Impact of monocarbonyl analogs of curcumin (MACs) C66 and B2BrBC on the expression of diabetes-associated genes in streptozotocin-treated rat pancreatic RIN-m cells—Quantitative RT-PCR array data
Radoslav Stojchevski,
Sara Velichkovikj,
Jane Bogdanov,
Nikola Hadzi-Petrushev,
Mitko Mladenov,
Leonid Poretsky,
Dimiter Avtanski
Affiliations
Radoslav Stojchevski
Friedman Diabetes Institute, Lenox Hill Hospital, Northwell Health, New York, NY, USA; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Feinstein Institutes for Medical Research, Manhasset, NY, USA
Sara Velichkovikj
Department of Medicine, Lenox Hill Hospital, Northwell Health, New York, NY, USA
Jane Bogdanov
Faculty of Natural Sciences and Mathematics, Institute of Chemistry, Ss. Cyril and Methodius University, Skopje, North Macedonia
Nikola Hadzi-Petrushev
Faculty of Natural Sciences and Mathematics, Institute of Biology, Ss. Cyril and Methodius University, Skopje, North Macedonia
Mitko Mladenov
Faculty of Natural Sciences and Mathematics, Institute of Biology, Ss. Cyril and Methodius University, Skopje, North Macedonia
Leonid Poretsky
Friedman Diabetes Institute, Lenox Hill Hospital, Northwell Health, New York, NY, USA; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Feinstein Institutes for Medical Research, Manhasset, NY, USA
Dimiter Avtanski
Friedman Diabetes Institute, Lenox Hill Hospital, Northwell Health, New York, NY, USA; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Feinstein Institutes for Medical Research, Manhasset, NY, USA; Corresponding author at: Friedman Diabetes Institute, Lenox Hill Hospital, Northwell Health, 110E 59th Street, Suite 8B, Room 837, New York, NY 10022, USA.
This paper presents a dataset obtained from an RT2-qPCR array analysis of rat pancreatic RIN-m cells treated with two monocarbonyl analogs of curcumin (MACs), C66 and B2BrBC in the presence or absence of streptozotocin (STZ). The array quantified the expression of 84 genes associated with the onset, development, and progression of diabetes. This dataset provides information on the gene expression profiles of pancreatic cells modulated by two specific MACs in a diabetic context. The data can serve as a foundation for developing new hypotheses, designing follow-up experiments, and identifying novel targets for treatment. It can be used to investigate further the molecular mechanisms underlying the therapeutic effects of these MACs and in comparative studies using other experimental antidiabetic compounds.
