Starch and prolamin composition and content are important indexes for determining the processing and nutritional quality of wheat (Triticum aestivum L.) grains. Several transcription factors (TFs) regulate gene expression during starch and protein biosynthesis in wheat. Storage protein activator (TaSPA), a member of the basic leucine zipper (bZIP) family, has been reported to activate glutenin genes and is correlated to starch synthesis related genes. In this study, we generated TaSPA-B overexpressing (OE) transgenic wheat lines. Compared with wild-type (WT) plants, the starch content was slightly reduced and starch granules exhibited a more polarized distribution in the TaSPA-B OE lines. Moreover, glutenin and ω- gliadin contents were significantly reduced, with lower expression levels of related genes (e.g., By15, Dx2, and ω-1,2 gliadin gene). RNA-seq analysis identified 2023 differentially expressed genes (DEGs). The low expression of some DEGs (e.g., SUSase, ADPase, Pho1, Waxy, SBE, SSI, and SS II a) might explain the reduction of starch contents. Some TFs involved in glutenin and starch synthesis might be regulated by TaSPA-B, for example, TaPBF was reduced in TaSPA-B OE-3 lines. In addition, dual-luciferase reporter assay indicated that both TaSPA-B and TaPBF could transactivate the promoter of ω-1,2 gliadin gene. These results suggest that TaSPA-B regulates a complex gene network and plays an important role in starch and protein biosynthesis in wheat.

Wheat aphids are major pests during the seed filling stage of wheat. Plant lectins are toxic to sap-sucking pests such as wheat aphids. In this study, Pinellia pedatisecta agglutinin (ppa), a gene encoding mannose binding lectin, was cloned, and it shared 92.69% nucleotide similarity and 94% amino acid similarity with Pinellia ternata agglutinin (pta). The ppa gene, driven by the constitutive and phloem-specific ribulose bisphosphate carboxylase small subunit gene (rbcs) promoter in pBAC-rbcs-ppa expression vector, was transferred into the wheat cultivar Baofeng104 (BF104) by particle bombardment transformation. Fifty-four T₀ transgenic plants were generated. The inheritance and expression of the ppa gene were confirmed by PCR and RT-PCR analysis respectively, and seven homozygous transgenic lines were obtained. An aphid bioassay on detached leaf segments revealed that seven ppa transgenic wheat lines had lower aphid growth rates and higher inhibition rates than BF104. Furthermore, two-year aphid bioassays in isolated fields showed that aphid numbers per tiller of transgenic lines were significantly decreased, compared with wild type BF104. Therefore, ppa could be a strong biotechnological candidate to produce aphid-resistant wheat.

Accurate germplasm characterization is a vital step for accelerating crop genetic improvement, which remains largely infeasible for crops such as bread wheat (Triticum aestivum L.), which has a complex genome that undergoes frequent introgression and contains many structural variations. Here, we propose a genomic strategy called ggComp, which integrates resequencing data with copy number variations and stratified single-nucleotide polymorphism densities to enable unsupervised identification of pairwise germplasm resource-based Identity-By-Descent (gIBD) blocks. The reliability of ggComp was verified in wheat cultivar Nongda5181 by dissecting parental-descent patterns represented by inherited genomic blocks. With gIBD blocks identified among 212 wheat accessions, we constructed a multi-scale genomic-based germplasm network. At the whole-genome level, the network helps to clarify pedigree relationship, demonstrate genetic flow, and identify key founder lines. At the chromosome level, we were able to trace the utilization of 1RS introgression in modern wheat breeding by hitchhiked segments. At the single block scale, the dissected germplasm-based haplotypes nicely matched with previously identified alleles of “Green Revolution” genes and can guide allele mining and dissect the trajectory of beneficial alleles in wheat breeding. Our work presents a model-based framework for precisely evaluating germplasm resources with genomic data. A database, WheatCompDB (http://wheat.cau.edu.cn/WheatCompDB/), is available for researchers to exploit the identified gIBDs with a multi-scale network.