Multivariate analysis of low-dose radiation-associated changes in cytokine gene expression profiles using microarray technology

Abstract

Objective: The availability of microarray technology, which permits evaluation of the entire cellular transcriptome in a single experiment, has provided new insights on the function of the genome under normal and pathological conditions, as well as in response to genotoxic stimuli, including ionizing radiation. The aims of this study were to: 1) determine whether specific cytokine gene expression profiles can be delineated in individuals exposed to chronic, low-dose radiation; and 2) compare analyses from three multivariate analytic methodologies, hierarchical clustering analysis (HCA), principal component analysis (PCA), and projection pursuit (PP), in evaluating transcriptional responses in human mononuclear cells to low doses of ionizing radiation (IR), as determined by cDNA microarrays.

Materials and methods: Total RNA isolated from mononuclear cells of 19 apparently healthy adult individuals exposed to low doses of IR ranging from 0.18 mSv to 49 mSv over a period of 11 to 13 years, as a result of the Chernobyl Nuclear Power Plant catastrophe, was reverse transcribed in the presence of radioactive dATP to generate radiolabeled complementary cDNA. Target cDNA was hybridized to human cytokine and receptor arrays and mRNA transcriptional patterns were evaluated using HCA, PCA, and PP.

Results: Statistical analyses of the data generated from 19 microarrays revealed distinct gene expression patterns in mononuclear cells of individuals exposed to radiation doses of greater than 10 mSv or less than 10 mSv. Genes encompassed within clusters discerned by HCA, PCA, and PP varied depending on the methodology used to analyze the microarray data. The most frequently expressed genes across all radiation doses were serine/threonine protein kinase receptor (11/19), transforming growth factor (TGF) receptor (11/19), EB13 (10/19), and CD40 ligand.

Conclusions: Although our findings suggest that it may be possible to assign gene expression profiles to low-dose-irradiated individuals, we show that gene expression profiles vary, depending on the statistical method used to analyze the data. Since there is, as of yet, no consensus regarding the best method to analyze a multivariate dataset, and since discarding the raw data and repeating the experiment at a later date constitutes an unwarranted expenditure, it is important to submit microarray data to public databases where these data can be reevaluated and interpreted by investigators holding expertise in various fields within the scientific community, including radiation biology, statistics, and bioinformatics.