The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.
Lipids are a highly diverse class of molecules with crucial roles in cellular energy storage, structure and signaling. Lipid homeostasis is fundamental to maintain health, and lipid defects are central to the pathogenesis of important and devastating diseases. Newly emerging advances have facilitated the development of so-called lipidomics technologies and offer an opportunity to elucidate the mechanisms leading to disease. Furthermore, these advances also provide the tools to unravel the complexity of the 'allostatic forces' that allow maintenance of normal cellular/tissue phenotypes through the application of bioenergetically inefficient adaptive mechanisms. An alternative strategy is to focus on tissues with limited allostatic capacity, such as the eye, that could be used as readouts of metabolic stress over time. Identification of these allostatic mechanisms and pathological 'scares' might provide a window to unknown pathogenic mechanisms, as well as facilitate identification of early biomarkers of disease.