Abstract
Trace metals such as copper, iron, zinc, and manganese play important roles in several biochemical processes, including respiration and photosynthesis. Using a label-free, quantitative proteomics strategy (MS(E)), we examined the effect of deficiencies in these micronutrients on the soluble proteome of Chlamydomonas reinhardtii. We quantified >10(3) proteins with abundances within a dynamic range of 3 to 4 orders of magnitude and demonstrated statistically significant changes in ~200 proteins in each metal-deficient growth condition relative to nutrient-replete media. Through analysis of Pearson's coefficient, we also examined the correlation between protein abundance and transcript abundance (as determined via RNA-Seq analysis) and found moderate correlations under all nutritional states. Interestingly, in a subset of transcripts known to significantly change in abundance in metal-replete and metal-deficient conditions, the correlation to protein abundance is much stronger. Examples of new discoveries highlighted in this work include the accumulation of O(2) labile, anaerobiosis-related enzymes (Hyd1, Pfr1, and Hcp2) in copper-deficient cells; co-variation of Cgl78/Ycf54 and coprogen oxidase; the loss of various stromal and lumenal photosynthesis-related proteins, including plastocyanin, in iron-limited cells; a large accumulation (from undetectable amounts to over 1,000 zmol/cell) of two COG0523 domain-containing proteins in zinc-deficient cells; and the preservation of photosynthesis proteins in manganese-deficient cells despite known losses in photosynthetic function in this condition.
Highlights
The investigation of cellular responses to micronutrient deficiency has provided important insights into the utilization of metals in biochemistry
Because protein sequence data were not considered in the generation of these gene model sets, we reasoned that proteomics data could provide an independent means to assess the accuracy of each gene model set
Changes at the RNA Level Can Serve as Reliable Predictors of Changes at the Protein Level but Do Not Reveal All Protein Level Changes—The Pearson coefficient between transcript and protein data showed a moderate correlation ( ϭ 0.28 for copper-replete and ϭ 0.30 for copper-deficient cells)
Summary
Au10.2, Augustus 10.2; MnSOD, manganese-containing superoxide dismutase; MS, mass spectrometry; Oee, oxygen evolving enhancer protein; PS, photosystem; SAM, S-adenosylmethionine. Ferredoxin is known to decrease substantially in iron-starved cells [15], whereas the iron-containing superoxide dismutase is only mildly affected, suggesting that some iron-proteins are more dispensable than others [19] This illustrates the need for proteomics surveys to identify changes at the protein level that are not apparent at the transcript level and to help determine the dispensability of iron-proteins relative to one another. Prior examinations of the metal-deficient transcriptomes in Chlamydomonas provide excellent examples of the application of the genome data From these studies, a great deal of information was obtained on the effect of metal deficiency on the abundance of thousands of transcripts [13]. This study allows the examination of multiple metal-dependent proteomes, which provides a better understanding of the interrelationship between various metal metabolisms
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