Dynamic Protein Pathway Activation Mapping of Adipose-Derived Stem Cell Differentiation Implicates Novel Regulators of Adipocyte Differentiation

Abstract

Next to embryonic stem cell research, adult stem cell research is providing a promising alternative for enhanced tissue regeneration and transplantation. The key biochemical networks controlling the differentiation processes regulating stem cell biology remain largely disputed and or undefined, contributing to a lack of knowledge of the principle phosphoregulatory events propagating signal transduction. To effectively monitor these events relative to adipocyte differentiation, this study utilized a high throughput reverse phase protein microarray platform and characterized adult adipose-derived stem cell (ASC) differentiation through the monitoring of ϳ100 phosphospecific endpoints with 33 distinct time points examined across 14 days. This kinetic-based analysis showed time ordered signal transduction ultimately implicating pathways correlated with adipogenic differentiation. To further validate the causal significance of these network activations, pharmacological targeting was implemented to include the chemical inhibitors MAPK inhibitor PD169316, rapamycin, and HNMPA-(AM) 3 yielding partial or complete disruption of adipocytic differentiation, as noted by a decrease or lack of lipid formation within the mature adipocytes. Based on this analysis, v-crk sarcoma virus CT10 oncogene homolog (CRKII) and c-abl oncogene 1, non-receptor tyrosine kinase (c-ABL) were implicated as novel key regulators of adipocyte differentiation, with v-akt murine thymoma viral oncogene (AKT), mammalian target of rapamycin (mTOR), and SMAD family member (SMAD) pathways being implicated as secondary regulators. This dynamic molecular profiling provides a novel insight into the signaling architecture of mesenchymal stem cell differentiation and may be useful in the development of therapeutic modulators for clinical applications; in addition to advancing the collective understanding of key cellular processes, ultimately contributing to more confident stem cell manipulation. Molecular & Cellular

c-Abl tyrosine kinase promotes adipocyte differentiation by targeting PPAR-gamma 2

“…As mentioned above, c-Abl was found to be involved in differentiation of diverse cell types (15)(16)(17)(18), whereas a recent proteomic analysis showed that it may be a novel adipogenic regulator (21). As we demonstrated, c-Abl inhibition caused a dramatic adipogenic abrogation, underlying its role as a key regulator of this process.…”

“…Interestingly, c-Abl is highly expressed in human adipocytes (19) and activated upon insulin stimulation, where it participates in regulation of metabolic downstream signals (20). Furthermore, a recent proteomic analysis placed c-Abl as a potential key regulator of adipogenesis (21).…”

c-Abl inhibition mitigates diet-induced obesity through improving insulin sensitivity of subcutaneous fat in mice

“…There is evidence showing that c-Abl plays a central role in CML and neurodegenerative diseases [12,45]. Recently, extensive studies have linked c-Abl to islet beta cell survival and adipogenesis [20,21,23,24]. By utilising genetic ablation and pharmacological inhibition, we found a new role for c-Abl in regulating diet-induced obesity and insulin resistance in vivo.…”

“…A proteomic analysis revealed that c-Abl was a potential key regulator of adipogenesis via regulation of the expression and activity of peroxisome proliferator-activator receptor γ 2 (PPARγ2), the master adipogenic regulator controlling adipogenesis [23,24]. These findings indicate that c-Abl participates in adipocytes metabolism regulation in vitro.…”

Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases

“…DNBs have great significance in disease classification and monitoring, therapeutic response evaluation, early diagnosis and understanding of molecular pathogenesis 5 . As a few examples, a Bayesian network model has been developed to classify subjects with Alzheimer’s disease using longitudinal magnetic resonance data 26 , Yang et al 27 proposed a dynamic network approach to identify a prognostic signature for acute myeloid leukemia, and a dynamic protein pathway activation of adipose-derived stem cell differentiation identified novel biomarkers of adipocyte differentiation 28 .…”