The influence of metabolically engineered glucosinolates profiles in Arabidopsis thaliana on Plutella xylostella preference and performance

The oviposition preference and larval performance of the diamondback moth (DBM), Plutella xylostella, was studied using Arabidopsis thaliana plants with modified glucosinolate (GS) profiles containing novel GSs as a result of the introduction of individual CYP79 genes. The insect parameters were determined in a series of bioassays. The GS content of the plants as well as the number of trichomes were measured. Multivariate analysis was used to determine the possible relationships among insect and plant variables. The novel GSs in the tested lines did not appear to have any unequivocal effect on the DBM. Instead, the plant characteristics that affected larval performance and larval preference did not influence oviposition preference. Trichomes did not affect oviposition, but influenced larval parameters negatively. Although the tested A. thaliana lines had earlier been shown to influence disease resistance, in this study no clear results were found for P. xylostella

Genetic Networks Controlling Structural Outcome of Glucosinolate Activation across Development

Most phenotypic variation present in natural populations is under polygenic control, largely determined by genetic variation at quantitative trait loci (QTLs). These genetic loci frequently interact with the environment, development, and each other, yet the importance of these interactions on the underlying genetic architecture of quantitative traits is not well characterized. To better study how epistasis and development may influence quantitative traits, we studied genetic variation in Arabidopsis glucosinolate activation using the moderately sized Bayreuth×Shahdara recombinant inbred population, in terms of number of lines. We identified QTLs for glucosinolate activation at three different developmental stages. Numerous QTLs showed developmental dependency, as well as a large epistatic network, centered on the previously cloned large-effect glucosinolate activation QTL, ESP. Analysis of Heterogeneous Inbred Families validated seven loci and all of the QTL×DPG (days post-germination) interactions tested, but was complicated by the extensive epistasis. A comparison of transcript accumulation data within 211 of these RILs showed an extensive overlap of gene expression QTLs for structural specifiers and their homologs with the identified glucosinolate activation loci. Finally, we were able to show that two of the QTLs are the result of whole-genome duplications of a glucosinolate activation gene cluster. These data reveal complex age-dependent regulation of structural outcomes and suggest that transcriptional regulation is associated with a significant portion of the underlying ontogenic variation and epistatic interactions in glucosinolate activation

Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise

Quantitative genetic analysis has long been used to study how natural variation of genotype can influence an organism's phenotype. While most studies have focused on genetic determinants of phenotypic average, it is rapidly becoming understood that stochastic noise is genetically determined. However, it is not known how many traits display genetic control of stochastic noise nor how broadly these stochastic loci are distributed within the genome. Understanding these questions is critical to our understanding of quantitative traits and how they relate to the underlying causal loci, especially since stochastic noise may be directly influenced by underlying changes in the wiring of regulatory networks. We identified QTLs controlling natural variation in stochastic noise of glucosinolates, plant defense metabolites, as well as QTLs for stochastic noise of related transcripts. These loci included stochastic noise QTLs unique for either transcript or metabolite variation. Validation of these loci showed that genetic polymorphism within the regulatory network alters stochastic noise independent of effects on corresponding average levels. We examined this phenomenon more globally, using transcriptomic datasets, and found that the Arabidopsis transcriptome exhibits significant, heritable differences in stochastic noise. Further analysis allowed us to identify QTLs that control genomic stochastic noise. Some genomic QTL were in common with those altering average transcript abundance, while others were unique to stochastic noise. Using a single isogenic population, we confirmed that natural variation at ELF3 alters stochastic noise in the circadian clock and metabolism. Since polymorphisms controlling stochastic noise in genomic phenotypes exist within wild germplasm for naturally selected phenotypes, this suggests that analysis of Arabidopsis evolution should account for genetic control of stochastic variance and average phenotypes. It remains to be determined if natural genetic variation controlling stochasticity is equally distributed across the genomes of other multi-cellular eukaryotes

Wheat Mds-1 encodes a heat-shock protein and governs susceptibility towards the Hessian fly gall midge

Citation: Liu, X., . . . & Chen, M. (2013). Wheat Mds-1 encodes a heat-shock protein and governs susceptibility towards the Hessian fly gall midge. Nature Communication, 4(1), 2070. https://doi.org/10.1038/ncomms3070Gall midges induce formation of host nutritive cells and alter plant metabolism to utilize host resources. Here we show that the gene Mayetiola destructor susceptibility-1 (Mds-1) on wheat chromosome 3AS encodes a small heat-shock protein and is a major susceptibility gene for infestation of wheat by the gall midge M. destructor, commonly known as the Hessian fly. Transcription of Mds-1 and its homoeologs increases upon insect infestation. Ectopic expression of Mds-1 or induction by heat shock suppresses resistance of wheat mediated by the resistance gene H13 to Hessian fly. Silencing of Mds-1 by RNA interference confers immunity to all Hessian fly biotypes on normally susceptible wheat genotypes. Mds-1-silenced plants also show reduced lesion formation due to infection by the powdery mildew fungus Blumeria graminis f. sp. tritici. Modification of susceptibility genes may provide broad and durable sources of resistance to Hessian fly, B. graminis f. sp. tritici, and other pests

Heterodera schachtii nematodes interfere with aphid-plant relations on Brassica oleracea

Aboveground and belowground herbivore species modify plant defense responses differently. Simultaneous attack can lead to non-additive effects on primary and secondary metabolite composition in roots and shoots. We previously found that aphid (Brevicoryne brassicae) population growth on Brassica oleracea was reduced on plants that were infested with nematodes (Heterodera schachtii) prior (4 weeks) to aphid infestation. Here, we examined how infection with root-feeding nematodes affected primary and secondary metabolites in the host plant and whether this could explain the increase in aphid doubling time from 3.8 to 6.7 days. We hypothesized that the effects of herbivores on plant metabolites would depend on the presence of the other herbivore and that nematode-induced changes in primary metabolites would correlate with reduced aphid performance. Total glucosinolate concentration in the leaves was not affected by nematode presence, but the composition of glucosinolates shifted, as gluconapin concentrations were reduced, while gluconapoleiferin concentrations increased in plants exposed to nematodes. Aphid presence increased 4-methoxyglucobrassicin concentrations in leaves, which correlated positively with the number of aphids per plant. Nematodes decreased amino acid and sugar concentrations in the phloem. Aphid population doubling time correlated negatively with amino acids and glucosinolate levels in leaves, whereas these correlations were non-significant when nematodes were present. In conclusion, the effects of an herbivore on plant metabolites were independent of the presence of another herbivore. Nematode presence reduced aphid population growth and disturbed feeding relations between plants and aphids.