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    Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome.

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    Author
    Morine, Melissa J
    McMonagle, Jolene
    Toomey, Sinead
    Reynolds, Clare M
    Moloney, Aidan P
    Gormley, Isobel C
    O Gaora, Peadar
    Roche, Helen M.
    Keyword
    Trans-11-Conjugated Linoleic-Acid
    Cholesterol-Biosynthesis
    Cardiovascular Mortality
    Lipoprotein Metabolism
    Microarray Data
    Expression
    Selenium Deficiency
    Liver
    Cis-9
    Rat
    Date
    2010-10-07
    
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    URI
    http://hdl.handle.net/11019/230
    Citation
    Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome. Melissa Morine et al. BMC Bioinformatics 2010, 11(1):499. doi:10.1186/1471-2105-11-499
    Abstract
    Background Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p < 0.05), followed by muscle (601 genes) and adipose (16 genes). Results from modified GSEA showed that the high-CLA beef diet affected diverse biological processes across the three tissues, and that the majority of pathway changes reached significance only with the bi-directional test. Combining the liver tissue microarray results with plasma marker data revealed 110 CLA-sensitive genes showing strong canonical correlation with one or more plasma markers of metabolic health, and 9 significantly overrepresented pathways among this set; each of these pathways was also significantly changed by the high-CLA diet. Closer inspection of two of these pathways - selenoamino acid metabolism and steroid biosynthesis - illustrated clear diet-sensitive changes in constituent genes, as well as strong correlations between gene expression and plasma markers of metabolic syndrome independent of the dietary effect. Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of analysis has the potential to generate novel transcriptome-based biomarkers of disease.
    Funder
    Department of Agriculture, Food and the Marine; Irish Research Council for Science, Engineering and Technology; Science Foundation Ireland
    Grant Number
    5254
    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.1186/1471-2105-11-499
    Scopus Count
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    Animal & Bioscience
    Teagasc publications in Biomed Central

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