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Background: Handling the vast amount of gene expression data generated by genome-wide transcriptional profiling techniques is a challenging task, demanding an informed combination of pre-processing, filtering and analysis methods if meaningful biological conclusions are to be drawn. For example, a range of traditional statistical and computational pathway analysis approaches have been used to identify over-represented processes in microarray data derived from various disease states. However, most of these approaches tend not to exploit the full spectrum of gene expression data, or the various relationships and dependencies. Previously, we described a pathway enrichment analysis tool created in MATLAB that yields a Pathway Regulation Score (PRS) by considering signalling pathway topology, and the overrepresentation and magnitude of differentially-expressed genes (J Comput Biol 19:563-573, 2012). Herein, we extended this approach to include metabolic pathways, and described the use of a graphical user interface (GUI)., Results: Using input from a variety of microarray platforms and species, users are able to calculate PRS scores, along with a corresponding z-score for comparison. Further pathway significance assessment may be performed to increase confidence in the pathways obtained, and users can view Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway diagrams marked-up to highlight impacted genes., Conclusions: The PRS tool provides a filter in the isolation of biologically-relevant insights from complex transcriptomic data.

Investigators require intuitive tools to rationalize complex datasets generated by transcriptional profiling experiments. Pathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways to facilitate assessment of relative enrichment, lead investigators more effectively to biologically testable hypotheses. However, once a set of differentially expressed genes is isolated, pathway analysis approaches tend to ignore rich gene expression information and, moreover, do not exploit relationships between transcripts. In this article, we report the development of a new method in which both pathway topology and the magnitude of gene expression changes inform the scoring system, thereby providing a powerful filter in the enrichment of biologically relevant information. When four sample datasets were evaluated with this method, literature mining confirmed that those pathways germane to the physiological process under investigation were highlighted by our method relative to z-score overrepresentation calculations. Moreover, non-relevant processes were downgraded using the method described herein. The inclusion of expression and topological data in the calculation of a pathway regulation score (PRS) facilitated discrimination of key processes in real biological datasets. Specifically, by combining fold-change data for those transcripts exceeding a significance threshold, and by taking into account the potential for altered gene expression to impact upon downstream transcription, one may readily identify those pathways most relevant to pathophysiological processes.

The application of gene expression profiling to the study of chronological ageing has the potential to illuminate the molecular mechanisms underlying a complex and active process. For example, ageing of the skin and its constituent organs has myriad phenotypic consequences, and a better understanding of the means by which these changes arise has important corollaries for intervention strategies. We used a transcriptional profiling approach to investigate changes in gene expression associated with ageing of the large vibrissa follicle of the Wistar rat. Follicle mRNA isolated from male Wistar rats at 1 and 18 months of age was hybridized to Clontech Atlas 1.2 Rat cDNA macroarrays. Confirmation of array results was provided by the use of Northern blotting and immunohistochemistry. Seven transcripts displayed at least a 1·6-fold increase in expression with age, of which APOD (2·5-fold), GSTM2 (2·0-fold) and NPY (1·8-fold) showed the greatest increases. Decreased expression was found in 19 transcripts, most notably in ALOX12 (13·3-fold) and GAP43 (12·6-fold) expression. Follicular ageing is characterized by transcriptional changes associated with diverse aspects of keratinocyte metabolism, proliferation and development. [ABSTRACT FROM AUTHOR]