Abstract

BackgroundCurrently, 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.ResultsHere, 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.ConclusionBi-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.

Highlights

  • A number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment

  • Animal experimentation The animal feeding experiment was conducted at the BioResources Unit, Trinity College Dublin (TCD) Ireland according to European Union (EU) animal research welfare protocol, with approval for experimentation granted by the Department of Health and Children in Ireland (License number B100/3041)

  • High-conjugated linoleic acid (CLA) beef diet improved insulin sensitivity, lipoprotein profile and inflammatory status Feeding a diet enriched with natural beef derived cis-9, trans-11-CLA diet significantly reduced fasting plasma glucose (p = 5.6e-06) concentrations compared to control linoleic acid (LA)-enriched diet (Diet A) (Figure 1)

Read more

Summary

Introduction

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. Nutritional genomics strives to understand molecular-level metabolic effects of dietary components, and to develop sensitive tools to analyze these effects This has proven to be a formidable challenge, as many nutrients have ubiquitous metabolic effects that are both subtle and complex [2]. In the case of MetS, this is further complicated by the involvement of multiple organs, including adipose tissue, liver and skeletal muscle Traditional metabolic biomarkers, such as plasma glucose and triglycerides, have well-established associations with health [3,4,5], but do not reflect the vast complexity of inter-organ metabolic processes. In contrast to typical ranking based on fold-change or statistical evidence of differential expression, these correlation patterns can be used to rank the ‘importance’ of diet-sensitive genes based on the degree of correlation with diagnostic markers

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call