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Functional & Integrative Genomics

, Volume 20, Issue 1, pp 63–74 | Cite as

Probing early wheat grain development via transcriptomic and proteomic approaches

  • Mingming Yang
  • Yang Liu
  • Jian Dong
  • Wanchun Zhao
  • Samneet Kashyap
  • Xiang GaoEmail author
  • Sachin RustgiEmail author
  • Shanshan WenEmail author
Original Article

Abstract

To understand the molecular changes taking place during the early grain development in common wheat, we profiled transcriptome and proteome of two cultivars, “P271” and “Chinese Spring” (CS) with large and small grains, respectively. More than 85,000 genes and 7500 proteins were identified to express during early grain development in two wheat cultivars. We observed enrichment in the number of genes falling in the functional categories—carbohydrate metabolism, amino acid metabolism, lipid metabolism, and cofactor as well as vitamin metabolism with progression in grain development, which indicates towards the importance of these metabolic pathways during grain maturation. Many genes showed inconsistency between transcription and translation, which suggested a role of post-transcriptional events that determine the fate of nascent transcript/protein, in the early grain development. In silico localization of differentially expressed genes/proteins between CS and P271 to wheat chromosomes, exhibited a biased genomic distribution with chromosomes 1A, 4B, and 5B contributing primarily to it. These results corroborated the earlier findings, where chromosomes 1A, 4B, and 5B were reported to harbor genes/QTLs for yield contributing traits such as grain length and thickness. Collectively, this study reveals the molecular changes taking place during early grain development, through light on the regulation of these processes, and allows identification of the gene candidates contributing to the contrasting grain characteristics of CS and P721. This information has implications in the future wheat breeding for the enhanced grain yield.

Keywords

Triticum aestivum Grain development Transcriptome Proteome 

Notes

Acknowledgments

. We thank Guangzhou FitGene Biotechnology Co. Ltd., China, for technical assistance.

Author contribution statement

MY, XG, SR, and SW designed the research and wrote the manuscript; MY, JD, WZ, and SK performed experiments and analyzed data.

Funding information

This work was supported by grants from national science and technology projects for rural areas during the 12th five-year plan period (2011AA100501), China agricultural research system (CARS-3-2-47), Fundamental Research Funds for the Central Universities (Z109021623), Chinese post-doctoral science foundation (2016M602871), and partly supported by the open funds of the State Key Laboratory of Crop Genetics and Germplasm Enhancement (ZW201702), National Natural Science Foundation of China (31801443, 31701425), program of China Scholarships Council (201806305044), and NIFA Hatch/Multi-state grant (S009) to SR, the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2019JQ-514).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10142_2019_698_MOESM1_ESM.pdf (5.1 mb)
ESM 1. Fig S1. Bubble diagrams showing distribution of differentially expressed genes/proteins at 4 days post-anthesis (DPA) in P271 and Chinese Spring comparison (cf. Fig. 8a). The classification of DEGs was based on the KEGG pathway analysis. The top 20 metabolic pathways are shown here. Fig S2. Bubble diagrams showing distribution of differentially expressed genes/proteins at 8 days post-anthesis (DPA) in P271 and Chinese Spring comparison (cf. Fig. 8b). The classification of DEGs was based on the KEGG pathway analysis. The top 20 metabolic pathways are shown here. Fig S3. Bubble diagrams showing distribution of differentially expressed genes/proteins at 12 days post-anthesis (DPA) in P271 and Chinese Spring comparison (cf. Fig. 8c). The classification of DEGs was based on the KEGG pathway analysis. The top 20 metabolic pathways are shown here. Fig S4. (A) Number of differentially expressing genes/proteins identified to map on the selected chromosomes, and (B) relative contribution of each chromosome to the total number of DEG/DEPs identified per developmental stage. Dashed boxes mark the selected chromosomes (cf. Fig. 5A-C). Fig S5. Chromosomal distribution of differentially expressed genes/proteins on wheat chromosomes. cM location of genes and the centromere on each chromosome were determined on the basis of the wheat chromosome zippers. Peaks in the chromosome plots represent the number of DEGs/DEPs mapping to arbitrary chromosome bins of 50 cM. Fig S6. Genetic linkage maps of wheat chromosomes 2A, 4A, 1B, 2B, 3B, 4B, 5B, and 6B showing locations of differentially expressing genes/proteins in ration to the quantitative trait loci (QTLs) for the grain yield traits such as thousand grain weight (TGW), grain length (GL), grain width (GW), and grain thickness (GT). (Picture modified after Wu et al. 2015). (PDF 5178 kb)
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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Crop Stress Biology for Arid Areas, College of AgronomyNorthwest A&F UniversityYanglingPeople’s Republic of China
  2. 2.Wheat Engineering Research Center of Shaanxi ProvinceYanglingPeople’s Republic of China
  3. 3.Department of Plant and Environmental Sciences, Pee Dee Research and Education CenterClemson UniversityFlorenceUSA

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