SummaryMethionine-derived glucosinolates belong to a class of plant secondary metabolites that serve as chemoprotective compounds in plant biotic defense reactions and also exhibit strong anticancerogenic properties beneficial to human health. In a screen for the trans-activation potential of various transcription factors toward glucosinolate biosynthetic genes, we could identify the HAG1 (HIGH ALIPHATIC GLUCOSINOLATE 1, also referred to as MYB28) gene as a positive regulator of aliphatic methionine-derived glucosinolates. The content of aliphatic glucosinolates as well as transcript levels of aliphatic glucosinolate biosynthetic genes were elevated in gain-of-function mutants and decreased in HAG1 RNAi knock-down mutants. Pro HAG1 :GUS expression analysis revealed strong HAG1 promoter activity in generative organs and mature leaves of A. thaliana plants, the main sites of accumulation of aliphatic glucosinolates. Mechanical stimuli such as touch or wounding transiently induced HAG1/MYB28 expression in inflorescences of flowering plants, and HAG1/ MYB28 over-expression reduced insect performance as revealed by weight gain assays with the generalist lepidopteran herbivore Spodoptera exigua. Expression of HAG1/MYB28 was significantly induced by glucose, indicating a novel transcriptional regulatory mechanism for the integration of carbohydrate availability upon biotic challenge. We hypothesize that HAG1/MYB28 is a novel regulator of aliphatic glucosinolate biosynthesis that controls the response to biotic challenges.

SummaryGlucosinolates are a class of plant secondary metabolites that serve as antiherbivore compounds in plant defence. A previously identified Arabidopsis thaliana activation-tagged line, displaying altered levels of secondary metabolites, was shown here to be affected in the content of indolic and aliphatic glucosinolates. The observed chemotype was caused by activation of the R2R3-MYB transcription factor gene HIG1 (HIGH INDOLIC GLUCOSINOLATE 1, also referred to as MYB51). HIG1/MYB51 was shown to activate promoters of indolic glucosinolate biosynthetic genes leading to increased accumulation of indolic glucosinolates. The corresponding loss-of-function mutant hig1-1 contained low levels of glucosinolates. Overexpression of the related transcription factor ATR1/MYB34, which had previously been described as a regulator of indolic glucosinolate and indole-3-acetic acid homeostasis, in the hig1-1 mutant background led to a partial rescue of the mutant chemotype along with a severe high-auxin growth phenotype. Overexpression of MYB122, another close homologue of HIG1/MYB51, did not rescue the hig1-1 chemotype, but caused a high-auxin phenotype and increased levels of indolic glucosinolates in the wild-type. By contrast, overexpression of HIG1/MYB51 resulted in the specific accumulation of indolic glucosinolates without affecting auxin metabolism and plant morphology. Mechanical stimuli such as touch or wounding transiently induced the expression of HIG1/ MYB51 but not of ATR1/MYB34, and HIG1/MYB51 overexpression reduced insect herbivory as revealed by dual-choice assays with the generalist lepidopteran herbivore, Spodoptera exigua. We hypothesize that HIG1/ MYB51 is a regulator of indolic glucosinolate biosynthesis that also controls responses to biotic challenges.

With the establishment of advanced technology facilities for high throughput plant phenotyping, the problem of estimating plant biomass of individual plants from their two dimensional images is becoming increasingly important. The approach predominantly cited in literature is to estimate the biomass of a plant as a linear function of the projected shoot area of plants in the images. However, the estimation error from this model, which is solely a function of projected shoot area, is large, prohibiting accurate estimation of the biomass of plants, particularly for the salt-stressed plants. In this paper, we propose a method based on plant specific weight for improving the accuracy of the linear model and reducing the estimation bias (the difference between actual shoot dry weight and the value of the shoot dry weight estimated with a predictive model). For the proposed method in this study, we modeled the plant shoot dry weight as a function of plant area and plant age. The data used for developing our model and comparing the results with the linear model were collected from a completely randomized block design experiment. A total of 320 plants from two bread wheat varieties were grown in a supported hydroponics system in a greenhouse. The plants were exposed to two levels of hydroponic salt treatments (NaCl at 0 and 100 mM) for 6 weeks. Five harvests were carried out. Each time 64 randomly selected plants were imaged and then harvested to measure the shoot fresh weight and shoot dry weight. The results of statistical analysis showed that with our proposed method, most of the observed variance can be explained, and moreover only a small difference between actual and estimated shoot dry weight was obtained. The low estimation bias indicates that our proposed method can be used to estimate biomass of individual plants regardless of what variety the plant is and what salt treatment has been applied. We validated this model on an independent set of barley data. The technique presented in this paper may extend to other plants and types of stresses.

High-throughput phenotyping produces multiple measurements over time, which require new methods of analyses that are flexible in their quantification of plant growth and transpiration, yet are computationally economic. Here we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array. Two rice diversity panels, indica and aus, containing a total of 553 genotypes, are phenotyped in waterlogged conditions. Using cubic smoothing splines to estimate plant growth and transpiration, we identify four time intervals that characterize the early responses of rice to salinity. Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analysed using a new association model that takes into account the interaction between treatment (control and salt) and genetic marker. This model allows the identification of previously undetected loci affecting TUE on chromosome 11, providing insights into the early responses of rice to salinity, in particular into the effects of salinity on plant growth and transpiration.