Signaling pathway activation drift during aging: Hutchinson-Gilford Progeria Syndrome fibroblasts are comparable to normal middle-age and old-age cells

Abstract

For the past several decades, research in understanding the molecular basis of human aging has progressed significantly with the analysis of premature aging syndromes. Progerin, an altered form of lamin A, has been identified as the cause of premature aging in Hutchinson-Gilford Progeria Syndrome (HGPS), and may be a contributing causative factor in normal aging. However, the question of whether HGPS actually recapitulates the normal aging process at the cellular and organismal level, or simply mimics the aging phenotype is widely debated. In the present study we analyzed publicly available microarray datasets for fibroblasts undergoing cellular aging in culture, as well as fibroblasts derived from young, middle-age, and old-age individuals, and patients with HGPS. Using GeroScope pathway analysis and drug discovery platform we analyzed the activation states of 65 major cellular signaling pathways. Our analysis reveals that signaling pathway activation states in cells derived from chronologically young patients with HGPS strongly resemble cells taken from normal middle-aged and old individuals. This clearly indicates that HGPS may truly represent accelerated aging, rather than being just a simulacrum. Our data also points to potential pathways that could be targeted to develop drugs and drug combinations for both HGPS and normal aging.

Figure 1

Unsupervised hierarchical clustering heat map of signaling pathways for dataset E-MTAB-2086. Up- and down-regulated pathways are depicted in red and blue color, respectively. The first two clustered samples on the left-hand side of schematic represent the cells with maximum passages of eighty, then follow three samples that underwent 70 passages and four samples grown for 50 passages.

Figure 2

Pearson's correlation plot build for “Young”, “Middle”, “Old” and “HGPS” groups of fibroblasts. Samples from all datasets are combined and named according to the group they belong to. Scale bar colors indicate the sign and magnitude of Pearson's correlation coefficient between samples.

Figure 3

(A) PAS values of all samples were transformed into the first three principal components using principal component analysis (PCA). “Young”, “Middle”, “Old” and “HGPS” are depicted in green, yellow, blue and red color respectively. (B) Venn diagram representing the number of similarly up-/down-regulated pathways between “Young”, “Middle”, “Old” and “HGPS” groups. Similarity of PAS values distributions of different pathways for given groups were computed using equivalence T-test (pairwise comparison) and equivalence F-test (comparison of three and four groups).

Figure 4

Distribution of PAS values in “Young” (Y), “Middle” (M), “Old” (O) and “HGPS” (P) groups in 14 different signaling pathways.