title: How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes.
abstract: A key assumption in studying mRNA expression is that it is informative in the prediction of protein expression. However, only limited studies have explored the mRNA-protein expression correlation in yeast or human tissues and the results have been relatively inconsistent. We carried out correlation analyses on mRNA-protein expressions in freshly isolated human circulating monocytes from 30 unrelated women. The expressed proteins for 71 genes were quantified and identified by 2-D electrophoresis coupled with mass spectrometry. The corresponding mRNA expressions were quantified by Affymetrix gene chips. Significant correlation (r=0.235, P<0.0001) was observed for the whole dataset including all studied genes and all samples. The correlations varied in different biological categories of gene ontology. For example, the highest correlation was achieved for genes of the extracellular region in terms of cellular component (r=0.643, P<0.0001) and the lowest correlation was obtained for genes of regulation (r=0.099, P=0.213) in terms of biological process. In the genome, half of the samples showed significant positive correlation for the 71 genes and significant correlation was found between the average mRNA and the average protein expression levels in all samples (r=0.296, P<0.01). However, at the study group level, only five studied genes had significant positive correlation across all the samples. Our results showed an overall positive correlation between mRNA and protein expression levels. However, the moderate and varied correlations suggest that mRNA expression might be sometimes useful, but certainly far from perfect, in predicting protein expression levels.
authors.affiliation: Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, China.
authors.forename: Yanfang
authors.initials: Y
authors.lastname: Guo
authors.forename: Peng
authors.initials: P
authors.lastname: Xiao
authors.forename: Shufeng
authors.initials: S
authors.lastname: Lei
authors.forename: Feiyan
authors.initials: F
authors.lastname: Deng
authors.forename: Gary Guishan
authors.initials: GG
authors.lastname: Xiao
authors.forename: Yaozhong
authors.initials: Y
authors.lastname: Liu
authors.forename: Xiangding
authors.initials: X
authors.lastname: Chen
authors.forename: Liming
authors.initials: L
authors.lastname: Li
authors.forename: Shan
authors.initials: S
authors.lastname: Wu
authors.forename: Yuan
authors.initials: Y
authors.lastname: Chen
authors.forename: Hui
authors.initials: H
authors.lastname: Jiang
authors.forename: Lijun
authors.initials: L
authors.lastname: Tan
authors.forename: Jingyun
authors.initials: J
authors.lastname: Xie
authors.forename: Xuezhen
authors.initials: X
authors.lastname: Zhu
authors.forename: Songping
authors.initials: S
authors.lastname: Liang
authors.forename: Hongwen
authors.initials: H
authors.lastname: Deng
mesh_terms: D001798:Blood Proteins / Q000032:analysis / Q000235:genetics* / Q000378:metabolism*; D002478:Cells, Cultured; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009000:Monocytes / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012333:RNA, Messenger / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic
chemical_list: D001798:Blood Proteins; D012333:RNA, Messenger