Submitted to: Plant Physiology
Publication Type: Peer reviewed journal
Publication Acceptance Date: 1/29/2010
Publication Date: 4/1/2010
Citation: Hajduch, M., Hearne, L.B., Miernyk, J.A., Casteel, J.E., Joshi, T., Agrawal, G.K., Song, Z., Zhou, M., Xu, D., Thalen, J.J. 2010. Systems Analysis of Seed Filling in Arabidopsis Thaliana: Using General Linear Modeling to Assess Concordance of Transcript and Protein Expression. Plant Physiology. 152:2078-2087. Interpretive Summary: Proteins are an important component of plant seeds, but unfortunately proteins can be relatively difficult to study. Another class of chemicals, called nucleic acids, are responsible for how much protein and how many different proteins are made in all living cells. These nucleic acids are relatively easy to study. Because they are easy to study, many researchers measure nucleic acids and then imply that the results are the same as they would be if they actually studied proteins. We have developed a method of statistical analysis to show that in mouse-ear cress seeds the levels of proteins and the levels of nucleic acids are equivalent only half of the time. We conclude that studying proteins directly will allow us to better understand plant seeds. The method will allow identification of instances where protein and nucleic acid levels are not equivalent so that the reasons for the difference can be studied. These results will be useful to other researchers in their efforts to improve agricultural production through classical breeding or application of biotechnology.
Technical Abstract: Previous systems analyses in plants have focused on a single developmental stage or time point, although it is often important to additionally consider time-index changes. During seed development a cascade of events occurs within a relatively brief time-scale. We have collected protein and transcript expression data from five sequential stages of Arabidopsis thaliana seed development encompassing the period of reserve polymer accumulation. Protein expression profiling employed 2-dimensional gel electrophoresis coupled with tandem mass spectrometry, while transcript profiling used oligonucleotide microarrays. Analyses in biological triplicate yielded robust expression information for 523 proteins and 22,746 genes across the five developmental stages, and established 319 protein/transcript pairs for subsequent pattern analysis. General linear modeling was used to evaluate the protein/transcript expression patterns. Overall, application of this statistical assessment technique showed concurrence for a slight majority (56%) of expression pairs. Many specific examples of discordant protein/transcript expression patterns were detected, suggesting that this approach will be useful in revealing examples of post-transcriptional regulation.