The oral hypoxia-inducible factor prolyl hydroxylase inhibitor enarodustat counteracts alterations in renal energy metabolism in the early stages of diabetic kidney disease
2019; Elsevier BV; Volume: 97; Issue: 5 Linguagem: Inglês
10.1016/j.kint.2019.12.007
ISSN1523-1755
AutoresS. Hasegawa, Tetsuhiro Tanaka, Tomoyuki Saito, Kenji Fukui, Takeshi Wakashima, Etsuo A. Susaki, Hiroki R. Ueda, Masaomi Nangaku,
Tópico(s)Muscle metabolism and nutrition
ResumoHypoxia-inducible factor (HIF) prolyl hydroxylase inhibitors, also known as HIF stabilizers, increase endogenous erythropoietin production and serve as novel therapeutic agents against anemia in chronic kidney disease. HIF induces the expression of various genes related to energy metabolism as an adaptive response to hypoxia. However, it remains obscure how the metabolic reprogramming in renal tissue by HIF stabilization affects the pathophysiology of kidney diseases. Previous studies suggest that systemic metabolic disorders such as hyperglycemia and dyslipidemia cause alterations of renal metabolism, leading to renal dysfunction including diabetic kidney disease. Here, we analyze the effects of enarodustat (JTZ-951), an oral HIF stabilizer, on renal energy metabolism in the early stages of diabetic kidney disease, using streptozotocin-induced diabetic rats and alloxan-induced diabetic mice. Transcriptome analysis revealed that enarodustat counteracts the alterations in diabetic renal metabolism. Transcriptome analysis showed that fatty acid and amino acid metabolisms were upregulated in diabetic renal tissue and downregulated by enarodustat, whereas glucose metabolism was upregulated. These symmetric changes were confirmed by metabolome analysis. Whereas glycolysis and tricarboxylic acid cycle metabolites were accumulated and amino acids reduced in renal tissue of diabetic animals, these metabolic disturbances were mitigated by enarodustat. Furthermore, enarodustat increased the glutathione to glutathione disulfide ratio and relieved oxidative stress in renal tissue of diabetic animals. Thus, HIF stabilization counteracts alterations in renal energy metabolism occurring in incipient diabetic kidney disease. Hypoxia-inducible factor (HIF) prolyl hydroxylase inhibitors, also known as HIF stabilizers, increase endogenous erythropoietin production and serve as novel therapeutic agents against anemia in chronic kidney disease. HIF induces the expression of various genes related to energy metabolism as an adaptive response to hypoxia. However, it remains obscure how the metabolic reprogramming in renal tissue by HIF stabilization affects the pathophysiology of kidney diseases. Previous studies suggest that systemic metabolic disorders such as hyperglycemia and dyslipidemia cause alterations of renal metabolism, leading to renal dysfunction including diabetic kidney disease. Here, we analyze the effects of enarodustat (JTZ-951), an oral HIF stabilizer, on renal energy metabolism in the early stages of diabetic kidney disease, using streptozotocin-induced diabetic rats and alloxan-induced diabetic mice. Transcriptome analysis revealed that enarodustat counteracts the alterations in diabetic renal metabolism. Transcriptome analysis showed that fatty acid and amino acid metabolisms were upregulated in diabetic renal tissue and downregulated by enarodustat, whereas glucose metabolism was upregulated. These symmetric changes were confirmed by metabolome analysis. Whereas glycolysis and tricarboxylic acid cycle metabolites were accumulated and amino acids reduced in renal tissue of diabetic animals, these metabolic disturbances were mitigated by enarodustat. Furthermore, enarodustat increased the glutathione to glutathione disulfide ratio and relieved oxidative stress in renal tissue of diabetic animals. Thus, HIF stabilization counteracts alterations in renal energy metabolism occurring in incipient diabetic kidney disease. see commentary on page 855 see commentary on page 855 Translational StatementHypoxia-inducible factor (HIF) prolyl hydroxylase inhibitors (also known as HIF stabilizers) increase endogenous erythropoietin production and serve as novel therapeutic agents against anemia in chronic kidney disease. Our transcriptome and metabolome analyses of renal tissue in rat and mouse diabetic models have revealed that enarodustat (JTZ-951), an oral HIF stabilizer, counteracts renal energy metabolism alterations in the early stages of diabetic kidney disease. The results provide important data for extrapolating the effects of HIF stabilizers on renal energy metabolism in clinical settings, although further studies are needed to clarify how this renal metabolism reprogramming by HIF stabilizers affects the progression of diabetic kidney disease. Hypoxia-inducible factor (HIF) prolyl hydroxylase inhibitors (also known as HIF stabilizers) increase endogenous erythropoietin production and serve as novel therapeutic agents against anemia in chronic kidney disease. Our transcriptome and metabolome analyses of renal tissue in rat and mouse diabetic models have revealed that enarodustat (JTZ-951), an oral HIF stabilizer, counteracts renal energy metabolism alterations in the early stages of diabetic kidney disease. The results provide important data for extrapolating the effects of HIF stabilizers on renal energy metabolism in clinical settings, although further studies are needed to clarify how this renal metabolism reprogramming by HIF stabilizers affects the progression of diabetic kidney disease. Hypoxia-inducible factor (HIF) prolyl hydroxylase inhibitors increase endogenous erythropoietin production and serve as novel therapeutic agents against anemia in chronic kidney disease.1Hasegawa S. Tanaka T. Nangaku M. Hypoxia-inducible factor stabilizers for treating anemia of chronic kidney disease.Curr Opin Nephrol Hypertens. 2018; 27: 331-338Crossref PubMed Scopus (30) Google Scholar Cells are endowed with a defensive mechanism against hypoxia, and HIF is a master regulator of this defense.2Maxwell P. HIF-1: an oxygen response system with special relevance to the kidney.J Am Soc Nephrol. 2003; 14: 2712-2722Crossref PubMed Scopus (118) Google Scholar,3Marx J. Cell biology: how cells endure low oxygen.Science. 2004; 303: 1454-1456Crossref PubMed Scopus (69) Google Scholar Kidneys are physiologically exposed to hypoxia, and chronic hypoxia is recognized as a final common pathway leading to end-stage kidney disease.4Nangaku M. Chronic hypoxia and tubulointerstitial injury: a final common pathway to end-stage renal failure.J Am Soc Nephrol. 2006; 17: 17-25Crossref PubMed Scopus (877) Google Scholar,5Tanaka T. Miyata T. Inagi R. et al.Hypoxia in renal disease with proteinuria and/or glomerular hypertension.Am J Pathol. 2004; 165: 1979-1992Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar Considering that HIF induces the expression of various genes related to hypoxia responses, HIF stabilizers might have pleiotropic effects on the progression of kidney diseases as well as improvement in anemia in chronic kidney disease. Interestingly, HIF induces the expression of glycolytic genes and pyruvate dehydrogenase kinase 1, which inhibits pyruvate dehydrogenase from using pyruvate to fuel the mitochondrial tricarboxylic acid (TCA) cycle.6Kim J.W. Tchernyshyov I. Semenza G.L. et al.HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia.Cell Metab. 2006; 3: 177-185Abstract Full Text Full Text PDF PubMed Scopus (2593) Google Scholar,7Papandreou I. Cairns R.A. Fontana L. et al.HIF-1 mediates adaptation to hypoxia by actively downregulating mitochondrial oxygen consumption.Cell Metab. 2006; 3: 187-197Abstract Full Text Full Text PDF PubMed Scopus (1614) Google Scholar This metabolic reprogramming from TCA cycle to glycolysis represses oxygen consumption and is critical for adaptation of cells exposed to hypoxic environments. However, it remains obscure how the metabolic reprogramming of renal tissue by HIF stabilization affects the pathophysiology of kidney diseases. Diabetic kidney disease (DKD) is the major cause of end-stage kidney disease.8Thomas M.C. Cooper M.E. Zimmet P. Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease.Nat Rev Nephrol. 2016; 12: 73-81Crossref PubMed Scopus (314) Google Scholar Systemic metabolic disorders such as hyperglycemia and dyslipidemia cause renal metabolism alterations, leading to renal dysfunction including DKD.9Hasegawa S. Jao T.M. Inagi R. Dietary metabolites and chronic kidney disease.Nutrients. 2017; 9: 358Crossref PubMed Scopus (28) Google Scholar Previous studies have shown increased metabolic flux and accumulation of glucose and TCA cycle metabolites in diabetic renal cortical tissue,10Sas K.M. Kayampilly P. Byun J. et al.Tissue-specific metabolic reprogramming drives nutrient flux in diabetic complications.JCI Insight. 2016; 1e86976Crossref PubMed Scopus (142) Google Scholar,11Tanaka S. Sugiura Y. Saito H. et al.Sodium-glucose cotransporter 2 inhibition normalizes glucose metabolism and suppresses oxidative stress in the kidneys of diabetic mice.Kidney Int. 2018; 94: 912-925Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar which might be related to mitochondrial dysfunction and DKD progression.12Sharma K. Mitochondrial dysfunction in the diabetic kidney.Adv Exp Med Biol. 2017; 982: 553-562Crossref PubMed Scopus (28) Google Scholar We hypothesized that HIF stabilizers might reverse the metabolism alterations in diabetic renal cortical tissue, considering that HIF, as an adaptive response to hypoxia, reduces metabolic flux in cells to repress oxygen consumption. Thus, utilizing transcriptome and metabolome analyses, we conducted a proof-of-concept study to comprehensively understand how enarodustat (JTZ-951),13Ogoshi Y. Matsui T. Mitani I. et al.Discovery of JTZ-951: a HIF prolyl hydroxylase inhibitor for the treatment of renal anemia.ACS Med Chem Lett. 2017; 8: 1320-1325Crossref PubMed Scopus (38) Google Scholar,14Akizawa T. Nangaku M. Yamaguchi T. et al.A placebo-controlled, randomized trial of enarodustat in patients with chronic kidney disease followed by long-term trial.Am J Nephrol. 2019; 49: 165-174Crossref PubMed Scopus (51) Google Scholar an oral HIF stabilizer, affects renal metabolism alterations occurring in the early stages of DKD. As renal cortex is mainly composed of proximal tubules, we first examined the effects of enarodustat on the metabolic flux of renal proximal tubule cells in vitro. First, Mito Stress Test (Agilent Technologies, Inc., Santa Clara, CA) and Glycolytic Rate Assay (Agilent) were conducted in cultured HK-2 cells,15Ryan M.J. Johnson G. Kirk J. et al.HK-2: an immortalized proximal tubule epithelial cell line from normal adult human kidney.Kidney Int. 1994; 45: 48-57Abstract Full Text PDF PubMed Scopus (721) Google Scholar a human proximal tubule epithelial cell line (Figure 1a). Enarodustat significantly reduced mitochondrial respiration (TCA cycle) and increased basal glycolysis, indicating metabolic reprogramming from TCA cycle to glycolysis (Figure 1; Supplementary Figure S1). We also conducted an experiment using small interfering RNA (siRNA) for HIF-1 (Figure 2; Supplementary Figure S2). HIF-1 knockdown by siRNA reversed metabolic alterations (basal respiration, maximal respiration, spare respiratory capacity, and adenosine triphosphate [ATP] production) induced by enarodustat, which showed that the metabolic reprogramming was mainly through HIF-1 stabilization (Figure 2; Supplementary Figure S2). Pyruvate dehydrogenase activity, an important factor for cells to use pyruvate to fuel TCA cycle, was also reduced by enarodustat (Figure 2f), which was compatible with the previously published observations.6Kim J.W. Tchernyshyov I. Semenza G.L. et al.HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia.Cell Metab. 2006; 3: 177-185Abstract Full Text Full Text PDF PubMed Scopus (2593) Google Scholar,7Papandreou I. Cairns R.A. Fontana L. et al.HIF-1 mediates adaptation to hypoxia by actively downregulating mitochondrial oxygen consumption.Cell Metab. 2006; 3: 187-197Abstract Full Text Full Text PDF PubMed Scopus (1614) Google ScholarFigure 2Metabolic reprogramming by enarodustat occurs mainly through hypoxia-inducible factor-1 (HIF-1) stabilization. (a) Study protocols are shown. (b) Western blotting for HIF-1 protein at the time of the Mito Stress Test is shown. HIF-1 stabilization induced by enarodustat was successfully reversed by 2 types of small, interfering HIF-1 (siHIF-1; #1 and #2). (c) O2 consumption rates (OCR) were measured in real time under basal conditions and in response to indicated mitochondrial inhibitors (oligomycin, carbonyl cyanide-p-trifluoromethoxyphenylhydrazone [FCCP], and rotenone + antimycin A [Rot/AA]). (d,e) HIF-1 knockdown reversed the OCR (basal respiration, maximal respiration, spare respiratory capacity, and adenosine triphosphate [ATP] production) decrease induced by enarodustat (n = 18 for each group, ∗∗∗∗P < 0.0001). The experiments using another siHIF-1 (#2) showed the same result (see Supplementary Figure S2). (f) Pyruvate dehydrogenase (PDH) activities of these cells are shown. Enarodustat significantly reduced PDH activity through HIF-1 stabilization (n = 8, ∗∗P < 0.01, ∗∗∗∗P < 0.0001). All data are expressed as mean ± SD. μM, μmol/l; siNC, siRNA negative control. To optimize viewing of this image, please see the online version of this article at www.kidney-international.org.View Large Image Figure ViewerDownload Hi-res image Download (PPT) From the results of in vitro experiments, we hypothesized that HIF stabilizers might alleviate metabolism alterations in diabetic renal cortical tissue through metabolic reprogramming from TCA cycle to glycolysis. We first chose streptozotocin (STZ)-induced diabetic rats as the model for the proof-of-concept experiment to test the above-mentioned hypothesis. In this model, diabetes is rapidly induced, allowing us to observe the net effects of diabetes and HIF stabilizers on the energy metabolism in renal tissue within a short period of time. The study protocol and basic data from experiments in STZ-induced diabetic rats are shown in Figure 3a. We divided the rats into 3 groups: group A (sham), group B (DKD), and group C (DKD + enarodustat). Blood plasma glucose, glycosylated hemoglobin HbA1c, triglyceride, and total cholesterol levels on day 14 were significantly increased in diabetic groups as compared with group A, whereas there were no significant differences between groups B and C (Figure 3d–g). Although plasma creatinine levels were not different between groups, urinary albumin excretion was significantly increased and glomerulomegaly was noticeable in group B as compared with group A, and enarodustat tended to reverse these changes (Figure 4). Blood urea nitrogen levels were higher in diabetic groups, reflecting dehydration due to diabetes. In summary, the kidneys of STZ-treated rats in our study represent the early stages of DKD. Transcriptome and metabolome analyses were conducted using renal cortical tissue of these rats.Figure 4Basic renal parameters and pathologies in streptozotocin-induced diabetic rats. (a) Blood urea nitrogen (BUN), (b) plasma creatinine (Cre), and (c) urinary albumin levels on day 14 are shown. (d) The glomerular area measured in pathological images was markedly increased in group B, and enarodustat reversed this increase (see the periodic acid–Schiff staining images). (e) Representative pathological images are shown. (Upper) Periodic acid–Schiff staining images. Bar = 100 μm. (Lower) Electron microscopy images. Glomerular basement membrane thickening was noticeable in group B. Bar = 1 μm. All data are expressed as mean ± SD. For multiplex comparisons, 1-way analysis of variance was applied, followed by the Tukey multiple comparisons test, if appropriate (∗P < 0.05, ∗∗P < 0.01, ∗∗∗∗P < 0.0001). To optimize viewing of this image, please see the online version of this article at www.kidney-international.org.View Large Image Figure ViewerDownload Hi-res image Download (PPT) The results of transcriptome analysis of renal cortical tissue are shown in Figures 5 and 6. Principal component analysis and hierarchical clustering analysis indicated that groups A, B, and C were separated into different clusters, respectively (Figure 5a and b). Differentially expressed genes were selected by |log2 fold-change (FC)| ≥ 0.5 and Q value < 0.05. Gene ontology and canonical pathway analyses revealed that genes related to fatty-acid metabolism were upregulated in group B compared with group A. In contrast, genes related to glucose metabolism and hypoxia response including HIF-1 network were upregulated in group C compared with group B (Figure 5c and d).Figure 6Gene set enrichment analysis (GSEA) using the transcriptome data. GSEA revealed symmetric metabolism alterations (diabetes vs. enarodustat): fatty-acid and amino-acid metabolisms were upregulated in group B (diabetic kidney disease [DKD]) compared with group A (sham). In contrast, they were downregulated, and glycolysis was upregulated in group C (DKD + enarodustat) compared with group B. Detailed results of GSEA are shown in Tables 1 and 2.View Large Image Figure ViewerDownload Hi-res image Download (PPT) We also conducted gene set enrichment analysis (GSEA) using the transcriptomics data (Figure 6, Tables 1 and 2). Gene sets of fatty-acid and amino-acid metabolism were upregulated in group B compared with group A (Figure 6). In contrast, these gene sets were downregulated, and gene sets of glucose metabolism were upregulated in group C as compared with group B, showing that enarodustat reversed metabolism alterations induced by diabetes (Figure 6). Moreover, gene sets of TCA cycle were downregulated in group C compared with group B (Table 2), which is compatible with the notion of enarodustat-induced metabolic reprogramming in proximal tubules observed in our in vitro study. In summary, enarodustat counteracted diabetic renal metabolism alterations from transcriptomic perspectives.Table 1Results of GSEA in group B (DKD)/A (sham)KEGG canonical pathway (FDR < 0.25)Gene setsNES-Log10 (FDR)KEGG_PPAR signaling pathway2.26>10KEGG_Complement and coagulation cascades2.22>10KEGG_Drug metabolism cytochrome P4502.002.3KEGG_Metabolism of xenobiotics by cytochrome P4501.982.3KEGG_P53 signaling pathway1.821.4KEGG_Porphyrin and chlorophyll metabolism1.791.3KEGG_Bladder cancer1.660.9KEGG_Tyrosine metabolismaGene sets related to energy metabolism.1.620.8KEGG_ABC transporters1.610.8KEGG_Adipocytokine signaling pathway1.590.8KEGG_Biosynthesis of unsaturated fatty acidsaGene sets related to energy metabolism.1.580.8KEGG_Drug metabolism other enzymes1.560.8KEGG_Fatty-acid metabolismaGene sets related to energy metabolism.1.560.8KEGG_Prion diseases1.550.8KEGG_Alanine, aspartate, and glutamate metabolismaGene sets related to energy metabolism.1.520.7KEGG_Cytokine–cytokine receptor interaction1.510.7KEGG_Valine, leucine, and isoleucine degradationaGene sets related to energy metabolism.1.510.7KEGG_Chronic myeloid leukemia1.480.7KEGG_Cell cycle1.470.7KEGG_ErbB signaling pathway1.440.6KEGG_Starch and sucrose metabolismaGene sets related to energy metabolism.1.440.7KEGG_Peroxisome1.430.7KEGG_Toll-like receptor signaling pathway1.430.7KEGG_Primary immunodeficiency1.420.7KEGG_Glutathione metabolismaGene sets related to energy metabolism.1.400.6KEGG_O-glycan biosynthesis−1.660.7KEGG_Selenoamino acid metabolismaGene sets related to energy metabolism.−1.740.7ABC, adenosine triphosphate binding cassette; DKD, diabetic kidney disease; FDR, false discovery rate; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopdia of Genes and Genomes; NES, normalized enrichment score; PPAR, peroxisome proliferator activated receptor.Gene sets with FDR < 0.25 are raised in the table. The gene sets with NES > 0 were upregulated and those with NES < 0 were downregulated in group B (DKD) compared with group A (sham).a Gene sets related to energy metabolism. Open table in a new tab Table 2Results of GSEA in group C (DKD = enarodustat)/B (DKD)KEGG canonical pathway (FDR < 0.25)Gene setsNES−Log10 (FDR)KEGG_Pentose phosphate pathwayaGene sets related to energy metabolism.2.22>10KEGG_RIG-I–like receptor signaling pathway1.881.4KEGG_Glycolysis, gluconeogenesisaGene sets related to energy metabolism.1.881.6KEGG_Fructose and mannose metabolismaGene sets related to energy metabolism.1.791.3KEGG_Primary immunodeficiency1.761.3KEGG_DNA replication1.731.3KEGG_Systemic lupus erythematosus1.701.2KEGG_Complement and coagulation cascades1.661.1KEGG_Cytosolic DNA-sensing pathway1.641.1KEGG_Prion diseases1.601.0KEGG_P53-signaling pathway1.581.0KEGG_Galactose metabolismaGene sets related to energy metabolism.1.581.0KEGG_Cytokine–cytokine receptor interaction1.541.0KEGG_Hematopoietic cell lineage1.541.0KEGG_Toll-like receptor signaling pathway1.521.0KEGG_Cell cycle1.490.9KEGG_Cell adhesion molecules (CAMs)1.410.7KEGG_Progesterone-mediated oocyte maturation1.360.6KEGG_Proximal tubule bicarbonate reclamation–1.470.8KEGG_Retinol metabolism–1.470.8KEGG_Butanoate metabolism–1.490.8KEGG_Spliceosome–1.490.8KEGG_Histidine metabolismaGene sets related to energy metabolism.–1.490.8KEGG_Fatty-acid metabolismaGene sets related to energy metabolism.–1.500.8KEGG_Tyrosine metabolismaGene sets related to energy metabolism.–1.581.1KEGG_One carbon pool by folateaGene sets related to energy metabolism.–1.591.1KEGG_Alanine, aspartate, and glutamate metabolismaGene sets related to energy metabolism.–1.611.1KEGG_Peroxisome–1.641.2KEGG_RNA polymerase–1.641.2KEGG_Propanoate metabolism–1.661.2KEGG_Oxidative phosphorylationaGene sets related to energy metabolism.–1.661.2KEGG_Steroid hormone biosynthesis–1.711.4KEGG_Metabolism of xenobiotics by cytochrome P450–1.751.6KEGG_Pyruvate metabolismaGene sets related to energy metabolism.–1.781.7KEGG_Glycine, serine, and threonine metabolismaGene sets related to energy metabolism.–1.801.8KEGG_Valine, leucine, and isoleucine degradationaGene sets related to energy metabolism.–1.821.8KEGG_Citrate cycle (TCA cycle)aGene sets related to energy metabolism.–1.892.2KEGG_Aminoacyl-tRNA biosynthesis–1.922.4KEGG_Drug metabolism cytochrome P450–1.932.2KEGG_Tryptophan metabolismaGene sets related to energy metabolism.–2.152.9DKD, diabetic kidney disease; FDR, false discovery rate; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopdia of Genes and Genomes; NES, normalized enrichment score; RIG-I, retinoic acid inducible gene I; TCA, tricarboxylic acid; tRNA, transfer RNA.Gene sets with FDR < 0.25 are raised in the table. The gene sets with NES > 0 were upregulated and those with NES < 0 were downregulated in group C (DKD + enarodustat) compared with group B (DKD).a Gene sets related to energy metabolism. Open table in a new tab ABC, adenosine triphosphate binding cassette; DKD, diabetic kidney disease; FDR, false discovery rate; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopdia of Genes and Genomes; NES, normalized enrichment score; PPAR, peroxisome proliferator activated receptor. Gene sets with FDR < 0.25 are raised in the table. The gene sets with NES > 0 were upregulated and those with NES < 0 were downregulated in group B (DKD) compared with group A (sham). DKD, diabetic kidney disease; FDR, false discovery rate; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopdia of Genes and Genomes; NES, normalized enrichment score; RIG-I, retinoic acid inducible gene I; TCA, tricarboxylic acid; tRNA, transfer RNA. Gene sets with FDR < 0.25 are raised in the table. The gene sets with NES > 0 were upregulated and those with NES < 0 were downregulated in group C (DKD + enarodustat) compared with group B (DKD). We measured the absolute concentrations of 116 energy-related metabolites in renal cortical tissue of rats (n = 4, for each group) randomly selected from each group (Supplementary Table S1; Supplementary Figure S3). Partial least squares discriminant analysis (PLS-DA) was performed to assess the significance of class discrimination (Figure 7). We conducted metabolite set enrichment analysis (MSEA) using the metabolites with PLS-DA variable importance in projection (VIP) score ≥ 1. Differences in metabolism of amino acids, such as glycine, serine, methionine, aspartate, glutamate, arginine, proline, and β-alanine between groups A and B were noted. Differences in amino-acid metabolism were also observed between groups B and C. Moreover, glucose metabolism processes, such as glycolysis and gluconeogenesis, showed different trends between groups B and C (Figure 7). We visualized transcriptome and metabolome data for comprehensive understanding of energy metabolism alterations (Figure 8). Glycolysis and TCA cycle metabolites were found to be accumulated in group B compared with group A, which might be due to the excessive glucose inflow and upregulation of fatty-acid metabolism in DKD. Amino acid concentration was reduced in group B compared with group A, reflecting the upregulation of amino-acid metabolism (Figure 8a). In contrast, the accumulation of glycolysis metabolites was relieved by enarodustat, due to the facilitated flow of glycolysis. Enarodustat also reversed diabetes-induced changes in TCA cycle metabolites and amino acids (Figure 8b). Moreover, enarodustat alleviated the accumulation of glutathione disulfide (GSSG) in diabetic renal tissue and thus showed higher glutathione/GSSG ratio, which suggested that enarodustat relieved oxidative stress in DKD (Figure 8a and b). The reduction in oxidative stress was confirmed by the levels of the lipid peroxidation marker malondialdehyde in renal cortical tissue: enarodustat reversed the accumulation of malondialdehyde in diabetic renal cortical tissue (Supplementary Figure S4). In summary, integration of transcriptome and metabolome data has demonstrated that enarodustat counteracts renal energy metabolism alterations occurring in the early stages of DKD. We conducted transcriptome analysis in alloxan-induced diabetic mice, another animal model of diabetes, to confirm our findings in the rat model. Study protocols and background data for the mouse model are shown in Figure 9. Urinary albumin excretion was significantly increased in group B compared with group A, which was reversed by enarodustat (Figure 9d). In addition, we applied comprehensive 3-dimensional analysis (Clear, Unobstructed Brain/Body Imaging Cocktails, and Computational analysis [CUBIC]–kidney)16Hasegawa S. Susaki E.A. Tanaka T. et al.Comprehensive three-dimensional analysis (CUBIC-kidney) visualizes abnormal renal sympathetic nerves after ischemia/reperfusion injury.Kidney Int. 2019; 96: 129-138Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar to visualize glomeruli in the kidney. Glomerulomegaly was noticeable in group B compared with group A, which was reversed by enarodustat (Figure 9e). Transcriptome analysis of renal tissue in alloxan-induced diabetic mice showed symmetric metabolism alterations (diabetes vs. enarodustat) in the same way as in the STZ-induced diabetic rat model (Figure 10): fatty-acid metabolism was upregulated by diabetes, whereas glucose metabolism was upregulated by enarodustat. Furthermore, amino-acid metabolism was upregulated by diabetes and downregulated by enarodustat. Thus, enarodustat counteracted renal energy metabolism alterations occurring in the early stages of DKD in the alloxan-induced diabetic mouse model as well as in the STZ-induced diabetic rat model.Figure 10Transcriptome analysis of renal tissue in alloxan-induced diabetic mice. Symmetric metabolism alterations (diabetes vs. enarodustat) were confirmed by transcriptome analysis of renal tissue in alloxan-induced diabetic mice. (a) Gene ontology (GO) analysis of differentially expressed genes (DEGs) and gene set enrichment analysis (GSEA) in group B (diabetic kidney disease [DKD])/A (sham). DEGs were selected by |log2 fold-change (FC)| ≥ 0.5 and Q value < 0.05 (1085 probes). Fatty-acid and amino-acid metabolisms were upregulated by diabetes. (b) Canonical pathway analysis of DEGs and GSEA in group C (DKD + enarodustat)/B. DEGs were selected as described (65 probes). Glucose metabolism was upregulated, and amino-acid metabolism was downregulated by enarodustat. HIF-1, hypoxia-inducible factor 1; NAD, nicotinamide adenine dinucleotide; NADP, NAD phosphate.View Large Image Figure ViewerDownload Hi-res image Download (PPT) In this study, we have demonstrated that enarodustat (JTZ-951), an oral HIF stabilizer, counteracts renal energy metabolism alterations occurring in the early stages of DKD in rat and mouse models of diabetes. Transcriptome and metabolome analyses have shown symmetric metabolism alterations in renal tissue (diabetes vs. enarodustat): fatty-acid and amino-acid metabolisms were upregulated in DKD, whereas enarodustat downregulated these pathways and additionally upregulated glucose metabolism (Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10). It has not been fully elucidated whether HIF stabilization has protective effects on the pathophysiology of DKD or not. Previous studies have shown that diabetic renal tissue is exposed to hypoxia17Laustsen C. Lycke S. Palm F. et al.High altitude may alter oxygen availability and renal metabolism in diabetics as measured by hyperpolarized [1-(13)C]pyruvate magnetic resonance imaging.Kidney Int. 2014; 86: 67-74Abstract Full Te
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