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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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Microarray data analysis pipeline. Analysis pipeline for control and ethanol exposed microarray data sets.
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Figure 1: Microarray data analysis pipeline. Analysis pipeline for control and ethanol exposed microarray data sets.

Mentions: The entire probe set list and the individual expression pattern-clustered probe sets, were analyzed with Ingenuity Pathway Analysis, IPA (version 8.7, Ingenuity® Systems, Inc., http://www.ingenuity.com; Redwood City, CA) and the Database for Annotation, Visualization, and Integrated Discovery, DAVID [40]. The BioGPS database was used to evaluate tissue-specific gene expression using the Human U133A/GNF1H Gene Atlas dataset, (GEO-GSE1133, [41,42] ). The BIOBASE [43] ExPlain™ [44] Mammalian Module 3.0 was used with the application Match™ to examine the promoter regions of the cluster genes for transcription factor binding matrices using the BIOBASE TRANSFAC® database. The RMA summarized data set filtered for average log2 (expression) > 6 and with the 203 candidate genes removed was used for the No-set (background). The vertebrate_h0.01 profile was used. High-specific matrices with cut-offs minFP were used for a 1200 base promoter window from −1000 to 200. Both cut-off and window position were optimized with a p-value threshold of 0.001. The Match matrix output was filtered for a Yes/No ratio of >1.5, P-value <0.01 and Matched promoters P-value <0.01. The weight matrices profile was used to create a transcription factor gene set and filtered for human specific factors. The gene set was mapped on canonical pathways using the BIOBASE Transpath application using P-value <0.01, minimal hits to group of two. A Transpath gene set linking the transcription factors to pathways was generated for each cluster. The resulting output was examined for signaling pathways and transcription factors predicted to affect genes on our list. Figure 1 shows the analysis pipeline.


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)

Microarray data analysis pipeline. Analysis pipeline for control and ethanol exposed microarray data sets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750403&req=5

Figure 1: Microarray data analysis pipeline. Analysis pipeline for control and ethanol exposed microarray data sets.
Mentions: The entire probe set list and the individual expression pattern-clustered probe sets, were analyzed with Ingenuity Pathway Analysis, IPA (version 8.7, Ingenuity® Systems, Inc., http://www.ingenuity.com; Redwood City, CA) and the Database for Annotation, Visualization, and Integrated Discovery, DAVID [40]. The BioGPS database was used to evaluate tissue-specific gene expression using the Human U133A/GNF1H Gene Atlas dataset, (GEO-GSE1133, [41,42] ). The BIOBASE [43] ExPlain™ [44] Mammalian Module 3.0 was used with the application Match™ to examine the promoter regions of the cluster genes for transcription factor binding matrices using the BIOBASE TRANSFAC® database. The RMA summarized data set filtered for average log2 (expression) > 6 and with the 203 candidate genes removed was used for the No-set (background). The vertebrate_h0.01 profile was used. High-specific matrices with cut-offs minFP were used for a 1200 base promoter window from −1000 to 200. Both cut-off and window position were optimized with a p-value threshold of 0.001. The Match matrix output was filtered for a Yes/No ratio of >1.5, P-value <0.01 and Matched promoters P-value <0.01. The weight matrices profile was used to create a transcription factor gene set and filtered for human specific factors. The gene set was mapped on canonical pathways using the BIOBASE Transpath application using P-value <0.01, minimal hits to group of two. A Transpath gene set linking the transcription factors to pathways was generated for each cluster. The resulting output was examined for signaling pathways and transcription factors predicted to affect genes on our list. Figure 1 shows the analysis pipeline.

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