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Fakultät Statistik

P4: SecProt-InterAna

Model-based interaction analysis of secretome and proteome data from intrinsically and extrinsically aged cells

In project P4, aging processes will be comprehensively analysed by employing statistical methods, focusing on human fibroblasts that have aged in situ in human skin.

Aging processes are driven by genetic factors (intrinsic aging) as well as environmental factors (extrinsic aging). To assess if intrinsic and extrinsic aging develop independently of each other or if these two types of aging influence each other in their development, intrinsically and extrinsically aged skin fibroblasts of the same individuals belonging to different age groups will be statistically analysed.

In previous work, the secretomes and proteomes of intrinsically aged fibroblasts of individuals belonging to three age groups (18-25 years of age, 35-49 years of age, 60-67 years of age) were analysed (Waldera-Lupa et al., 2014, 2015). Besides other analyses, a one-way analysis of variance was applied to each protein to test whether the protein levels differ between the three age groups. Afterwards, proteins that showed a significant difference at a 5% level were considered in network and gene ontology (GO) analyses in order to identify GO biological processes in which the identified proteins were overrepresented.

The goal of the present project is to investigate, (i) whether the secretromes and/or proteomes of the intrinsically aged skin cells differ from the secretomes or proteomes of the extrinsically aged skin cells, and (ii) whether the two secretomes and/or proteomes develop independently or influence each other's development.

In order to answer these questions, suitable regression models will be constructed and developed to analyse the proteins of the secretome and the proteome individually. One of the models will, e.g., be employed to investigate whether the interaction between age and type of aging (intrinsic vs. extrinsic) has an effect on the secretome and/or proteome, taking the intraindividual differences between intrinsic and extrinsic aging as well as the differences between the different individuals into account.

Since both intrinsically and extrinsically aged skin cells were collected from the same individuals, network and GO analyses similar to the ones performed by Waldera-Lupa et al. (2014, 2015) will be performed separately for the secretome and the proteome. The results will then be compared between the two secretomes and proteomes, respectively. For this purpose, a statistical method will be developed that can be used to detect subnetworks that differ between two networks or are identical in both networks. Furthermore, the (test) results of the regression analyses will be employed in network and GO analyses to uncover GO biological processes that differ between intrinsically and extrinsically aged skin cells.

These results will also be used in protein set analyses, where all proteins that belong, e.g., to the same pathway or GO term will be analysed together. This enables the identification of groups of proteins that might individually not show substantially different intensities between the different types of skin cells, but when considered jointly might exhibit an intensity pattern that differs substantially between the cell types. For this purpose, different procedures for gene set analysis will be adapted and possibly further developed. Afterwards, they will be applied to the secretome and proteome data and the results will be compared with each other.

Moreover, the profiles of the protein intensities, i.e. the temporal progression of the intensity over the three age groups, will be analysed for the proteins showing intensities differing substantially between the age groups. In this way, proteins with a similar profile, i.e. with a similar progression of the protein intensities, can be identified separately for both types of skin cells. This approach will also be possible to detect (groups of) proteins that show profiles that differ between the cell types. For this purpose, similar to Gabdoulline et al. (2014), cluster methods will be employed to construct clusters containing proteins with similar profiles. For this application, a procedure will be devised that not only takes into account whether the (average) intensity increases, decreases, or remains constant between two successive age groups, but which also considers the magnitude of the degree of increase/decrease. Afterwards, the identified clusters of proteins will be compared and GO analysis will be performed to examine which GO terms are overrepresented in the clusters.


  • Gabdoulline R, Kaisers W, Gaspar A, Meganathan K, Doss MX, Jagtap S, Hescheler J, Sachinidis A, Schwender H (2015). Differences in the early development of human and mouse embryonic stem cells. PLOS ONE 10, e0140803, doi: 10.1371/journal.pone.0140803.
  • Waldera-Lupa DM, Kalfalah F, Boukamp P, Safferling K, Poschmann G, Gotz-Rosch C, Bernerd F, Haag L, Huebenthal U, Fritsche E, Grabe N, Boege F, Tigges F*, Stühler K*, Krutmann J* (2015). Characterization of skin aging associated secreted proteins (SAASP) produced by dermal fibroblasts isolated from intrinsically aged human skin. J Invest Dermatol 135, 1954-68, doi: 10.1038/jid.2015.120. (* equal contribution)
  • Waldera-Lupa DM, Kalfalah F, Florea AM, Sass S, Kruse F, Rieder V, Tigges J, Fritsche E, Krutmann J, Busch H, Boerries M, Meyer HE, Boege F, Theis F, Reifenberger G, Stühler K (2014). Proteome-wide analysis reveals age-associated cellular phenotype of in situ aged human fibroblasts. Aging 6, 856-72, doi: 10.18632/aging.100698.