Characterization of the SGLT2 interaction network and its regulation by SGLT2 inhibitors: a bioinformatic analysis

Abstract

Abstract Background SGLT2 (Solute Carrier Family 5 Member 2, SLC5A2) is a promising target of a new class of drugs primarily established as kidney-targeting, effective glucose-lowering agents used in diabetes mellitus (DM) patients. Rising evidence indicates that besides renal effects, SGLT2 inhibitors (SGLT2i) have also a systemic impact via indirectly targeting the heart, endothelial cells, liver, adipose tissue. Our hypothesis states that the pleiotropic effects of SGLT2i are associated with its binding force, location of their targets in the SGLT2 networks, target involvement in signaling pathways, and their tissue-specific expression. Methods Thus to investigate differences in SGLT2i impact on human organisms, we re-created the SGLT2 interaction network incorporating its inhibitors and analyzed its tissue-specific expression using publicly available datasets. We analyzed it in the context of the so-called key terms (Autophagy, Oxidative stress, Aging, Senescence, Inflammation, AMPK pathways, mTOR pathways) which seems to be crucial to elucidate SGLT2 role in a variety of clinical manifestations. Results Our analysis focused on selected organs using the TISSUES database identified in the following order: kidney, liver, adipose tissue, blood, heart, muscle, intestine, brain, and artery showed the highest expression confidence of SGLT2 and its network components. Drug repurposing analysis of known SGLT2i pointed out the influence of SGLT1 regulators on the heart and intestine tissue. Additionally, Dapagliflozin seems to also have a stronger impact on brain tissue through the regulation of SGLT3 and SLC5A11. Shortest paths analysis identified interaction SIRT1-SGLT2 among the top five interactions across six from seven analyzed networks associated with the key terms. Other top first-level SGLT2 interactors associated with key terms were ADIPOQ, INS, GLUT4, ACE, and GLUT1, but also less recognized ILK, ADCY7. Among interactors not associated with key-terms, but which appeared in multiple shortest-paths analyzes were GPT, COG2 and MGAM. Enrichment analysis of SGLT2 network components showed the highest overrepresentation of hypertensive disease, DM related diseases for both levels of SGLT2 interactors. Additionally, for the extended SGLT2 network, we observed enrichment in obesity (including SGLT1) and cancer-related terms. Conclusion This study provides comprehensive and ranked information regarding the SGLT2 interaction network in the context of tissue expression and might be helpful to predict the clinical effects of the most commonly used SGLT2i. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Agencja badań medycznych - grant number 2019/ABM/01/00037Medical University of Warsaw - grant number 1M9/2/M/MG/N/21