Global gene expression profiling in whole-blood samples from individuals exposed to metal fumes - PubMed (original) (raw)
Global gene expression profiling in whole-blood samples from individuals exposed to metal fumes
Zhaoxi Wang et al. Environ Health Perspect. 2005 Feb.
Abstract
Accumulating evidence demonstrates that particulate air pollutants can cause both pulmonary and airway inflammation. However, few data show that particulates can induce systemic inflammatory responses. We conducted an exploratory study using microarray techniques to analyze whole-blood total RNA in boilermakers before and after occupational exposure to metal fumes. A self-controlled study design was used to overcome the problems of larger between-individual variation interferences with observations of relatively smaller changes caused by environmental exposure. Moreover, we incorporated the dichotomous data of absolute gene expression status in the microarray analyses. Compared with nonexposed controls, we observed that genes with altered expression in response to particulate exposure were clustered in biologic processes related to inflammatory response, oxidative stress, intracellular signal transduction, cell cycle, and programmed cell death. In particular, the preinflammatory cytokine interleukin 8 and one of its receptors, chemokine receptor 4, seemed to play important roles in early-stage response to heavy metal exposure and were down-regulated. Furthermore, most observed expression variations were from nonsmoking exposed individuals, suggesting that smoking profoundly affects whole-blood expression profiles. Our study is the first to demonstrate that with a paired sampling study design of pre- and postexposed individuals, small changes in gene expression profiling can be measured in whole-blood total RNA from a population-based study. This technique can be applied to evaluate the host response to other forms of environmental exposures.
Figures
Figure 1. Cluster analysis 44 RNA samples using 139 genes identified by paired _t_-test in welders. The clustering display was generated by dChip software with two-way data clustering. Each row represents an individual gene, and each column corresponds to an individual array. Gene expression values were standardized and color coded relative to the mean: blue, values less than the mean; red, values greater than the mean. RNA samples from the same individual were labeled with the same sample ID with different suffixes, representing different collection time points. Smoking status: N, nonsmoking; S, smoking. Exposure status: N, controls; Y, welders. Time point: B, baseline; P, postexposure. Experiment: the number indicates the hybridization batch in which a sample was analyzed.
Figure 2. GoSurfer graphic view of hypergeometric distribution testing of gene clustering. Each node represents a GO biologic process, and a line connecting nodes represents parent–child relationship in the top-down direction. Because GO allows multiple parent–child relationships toward one biologic process but GoSurfer only plots one upstream and one downstream relationship for each node, one biologic process may appear several times in the GoSurfer plot. Red nodes represent significant GO bioprocesses tested by hypergeometric distribution as described in “Materials and Methods.” Numbered GO bioprocesses were used in calculation in hypergeometric distribution testing: 1, biologic process; 2.1, cellular process; 2.2, development; 2.3, physiologic processes; 3.1, cell communication; 3.2, cell growth and/or maintenance; 3.3, metabolism; 3.4, response to external stimulus; 3.5; response to stress; and 3.6, death.
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