Saturday 18 January 2014

Bioinformatics analysis of aging microarray profiles

Changes in gene expression are associated with numerous biological processes, cellular responses and disease states. However, elucidating the transcriptional features of aging and how these relate to physiological, biochemical and pathological changes with age remains a critical problem.

Considering the number of aging gene expression studies conducted to date it is impossible to analyse them without means of bioinformatics. Because the underlying molecular mechanisms of aging remain a subject of debate,  whether independent transcriptional programs can drive aging in different tissues is unknown. Previous results suggest that most genes differentially expressed with age in a given tissue are not genes specifically expressed in that tissue, suggesting that only a small fraction of transcriptional responses are tissue-specific.

The results of article by de Magalhães et al (2009) reveal several signatures of aging most notably involving an activation of inflammation/immune response genes. The most significant gene was APOD or apolipoprotein D, previously associated with Alzheimer's diseases. In addition, numerous genes overexpressed with age play roles in inflammation, such as CTSS, FCGR2B, IGJ, C3, C1QA and C1QB. Other genes consistently overexpressed with age included lysozyme (LYZ), clusterin (CLU), microsomal glutathione S-transferase 1 (MGST1), glutathione S-transferase A1 (GSTA1), S100 calcium binding protein A4 (S100A4) and A6 (S100A6), and annexin A3 (ANXA3) and A5 (ANXA5).B and include four genes encoding mitochondrial proteins (ATP5G3, NDUFB11, UQCRQ and UQCRFS1) and three collagen genes (COL3A1, COL1A1 and COL4A5).

These differentially expressed genes may serve as a basis for further studies, for example, for deriving reliable biomarkers of aging.

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