Pancreatic islet α cell function and proliferation requires the arginine transporter SLC7A2.
Erick SpearsJade E StanleyMatthew ShouLinlin YinXuan LiChunhua DaiAmber M BradleyKatelyn SellickGreg PoffenbergerKatie C CoateShristi ShresthaRegina JenkinsKyle W SloopKeith T WilsonAlan D AttieMark P KellerWenbiao ChenSimeon I TaylorE Danielle DeanPublished in: bioRxiv : the preprint server for biology (2023)
Interrupting glucagon signaling decreases gluconeogenesis and the fractional extraction of amino acids by liver from blood resulting in lower glycemia. The resulting hyperaminoacidemia stimulates α cell proliferation and glucagon secretion via a liver-α cell axis. We hypothesized that α cells detect and respond to circulating amino acids levels via a unique amino acid transporter repertoire. We found that Slc7a2ISLC7A2 is the most highly expressed cationic amino acid transporter in α cells with its expression being three-fold greater in α than β cells in both mouse and human. Employing cell culture, zebrafish, and knockout mouse models, we found that the cationic amino acid arginine and SLC7A2 are required for α cell proliferation in response to interrupted glucagon signaling . Ex vivo and in vivo assessment of islet function in Slc7a2 -/- mice showed decreased arginine-stimulated glucagon and insulin secretion. We found that arginine activation of mTOR signaling and induction of the glutamine transporter SLC38A5 was dependent on SLC7A2, showing that both's role in α cell proliferation is dependent on arginine transport and SLC7A2. Finally, we identified single nucleotide polymorphisms in SLC7A2 associated with HbA1c. Together, these data indicate a central role for SLC7A2 in amino acid-stimulated α cell proliferation and islet hormone secretion.
Keyphrases
- amino acid
- cell proliferation
- induced apoptosis
- cell cycle arrest
- pi k akt
- cell cycle
- signaling pathway
- nitric oxide
- endoplasmic reticulum stress
- mouse model
- oxidative stress
- mesenchymal stem cells
- cell death
- type diabetes
- poor prognosis
- bone marrow
- skeletal muscle
- metabolic syndrome
- electronic health record
- deep learning
- clinical evaluation