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Microarray characterization of gene expression changes in blood during acute ethanol exposure.

Kupfer DM, White VL, Strayer DL, Crouch DJ, Burian D - BMC Med Genomics (2013)

Bottom Line: Microarray data was analyzed in a pipeline fashion to summarize and normalize and the results evaluated for relative expression across time points with multiple methods.The results of this study provide a first look at changing gene expression patterns in human blood during an acute rise in blood ethanol concentration and its depletion because of metabolism and excretion, and demonstrate that it is possible to detect changes in gene expression using total RNA isolated from whole blood.The analysis approach for this study serves as a workflow to investigate the biology linked to expression changes across a time course and from these changes, to identify target genes that could serve as biomarkers linked to pilot performance.

View Article: PubMed Central - HTML - PubMed

Affiliation: Civil Aerospace Medical Institute, AAM 610, Federal Aviation Administration, Bioaeronautical Sciences Research Laboratory, Oklahoma City, OK 73169, USA. doris.kupfer@faa.gov

ABSTRACT

Background: As part of the civil aviation safety program to define the adverse effects of ethanol on flying performance, we performed a DNA microarray analysis of human whole blood samples from a five-time point study of subjects administered ethanol orally, followed by breathalyzer analysis, to monitor blood alcohol concentration (BAC) to discover significant gene expression changes in response to the ethanol exposure.

Methods: Subjects were administered either orange juice or orange juice with ethanol. Blood samples were taken based on BAC and total RNA was isolated from PaxGene™ blood tubes. The amplified cDNA was used in microarray and quantitative real-time polymerase chain reaction (RT-qPCR) analyses to evaluate differential gene expression. Microarray data was analyzed in a pipeline fashion to summarize and normalize and the results evaluated for relative expression across time points with multiple methods. Candidate genes showing distinctive expression patterns in response to ethanol were clustered by pattern and further analyzed for related function, pathway membership and common transcription factor binding within and across clusters. RT-qPCR was used with representative genes to confirm relative transcript levels across time to those detected in microarrays.

Results: Microarray analysis of samples representing 0%, 0.04%, 0.08%, return to 0.04%, and 0.02% wt/vol BAC showed that changes in gene expression could be detected across the time course. The expression changes were verified by qRT-PCR.The candidate genes of interest (GOI) identified from the microarray analysis and clustered by expression pattern across the five BAC points showed seven coordinately expressed groups. Analysis showed function-based networks, shared transcription factor binding sites and signaling pathways for members of the clusters. These include hematological functions, innate immunity and inflammation functions, metabolic functions expected of ethanol metabolism, and pancreatic and hepatic function. Five of the seven clusters showed links to the p38 MAPK pathway.

Conclusions: The results of this study provide a first look at changing gene expression patterns in human blood during an acute rise in blood ethanol concentration and its depletion because of metabolism and excretion, and demonstrate that it is possible to detect changes in gene expression using total RNA isolated from whole blood. The analysis approach for this study serves as a workflow to investigate the biology linked to expression changes across a time course and from these changes, to identify target genes that could serve as biomarkers linked to pilot performance.

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Expression pattern clustering. Each panel A-H shows the expression pattern for individual members of each gene cluster based on microarray expression data for the gene probe sets at each of the five BAC points. A. Cluster 1, 23 members with expression pattern down at 4 up at 5 toward or above baseline. B. Cluster 2, 44 members with expression pattern up from 2, no return to baseline. C. Cluster 3, 47 members with expression pattern down at 2, up to or above baseline at 5. D. Cluster 4, nine members with expression pattern down at 2, up 3, no return to baseline. E. Cluster 5, 67 members with expression pattern down at 2 no return to baseline. F. Cluster 6, four members with expression pattern up at 4 down 5 toward or to baseline. G. Cluster 7, five members with expression pattern up at 4 and 5. H. Unclustered, four genes which did not fall into any cluster. Percent blood ethanol concentrations, BAC1 = 0%, BAC2 = 0.04%, BAC3 = 0.08%, BAC4 = 0.04%, BAC5 = 0.02%.
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Figure 2: Expression pattern clustering. Each panel A-H shows the expression pattern for individual members of each gene cluster based on microarray expression data for the gene probe sets at each of the five BAC points. A. Cluster 1, 23 members with expression pattern down at 4 up at 5 toward or above baseline. B. Cluster 2, 44 members with expression pattern up from 2, no return to baseline. C. Cluster 3, 47 members with expression pattern down at 2, up to or above baseline at 5. D. Cluster 4, nine members with expression pattern down at 2, up 3, no return to baseline. E. Cluster 5, 67 members with expression pattern down at 2 no return to baseline. F. Cluster 6, four members with expression pattern up at 4 down 5 toward or to baseline. G. Cluster 7, five members with expression pattern up at 4 and 5. H. Unclustered, four genes which did not fall into any cluster. Percent blood ethanol concentrations, BAC1 = 0%, BAC2 = 0.04%, BAC3 = 0.08%, BAC4 = 0.04%, BAC5 = 0.02%.

Mentions: Genes in this cluster exhibit a decreased expression level at BAC4 followed by a sharp increase at BAC5 (Figure 2), suggesting that these genes constitute a late response of increased expression well after alcohol levels begin to decrease. Of the 23 genes in Cluster 1, IPA created a single network from 14 members with the top biological functions of Infectious Disease, Cell Signaling, and Small Molecule Biochemistry (Table 3).


Microarray characterization of gene expression changes in blood during acute ethanol exposure.

Kupfer DM, White VL, Strayer DL, Crouch DJ, Burian D - BMC Med Genomics (2013)

Expression pattern clustering. Each panel A-H shows the expression pattern for individual members of each gene cluster based on microarray expression data for the gene probe sets at each of the five BAC points. A. Cluster 1, 23 members with expression pattern down at 4 up at 5 toward or above baseline. B. Cluster 2, 44 members with expression pattern up from 2, no return to baseline. C. Cluster 3, 47 members with expression pattern down at 2, up to or above baseline at 5. D. Cluster 4, nine members with expression pattern down at 2, up 3, no return to baseline. E. Cluster 5, 67 members with expression pattern down at 2 no return to baseline. F. Cluster 6, four members with expression pattern up at 4 down 5 toward or to baseline. G. Cluster 7, five members with expression pattern up at 4 and 5. H. Unclustered, four genes which did not fall into any cluster. Percent blood ethanol concentrations, BAC1 = 0%, BAC2 = 0.04%, BAC3 = 0.08%, BAC4 = 0.04%, BAC5 = 0.02%.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3750403&req=5

Figure 2: Expression pattern clustering. Each panel A-H shows the expression pattern for individual members of each gene cluster based on microarray expression data for the gene probe sets at each of the five BAC points. A. Cluster 1, 23 members with expression pattern down at 4 up at 5 toward or above baseline. B. Cluster 2, 44 members with expression pattern up from 2, no return to baseline. C. Cluster 3, 47 members with expression pattern down at 2, up to or above baseline at 5. D. Cluster 4, nine members with expression pattern down at 2, up 3, no return to baseline. E. Cluster 5, 67 members with expression pattern down at 2 no return to baseline. F. Cluster 6, four members with expression pattern up at 4 down 5 toward or to baseline. G. Cluster 7, five members with expression pattern up at 4 and 5. H. Unclustered, four genes which did not fall into any cluster. Percent blood ethanol concentrations, BAC1 = 0%, BAC2 = 0.04%, BAC3 = 0.08%, BAC4 = 0.04%, BAC5 = 0.02%.
Mentions: Genes in this cluster exhibit a decreased expression level at BAC4 followed by a sharp increase at BAC5 (Figure 2), suggesting that these genes constitute a late response of increased expression well after alcohol levels begin to decrease. Of the 23 genes in Cluster 1, IPA created a single network from 14 members with the top biological functions of Infectious Disease, Cell Signaling, and Small Molecule Biochemistry (Table 3).

Bottom Line: Microarray data was analyzed in a pipeline fashion to summarize and normalize and the results evaluated for relative expression across time points with multiple methods.The results of this study provide a first look at changing gene expression patterns in human blood during an acute rise in blood ethanol concentration and its depletion because of metabolism and excretion, and demonstrate that it is possible to detect changes in gene expression using total RNA isolated from whole blood.The analysis approach for this study serves as a workflow to investigate the biology linked to expression changes across a time course and from these changes, to identify target genes that could serve as biomarkers linked to pilot performance.

View Article: PubMed Central - HTML - PubMed

Affiliation: Civil Aerospace Medical Institute, AAM 610, Federal Aviation Administration, Bioaeronautical Sciences Research Laboratory, Oklahoma City, OK 73169, USA. doris.kupfer@faa.gov

ABSTRACT

Background: As part of the civil aviation safety program to define the adverse effects of ethanol on flying performance, we performed a DNA microarray analysis of human whole blood samples from a five-time point study of subjects administered ethanol orally, followed by breathalyzer analysis, to monitor blood alcohol concentration (BAC) to discover significant gene expression changes in response to the ethanol exposure.

Methods: Subjects were administered either orange juice or orange juice with ethanol. Blood samples were taken based on BAC and total RNA was isolated from PaxGene™ blood tubes. The amplified cDNA was used in microarray and quantitative real-time polymerase chain reaction (RT-qPCR) analyses to evaluate differential gene expression. Microarray data was analyzed in a pipeline fashion to summarize and normalize and the results evaluated for relative expression across time points with multiple methods. Candidate genes showing distinctive expression patterns in response to ethanol were clustered by pattern and further analyzed for related function, pathway membership and common transcription factor binding within and across clusters. RT-qPCR was used with representative genes to confirm relative transcript levels across time to those detected in microarrays.

Results: Microarray analysis of samples representing 0%, 0.04%, 0.08%, return to 0.04%, and 0.02% wt/vol BAC showed that changes in gene expression could be detected across the time course. The expression changes were verified by qRT-PCR.The candidate genes of interest (GOI) identified from the microarray analysis and clustered by expression pattern across the five BAC points showed seven coordinately expressed groups. Analysis showed function-based networks, shared transcription factor binding sites and signaling pathways for members of the clusters. These include hematological functions, innate immunity and inflammation functions, metabolic functions expected of ethanol metabolism, and pancreatic and hepatic function. Five of the seven clusters showed links to the p38 MAPK pathway.

Conclusions: The results of this study provide a first look at changing gene expression patterns in human blood during an acute rise in blood ethanol concentration and its depletion because of metabolism and excretion, and demonstrate that it is possible to detect changes in gene expression using total RNA isolated from whole blood. The analysis approach for this study serves as a workflow to investigate the biology linked to expression changes across a time course and from these changes, to identify target genes that could serve as biomarkers linked to pilot performance.

Show MeSH
Related in: MedlinePlus