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ARS Home » Midwest Area » Columbia, Missouri » Plant Genetics Research » Research » Publications at this Location » Publication #188089
Title: Using quantitative proteomics of Arabidopsis roots and leaves to predict metabolic activity

Author
item MOONEY, BRIAN - UNIVERSITY OF MISSOURI
item MIERNYK, JAN
item GREENLIEF, C. MICHAEL - UNIVERSITY OF MISSOURI
item THELEN, JAY - UNIVERSITY OF MISSOURI

Submitted to: Physiologia Plantarum
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/30/2006
Publication Date: 10/1/2006
Citation: Mooney, B.P., Miernyk, J.A., Greenlief, C., Thelen, J.J. 2006. Using quantitative proteomics of Arabidopsis roots and leaves to predict metabolic activity. Physiologia Plantarum. 128(2):237-250.

Interpretive Summary: Identification of the complete protein composition (proteome) of plant cells, tissues, and organs is a key part of functional genomics. The model plant Thale Cress was used in these experiments because the complete genome has been defined, which simplifies subsequent protein identification. A total protein fraction was isolated from leaves or roots of the model plant Thale Cress. The individual protein components of these fractions were separated and identified by chemical methods. Additionally, a method was developed to quantify the levels of the individual proteins. The quantitative results were combined into a model of plant respiration. This model can be tested in the future using genetic, chemical, or molecular tools. This information will be important to researchers in their attempts to increase plant productivity through biotechnology, and to other plant scientists who will try to design more efficient crop plants through classical breeding.

Technical Abstract: Proteins isolated from developing roots and leaves of Arabidopsis thaliana were separated by high-resolution two-dimensional (2-D) electrophoresis. The resulting 2-D proteome maps are markedly different. Quantitative analysis of root and leaf protein spot pairs revealed that in most instances there was at least a 1.5-fold differential. Peptide mass fingerprint (PMF) analysis of the 288 most abundant 2-D spots from each organ allowed 156 and 126 protein assignments for roots and leaves respectively, 54 of which were in common. Metabolism-related proteins accounted for 20% of assignments in samples from both organs, whereas energy-related proteins comprised 25% and 18% of leaf and root samples, respectively. Proteins involved in disease resistance and defense encompass 13% of root proteins, but only 7% of leaf proteins. The most abundant proteins in roots are beta-glucosidase, homocysteine S-methyltransferase, glutathione S-transferase, a putative latex protein, and a lectin, which in toto comprise nearly one third of total root protein. Other prominent detoxification proteins in roots are monodehydroascorbate reductase and ascorbate peroxidase. By far the most abundant leaf proteins are ribulose-1,5-bisphosphate carboxylase/oxygenase, and the ER-lumenal molecular chaperone BiP. Comparison of protein abundance with transcript abundance, using previously reported microarray data, yielded a correlation coefficient of approximately 0.6. Based upon these data, we conclude that it is inappropriate to make protein level or metabolic conclusions based solely upon data from transcript profiling. A comparative model of root and leaf metabolism was developed, based upon protein abundance. Results from this model are consistent with elevated one-carbon and tricarboxylic acid metabolism in roots relative to leaves.