A metabolomics-based molecular pathway analysis of how the sodium-glucose co-transporter-2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes

Skander Mulder, Ann Hammarstedt, Sunil B Nagaraj, Viji Nair, Wenjun Ju, Jonatan Hedberg, Peter J Greasley, Jan W Eriksson, Jan Oscarsson, Hiddo J L Heerspink, Skander Mulder, Ann Hammarstedt, Sunil B Nagaraj, Viji Nair, Wenjun Ju, Jonatan Hedberg, Peter J Greasley, Jan W Eriksson, Jan Oscarsson, Hiddo J L Heerspink

Abstract

Aim: To investigate which metabolic pathways are targeted by the sodium-glucose co-transporter-2 inhibitor dapagliflozin to explore the molecular processes involved in its renal protective effects.

Methods: An unbiased mass spectrometry plasma metabolomics assay was performed on baseline and follow-up (week 12) samples from the EFFECT II trial in patients with type 2 diabetes with non-alcoholic fatty liver disease receiving dapagliflozin 10 mg/day (n = 19) or placebo (n = 6). Transcriptomic signatures from tubular compartments were identified from kidney biopsies collected from patients with diabetic kidney disease (DKD) (n = 17) and healthy controls (n = 30) from the European Renal cDNA Biobank. Serum metabolites that significantly changed after 12 weeks of dapagliflozin were mapped to a metabolite-protein interaction network. These proteins were then linked with intra-renal transcripts that were associated with DKD or estimated glomerular filtration rate (eGFR). The impacted metabolites and their protein-coding transcripts were analysed for enriched pathways.

Results: Of all measured (n = 812) metabolites, 108 changed (P < 0.05) during dapagliflozin treatment and 74 could be linked to 367 unique proteins/genes. Intra-renal mRNA expression analysis of the genes encoding the metabolite-associated proteins using kidney biopsies resulted in 105 genes that were significantly associated with eGFR in patients with DKD, and 135 genes that were differentially expressed between patients with DKD and controls. The combination of metabolites and transcripts identified four enriched pathways that were affected by dapagliflozin and associated with eGFR: glycine degradation (mitochondrial function), TCA cycle II (energy metabolism), L-carnitine biosynthesis (energy metabolism) and superpathway of citrulline metabolism (nitric oxide synthase and endothelial function).

Conclusion: The observed molecular pathways targeted by dapagliflozin and associated with DKD suggest that modifying molecular processes related to energy metabolism, mitochondrial function and endothelial function may contribute to its renal protective effect.

Keywords: bioinformatics; dapagliflozin; kidney function; metabolomics; sodium-glucose co-transporter-2; type 2 diabetes.

Conflict of interest statement

S.M., S.B.N., V.N. and W.J. have no conflict of interest. A.H, P.J.G., J.H. and J.O. are employed by AstraZeneca. Biopharmaceutical R&D. H.J.L.H. serves as a consultant for AbbVie, AstraZeneca, Boehringer Ingelheim, CSL Pharma, Fresenius, Gilead, Janssen, Merck, Mitsubishi Tanabe, Mundi Pharma and Retrophin. J.W.E. has received honoraria or research support from AstraZeneca, Merck‐Sharpe and Dohme, NovoNordisk, Mundipharma and Bayer.

© 2020 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

Figures

Figure 1
Figure 1
Schematic overview of a metabolomics to intra‐renal transcriptomics approach to identify molecular pathways targeted by dapagliflozin and associated with progressive kidney function decline. (A) Metabolomics were performed in the EFFECT‐II randomized controlled trial. (B) Metabolites changed during dapagliflozin were identified. (C) To link the metabolomic features with kidney‐specific pathophysiology context, unique protein‐coding genes derived from metabolomic features that significantly changed during dapagliflozin treatment were identified, and the gene expression profiles measured in kidney tissues from ERCB participants representing these genes were selected. (D) The gene expressions were then associated with estimated glomerular filtration rate decline, and significant features were selected. (E) Pathway analysis was then performed based on selected metabolomics and transcriptomic features, and (F) integration analysis of enriched molecular pathways based on metabolites and intra‐renal transcripts was performed to select molecular pathways targeted by dapagliflozin and associated with diabetic kidney disease progression
Figure 2
Figure 2
Pathways significantly enriched in features based on metabolites affected by dapagliflozin. Significant genes (green, left column) derived from the renal tissue transcriptomics and associated with estimated glomerular filtration rate or significantly different between patients with diabetic kidney disease and healthy donors are shown. Metabolites which significantly changed during dapagliflozin and represented in the enriched pathways are shown in red on the right side of the figure. In the middle, enriched pathways based on the metabolites are shown in blue, with the bold pathways also having significant enrichment in the kidney transcriptome
Figure 3
Figure 3
Identified molecular pathway based on metabolite and intra‐renal transcripts integration. Molecular pathways highlighted in light orange indicate pathways targeted by dapagliflozin and associated with diabetic kidney disease progression

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