Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: A multiple regression to identify sources of variations

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Abstract

Parallel profiling of mRNA and protein on a global scale and integrative analysis of these two data types could provide additional insights into the metabolic mechanisms underlying complex biological systems. However, because mRNA and protein abundance are affected by many cellular and physical processes, there have been conflicting results on their correlation. Using whole-genome microarray and LC–MS/MS proteomic data collected from Desulfovibrio vulgaris grown under three different conditions, we systematically investigate the relationship between mRNA and protein abundance by a multiple regression approach, in which some of the key covariates that may affect mRNA–protein relationship were included. The results showed that mRNA abundance alone can explain only 20–28% of the total variation of protein abundance, suggesting mRNA–protein correlation can not be determined by mRNA abundance alone. Among various covariates, analytic variation of protein abundance is the major source for the variation of mRNA–protein correlation, which contributes to 34–44% of the total variation of mRNA–protein correlation. The cellular functional category of genes/proteins contributes 10–15% of the total variation of mRNA–protein correlation, with a more pronounced correlation of the two properties was observed for “central intermediary metabolism” and “energy metabolism” categories. In addition, protein stability also contributes 5% of the total variation of mRNA–protein correlation. The study presents the first quantitative analysis of the contributions of various biochemical and physical sources to the correlation of mRNA and protein abundance in D. vulgaris.

Section snippets

Materials and methods

Microarray analysis. Oligonucleotide microarrays containing 3507 ORFs of the D. vulgaris genome were designed by NimbleGen Systems (Madison, WI) [13], [16]. The raw intensity data were normalized using tools available through the Bioconductor project (http://www.bioconductor.org). For each experimental condition, there were four measurements for each gene: two replicates (each containing a pool of three biological replicates) that were each hybridized to duplicate microarrays [14]. Four

Quality and distribution pattern analysis of mRNA and protein abundance data

Four measurements of mRNA levels were obtained for all genes in the D. vulgaris genome under three different growth conditions. The quality of the microarray data was evaluated with Pearson’s correlation coefficient analysis among four measurements. Pearson’s correlation coefficients indicated excellent reproducibility of the microarray experiments, with variations from 0.96 to 0.99 for samples collected from lactate-based medium at exponential phase (LE), from 0.97 to 0.99 for samples

Discussion

High-throughput experimentation measuring mRNA and protein expression provides rich sources of genomic information [30]. Integrative analysis of these two data types could provide additional insights into the metabolic mechanisms underlying complex biological systems. However, a challenging question facing the integrative analysis of these large-scale datasets is to identify the correlation patterns between mRNA and protein abundance, and various factors affecting the relationship. Moreover,

Acknowledgments

The research described in this paper was conducted under the Laboratory Directed Research and Development, LDRD Program at the Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy under Contract DE-AC056-76RLO1830. This study is also partially supported by the Award 0317349 from the National Science Foundation’s Division of Biological Infrastructure, Program in Biological Databases and Informatics.

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