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Updated: Dec 16, 2025

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling
Published on: May 21, 2020
Deciphering carbohydrate metabolism during wheat grain development via integrated transcriptome and proteome dynamics
Ayesha Tahir1,2, Jun Kang3, Frederic Choulet4
1Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan. ayesha.tahir2007@gmail.com.
Abstract:
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.
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