Cells Derived from Diabetic Patients Selected from a Cohort Study Using a Visual Search Tool (VST) Are Used as Models for Metformin Resistance - Marjan Nouri-Keshtkar (15431001) (2023)
Marjan Nouri-Keshtkar (University of Science and Culture, Royan Institute for Stem Cell Biology and Technology), Hassan Ketabi (MCI), Maryam Alasadi (University of Science and Culture, Royan Institute for Stem Cell Biology and Technology), Roya Hossein (Royan Institute for Reproductive Medicine), Reza Homayounfar (Fasa University of Medical Sciences, Shahid Beheshti University of Medical Sciences) Mehdi Totonchi (Royan Institute for Reproductive Medicine), Yaser Tahamtani (University of Science and Culture, Royan Institute for Stem Cell Biology and Technology, Roya Institute for Reproductive Medicine) Background: Metformin is the first line antidiabetic drug for type 2 diabetics.  Despite its widespread use, it doesn't work optimally for all and about 30% of patients are resistant to metformin. Different studies showed that there are genetic factors affecting this phenomenon. However, due to many influencing environmental factors, developing a cell-based modeling system can be utilized as a tool to better understand the nature of metformin response.  Objective: In this study, we selected our experimental sources (metformin responders/non-responders) from Fasa PERSIAN cohort study which is a population-based cohort of >10K individuals aged 35-70 years established in Fasa city in IRAN. For exact selection of patients, we developed a visual search tool (VST). Fibroblast cells were collected from selected patients to be used for further experiments. Methods: To develop the VST, each patients’ data was indexed as one record in elastic search. The user interface is a customized Kibana dashboard that enhances Lucene based full-text search of associations with interactive aggregation visualizations. All patients entered this study based on informed consent and skin biopsies were collected by taking a punch-size piece of skin. Fibroblast cells were isolated by enzymatic digestion, expanded and adequately banked. To detect metformin effects at the cellular level, at first step, fibroblasts were treated by 10 to 100 mM metformin for 72 hours to find the optimal dose and the phosphorylation of AMP-activated protein kinase (AMPK) was measured by western blotting technique. Next steps of this study will include generation of fibroblast derived induced pluripotent stem cells (iPSCs) and iPSCs -derived hepatocytes.  Results: At the first step, among the population of the Fasa PERSIAN cohort, 252 patients were selected using VST for HbA1c test, among them 62 patients were available and tested for HbA1c. Accordingly, six patients who had a HbA1c level higher than 7.5%, and 19 patients who had a HbA1c level less than 7.5% were selected as non-responders and responder patients respectively. After being interviewed by an endocrinologist, two patients were selected for each groups. The isolated Fibroblasts were cultured up to passage number 15 and they were morphologically fibroblast-like cells. The western blot results for 10 mM to 100 mM metformin-treated fibroblasts showed that 10mM of metformin leads to the phosphorylation of APMK as a cell energy sensor through the change in AMP/ATP ratio. Conclusion: Most metformin-resistant patients are unaware of their drug resistance, so we required a protocol to identify them. Identifying these patients is a specialized and time-consuming process, which requires interpretation and analysis tasks. However, by using VST, which provides a user-friendly platform, medical researchers were enabled to perform complex analyses by entering a few queries in a short time; the capability that can be also used for other cohort-driven patient selection studies. By having access to the patient-derived iPSCs, we would be able to differentiate them toward all target cell types such as intestinal epithelial cells and hepatocytes, and explore the differences between different groups at cellular level. Such a cell-based modeling platform also can be an effective tool for conducting drug discovery studies.