Background: Fusarium head blight (FHB), caused mainly by Fusarium graminearum (Fg) Schwabe (teleomorph: Gibberellazeae Schwble), brings serious damage to wheat production. Chinese wheat landrace Wangshuibai is one of the most important resistance sources in the world. The knowledge of mechanism underlying its resistance to FHB is still limited. Results: To get an overview of transcriptome characteristics of Wangshuibai during infection by Fg, a high-throughput RNA sequencing based on next generation sequencing (NGS) technology (Illumina) were performed. Totally, 165,499 unigenes were generated and assigned to known protein databases including NCBI non-redundant protein database (nr) (82,721, 50.0%), Gene Ontology (GO) (38,184, 23.1%), Swiss-Prot (50,702, 30.6%), Clusters of orthologous groups (COG) (51,566, 31.2%) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (30,657, 18.5%), as determined by Blastx search. With another NGS based platform, a digital gene expression (DGE) system, gene expression in Wangshuibai and its FHB susceptible mutant NAUH117 was profiled and compared at two infection stages by inoculation of Fg at 24 and 48 hour, with the aim of identifying genes involved in FHB resistance. Conclusion: Pathogen-related proteins such as PR5, PR14 and ABC transporter and JA signaling pathway were crucial for FHB resistance, especially that mediated by Fhb1. ET pathway and ROS/NO pathway were not activated in Wangshuibai and may be not pivotal in defense to FHB. Consistent with the fact that in NAUH117 there presented a chromosome fragment deletion, which led to its increased FHB susceptibility, in Wangshuibai, twenty out of eighty-nine genes showed changed expression patterns upon the infection of Fg. The up-regulation of eight of them was confirmed by qRT-PCR, revealing they may be candidate genes for Fhb1 and need further functional analysis to confirm their roles in FHB resistance.

Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R²) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.

The APETALA 2/Ethylene-responsive element binding factor (AP2/ERF) transcription factor gene family is widely involved in the biotic and abiotic stress regulation. Haynaldia villosa (VV, 2n = 14), a wild species of wheat, is a potential gene pool for wheat improvement. H. villosa confers high resistance to several wheat diseases and high tolerance to some abiotic stress. In this study, ERF1-V, an ethylene-responsive element-binding factor gene of the AP2/ERF transcription factor gene family from wild H. villosa, was cloned and characterized. Sequence and phylogenetic analysis showed that ERF1-V is a deduced B2 type ERF gene. ERF1-V was first identified as a Blumeria graminis f. sp. tritici (Bgt) up-regulated gene, and later found to be induced by drought, salt and cold stresses. In responses to hormones, ERF1-V was up-regulated by ethylene and abscisic acid, but down-regulated by salicylic acid and jasmonic acid. Over expression of ERF1-V in wheat could improve resistance to powdery mildew, salt and drought stress. Chlorophyll content, malondialdehyde content, superoxide dismutase and peroxidase activity were significantly differences between the recipient Yangmai158 and the transgenic plants following salt treatment. Furthermore, the expression levels of some stress responsive genes were differences after drought or salt treatments. Although ERF1-V was activated by the constitutive promoter, the agronomic traits, including flowering time, plant height, effective tiller number, spikelet number per spike and grain size, did not changed significantly. ERF1-V is a valuable gene for wheat improvement by genetic engineering.