Deciphering carbohydrate metabolism during wheat grain development via integrated transcriptome and proteome dynamics.

Grain development of Triticum aestivum is being studied extensively using individual OMICS tools. However, integrated transcriptome and proteome studies are limited mainly due to complexity of genome. Current study focused to unravel the transcriptome-proteome coordination of key mechanisms underlying carbohydrate metabolism during whole wheat grain development. Wheat grains were manually dissected to obtain grain tissues for proteomics and transcriptomics analyses. Differentially expressed proteins and transcripts at the 11 stages of grain development were compared. Computational workflow for integration of two datasets related to carbohydrate metabolism was designed. For CM proteins, output peptide sequences of proteomic analyses (via LC-MS/MS) were used as source to search corresponding transcripts. The transcript that turned out with higher number of peptides was selected as bona fide ribonucleotide sequence for respective protein synthesis. More than 90% of hits resulted in successful identification of respective transcripts. Comparative analysis of protein and transcript expression profiles resulted in overall 32% concordance between these two series of data. However, during grain development correlation of two datasets gradually increased up to ~ tenfold from 152 to 655 °Cd and then dropped down. Proteins involved in carbohydrate metabolism were divided in five categories in accordance with their functions. Enzymes involved in starch and sucrose biosynthesis showed the highest correlations between proteome-transcriptome profiles. High percentage of identification and validation of protein-transcript hits highlighted the power of omics data integration approach over existing gene functional annotation tools. We found that correlation of two datasets is highly influenced by stage of grain development. Further, gene regulatory networks would be helpful in unraveling the mechanisms underlying the complex and significant traits such as grain weight and yield.

wDBTF: an integrated database resource for studying wheat transcription factor families.

BACKGROUND: Transcription factors (TFs) regulate gene expression by interacting with promoters of their target genes and are classified into families based on their DNA-binding domains. Genes coding for TFs have been identified in the sequences of model plant genomes. The rice (Oryza sativa spp. japonica) genome contains 2,384 TF gene models, which represent the mRNA transcript of a locus, classed into 63 families. RESULTS: We have created an extensive list of wheat (Triticum aestivum L) TF sequences based on sequence homology with rice TFs identified and classified in the Database of Rice Transcription Factors (DRTF). We have identified 7,112 wheat sequences (contigs and singletons) from a dataset of 1,033,960 expressed sequence tag and mRNA (ET) sequences available. This number is about three times the number of TFs in rice so proportionally is very similar if allowance is made for the hexaploidy of wheat. Of these sequences 3,820 encode gene products with a DNA-binding domain and thus were confirmed as potential regulators. These 3,820 sequences were classified into 40 families and 84 subfamilies and some members defined orphan families. The results were compiled in the Database of Wheat Transcription Factor (wDBTF), an inventory available on the web http://wwwappli.nantes.inra.fr:8180/wDBFT/. For each accession, a link to its library source and its Affymetrix identification number is provided. The positions of Pfam (protein family database) motifs were given when known. CONCLUSIONS: wDBTF collates 3,820 wheat TF sequences validated by the presence of a DNA-binding domain out of 7,112 potential TF sequences identified from publicly available gene expression data. We also incorporated in silico expression data on these TFs into the database. Thus this database provides a major resource for systematic studies of TF families and their expression in wheat as illustrated here in a study of DOF family members expressed during seed development.

Nucleotide polymorphism in the wheat transcriptional activator Spa influences its pattern of expression and has pleiotropic effects on grain protein composition, dough viscoelasticity, and grain hardness.

Storage protein activator (SPA) is a key regulator of the transcription of wheat (Triticum aestivum) grain storage protein genes and belongs to the Opaque2 transcription factor subfamily. We analyzed the sequence polymorphism of the three homoeologous Spa genes in hexaploid wheat. The level of polymorphism in these genes was high particularly in the promoter. The deduced protein sequences of each homoeolog and haplotype show greater than 93% identity. Two major haplotypes were studied for each Spa gene. The three Spa homoeologs have similar patterns of expression during grain development, with a peak in expression around 300 degree days after anthesis. On average, Spa-B is 10 and seven times more strongly expressed than Spa-A and Spa-D, respectively. The haplotypes are associated with significant quantitative differences in Spa expression, especially for Spa-A and Spa-D. Significant differences were found in the quantity of total grain nitrogen allocated to the gliadin protein fractions for the Spa-A haplotypes, whereas the synthesis of glutenins is not modified. Genetic association analysis between Spa and dough viscoelasticity revealed that Spa polymorphisms are associated with dough tenacity, extensibility, and strength. Except for Spa-A, these associations can be explained by differences in grain hardness. No association was found between Spa markers and the average single grain dry mass or grain protein concentration. These results demonstrate that in planta Spa is involved in the regulation of grain storage protein synthesis. The associations between Spa and dough viscoelasticity and grain hardness strongly suggest that Spa has complex pleiotropic functions during grain development.