title: Multiple correlations of mRNA expression and protein abundance in human cytokine profile.
abstract: With the development of genomic study, researchers found that it is insufficient to predict protein expression from quantitative mRNA data in large scale, which is contrary to the traditional opinion that mRNA expression correlates with protein abundance at the single gene level. To try to solve the apparent conflicting views, here we set up a series of research models and chose soluble cytokines as targets. First, human peripheral blood mononuclear cell (PBMC) from one health donor was treated with 16 continuously changing conditions, the protein and mRNA profile were analyzed by multiplex Luminex and genomic microarray, respectively. Among the tested genes, around half mRNA correlated well with their corresponding proteins (ρ > 0.8), however if we put all the genes together, the correlation coefficient for the 16 conditions varied from 0.29 to 0.71. Second, PBMC from 14 healthy donors were stimulated with the same condition and it was found that the correlation coefficient went down (ρ < 0.6). Third, 28 rheumatoid arthritis (RA) patients were tested for their response to the same external stimuli and it turned out different individual displayed different protein expression pattern as expect. Lastly, autoimmune disease cohorts (8 diseases including RA, 103 patients in total) were assayed on the whole view. It was observed that there was still some similarity in the protein profile among patients from the single disease type although completely different patterns were displayed across different disease categories. This study built a good bridge between single gene analysis and the whole genome study and may give a reasonable explanation for the two conflicting views in current biological science.
authors.affiliation: School of Chemistry & Biological Engineering, University of Science & Technology Beijing, Beijing, 100083, China, hongwudu@ustb.edu.cn.
authors.forename: Hong-Wu
authors.initials: HW
authors.lastname: Du
authors.forename: Guang-Yu
authors.initials: GY
authors.lastname: Chen
authors.forename: Chun-He
authors.initials: CH
authors.lastname: Yang
authors.forename: Chuan-Bao
authors.initials: CB
authors.lastname: Li
authors.forename: Yi-Ping
authors.initials: YP
authors.lastname: Xun
authors.forename: Jia
authors.initials: J
authors.lastname: Liu
authors.forename: Peng
authors.initials: P
authors.lastname: Chen
authors.forename: Li-Li
authors.initials: LL
authors.lastname: Shi
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology; D016207:Cytokines / Q000235:genetics* / Q000378:metabolism*; D020869:Gene Expression Profiling; D006801:Humans; D007963:Leukocytes, Mononuclear / Q000378:metabolism; D020543:Proteome; D040901:Proteomics; D059467:Transcriptome
chemical_list: D016207:Cytokines; D020543:Proteome