bioRxivSystems BiologyPreprint
Elucidating Interactome Dynamics of the A2A Adenosine Receptor
Wonseok LeeiD, Ahrum SoniD, Woojin KimiD, Jongham ParkiD, Ja Young ChoiD, Ju-Won KimiD, Young‐Ok KimiD, Hee Jeong KongiD, Hyunsoo KimiD
Abstract
Polydeoxyribonucleotide (PDRN) is a tissue regeneration substance that resembles human DNA and is present in human cells, mullets, salmons, and flatfish. It stimulates physiological regeneration and metabolic activity. The regenerative and metabolic effects of PDRN are attributed to the activation of Adenosine A2A receptors (ADORA2A), which increases the production of angiogenesis factors and growth factors. Activation of ADORA2A leads to the activation of ADORA2A-interacting proteins with similar functions. To investigate the changes and dynamics of proteins in the presence of PDRN, we conducted selected reaction monitoring-mass spectrometry (SRM-MS) to quantify 491 proteins, 3,852 peptides. Through peptide-level analysis, we founded 374 proteins and 1,193 peptides demonstrating both up-regulation and down-regulation in expression. We conducted gene ontology (GO) analysis and physical network analysis and discovered a novel network of proteins related to neuronal differentiation among the ADORA2A-interacting protein. Based on network analysis, we found indirect interactions with MAPK1 and MP2K1, which are known to influence neuronal cell differentiation and suggest the formation of a network involving MAPK signaling transduction. Finally, using AlphaFold multimer, we were able to predict interaction sites among ADORA2A-interacting proteins in the network associated with neuronal cell differentiation. Specifically, we predicted five interaction sites between ADORA2A and NTRK1 (which interacts with ADORA2A), forming the edge. Thus, we provided indicators for further research using ADORA2A present in robust network and highlighted the potential of PDRN to impact neuronal differentiation.
- organism: human
- assay: proteomics
- research focus: cell-cell communication
- research focus: protein structure prediction
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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