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A genome-wide survey of maize lipid-related genes: candidate genes mining, digital gene expression profiling and co-location with QTL for maize kernel oil

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Abstract

Lipids play an important role in plants due to their abundance and their extensive participation in many metabolic processes. Genes involved in lipid metabolism have been extensively studied in Arabidopsis and other plant species. In this study, a total of 1003 maize lipid-related genes were cloned and annotated, including 42 genes with experimental validation, 732 genes with full-length cDNA and protein sequences in public databases and 229 newly cloned genes. Ninety-seven maize lipid-related genes with tissue-preferential expression were discovered by in silico gene expression profiling based on 1984483 maize Expressed Sequence Tags collected from 182 cDNA libraries. Meanwhile, 70 QTL clusters for maize kernel oil were identified, covering 34.5% of the maize genome. Fifty-nine (84%) QTL clusters co-located with at least one lipid-related gene, and the total number of these genes amounted to 147. Interestingly, thirteen genes with kernel-preferential expression profiles fell within QTL clusters for maize kernel oil content. All the maize lipid-related genes identified here may provide good targets for maize kernel oil QTL cloning and thus help us to better understand the molecular mechanism of maize kernel oil accumulation.

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References

  1. Sprong H, van der Sluijs P, van Meer G. How proteins move lipids and lipids move proteins. Nat Rev Mol Cell Biol, 2001, 2: 504–513 10.1038/35080071, 11433364, 1:CAS:528:DC%2BD3MXltlSmtLY%3D

    Article  PubMed  CAS  Google Scholar 

  2. Wallis J G, Browse J. Mutants of Arabidopsis reveal many roles for membrane lipids. Prog Lipid Res, 2002, 41: 254–278 10.1016/S0163-7827(01)00027-3, 11814526, 1:CAS:528:DC%2BD38Xjtlent74%3D

    Article  PubMed  CAS  Google Scholar 

  3. Broun P, Gettner S, Somerville C. Genetic engineering of plant lipids. Annu Rev Nutr, 1999, 19: 197–216 10.1146/annurev.nutr.19.1.197, 10448522, 1:CAS:528:DyaK1MXlt1ejsL8%3D

    Article  PubMed  CAS  Google Scholar 

  4. Schneider K, Kienow L, Schmelzer E, et al. A new type of peroxisomal acyl-coenzyme a synthetase from Arabidopsis thaliana has the catalytic capacity to activate biosynthetic precursors of jasmonic acid. J Biol Chem, 2005, 280: 13962–13972 10.1074/jbc.M413578200, 15677481, 1:CAS:528:DC%2BD2MXivV2rsrs%3D

    Article  PubMed  CAS  Google Scholar 

  5. Li C, Schilmiller A L, Liu G, et al. Role of β-oxidation in jasmonate biosynthesis and systemic wound signaling in tomato. Plant Cell, 2005, 17: 971–986 10.1105/tpc.104.029108, 15722469, 1:CAS:528:DC%2BD2MXis1ygt7c%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  6. Angelo A J S, Altschul A M. Lipolysis and the free fatty acid pool in seedlings. Plant Physiol, 1964, 39: 880–883 10.1104/pp.39.6.880, 16656027, 1:STN:280:DC%2BD28zhsVansg%3D%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Gueguen V, Macherel D, Jaquinod M, et al. Fatty acid and lipoic acid biosynthesis in higher plant mitochondria. J Biol Chem, 2000, 275: 5016–5025 10.1074/jbc.275.7.5016, 10671542, 1:CAS:528:DC%2BD3cXhsVymsb8%3D

    Article  PubMed  CAS  Google Scholar 

  8. Thelen J J, Ohlrogge J B. Metabolic engineering of fatty acid biosynthesis in plants. Metab Eng, 2002, 4: 12–21 10.1006/mben.2001.0204, 11800570, 1:CAS:528:DC%2BD38XlsVSrtA%3D%3D

    Article  PubMed  CAS  Google Scholar 

  9. Beaudoin F, Napier J A. Biosynthesis and compartmentation of triacylglycerol in higher plants. In Daum G. ed. Lipid Metabolism and Membrane Bioiogenesis. Berlin: Springer, 2004. 267–287

    Google Scholar 

  10. Kunst L, Samuels A L. Biosynthesis and secretion of plant cuticular wax. Prog Lipid Res, 2003, 42: 51–80 10.1016/S0163-7827(02)00045-0, 12467640, 1:CAS:528:DC%2BD38XptFGku7c%3D

    Article  PubMed  CAS  Google Scholar 

  11. Reina-Pinto J J, Yephremov A. Surface lipids and plant defenses. Plant Physiol Biochem, 2009, 47: 540–549 10.1016/j.plaphy.2009.01.004, 19230697, 1:CAS:528:DC%2BD1MXltFWlu70%3D

    Article  PubMed  CAS  Google Scholar 

  12. Hynes M J, Murray S L, Duncan A, et al. Regulatory genes controlling fatty acid catabolism and peroxisomal functions in the filamentous fungus aspergillus nidulans. Eukaryot Cell, 2006, 5: 794–805 10.1128/EC.5.5.794-805.2006, 16682457, 1:CAS:528:DC%2BD28XlsVGgt7o%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Graham I A, Eastmond P J. Pathways of straight and branched chain fatty acid catabolism in higher plants. Prog Lipid Res, 2002, 41: 156–181 10.1016/S0163-7827(01)00022-4, 11755682, 1:CAS:528:DC%2BD38XhvFGnsLs%3D

    Article  PubMed  CAS  Google Scholar 

  14. Goepfert S, Poirier Y. β-Oxidation in fatty acid degradation and beyond. Curr Opin Plant Biol, 2007, 10: 245–251 10.1016/j.pbi.2007.04.007, 17434787, 1:CAS:528:DC%2BD2sXlt1SitrY%3D

    Article  PubMed  CAS  Google Scholar 

  15. Bessoule J J, Moreau P. Lipid Metabolism and Membrane Biogenesis. Berlin: Springer. 2003. 1610–2096

    Google Scholar 

  16. Mueller-Roeber B, Pical C. Inositol phospholipid metabolism in arabidopsis characterized and putative isoforms of inositol phospholipid kinase and phosphoinositide-specific phospholipase C. Plant Physiol, 2002, 130: 22–46 10.1104/pp.004770, 12226484, 1:CAS:528:DC%2BD38XntFOrsLo%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  17. Chapman K D. Emerging physiological roles for N-acylphosphatidylethanolamine metabolism in plants: signal transduction and membrane protection. Chem Phys Lipids, 2000, 108: 221–230 10.1016/S0009-3084(00)00198-5, 11106793, 1:CAS:528:DC%2BD3cXosFeqtLY%3D

    Article  PubMed  CAS  Google Scholar 

  18. Munnik T. Phosphatidic acid: an emerging plant lipid second messenger. Trends Plant Sci, 2001, 6: 227–233 10.1016/S1360-1385(01)01918-5, 11335176, 1:CAS:528:DC%2BD3MXlsFKmtbs%3D

    Article  PubMed  CAS  Google Scholar 

  19. Wang X M. Plant phsopholipases. Plant Mol Biol, 2001, 52: 211–231 10.1146/annurev.arplant.52.1.211, 1:CAS:528:DC%2BD3MXkslWgsLs%3D

    CAS  Google Scholar 

  20. Futerman A H, Riezman H. The ins and outs of sphingolipid synthesis. Tr Cell Biol, 2005, 15: 312–318 10.1016/j.tcb.2005.04.006, 1:CAS:528:DC%2BD2MXlt1SrtrY%3D

    Article  CAS  Google Scholar 

  21. Sato N, Moriyama T. Genomic and biochemical analysis of lipid biosynthesis in the unicellular rhodophyte cyanidioschyzon merolae: lack of a plastidic desaturation pathway results in the coupled pathway of galactolipid synthesis. Eukaryot Cell, 2007, 8: 1006–1017 10.1128/EC.00393-06, 1:CAS:528:DC%2BD2sXns1yjt74%3D

    Article  Google Scholar 

  22. Benning C. Biosynthesis and function of the sulfolipid sulfoquinovosyl diacylglycerol. Plant Mol Biol, 1998, 49: 53–75 10.1146/annurev.arplant.49.1.53, 1:CAS:528:DyaK1cXjvVSiurw%3D

    CAS  Google Scholar 

  23. Baysal T, Demirdoven A. Lipoxygenase in fruits and vegetables: a review. Enzyme Microb Technol, 2007, 40: 491–496 10.1016/j.enzmictec.2006.11.025, 1:CAS:528:DC%2BD2sXit1Glu7w%3D

    Article  CAS  Google Scholar 

  24. Feussner I, Wasternack C. The lipoxygenase pathway. Annu Rev Plant Biol, 2002, 53: 275–297 10.1146/annurev.arplant.53.100301.135248, 12221977, 1:CAS:528:DC%2BD38XlsVWhur4%3D

    Article  PubMed  CAS  Google Scholar 

  25. Pollard M, Ohlrogge J. Testing models of fatty acid transfer and lipid synthesis in spinach leaf using in vivo oxygen-18 labeling. Plant Physiol, 1999, 121: 1217–1226 10.1104/pp.121.4.1217, 10594108, 1:CAS:528:DyaK1MXotFyisr4%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  26. Germain V, Rylott E L, Larson T R, et al. Requirement for 3-ketoacyl-CoA thiolase-2 in peroxisome development fatty acid beta-oxidation and breakdown of triacylglycerol in lipid bodies of Arabidopsis seedlings. Plant J, 2001, 28: 1–12 10.1046/j.1365-313X.2001.01095.x, 11696182, 1:CAS:528:DC%2BD3MXosVWqsrs%3D

    Article  PubMed  CAS  Google Scholar 

  27. Stymne S, Stobart A K, Glad G. The role of the acyl-CoA pool in the synthesis of polyunsaturated 18-carbon fatty acids and triacylglycerol production in the microsomes of developing safflower seeds. Lipids and Lipid Metabolism, 1983, 752: 198–208 1:CAS:528:DyaL3sXkvVers70%3D

    Article  CAS  Google Scholar 

  28. International Rice Genome Sequencing Project. The map-based sequence of the rice genome. Nature, 2005, 436: 793–800 10.1038/nature03895, 1:CAS:528:DC%2BD2MXnt1Shsr4%3D

    Article  Google Scholar 

  29. Paterson A H, Bowers J E, Bruggmann R, Dubchak I et al. The sorghum bicolor genome and the diversification of grasses. Nature, 2009, 457: 551–556 10.1038/nature07723, 19189423, 1:CAS:528:DC%2BD1MXhtFOmsb4%3D

    Article  PubMed  CAS  Google Scholar 

  30. Borevitz J O, Chory J. Genomics tools for QTL analysis and gene discovery. Curr Opin Plant Biol, 2004, 7: 132–136 10.1016/j.pbi.2004.01.011, 15003212, 1:CAS:528:DC%2BD2cXhslCiu7k%3D

    Article  PubMed  CAS  Google Scholar 

  31. Varshney R K, Hoisington D A, Tyagi A K. Advances in cereal genomics and applications in crop breeding. Trends Biotechnol, 2006, 24: 490–499 10.1016/j.tibtech.2006.08.006, 16956681, 1:CAS:528:DC%2BD28XhtVOrt77P

    Article  PubMed  CAS  Google Scholar 

  32. Henrissat B, Coutinho P M, Davies G J. A census of carbohydrate-active enzymes in the genome of Arabidopsis thaliana. Plant Mol Biol, 2001, 47: 55–72 10.1023/A:1010667012056, 11554480, 1:CAS:528:DC%2BD3MXmslGmsLY%3D

    Article  PubMed  CAS  Google Scholar 

  33. Ward J M. Identification of novel families of membrane proteins from the model plant Arabidopsis thaliana. Bioinformatics, 2001, 17: 560–563 10.1093/bioinformatics/17.6.560, 11395435, 1:CAS:528:DC%2BD3MXltFOgsbs%3D

    Article  PubMed  CAS  Google Scholar 

  34. Beisson F, Koo A J K, Ruuska S, et al. Arabidopsis genes involved in acyl lipid metabolism a 2003 census of the candidates, a study of the distribution of expressed sequence tags in organs, and a web-based database. Plant Physiol, 2003, 132: 681–697 10.1104/pp.103.022988, 12805597, 1:CAS:528:DC%2BD3sXkslertrw%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  35. Altschul S F, Madden T L, Schaffer A A, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 1997, 25: 3389–3402 10.1093/nar/25.17.3389, 9254694, 1:CAS:528:DyaK2sXlvFyhu7w%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  36. Zdobnov E M, Apweiler R. InterProScan — an integration platform for the signature-recognition methods in InterPro. Bioinformatics, 2001, 17: 847–849 10.1093/bioinformatics/17.9.847, 11590104, 1:CAS:528:DC%2BD3MXotFehsro%3D

    Article  PubMed  CAS  Google Scholar 

  37. Huang X, Madan A. CAP3: A DNA sequence assembly program. Genome Res, 1999, 9: 868–877 10.1101/gr.9.9.868, 10508846, 1:CAS:528:DyaK1MXmslKgs7Y%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  38. Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J Mol Biol, 1997, 268: 78–94 10.1006/jmbi.1997.0951, 9149143, 1:CAS:528:DyaK2sXjtlSqtL4%3D

    Article  PubMed  CAS  Google Scholar 

  39. Salamov A A, Solovyev V V. Ab initio gene finding in Drosophila genomic DNA. Genome Res, 2000, 10: 516–522 10.1101/gr.10.4.516, 10779491, 1:CAS:528:DC%2BD3cXjtVKrs7Y%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  40. Stekel D J, Git Y, Falciani F. The comparison of gene expression from multiple cDNA Libraries. Genome Res, 2000, 10: 2055–2061 10.1101/gr.GR-1325RR, 11116099, 1:CAS:528:DC%2BD3MXjslY%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  41. Varuzza L, Lauretto M S, Brentani H, et al. Significance index for digital expression. In: Clamp M, ed. Genome Information Meeting, on CSHL. UK: University of Dundee, 2007.

    Google Scholar 

  42. Li L, Yan J B, Lei H Z, et al. QTL-Finder: a bioinformatics tool for QTL integration, comparison and discovery of candidate genes across genomes and experiments. In: Zhang Q F, ed. The Abstract of Plant Genomics Conference in China VIII. Plant Genomics Conference in China. Shanghai. 2007. 56–56

  43. Lee M, Sharopova N, Beavis W D, et al. Expanding the genetic map of maize with the intermated B73 x Mo17 (IBM) population. Plant Mol Biol, 2002, 48: 453–461 10.1023/A:1014893521186, 11999829, 1:CAS:528:DC%2BD38XjsFWrsbg%3D

    Article  PubMed  CAS  Google Scholar 

  44. Song X F, Song T M, Dai J R, et al. QTL mapping of kernel oil concentration with high-oil maize by SSR markers. Maydica, 2004, 49: 41–48

    Google Scholar 

  45. Zhang J, Lu X Q, Song X F, et al. Mapping quantitative trait loci for oil, starch, and protein concentrations in grain with high-oil Maize by SSR Markers. Euphytica, 2008, 162: 335–344 10.1007/s10681-007-9500-9, 1:CAS:528:DC%2BD1cXnvVyis7k%3D

    Article  CAS  Google Scholar 

  46. Yang X H, Guo Y Q, Yan J B, et al. Major and minor QTL and epistasis contribute to fatty acid composition and oil content in high-oil maize. Theor Appl Genet, 2010, 120: 665–678 10.1007/s00122-009-1184-1, 19856173, 1:CAS:528:DC%2BC3cXotV2iuw%3D%3D

    Article  PubMed  CAS  Google Scholar 

  47. Goldman I, Rocheford T R, Dudley J W. Molecular markers associated with maize kernel oil concentration in an Illinois high protein x Illinois low protein cross. Crop Sci, 1994, 34: 908–915

    Article  Google Scholar 

  48. Alrefai R, Berke T G, Rocheford T R. Quantitative trait locus analysis of fatty acid concentration in maize. Genome, 1995, 38: 894–901 10.1139/g95-118, 18470215, 1:CAS:528:DyaK28Xhs12rsA%3D%3D

    Article  PubMed  CAS  Google Scholar 

  49. Berke T, Rocheford T R. Quantitative trait loci for flowering, plant and ear height, and kernel traits in maize. Crop Sci, 1995, 35: 1542–1549

    Article  Google Scholar 

  50. Clark D, Dudley J W, Rocheford T R, et al. Genetic analysis of corn kernel chemical composition in the random mated 10 generation of the cross of generation 70 of IHO × ILO. Crop Sci, 2006, 46: 807–819 10.2135/cropsci2005.06-0153

    Article  Google Scholar 

  51. Dudley J W, Clark D, Rocheford T R, et al. Genetic Analysis of corn kernel chemical composition in the random mated 7 generation of the cross of generations 70 of IHP×ILP. Crop Sci, 2007, 47: 45–57 10.2135/cropsci2006.03.0207, 1:CAS:528:DC%2BD2sXjsF2nur4%3D

    Article  CAS  Google Scholar 

  52. Dudley J W. Epistatic interactions in crosses of Illinois High Oil × Illinois Low Oil and of Illinois High Protein × Illinois Low Protein corn strains. Crop Sci, 2008, 48: 59–68 10.2135/cropsci2007.04.0242

    Article  Google Scholar 

  53. Wassom J J, Wong J C, Martinez E, et al. QTL associated with maize kernel oil, protein, and starch concentrations; kernel mass; and grain yield in Illinois High Oil × B73 backcross-derived lines. Crop Sci, 2008, 48: 243–252 10.2135/cropsci2007.04.0205

    Article  Google Scholar 

  54. Wassom J J, Mikkelineni V, Bohn M O, et al. QTL for fatty acid composition of maize kernel oil in Illinois High Oil × B73 backcross-derived lines. Crop Sci, 2008, 48: 69–78 10.2135/cropsci2007.04.0208, 1:CAS:528:DC%2BD1cXit12htrs%3D

    Article  CAS  Google Scholar 

  55. Mangolin C A, de Souza Jr C L, Garcia A A F, et al. Mapping QTLs for kernel oil content in a tropical maize population. Euphytica, 2004, 137: 251–259 10.1023/B:EUPH.0000041588.95689.47, 1:CAS:528:DC%2BD2cXnsFOrsLk%3D

    Article  CAS  Google Scholar 

  56. Guigo R, Knudsen S, Drake N, et al. Prediction of gene structure. J Mol Biol, 1992, 226: 141–157 10.1016/0022-2836(92)90130-C, 1619647, 1:CAS:528:DyaK38XkvVCgsb0%3D

    Article  PubMed  CAS  Google Scholar 

  57. Snyder E E, Stormo G D. Identification of coding regions in genomic DNA sequences: an application of dynamic programming and neural networks. Nucleic Acids Res, 1993, 21: 607–613 10.1093/nar/21.3.607, 8441672, 1:CAS:528:DyaK3sXhs1antr4%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  58. Uberbacher E C, Mural R J. Locating protein-codeing regions in human DNA sequences by a multiple sensor-neural network approach. Proc Natl Acad Sci USA, 1991, 88: 11261–11265 10.1073/pnas.88.24.11261, 1763041, 1:CAS:528:DyaK38XnsleltQ%3D%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  59. Wang Z, Chen Y, Li Y. A brief review of computational gene prediction methods. Geno Prot Bioinfo, 2004, 2: 216–221 1:CAS:528:DC%2BD2MXlsl2qsLg%3D

    CAS  Google Scholar 

  60. Zheng P, Allen W B, Roesler K et al. A phenylalanine in DGAT is a key determinant of oil content and composition in maize. Nat Genet, 2008, 40: 367–372 10.1038/ng.85, 18278045, 1:CAS:528:DC%2BD1cXisVKhtrk%3D

    Article  PubMed  CAS  Google Scholar 

  61. Lee K, Huang A H C. Genes encoding oleosins in maize kernel of inbreds Mo17 and B73. Plant Mol Biol, 2004, 26: 1981–1987 10.1007/BF00019508

    Article  Google Scholar 

  62. Beló A, Zheng P Z, Luck S, et al. Whole genome scan detects an allelic variant of fad2 associated with increased oleic acid levels in maize. Mol Genet Genomics, 2008, 279: 1–10 10.1007/s00438-007-0289-y, 17934760, 1:CAS:528:DC%2BD2sXhsVWisb%2FN

    Article  PubMed  Google Scholar 

  63. Hill W G. A century of corn selection. Science, 2005, 307: 683–684 10.1126/science.1105459, 15692038, 1:CAS:528:DC%2BD2MXhtFartLg%3D

    Article  PubMed  CAS  Google Scholar 

  64. Voelker T A, Hayes T R, Cranmer A M, et al. Genetic engineering of a quantitative trait: Metabolic and genetic parameters influencing the accumulation of laurate in rapeseed. Plant J, 1996, 9: 229–241 10.1046/j.1365-313X.1996.09020229.x, 1:CAS:528:DyaK28XitVyjurk%3D

    Article  CAS  Google Scholar 

  65. Domann P, Voelker T A, Ohlrogge J B. Accumulation of palmitate in Arabidopsis mediated by the acyl-acyl carrier protein thioesterase FATB1. Plant Physiol, 2000, 123: 637–643 10.1104/pp.123.2.637

    Article  Google Scholar 

  66. Liu Q, Singh S, Green A. Genetic modification of cotton seed oil using inverted-repeat gene-silencing techniques. Biochem Soc Trans, 2000, 28: 927–929 10.1042/BST0280927, 11288706, 1:CAS:528:DC%2BD3MXhsVGqu74%3D

    Article  PubMed  CAS  Google Scholar 

  67. Ye X, Al-Babili S, Klöti A, et al. Engineering provitamin A (β-carotene) biosynthetic pathway into (carotenoid-free) rice endosperm. Science, 2000, 287: 303–305 10.1126/science.287.5451.303, 10634784, 1:CAS:528:DC%2BD3cXlvVWqtw%3D%3D

    Article  PubMed  CAS  Google Scholar 

  68. Zhu C F, Naqvi S, Breitenbach J, et al. Combinatorial genetic transformation generates a library of metabolic phenotypes for the carotenoid pathway in Maize. Proc Natl Acad Sci USA, 2008, 105: 18232–18237 10.1073/pnas.0809737105, 19011084

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  69. Harjes C E, Rocheford T R, Bai L, et al. Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science, 2008, 319: 330–333 10.1126/science.1150255, 18202289, 1:CAS:528:DC%2BD1cXmt1yjsQ%3D%3D

    Article  PubMed  CAS  PubMed Central  Google Scholar 

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Li, L., Li, H., Li, J. et al. A genome-wide survey of maize lipid-related genes: candidate genes mining, digital gene expression profiling and co-location with QTL for maize kernel oil. Sci. China Life Sci. 53, 690–700 (2010). https://doi.org/10.1007/s11427-010-4007-3

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