Theoretical and Applied Genetics

, Volume 124, Issue 4, pp 755–768 | Cite as

Using a physiological framework for improving the detection of quantitative trait loci related to nitrogen nutrition in Medicago truncatula

  • Delphine Moreau
  • Judith Burstin
  • Grégoire Aubert
  • Thierry Huguet
  • Cécile Ben
  • Jean-Marie Prosperi
  • Christophe Salon
  • Nathalie Munier-Jolain
Original Paper


Medicago truncatula is used as a model plant for exploring the genetic and molecular determinants of nitrogen (N) nutrition in legumes. In this study, our aim was to detect quantitative trait loci (QTL) controlling plant N nutrition using a simple framework of carbon/N plant functioning stemming from crop physiology. This framework was based on efficiency variables which delineated the plant’s efficiency to take up and process carbon and N resources. A recombinant inbred line population (LR4) was grown in a glasshouse experiment under two contrasting nitrate concentrations. At low nitrate, symbiotic N2 fixation was the main N source for plant growth and a QTL with a large effect located on linkage group (LG) 8 affected all the traits. Significantly, efficiency variables were necessary both to precisely localize a second QTL on LG5 and to detect a third QTL involved in epistatic interactions on LG2. At high nitrate, nitrate assimilation was the main N source and a larger number of QTL with weaker effects were identified compared to low nitrate. Only two QTL were common to both nitrate treatments: a QTL of belowground biomass located at the bottom of LG3 and another one on LG6 related to three different variables (leaf area, specific N uptake and aboveground:belowground biomass ratio). Possible functions of several candidate genes underlying QTL of efficiency variables could be proposed. Altogether, our results provided new insights into the genetic control of N nutrition in M. truncatula. For instance, a novel result for M. truncatula was identification of two epistatic interactions in controlling plant N2 fixation. As such this study showed the value of a simple conceptual framework based on efficiency variables for studying genetic determinants of complex traits and particularly epistatic interactions.



Aboveground:belowground biomass ratio


Linkage group




N into leaf area conversion efficiency


Quantitative trait loci


Recombinant inbred line


Radiation use efficiency


Specific N uptake



We thank P. Mathey, C. Jeudy, V. Durey, A. Larmure, A.L. Santoni, V. Pellissier, F. Strbik, H. Busset, F. Monraisin and S. Brunel-Muguet for their excellent technical assistance, S. Jasson, B. Mangin, A. Bordat and V. Savois for their help in QTL detection, B. Julier and B. Teulat-Merah for useful discussions about QTL analysis. We also thank R. Thompson for critical reading of the manuscript and its improvement. This work was supported by the European Project Grain Legumes (FP6-20 02-FOOD-1-506223), UNIP (Union Nationale Interprofessionnelle des plantes riches en Protéines) and the Regional Council of Burgundy (France).

Supplementary material

122_2011_1744_MOESM1_ESM.doc (86 kb)
Supplementary material 1 (DOC 86 kb)


  1. Ameline-Torregrosa C, Cazaux M, Danesh D, Chardon F, Cannon SB, Esquerre-Tugaye MT, Dumas B, Young ND, Samac DA, Huguet T, Jacquet C (2008) Genetic dissection of resistance to anthracnose and powdery mildew in Medicago truncatula. Mol Plant Microbe Interact 21:61–69PubMedCrossRefGoogle Scholar
  2. AOAC (1965) Official methods of analysis of the Association of Official Agricultural Chemists, 10th edn, pp 15–16Google Scholar
  3. Baier MC, Barsch A, Kuster H, Hohnjec N (2007) Antisense repression of the Medicago truncatula nodule-enhanced sucrose synthase leads to a handicapped nitrogen fixation mirrored by specific alterations in the symbiotic transcriptome and metabolome. Plant Physiol 145:1600–1618PubMedCrossRefGoogle Scholar
  4. Barker DG, Bianchi S, Blondon F, Dattee Y, Duc G, Essad S, Flament P, Gallusci P, Genier G, Guy P, Muel X, Tourneur J, Dénarié J, Huguet T (1990) Medicago truncatula, a model plant for studying the molecular genetics of the Rhizobium-legume symbiosis. Plant Mol Biol Rep 8:40–49CrossRefGoogle Scholar
  5. Blondon F, Marie D, Brown S, Kondorosi A (1994) Genome size and base composition in Medicago sativa and M. truncatula species. Genome 37:264–270PubMedCrossRefGoogle Scholar
  6. Bonhomme R (2000) Bases and limits to using ‘’ units. Eur J Agron 13:1–10CrossRefGoogle Scholar
  7. Bordat A, Savois V, Nicolas M, Salse J, Chauveau A, Bourgeois M, Potier J, Houtin H, Rond C, Murat F, Marget P, Aubert G, Burstin J (2011) Translational genomics in legumes allowed placing in silico 5460 Unigenes on the pea functional map and identified candidate genes in Pisum sativum L. Genes Genomes Genet 1:93–103Google Scholar
  8. Bourion V, Hasan Rizvi SM, Fournier S, de Larambergue H, Galmiche F, Marget P, Duc G, Burstin J (2010) Genetic dissection of nitrogen nutrition in pea through a QTL approach of root, nodule, and aboveground variability. Theor Appl Genet 121:71–86PubMedCrossRefGoogle Scholar
  9. Calenge F, Saliba-Colombani V, Mahieu S, Loudet O, Daniel-Vedele F, Krapp A (2006) Natural variation for carbohydrate content in Arabidopsis. Interaction with complex traits dissected by quantitative genetics. Plant Physiol 141:1630–1643PubMedCrossRefGoogle Scholar
  10. Colebatch G, Trevaskis B, Udvardi M (2002) Symbiotic nitrogen fixation research in the postgenomics area. New Phytol 153:37–42CrossRefGoogle Scholar
  11. Cook DR (1999) Medicago truncatula–a model in the making! Curr Opin Plant Biol 2:301–304PubMedCrossRefGoogle Scholar
  12. Djebali N, Jauneau, Ameline-Torregrosa C, Chardon F, Jaulneau V, Mathe C, Bottin A, Cazaux M, Pilet-Nayel ML, Baranger A, Aouani ME, Esquerre-Tugaye MT, Dumas B, Huguet T, Jacquet C (2009) Partial resistance of Medicago truncatula to Aphanomyces euteiches is associated with protection of the root stele and is controlled by a major QTL rich in proteasome-related genes. Mol Plant Microbe Interact 22:1043–1055PubMedCrossRefGoogle Scholar
  13. Gallusci P, Dedieu A, Journet EP, Huguet T, Barker DG (1991) Synchronous expression of leghaemoglobin genes in Medicago truncatula during nitrogen-fixing root nodule development and response to exogenously supplied nitrate. Plant Mol Biol 17:335–349PubMedCrossRefGoogle Scholar
  14. Gastal F, Lemaire G (2002) N uptake and distribution in crops: an agronomical and ecophysiological perspective. J Exp Bot 53:789–799PubMedCrossRefGoogle Scholar
  15. Huguet T, Thoquet P, Ghérardi M, Cardinet G, Prioul S, Lazrek F, Aouani ME, Laouar M, Abdelguerfi A, Kurchak O, Jacquet C, Torregrosa C, Julier B, Kiss E, Batut J, Prosperi JM (2004) A post-genomic approach of the natural variations of the model-legume species Medicago truncatula. Legumes for the benefit of agriculture, nutrition and the environment: their genomics, their products and their improvement. AEP, Dijon, pp 169–170Google Scholar
  16. Institute SAS (2000) SAS/STAT user’s guide. SAS Institute, CaryGoogle Scholar
  17. Jeudy C, Ruffel S, Freixes S, Tillard P, Santoni AL, Morel S, Journet EP, Duc G, Gojon A, Lepetit M, Salon C (2009) Adaptation of Medicago truncatula to nitrogen limitation is modulated via local and systemic nodule developmental responses. New Phytol 185:817–828PubMedCrossRefGoogle Scholar
  18. Jourjon MF, Jasson S, Marcel J, Ngom B, Mangin B (2005) MCQTL: multi allelic QTL mapping in multi-cross design. Bioinformatics 21:128–130PubMedCrossRefGoogle Scholar
  19. Julier B, Huguet T, Chardon F, Ayadi R, Pierre JB, Prosperi JM, Barre P, Huyghe C (2007) Identification of quantitative trait loci influencing aerial morphogenesis in the model legume Medicago truncatula. Theor Appl Genet 114:1391–1406PubMedCrossRefGoogle Scholar
  20. Kamphuis LG, Lichtenzveig J, Oliver RP, Ellwood SR (2008) Two alternative recessive quantitative trait loci influence resistance to spring black stem and leaf spot in Medicago truncatula. BMC Plant Biol 8:30PubMedCrossRefGoogle Scholar
  21. Laperche A, Devienne-Barret F, Maury O, Le Gouis J, Ney B (2006) A simplified conceptual model of carbon/nitrogen functioning for QTL analysis of winter wheat adaptation to nitrogen deficiency. Theor Appl Genet 113:1131–1146PubMedCrossRefGoogle Scholar
  22. Laporte P, Satiat-Jeunemaître B, Velasco I, Csorba T, Van de Velde W, Campalans A, Burgyan J, Arevalo-Rodriguez M, Crespi M (2010) A novel RNA-binding peptide regulates the establishment of the Medicago truncatulaSinorhizobium meliloti nitrogen-fixing symbiosis. Plant J 62:24–38PubMedCrossRefGoogle Scholar
  23. Mathey P, Moreau D, Munier-Jolain N (2011) Standardisation de la prise de photographies en conditions contrôlées pour l’estimation non destructive de la surface foliaire. Cahiers Tech de l’INRA 72:31–35Google Scholar
  24. Menna Baretto Dias P, Brunel-Muguet S, Dürr C, Huguet T, Demilly D, Teulat-Meurah B (2011) QTL analysis of seed germination and pre-emergence growth at extreme temperatures in Medicago truncatula. Theor Appl Genet 122:429–444CrossRefGoogle Scholar
  25. Minchin FR, Summerfield RJ, Hadley P, Roberts EH, Rawsthorne S (1981) Carbon and nitrogen nutrition of nodulated roots of grain legumes. Plant Cell Environ 4:5–26CrossRefGoogle Scholar
  26. Mitra RM, Shaw SL, Long SR (2004) Six nonnodulating plant mutants defective for Nod factor-induced transcriptional changes associated with the legume-Rhizobia symbiosis. Proc Natl Acad Sci USA 101:10217–10222PubMedCrossRefGoogle Scholar
  27. Monteith J (1977) Climate and the efficiency of crop production in Britain. Philos Trans R Soc Lond B Biol Sci 281:277–294CrossRefGoogle Scholar
  28. Moreau D, Salon C, Munier-Jolain N (2006) Using a standard framework for the phenotypic analysis of Medicago truncatula: an effective method for characterizing the plant material used for functional genomics approaches. Plant Cell Environ 29:1087–1098PubMedCrossRefGoogle Scholar
  29. Moreau D, Voisin AS, Salon C, Munier-Jolain N (2008) The model symbiotic association between Medicago truncatula cv. Jemalong, Rhizobium meliloti strain 2011 leads to N-stressed plants when symbiotic N2 fixation is the main N source for plant growth. J Exp Bot 59:3509–3522PubMedCrossRefGoogle Scholar
  30. Moreau D, Schneider C, Huguet T, Munier-Jolain N (2009) Can differences of nitrogen nutrition level among Medicago truncatula genotypes be assessed non-destructively? Probing with a recombinant inbred lines population. Plant Signal Behav 4:30–32PubMedCrossRefGoogle Scholar
  31. Nakagawa H, Yamagishi J, Miyamoto N, Motoyama M, Yano M, Nemoto K (2005) Flowering response of rice to photoperiod and temperature: a QTL analysis using a phenological model. Theor Appl Genet 110:778–786PubMedCrossRefGoogle Scholar
  32. Penmetsa RV, Cook DR (1997) A legume ethylene-insensitive mutant hyperinfected by its rhizobial symbiont. Science 275:527–530PubMedCrossRefGoogle Scholar
  33. Penmetsa RV, Frugoli JA, Smith LS, Long SR, Cook DR (2003) Dual genetic pathways controlling nodule number in Medicago truncatula. Plant Physiol 131:998–1008PubMedCrossRefGoogle Scholar
  34. Pierre JB, Huguet T, Barre P, Huyghe C, Julier B (2008) Detection of QTLs for flowering date in three mapping populations of the model legume species Medicago truncatula. Theor Appl Genet 117:609–620PubMedCrossRefGoogle Scholar
  35. Pilet-Nayel ML, Prosperi JM, Hamon C, Lesne A, Lecointe R, Le Goff, Herve M, Deniot G, Delalande M, Huguet T, Jacquet C, Baranger A (2009) AER1, a major gene conferring resistance to Aphanomyces euteiches in Medicago truncatula. Phytopathology 99:203–208PubMedCrossRefGoogle Scholar
  36. Quilot B, Génard M, Kervella J, Lescourret F (2004) Analysis of genotypic variation in fruit flesh total sugar content via an ecophysiological model applied to peach. Theor Appl Genet 109:440–449PubMedGoogle Scholar
  37. Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyse the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol 131:664–675PubMedCrossRefGoogle Scholar
  38. Sagan M, Ney B, Duc G (1993) Plant symbiotic mutants as a tool to analyse nitrogen and yield relationship in field-grown peas (Pisum sativum L.). Plant Soil 153:33–45CrossRefGoogle Scholar
  39. Sagan M, Morandi D, Tarenghi E, Duc G (1995) Selection of nodulation and mycorrhizal mutants in the model plant Medicago truncatula (Gaertn.) after gamma-ray mutagenesis. Plant Sci 111:63–71CrossRefGoogle Scholar
  40. Salon C, Lepetit M, Gamas P, Jeudy C, Moreau S, Moreau D, Voisin AS, Duc G, Bourion V, Munier-Jolain N (2009) Analysis and modeling of the integrative response of Medicago truncatula to nitrogen constraints. C R Biol 332:1022–1033PubMedCrossRefGoogle Scholar
  41. Sankaran RP, Huguet T, Grusak MA (2009) Identification of QTL affecting seed mineral concentrations and content in the model legume Medicago truncatula. Theor Appl Genet 119:241–253PubMedCrossRefGoogle Scholar
  42. Schnabel E, Journet EP, Carvalho-Niebel F, Duc G, Frugoli J (2005) The Medicago truncatula SUNN gene encodes a CLV1-like leucine-rich repeat receptor kinase that regulates nodule number and root length. Plant Mol Biol 58:809–822PubMedCrossRefGoogle Scholar
  43. Souza AA, Boscariol RL, Moon DH, Camargo LEA, Tsai SM (2000) Effects of Phaseolus vulgaris QTL in controlling host-bacteria interactions under two levels of nitrogen fertilization. Genetics Mol Biol 23:155–161CrossRefGoogle Scholar
  44. Stacey G, Libault M, Brechenmacher L, Wan J, May GD (2006) Genetics and functional genomics of legume nodulation. Curr Opin Plant Biol 9:110–121PubMedCrossRefGoogle Scholar
  45. Suganuma N, Nakamura Y, Yamamoto M, Ohta T, Koiwa H, Akao S, Kawaguchi M (2003) The Lotus japonicus Sen1 gene controls rhizobial differentiation into nitrogen-fixing bacteroids in nodules. Mol Gen Genomics 269:312–320CrossRefGoogle Scholar
  46. Sulieman S, Schulze J (2010) The efficiency of nitrogen fixation of the model legume Medicago truncatula (Jemalong A17) is low compared to Medicago sativa. J Plant Physiol 167:683–692PubMedCrossRefGoogle Scholar
  47. Tardieu F (2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends Plant Sci 8:9–14PubMedCrossRefGoogle Scholar
  48. Terpolilli JJ, O’Hara GW, Tiwari RP, Dilworth MJ, Howieson JG (2008) The model legume Medicago truncatula A17 is poorly matched for N2 fixation with the sequenced microsymbiont Sinorhizobium meliloti 1021. New Phytol 179:62–66PubMedCrossRefGoogle Scholar
  49. Thoquet P, Ghérardi M, Journet EP, Kereszt A, Ané JM, Prosperi JM, Huguet T (2002) The molecular genetic linkage map of the model legume Medicago truncatula: an essential tool for comparative legume genomics and the isolation of agronomically important genes. BMC Plant Biol 2:1PubMedCrossRefGoogle Scholar
  50. Tirichine L, de Billy F, Huguet T (2000) Mtsym6, a gene conditioning Sinorhizobium strain-specific nitrogen fixation in Medicago truncatula. Plant Physiol 123:845–851PubMedCrossRefGoogle Scholar
  51. Tsai SM, Nodari RO, Moon DH, Camargo LEA, Vencovsky R, Gepts P (1998) QTL mapping for nodule number and common bacterial blight in Phaseolus vulgaris L. Plant Soil 204:135–145CrossRefGoogle Scholar
  52. Vailleau F, Sartorel E, Jardinaud MF, Chardon F, Genin S, Huguet T, Gentzbittel L, Petitprez M (2007) Characterization of the interaction between the bacterial wilt pathogen Ralstonia solanacearum and the model legume plant Medicago truncatula. Mol Plant Microbe Interact 20:159–167PubMedCrossRefGoogle Scholar
  53. Vandecasteele C, Teulat-Merah B, Morère-Le Paven MC, Leprince O, Ly Vu B, Viau L, Ledroit L, Pelletier S, Payet N, Satour P, Lebras C, Gallardo K, Huguet T, Limami AM, Prosperi JM, Buitink J (2011) QTL analysis reveals a correlation between the ratio of sucrose/raffinose family oligosaccharides and seed vigour in Medicago truncatula. Plant Cell Environ. doi:10.1111/j.1365-3040.2011.02346.x
  54. Voisin AS, Salon C, Munier-Jolain NG, Ney B (2002) Effect of mineral nitrogen on nitrogen nutrition and biomass partitioning between the aboveground and the roots of pea (Pisum sativum L.). Plant Soil 242:251–262CrossRefGoogle Scholar
  55. Voorips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77–78CrossRefGoogle Scholar
  56. Wais RJ, Galera C, Oldroyd G, Catoira R, Penmetsa RV, Cook D, Gough C, Dénarié J, Long SR (2000) Genetic analysis of calcium spiking responses in nodulation mutants of Medicago truncatula. Proc Natl Acad Sci USA 97:13407–13412PubMedCrossRefGoogle Scholar
  57. Yin X, Struik PC, van Eeuwijk FA, Stam P, Tang J (2005) QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley. J Exp Bot 56:967–976PubMedCrossRefGoogle Scholar
  58. Zhu H, Choi HK, Cook DR, Shoemaker RC (2005) Bridging model and crop legumes through comparative genomics. Plant Physiol 137:1189–1196PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Delphine Moreau
    • 1
    • 2
  • Judith Burstin
    • 1
  • Grégoire Aubert
    • 1
  • Thierry Huguet
    • 3
  • Cécile Ben
    • 4
    • 5
  • Jean-Marie Prosperi
    • 6
  • Christophe Salon
    • 1
  • Nathalie Munier-Jolain
    • 1
  1. 1.INRA, UMR 102 Génétique et Ecophysiologie des LégumineusesDijon cedexFrance
  2. 2.INRA, UMR 1210 Biologie et Gestion des AdventicesDijon cedexFrance
  3. 3.INP-ENSAT, Laboratoire Symbioses et Pathologies des PlantesCastanet TolosanFrance
  4. 4.Université de Toulouse, INP, UPS, UMR 5245 EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), ENSATCastanet TolosanFrance
  5. 5.CNRS, EcoLabCastanet TolosanFrance
  6. 6.INRA, IRD, Montpellier SupAgroUniversité de Montpellier 2MauguioFrance

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