Separate loci underlie resistance to root infection and leaf scorch during soybean sudden death syndrome

Soybean [Glycine max (L.) Merr.] cultivars show differences in their resistance to both the leaf scorch and root rot of sudden death syndrome (SDS). The syndrome is caused by root colonization by Fusarium virguliforme (ex. F. solani f. sp. glycines). Root susceptibility combined with reduced leaf scorch resistance has been associated with resistance to Heterodera glycines HG Type 1.3.6.7 (race 14) of the soybean cyst nematode (SCN). In contrast, the rhg1 locus underlying resistance to Hg Type 0 was found clustered with three loci for resistance to SDS leaf scorch and one for root infection. The aims of this study were to compare the inheritance of resistance to leaf scorch and root infection in a population that segregated for resistance to SCN and to identify the underlying quantitative trait loci (QTL). “Hartwig”, a cultivar partially resistant to SDS leaf scorch, F. virguliforme root infection and SCN HG Type 1.3.6.7 was crossed with the partially susceptible cultivar “Flyer”. Ninety-two F5-derived recombinant inbred lines and 144 markers were used for map development. Four QTL found in earlier studies were confirmed. One contributed resistance to leaf scorch on linkage group (LG) C2 (Satt277; P = 0.004, R 2 = 15%). Two on LG G underlay root infection at R8 (Satt038; P = 0.0001 R 2 = 28.1%; Satt115; P = 0.003, R 2 = 12.9%). The marker Satt038 was linked to rhg1 underlying resistance to SCN Hg Type 0. The fourth QTL was on LG D2 underlying resistance to root infection at R6 (Satt574; P = 0.001, R 2 = 10%). That QTL was in an interval previously associated with resistance to both SDS leaf scorch and SCN Hg Type 1.3.6.7. The QTL showed repulsion linkage with resistance to SCN that may explain the relative susceptibility to SDS of some SCN resistant cultivars. One additional QTL was discovered on LG G underlying resistance to SDS leaf scorch measured by disease index (Satt130; P = 0.003, R 2 = 13%). The loci and markers will provide tagged alleles with which to improve the breeding of cultivars combining resistances to SDS leaf scorch, root infection and SCN HG Type 1.3.6.7

Metabolic Profiling of a Mapping Population Exposes New Insights in the Regulation of Seed Metabolism and Seed, Fruit, and Plant Relations

To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping