Aging is accompanied by considerable heterogeneity with possible co-expression of differentiation pathways. The present study investigates the interplay between crucial myogenic, adipogenic, and Wnt-related genes orchestrating aged myogenic progenitor differentiation (AMPD) using clonal gene expression profiling in conjunction with Bayesian structure learning (BSL) techniques. The expression of three myogenic regulatory factor genes (Myogenin, Myf-5, MyoD1), four genes involved in regulating adipogenic potential (C/EBPα, DDIT3, FoxC2, PPARγ), and two genes in the Wnt signaling pathway (Lrp5, Wnt5a) known to influence both differentiation programs were determined across 34 clones by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Three control genes were used for normalization of the clonal expression data (18S, GAPDH, and B2M). Constraint-based BSL techniques, namely (a) PC Algorithm, (b) Grow-shrink (GS) algorithm, and (c) Incremental Association Markov Blanket (IAMB) were used to model the functional relationships (FRs) in the form of acyclic networks from the clonal expression profiles. A novel resampling approach that obviates the need for a user-defined confidence threshold is proposed to identify statistically significant FRs at small sample sizes. Interestingly, the resulting acyclic network consisted of FRs corresponding to myogenic, adipogenic, Wnt-related genes and their interaction. A significant number of these FRs were robust to normalization across the three house-keeping genes and the choice of the BSL technique. The results presented elucidate the delicate balance between differentiation pathways (i.e., myogenic as well as adipogenic) and possible cross-talk between pathways in AMPD.
Keywords: functional relationships, aged myogenic progenitor differentiation, Bayesian structure learning