2001) Tight transcriptional control of the ETS domain factors Erm and Pea3 by Fgf signaling during early zebrafish development. Mech Dev 107:105–117PubMedCrossRefGoogle Scholar
  40. Renaut S, Nolte AW, Bernatchez L (2009) Gene expression divergence and hybrid misexpression between lake whitefish species pairs (Coregonus spp. Salmonidae). Mol Biol Evol 26:925–936PubMedCrossRefGoogle Scholar
  41. Robison BD, Wheeler PA, Sundin K, Sikka P, Thorgaard GH (2001) Composite interval mapping reveals a major locus influencing embryonic development rate in rainbow trout (Oncorhynchus mykiss). J Hered 92:16–22PubMedCrossRefGoogle Scholar
  42. Rodriguez-Zas SL, Schellander K, Lewin HA (2007) Biological interpretations of transcriptomic profiles in mammalian oocytes and embryos. Reprocution 135:129–139Google Scholar
  43. Salem M, Kenney PB, Rexroad CE 3rd, Yao J (2006) Microarray gene expression analysis in atrophying rainbow trout muscle: a unique nonmammalian muscle degradation model. Physiol Genomics 28:33–45PubMedCrossRefGoogle Scholar
  44. Salem M, Silverstein J, Rexroad CE 3rd, Yao J (2007) Effect of starvation on global gene expression and proteolysis in rainbow trout (Oncorhynchus mykiss). BMC Genomics 8:328PubMedCrossRefGoogle Scholar
  45. Schaub MC, Heizmann CW (2008) Calcium, troponin, calmodulin, S100 proteins: from myocardial basics to new therapeutic strategies. Biochem Biophys Res Commun 369:247–264PubMedCrossRefGoogle Scholar
  46. Schorderet DF, Menasche M, Morand S, Bonnel S, Buchillier V, Marchant D, Auderset K, Bonny C, Abitbol M, Munier FL (2000) Genomic characterization and embryonic expression of the mouse Bigh3 (Tgfbi) gene. Biochem Biophys Res Commun 274:267–274PubMedCrossRefGoogle Scholar
  47. Shockley KR, Churchill GA (2006) Gene expression analysis of mouse chromosome substitution strains. Mamm Genome 17:598–614PubMedCrossRefGoogle Scholar
  48. Singh A, Mcintyre L, Sherman L (2003) Microarray analysis of the genome-wide response to iron deficiency and iron reconstitution in the cyanobacterium Synechocystis sp. PCC 6803. Plant Physiol 132:1825–1839PubMedCrossRefGoogle Scholar
  49. Sladek R, Hudson TJ (2006) Elucidating cis- and trans-regulatory variation using genetical genomics. Trends Genet 22:245–250PubMedCrossRefGoogle Scholar
  50. St-Cyr J, Derome N, Bernatchez L (2008) The transcriptomics of life-history trade-offs in whitefish species pairs (Coregonus sp.). Mol Ecol 17:1850–1870PubMedCrossRefGoogle Scholar
  51. Street NR, Skogstrom O, Sjodin A, Tucker J, Rodriguez-Acosta M, Nilsson P, Jansson S, Taylor G (2006) The genetics and genomics of the drought response in Populus. Plant J 48:321–341PubMedCrossRefGoogle Scholar
  52. Sundin K, Brown KH, Drew RE, Nichols KM, Wheeler PA, Thorgaard GH (2005) Genetic analysis of a development rate QTL in backcrosses of clonal rainbow trout, Oncorhynchus mykiss. Aquaculture 247:75–83CrossRefGoogle Scholar
  53. Sundstrom LF, Lohmus M, Devlin RH (2005) Selection on increased intrinsic growth rates in coho salmon, Oncorhynchus kisutch. Evolution 59:1560–1569PubMedGoogle Scholar
  54. Tabakoff B, Bhave S, Hoffman P (2003) Selective breeding, quantitative trait locus analysis, and gene arrays identify candidate genes for complex drug-related behaviors. J Neurosci 23:4491–4498PubMedGoogle Scholar
  55. Tomancak P, Berman BP, Beaton A, Weiszmann R, Kwan E, Hartenstein V, Celniker SE, Rubin GM (2007) Global analysis of patterns of gene expression during Drosophila embryogenesis. Genome Biol 8:R145PubMedCrossRefGoogle Scholar
  56. Vallee M, Dufort I, Desrosiers S, Labbe A, Gravel C, Gilbert I, Robert C, Sirard MA (2009) Revealing the bovine embryo transcript profiles during early in vivo embryonic development. Reproduction 138:95–105PubMedCrossRefGoogle Scholar
  57. Verhoeven K, Simonsen K, Mcintyre L (2005) Implementing false discovery rate control: increasing your power. Oikos 108:643–647CrossRefGoogle Scholar
  58. Vernier J (1969) Table chonologique du developpement embryonaire de la truite are-en-ciel, Salmo gairdneri Rich. 1836. Ann Embryol Morphogen 2:495–520Google Scholar
  59. Von Schalburg KR, Rise ML, Cooper GA, Brown GD, Gibbs AR, Nelson CC, Davidson WS, Koop BF (2005) Fish and chips: various methodologies demonstrate utility of a 16,006 gene salmonid microarray. BMC Genomics 6:126CrossRefGoogle Scholar
  60. Voorips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTL. J Hered 93:77–78CrossRefGoogle Scholar
  61. Wagner RA, Tabibiazar R, Liao A, Quertermous T (2005) Genome-wide expression dynamics during mouse embryonic development reveal similarities to Drosophila development. Dev Biol 288:595–611PubMedCrossRefGoogle Scholar
  62. Wang YL, Barbacioru C, Hyland F, Xiao WM, Hunkapiller KL, Blake J, Chan F, Gonzalez C, Zhang L, Samaha RR (2006) Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays. BMC Genomics 7:59PubMedCrossRefGoogle Scholar
  63. Wayne ML, Mcintyre LM (2002) Combining mapping and arraying: an approach to candidate gene identification. P Natl Aca Sci 99:14903–14906CrossRefGoogle Scholar
  64. Whitehead A, Crawford DL (2006a) Neutral and adaptive variation in gene expression. P Natl Aca Sci USA 103:5425–5430CrossRefGoogle Scholar
  65. Whitehead A, Crawford DL (2006b) Variation within and among species in gene expression: raw material for evolution. Mol Ecol 15:1197–1211PubMedCrossRefGoogle Scholar
  66. Yang J, Aittomaki S, Pesu M, Carter K, Saarinen J, Kalkkinen N, Kieff E, Silvennoinen O (2002) Identification of p100 as a coactivator for STAT6 that bridges STAT6 with RNA polymerase II. Embo J 21:4950–4958PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Peng Xu
    • 1
    • 5
  • Lauren M. McIntyre
    • 2
  • Julie Scardina
    • 1
  • Paul A. Wheeler
    • 3
  • Gary H. Thorgaard
    • 3
  • Krista M. Nichols
    • 1
    • 4
  1. 1.Department of Biological SciencesPurdue UniversityWest LafayetteUSA
  2. 2.Department of Molecular Genetics and MicrobiologyUniversity of FloridaGainesvilleUSA
  3. 3.School of Biological Sciences and Center for Reproductive BiologyWashington State UniversityPullmanUSA
  4. 4.Department of Forestry and National ResourcesPurdue UniversityWest LafayetteUSA
  5. 5.Applied Aquatic Genomics CenterChinese Academy of Fishery SciencesBeijingChina

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