basic researchwww.kidney-international.org

The oral hypoxia-inducible factor prolylhydroxylase inhibitor enarodustat
counteracts alterations in renal energy
metabolism in the early stages of diabetic
kidney disease

see commentary on page 855
OPEN

Sho Hasegawa1,2, Tetsuhiro Tanaka1, Tomoyuki Saito3, Kenji Fukui1,3, Takeshi Wakashima1,3,
Etsuo A. Susaki4,5, Hiroki R. Ueda4,5,6 and Masaomi Nangaku1
1

Division of Nephrology and Endocrinology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan; 2Research Fellowship for
Young Scientists, Japan Society for the Promotion of Science, Tokyo, Japan; 3Biological and Pharmacological Research Laboratories,
Central Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Japan; 4Department of Systems Pharmacology, The Universityof Tokyo Graduate School of Medicine, Tokyo, Japan; 5Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research,
Suita, Japan; and 6WPI International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study (UTIAS),The University of Tokyo, Tokyo, Japan

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 inrenal tissue by HIF stabilization affects the pathophysiologyof kidney diseases. Previous studies suggest that systemic
metabolic disorders such as hyperglycemia anddyslipidemia 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 earlystages of diabetic kidney disease, using streptozotocininduced 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 aminoacid metabolisms were upregulated in diabetic renal tissueand downregulated by enarodustat, whereas glucose
metabolism was upregulated. These symmetric changes
were conﬁrmed by metabolome analysis. Whereas
glycolysis and tricarboxylic acid cycle metabolites wereaccumulated 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 andrelieved oxidative stress in renal tissue of diabetic animals.
Thus, HIF stabilization counteracts alterations in renal energy
metabolism occurring in incipient diabetic kidney disease.
Correspondence: Tetsuhiro Tanaka or Masaomi Nangaku, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8655, Japan. E-mail: tetsu-tky@umin.ac.jp ormnangaku-tky@umin.ac.jp
Received 15 May 2019; revised 11 November 2019; accepted 5
December 2019; published online 25 December 2019
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Kidney International (2020) 97, 934–950; https://doi.org/10.1016/
j.kint.2019.12.007
KEYWORDS: diabetic kidney disease; energy metabolism; hypoxia-inducible
factor 1; metabolome; prolyl hydroxylase inhibitors; transcriptome
Copyright ª 2020, International Society of Nephrology. Published by
Elsevier Inc. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Translational Statement
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 kidneydisease. Our transcriptome and metabolome analyses ofrenal tissue in rat and mouse diabetic models haverevealed that enarodustat (JTZ-951), an oral HIF stabilizer,
counteracts renal energy metabolism alterations in theearly 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 thisrenal metabolism reprogramming by HIF stabilizers affects the progression of diabetic kidney disease.

ypoxia-inducible factor (HIF) prolyl hydroxylase inhibitors increase endogenous erythropoietin production and serve as novel therapeutic agents against
anemia in chronic kidney disease.1
Cells are endowed with a defensive mechanism against
hypoxia, and HIF is a master regulator of this defense.2,3 Kidneys
are physiologically exposed to hypoxia, and chronic hypoxia isrecognized as a ﬁnal common pathway leading to end-stage
kidney disease.4,5 Considering that HIF induces the expressionof various genes related to hypoxia responses, HIF stabilizers
might have pleiotropic effects on the progression of kidney

H

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S Hasegawa et al.: Effects of HIF stabilization on renal metabolism

diseases as well as improvement in anemia in chronic kidneydisease. 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.6,7 This metabolic
reprogramming from TCA cycle to glycolysis represses oxygen
consumption and is critical for adaptation of cells exposed tohypoxic 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 endstage kidney disease.8 Systemic metabolic disorders such as
hyperglycemia and dyslipidemia cause renal metabolism alterations, leading to renal dysfunction including DKD.9 Previous studies have shown increased metabolic ﬂux andaccumulation of glucose and TCA cycle metabolites in diabetic renal cortical tissue,10,11 which might be related tomitochondrial dysfunction and DKD progression.12 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),13,14 an oral HIF stabilizer, affects renal
metabolism alterations occurring in the early stages of DKD.
RESULTS
Enarodustat induces metabolic reprogramming from TCA
cycle to glycolysis in renal proximal tubule cells

As renal cortex is mainly composed of proximal tubules, weﬁrst 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,15
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 experimentusing small interfering RNA (siRNA) for HIF-1 (Figure 2;
Supplementary Figure S2). HIF-1 knockdown by siRNAreversed 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 importantfactor for cells to use pyruvate to fuel TCA cycle, was also
reduced by enarodustat (Figure 2f), which was compatible
with the previously published observations.6,7

basic researchfrom TCA cycle to glycolysis. We ﬁrst chose streptozotocin(STZ)-induced diabetic rats as the model for the proof-ofconcept experiment to test the above-mentioned hypothesis.
In this model, diabetes is rapidly induced, allowing us toobserve the net effects of diabetes and HIF stabilizers on theenergy 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), andgroup C (DKD þ enarodustat). Blood plasma glucose, glycosylated hemoglobin HbA1c, triglyceride, and total cholesterollevels on day 14 were signiﬁcantly increased in diabetic groups
as compared with group A, whereas there were no signiﬁcantdifferences between groups B and C (Figure 3d–g). Although
plasma creatinine levels were not different between groups,
urinary albumin excretion was signiﬁcantly increased andglomerulomegaly was noticeable in group B as compared withgroup 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, thekidneys of STZ-treated rats in our study represent the earlystages of DKD. Transcriptome and metabolome analyses wereconducted using renal cortical tissue of these rats.
Transcriptome analysis of renal cortical tissue

The results of transcriptome analysis of renal cortical tissue areshown in Figures 5 and 6. Principal component analysis andhierarchical clustering analysis indicated that groups A, B, andC were separated into different clusters, respectively (Figure 5a
and b). Differentially expressed genes were selected by jlog2
fold-change (FC)j $ 0.5 and Q value < 0.05. Gene ontology
and canonical pathway analyses revealed that genes related tofatty-acid metabolism were upregulated in group B comparedwith 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).
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 wereupregulated 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 comparedwith group B, showing that enarodustat reversed metabolism
alterations induced by diabetes (Figure 6). Moreover, gene setsof TCA cycle were downregulated in group C compared withgroup 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.
Metabolome analysis of renal cortical tissue

Background data of streptozotocin-induced diabetic rats

From the results of in vitro experiments, we hypothesized that
HIF stabilizers might alleviate metabolism alterations in diabetic renal cortical tissue through metabolic reprogramming
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We measured the absolute concentrations of 116 energyrelated metabolites in renal cortical tissue of rats (n ¼ 4,for each group) randomly selected from each group(Supplementary Table S1; Supplementary Figure S3). Partial935

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Figure 1 | Enarodustat induces metabolic reprogramming from tricarboxylic acid (TCA) cycle to glycolysis in renal proximal tubule
cells. (a) Study protocols are shown. (b) Mito Stress Test: O2 consumption rates (OCR) were measured in real time under basal conditions andin response to indicated mitochondrial inhibitors (oligomycin, carbonyl cyanide-p-triﬂuoromethoxyphenylhydrazone [FCCP], and rotenone þ
antimycin A [Rot/AA]). The result of basal respiration (TCA cycle) is shown (n ¼ 36 for each group, ****P < 0.0001). (c) Glycolytic Rate Assay:
Glycolytic proton efﬂux rate was measured in real time under basal conditions and in response to Rot/AA (mitochondrial inhibitors)
and 2-deoxy-D-glucose (2-DG, a glycolysis inhibitor). The result of basal glycolysis is shown (n ¼ 36 for each group, *P < 0.05). All data
are expressed as mean  SD. Other parameters including spare respiration capacity and compensatory glycolysis are shown in
Supplementary Figure S1. mM, mmol/l.

least squares discriminant analysis (PLS-DA) was performed
to assess the signiﬁcance of class discrimination (Figure 7).
We conducted metabolite set enrichment analysis (MSEA)
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using the metabolites with PLS-DA variable importance inprojection (VIP) score $ 1. Differences in metabolism ofamino acids, such as glycine, serine, methionine, aspartate,
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Figure 2 | Metabolic 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 byenarodustat 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-ptriﬂuoromethoxyphenylhydrazone [FCCP], and rotenone þ 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 ¼ 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 ¼ 8, **P < 0.01, ****P < 0.0001). All data are expressed as mean  SD. mM, mmol/l; siNC, siRNA negative control. To optimize viewing of
this image, please see the online version of this article at www.kidney-international.org.

glutamate, arginine, proline, and b-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).
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Visualization of transcriptome and metabolome data on theenergy metabolic pathway map

We visualized transcriptome and metabolome data forcomprehensive understanding of energy metabolism alterations (Figure 8). Glycolysis and TCA cycle metabolites
were found to be accumulated in group B compared with937

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Figure 3 | Study protocols and basic data of streptozotocin (STZ)-induced diabetic rats. (a) Study protocols of streptozotocin-induced
diabetic rat experiments are shown. (b) Transition of the body weights are shown. Body weights were not signiﬁcantly different between
group B (diabetic kidney disease [DKD]) and group C (DKD þ enarodustat). (c) Hemoglobin (Hb) levels, (d) blood glucose levels, (e)glycosylated hemoglobin HbA1c levels, (f) triglyceride levels, and (g) total cholesterol levels on day 14 are shown. For multiplex comparisons,
1-way analysis of variance was applied, followed by the Tukey multiple comparisons test, if appropriate (**P < 0.01, ****P < 0.0001). Alldata are expressed as mean  SD.

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
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compared with group A, reﬂecting the upregulation ofamino-acid metabolism (Figure 8a). In contrast, the accumulation of glycolysis metabolites was relieved byKidney International (2020) 97, 934–950

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Figure 4 | Basic 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 wasmarkedly 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 ¼ 100 mm. (Lower) Electron microscopy images.
Glomerular basement membrane thickening was noticeable in group B. Bar ¼ 1 mm. 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.

enarodustat, due to the facilitated ﬂow of glycolysis. Enarodustat also reversed diabetes-induced changes in TCA
cycle metabolites and amino acids (Figure 8b). Moreover,
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enarodustat alleviated the accumulation of glutathione disulﬁde (GSSG) in diabetic renal tissue and thus showed
higher glutathione/GSSG ratio, which suggested that939

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Figure 5 | Transcriptome analysis of renal cortical tissue in streptozotocin-induced diabetic rats. (a) Principal component (PC) analysis
scores plot and (b) hierarchical clustering analysis show that groups A (sham), B (diabetic kidney disease [DKD]), and C (DKD þ enarodustat)
are separated to respectively different clusters. (c) Gene ontology (GO) and canonical pathway analysis of differentially expressed genes(DEGs) in group B/A; DEGs were selected by |log2 fold-change (FC)| $ 0.5 and Q value < 0.05 (1280 probes; 844 genes). Pathways related
to energy metabolism are highlighted in red. (d) GO and canonical pathway analysis of DEGs in group C/B; DEGs were selected as
described (167 probes; 114 genes). Pathways related to energy metabolism are highlighted in red. Pathways related to hypoxia response arehighlighted in green. HIF-1, hypoxia-inducible factor 1; PPAR, peroxisome proliferator–activated receptor.

enarodustat relieved oxidative stress in DKD (Figure 8a andb). The reduction in oxidative stress was conﬁrmed by thelevels of the lipid peroxidation marker malondialdehyde in940

renal cortical tissue: enarodustat reversed the accumulationof malondialdehyde in diabetic renal cortical tissue(Supplementary Figure S4). In summary, integration of
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Figure 6 | Gene 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 þ enarodustat) compared withgroup B. Detailed results of GSEA are shown in Tables 1 and 2.

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Table 2 | Results of GSEA in group C (DKD [ enarodustat)/B(DKD)

Table 1 | Results of GSEA in group B (DKD)/A (sham)
KEGG canonical pathway (FDR < 0.25)
Gene sets
KEGG_PPAR signaling pathway
KEGG_Complement and coagulation cascades
KEGG_Drug metabolism cytochrome P450
KEGG_Metabolism of xenobiotics by cytochrome
P450
KEGG_P53 signaling pathway
KEGG_Porphyrin and chlorophyll metabolism
KEGG_Bladder cancer
KEGG_Tyrosine metabolisma
KEGG_ABC transporters
KEGG_Adipocytokine signaling pathway
KEGG_Biosynthesis of unsaturated fatty acidsa
KEGG_Drug metabolism other enzymes
KEGG_Fatty-acid metabolisma
KEGG_Prion diseases
KEGG_Alanine, aspartate, and glutamate
metabolisma
KEGG_Cytokine–cytokine receptor interaction
KEGG_Valine, leucine, and isoleucine degradationa
KEGG_Chronic myeloid leukemia
KEGG_Cell cycle
KEGG_ErbB signaling pathway
KEGG_Starch and sucrose metabolisma
KEGG_Peroxisome
KEGG_Toll-like receptor signaling pathway
KEGG_Primary immunodeﬁciency
KEGG_Glutathione metabolisma
KEGG_O-glycan biosynthesis
KEGG_Selenoamino acid metabolisma

NES-Log10
(FDR)

2.26
2.22
2.00
1.98

>10
>10
2.3
2.3

1.82
1.79
1.66
1.62
1.61
1.59
1.58
1.56
1.56
1.55
1.52

1.4
1.3
0.9
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.7

1.51
1.51
1.48
1.47
1.44
1.44
1.43
1.43
1.42
1.40
1.66
1.74

0.7
0.7
0.7
0.7
0.6
0.7
0.7
0.7
0.7
0.6
0.7
0.7

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 wereupregulated and those with NES < 0 were downregulated in group B (DKD)
compared with group A (sham).
aGene sets related to energy metabolism.

transcriptome and metabolome data has demonstrated thatenarodustat counteracts renal energy metabolism alterations
occurring in the early stages of DKD.
Symmetric metabolism alterations (diabetes vs. enarodustat)
are conﬁrmed in an alternative model

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 ingroup B compared with group A, which was reversed byenarodustat (Figure 9d). In addition, we applied comprehensive 3-dimensional analysis (Clear, Unobstructed Brain/
Body Imaging Cocktails, and Computational analysis [CUBIC]–kidney)16 to visualize glomeruli in the kidney. Glomerulomegaly was noticeable in group B compared withgroup A, which was reversed by enarodustat (Figure 9e).
Transcriptome analysis of renal tissue in alloxan-induced
diabetic mice showed symmetric metabolism alterations
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KEGG canonical pathway (FDR < 0.25)
Gene sets

NES

LLog10(FDR)

KEGG_Pentose phosphate pathwaya
KEGG_RIG-I–like receptor signaling pathway
KEGG_Glycolysis, gluconeogenesisa
KEGG_Fructose and mannose metabolisma
KEGG_Primary immunodeﬁciency
KEGG_DNA replication
KEGG_Systemic lupus erythematosus
KEGG_Complement and coagulation cascades
KEGG_Cytosolic DNA-sensing pathway
KEGG_Prion diseases
KEGG_P53-signaling pathway
KEGG_Galactose metabolisma
KEGG_Cytokine–cytokine receptor interaction
KEGG_Hematopoietic cell lineage
KEGG_Toll-like receptor signaling pathway
KEGG_Cell cycle
KEGG_Cell adhesion molecules (CAMs)
KEGG_Progesterone-mediated oocyte maturation
KEGG_Proximal tubule bicarbonate reclamation
KEGG_Retinol metabolism
KEGG_Butanoate metabolism
KEGG_Spliceosome
KEGG_Histidine metabolisma
KEGG_Fatty-acid metabolisma
KEGG_Tyrosine metabolisma
KEGG_One carbon pool by folatea
KEGG_Alanine, aspartate, and glutamate
metabolisma
KEGG_Peroxisome
KEGG_RNA polymerase
KEGG_Propanoate metabolism
KEGG_Oxidative phosphorylationa
KEGG_Steroid hormone biosynthesis
KEGG_Metabolism of xenobiotics by cytochrome
P450
KEGG_Pyruvate metabolisma
KEGG_Glycine, serine, and threonine metabolisma
KEGG_Valine, leucine, and isoleucine degradationa
KEGG_Citrate cycle (TCA cycle)a
KEGG_Aminoacyl-tRNA biosynthesis
KEGG_Drug metabolism cytochrome P450
KEGG_Tryptophan metabolisma

2.22
1.88
1.88
1.79
1.76
1.73
1.70
1.66
1.64
1.60
1.58
1.58
1.54
1.54
1.52
1.49
1.41
1.36
–1.47
–1.47
–1.49
–1.49
–1.49
–1.50
–1.58
–1.59
–1.61

>10
1.4
1.6
1.3
1.3
1.3
1.2
1.1
1.1
1.0
1.0
1.0
1.0
1.0
1.0
0.9
0.7
0.6
0.8
0.8
0.8
0.8
0.8
0.8
1.1
1.1
1.1

–1.64
–1.64
–1.66
–1.66
–1.71
–1.75

1.2
1.2
1.2
1.2
1.4
1.6

–1.78
–1.80
–1.82
–1.89
–1.92
–1.93
–2.15

1.7
1.8
1.8
2.2
2.4
2.2
2.9

DKD, diabetic kidney disease; FDR, false discovery rate; GSEA, gene set enrichment
analysis; KEGG, Kyoto Encyclopdia of Genes and Genomes; NES, normalizedenrichment 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 wereupregulated and those with NES < 0 were downregulated in group C (DKD þ
enarodustat) compared with group B (DKD).
a
Gene sets related to energy metabolism.

(diabetes vs. enarodustat) in the same way as in the
STZ-induced diabetic rat model (Figure 10): fatty-acidmetabolism was upregulated by diabetes, whereas glucose
metabolism was upregulated by enarodustat. Furthermore,amino-acid metabolism was upregulated by diabetes anddownregulated by enarodustat. Thus, enarodustat counteracted renal energy metabolism alterations occurring in theearly stages of DKD in the alloxan-induced diabetic mousemodel as well as in the STZ-induced diabetic rat model.
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Figure 7 | Partial least squares discriminant analysis (PLS-DA) and metabolite set enrichment analysis (MSEA) using the metabolome
data. The absolute concentrations of 116 energy-related-metabolites were measured in renal cortical tissue of rats (n ¼ 4, for each group)
randomly selected from each group (see Supplementary Figure S3). (a) PLS-DA scores plot of group B (diabetic kidney disease [DKD])/A(sham) is shown on the left side. MSEA was conducted using metabolites with the PLS-DA variable importance in projection (VIP) score $ 1(51 metabolites, Supplementary Table S2). The MSEA results (shown on the right side) suggest changes in amino-acid metabolism(highlighted in green). (b) PLS-DA scores plot of group C (DKD þ enarodustat)/B is shown on the left side. MSEA was conducted using
metabolites with the PLS-DA VIP score $ 1 (43 metabolites, Supplementary Table S3). The MSEA results (shown on the right side) suggest
changes in glycolysis and gluconeogenesis (highlighted in brown) and in amino-acid metabolism (highlighted in green). FDR, false
discovery rate.

DISCUSSION

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 mousemodels of diabetes. Transcriptome and metabolome analyses
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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 (Figures 5–10).
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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 hypoxia17 and HIF expression in diabetic renal
tissue is insufﬁcient in responding to their hypoxic conditions, which is partially caused by oxidative stress.18 HIF
stabilization by cobalt chloride improved oxidative stressstatus and reduced proteinuria and tubulointerstitial damage
in STZ-induced DKD.19 Animal study results also indicatedthat HIF stabilization protected against the development ofobesity,20 improved insulin sensitivity,20 and lowered serum
cholesterol levels.21 These results suggest the protective effectsof HIF stabilization in the development of DKD. In contrast,
supraphysiologic HIF stabilization by von Hippel-Lindau
deletion was reported to induce renal ﬁbrosis.22 The role of
HIF stabilization in DKD progression may be contextdependent given the pleiotropic effects of HIF and the existence of varied DKD phenotypes.
The aim of this study was to clarify the net effects of HIF
stabilization on energy metabolism in diabetic kidney. Previous reports have indicated that energy metabolism alterations occur in DKD. Sas et al.10 showed increased energy
metabolic ﬂux and accumulation of glucose metabolites indiabetic renal cortical tissue, which might be related tomitochondrial dysfunction. You et al.23 reported that fumarate, a TCA cycle metabolite, was accumulated in diabetic
renal tissue and it directly contributed to the progression of
DKD. We have previously shown that TCA cycle metabolites
accumulated in diabetic renal tissue, which was reversed bysodium–glucose cotransporter 2 inhibition or calorie restriction.11 Moreover, Qi et al.24 showed that enzymes inglycolytic pathways including pyruvate kinase M2 (PKM2),
were upregulated in type 1 diabetic patients without DKD
compared with patients with DKD. PKM2 activation reversed
the accumulation of glycolysis metabolites and restored
mitochondrial function, partially by increasing glycolytic
ﬂux.24,25 These previous reports suggest that accumulation of
glycolysis and TCA cycle metabolites might affect the progression of DKD, which can be mitigated by facilitatedglycolysis including PKM2 activation.

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In our study, HIF stabilization by enarodustat reversed
energy metabolism alterations and mitigated the accumulation of glycolysis and TCA cycle metabolites in diabetic renal
cortical tissue (Figure 8). The expression of glycolytic enzymes including PKM2 was also upregulated by enarodustat(Supplementary Figure S4B). Moreover, enarodustat alleviated GSSG accumulation and increased the glutathione/GSSG
ratio, which suggests that enarodustat relieved the oxidative
stress in DKD (Figure 8). This enarodustat-induced reductionin oxidative stress was also conﬁrmed by changes in malondialdehyde levels in renal cortical tissue (Supplementary
Figure S4A). These results suggest that HIF stabilization
should have protective roles on the pathophysiology of DKD
at least from metabolic perspectives.
It is certain that we cannot examine whether the metabolic
reprogramming by HIF stabilization has direct effects on
DKD progression, because it may be impossible to discern the
net effects of metabolism alterations on the renal outcome
due to the pleiotropic role of HIF. However, in our study,
mild urinary albumin excretion and renal pathological abnormalities (glomerulomegaly and glomerular basement
membrane thickening) induced by diabetes were mitigated byenarodustat, in association with the normalization of renalenergy metabolism. We extracted the genes whose expression
changes correlated with urinary albumin levels (257 probes;
232 genes) from the microarray data in alloxan-induced
diabetic mouse model and conducted pathway enrichment
analysis (Supplementary Figure S5). As a result, pathways
related to mitochondrial membrane and respiratory electrontransport are upregulated associated with urinary albumin
levels, indicating that mitochondrial burden is closely related
to urinary albumin excretion. Thus, there is a possibility thatthe metabolic normalization of diabetic renal tissue by HIF
stabilizers might reduce the mitochondrial burden and mitigate the progression of DKD. Our data suggest the pathophysiology of DKD from metabolic perspectives as follows:
Hyperglycemia creates energy demand for glucose reabsorption in renal proximal tubules; thus, TCA cycle in mitochondria is forcibly activated to meet the energy demand in
the early stages of DKD (upregulation of fatty-acid and=Figure 8 | Visualization of transcriptome and metabolome data on the energy metabolic pathway map. (a) Transcriptome andmetabolome data of group B (diabetic kidney disease [DKD])/A (sham) are visualized (diabetes-induced changes). Accumulation of glycolysis
and tricarboxylic acid (TCA) cycle metabolites, amino-acid consumption, and glutathione disulﬁde (GSSG) accumulation can be recognizedin group B (DKD) compared with group A (sham). (b) Transcriptome and metabolome data of group C (DKD þ enarodustat)/B are visualized(hypoxia-inducible factor [HIF] stabilizer-induced changes). Metabolic reprogramming by enarodustat counteracted the diabetes-induced
metabolism alterations in renal tissue. ACAA1 (2), acetyl-CoA acyltransferase 1 (2); AcCoA, acetyl coenzyme A; ACO1, aconitase 1; Ala, alanine;
ALDOA (C), aldolase, fructose-bisphosphate A (C); AOX1, aldehyde oxidase 1; ASGL1, L-asparaginase; Asn, asparagine; ASNS, asparagine
synthetase; Asp, aspartate; BPG, bisphosphoglycerate; CNDP1, carnosine dipeptidase 1; CoA, coenzyme A; Cys, cysteine; DHAP,
dihydroxyacetone phosphate; DMG, dimethylglycine; EHHADH, enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase; ENO1, enolase 1;
FBP1, fructose-1,6-biphosphatase 1; FC, fold-change; GCLC, glutamate-cysteine ligase catalytic subunit; Gln, glutamine; Glu, glutamate; Gly,
glycine; GNMT, glycine N-methyltransferase; GPI, glucose-6-phosphate isomerase; GPT, glutamic–pyruvic transaminase; GPX1 (2), glutathione
peroxidase 1 (2); GSH, glutathione; GSS, glutathione synthetase; His, histidine; KIV, alpha-ketoisovaleric acid; ME1, malic enzyme 1; OG,
oxoglutarate; PCK1, phosphoenolpyruvate carboxykinase 1; PDK1, pyruvate dehydrogenase kinase 1; PDH, pyruvate dehydrogenase; PEP,
phosphoenolpyruvate; PFKL, phosphofructokinase, liver type; PFKP, phosphofructokinase, platelet; PG, prostaglandin; PHGDH, phosphoglycerate
dehydrogenase; PIPOX, pipecolic acid and sarcosine oxidase; PKLR, pyruvate kinase L/R; PSAT1, phosphoserine aminotransferase 1; PSPH,
phosphoserine phosphatase; SDS, serine dehydratase; Ser, serine; SucCoA, succinyl coenzyme A; Tyr, tyrosine; Val, valine.
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S Hasegawa et al.: Effects of HIF stabilization on renal metabolism

amino-acid metabolism). And HIF stabilizers might mitigate
this mitochondrial burden by the metabolic reprogrammingfrom TCA cycle to glycolysis (downregulation of fatty-acidand amino-acid metabolism and upregulation of glycolysis),which might have protective roles against DKD progression.
Further studies including single-cell omics are needed toclarify how the mitochondrial burden in renal tissue directly
affects the pathophysiology of DKD.
Another interesting question is whether HIF stabilizers
directly affect the glucose absorption in proximal tubules.
Our in vitro data showed that ATP production is signiﬁcantly reduced by enarodustat through metabolic reprogramming from TCA cycle to glycolysis (Figures 1 and 2).
Considering that ATP is required for glucose reabsorption
by sodium–glucose cotransporter 2, we ﬁrst hypothesizedthat glucose reabsorption might be inactivated by enarodustat. However, urinary glucose levels were not signiﬁcantly different between group B and group C in bothanimal models (Supplementary Figure S6). Probably, thereduction in ATP production by enarodustat is much
milder in a physiological situation compared with its effectin vitro; thus, HIF stabilization does not affect glucose absorption through sodium–glucose cotransporter 2, at leastin our animal experiments.
Our study had 2 limitations. First, we utilized STZinduced diabetic rats and alloxan-induced diabetic mice as
early-stage DKD models and observed the short-term effects
of diabetic conditions. However, in the clinical setting, HIF
stabilizers would be administered to patients with anemia inlate-stage DKD. More studies are needed to understand theeffect of HIF stabilization on renal metabolism alterations inlate-stage DKD. Second, the study lacked sufﬁcient statisticalpower with respect to the dispersion of metabolites’ concentrations in the metabolome data, whereas the transcriptome data had statistical power large enough to clarify
the whole picture of energy metabolism. Because not many
metabolites showed signiﬁcant difference between groups, we
conducted PLS-DA and selected the metabolites with PLS-DA
VIP score $ 1 for MSEA. Although it is difﬁcult to crossverify a metabolic change using metabolomics data alone,
the results of metabolome analysis were compatible with thetranscriptomic data (Figures 7 and 8), providing additional
support for the observed effects of transcriptome alterations
on renal energy metabolism.
In conclusion, enarodustat (JTZ-951), an oral HIF stabilizer, counteracts renal energy metabolism alterations in thebasic research

early stages of DKD. Our study suggests that HIF stabilization
may serve as a potential intervention that targets the dysregulated renal energy metabolism in DKD.
METHODS
Mito Stress Test and Glycolytic Rate Assay
HK-2 cells15 were seeded onto a 96-well microplate at a density of1  104 cells/well. On the next day, metabolic ﬂux was measured inreal time by Seahorse XFe96 Analyzer (Agilent) using Mito Stress
Test Kit (103015-100; Agilent) or Glycolytic Rate Assay Kit (103344100; Agilent). Concentrations of glucose, pyruvate, and glutamine inculture media were 10 mmol/l, 1 mmol/l, and 2 mmol/l, respectively.
siRNA transfection
HIF-1 knockdown was conducted by Stealth RNAi for human
HIF1A (HSS104774 [#1] and HSS104775 [#2]; Thermo Fisher Scientiﬁc, Waltham, MA). Stealth RNAi siRNA Negative Control Med
GC Duplex #2 (12935112; Thermo Fisher Scientiﬁc) was used as thenegative control.
Western blotting
Primary antibodies used for staining were anti-human HIF-1a
antibody (rabbit polyclonal, 1:500; Novus Biologicals, Littleton, CO)and anti-human actin antibody (rabbit polyclonal, 1:1000; SigmaAldrich, St. Louis, MO). Secondary antibody was the horseradish
peroxidase-conjugated goat anti-rabbit IgG antibody (170-6515,
1:5000; Bio-Rad Laboratories, Hercules, CA).
Animal experiments
Crl:CD (Sprague Dawley) rats and Crl:CD1 (Institute of Cancer
Research) mice were obtained from Charles River Laboratories Japan
Inc. (Yokohama, Japan). All experiments were approved by the
University of Tokyo Institutional Review Board (approval number
P17-110). All animal procedures were performed according to the
National Institutes of Health guidelines (Guide for the Care and Useof the Laboratory Animals). Study protocols are shown in Figures 3
and 9.
Transcriptome analysis
Total RNA from renal cortical tissue was isolated using GenElute
Mammalian Total RNA Miniprep Kit (RTN70; Sigma-Aldrich). Total
RNA (100 ng) was labeled using Low Input Quick Amp Labeling Kit(Agilent) and then hybridized to SurePrint G3 Rat GE v2 8x60K
Microarray (for rat; Agilent) and SurePrint G3 Mouse GE v2 8x60K
Microarray (for mouse), respectively. All microarray experiments
were performed by DNA Chip Research Inc. (Tokyo, Japan). Raw
data were processed with the R package limma in Bioconductor(http://www.bioconductor.org/) to perform background correction
and data normalization using the quantile normalization method.
Batch effects were removed by ComBat26,27 (Bioconductor) in the rat=

Figure 9 | Study protocols, background data, and 3-dimensional pathologies of alloxan-induced diabetic mice. (a) Study protocols of
experiments in alloxan-induced diabetic mice are shown. (b) Transition of the body weights are shown. Body weights were not signiﬁcantly
different between group B (diabetic kidney disease [DKD]) and group C (DKD þ enarodustat). (c) Blood glucose, glycosylated hemoglobin
HbA1c, triglyceride, and total cholesterol levels on day 16 are shown. (d) Blood urea nitrogen (BUN), plasma creatinine (Cre), and urinary
albumin levels on day 16 are shown. Urinary albumin excretion was signiﬁcantly increased in group B compared with group A (sham),which was reversed by enarodustat. (e) Comprehensive 3-dimensional imaging (Clear, Unobstructed Brain/Body Imaging Cocktails, andComputational analysis [CUBIC]–kidney) of glomeruli is shown. Glomerulomegaly was noticeable in group B compared with group A, which
was reversed by enarodustat. All data are expressed as mean  SD. For multiplex comparisons, 1-way analysis of variance (ANOVA) wasapplied, followed by the Tukey multiple comparisons test, if appropriate (*P < 0.05, **P < 0.01, ****P < 0.0001). To optimize viewing of thisimage, please see the online version of this article at www.kidney-international.org.
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Figure 10 | Transcriptome 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 ofdifferentially 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 wereupregulated by diabetes. (b) Canonical pathway analysis of DEGs and GSEA in group C (DKD þ enarodustat)/B. DEGs were selected as
described (65 probes). Glucose metabolism was upregulated, and amino-acid metabolism was downregulated by enarodustat. HIF-1, hypoxiainducible factor 1; NAD, nicotinamide adenine dinucleotide; NADP, NAD phosphate.

experiment. Differentially expressed genes were determined by jlog2
FCj $ 0.5 and Q value < 0.05. Gene ontology and canonical pathway
analyses were conducted using BaseSpace Correlation Engine
948(Illumina, San Diego, CA). Microarray data have been deposited in
the National Center for Biotechnology Information Gene Expression
Omnibus as series GSE131221 (rat) and GSE139317 (mouse).
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Gene Set Enrichment Analysis
GSEA is a computational method that determines whether an apriori–deﬁned set of genes shows statistically signiﬁcant, concordantdifferences between 2 biological states.28,29 GSEA was performed
using GSEA v.3.0 (Broad Institute, Cambridge, MA). The pathways
with a false discovery rate < 0.25 were considered signiﬁcant.
Metabolome analysis
Metabolome measurements were performed by Human Metabolome
Technologies Inc. (Tsuruoka, Japan).30,31 Metabolite concentrations
were calculated by normalizing the peak area of each metabolite withrespect to the area of the internal standard and by using standard
curves, which were obtained from 3-point calibrations. See also the
Supplementary Methods.
Metabolite set enrichment analysis
The analysis of metabolomics data was conducted using the integrated web-based platform MetaboAnalyst 4.0.32 PLS-DA was performed and associated VIP scores were calculated. The metabolites
with PLS-DA VIP score $ 1 were used for the MSEA.
Quantiﬁcation of glomerular areas in renal pathologies
We randomly took 3 pathological pictures from each sample’s periodic acid–Schiff staining image and measured every glomerulararea respectively using Fiji (a distribution of ImageJ; National Institutes of Health, Bethesda, MD).33
CUBIC-kidney
Comprehensive 3-dimensional imaging of glomeruli in renal tissue
was performed using CUBIC according to our previous
papers.16,34–36 In brief, ﬁxed mouse kidneys were immersed in
CUBIC-L for delipidation and then subjected to immunoﬂuorescent staining. Finally, the refractive index was matched by theplacement of the samples in CUBIC-Rþ.16 Comprehensive 3D–
dimensional images from transparent kidneys were acquired with acustom-built light-sheet ﬂuorescence microscopy (MVX10-LS;
Olympus, Tokyo, Japan).37,38 Primary antibody used for staining
was anti-podocin antibody (rabbit polyclonal, 1:100, P0372;
Sigma-Aldrich). Secondary antibody was Alexa Flour 555conjugated donkey anti-rabbit IgG (1:100, A-31572; Invitrogen,
Thermo Fisher Scientiﬁc).
Statistical analysis
For multiplex comparisons, 1-way analysis of variance followed by thepost hoc Tukey multiple comparisons test, if appropriate, was applied.
All statistical analyses were performed with GraphPad Prism 7 software (GraphPad Software, San Diego, CA). P < 0.05 was considered
statistically signiﬁcant. Data are presented as the mean  SD.
DISCLOSURE
MN has received honoraria, advisory fees, or research funding from KyowaHakko-Kirin, Akebia, Astellas, Chugai, GlaxoSmithKline, Japan Tobacco Inc. (JT),Torii, Tanabe-Mitsubishi, Daiichi-Sankyo, Takeda, Ono, Bayer, Boehringer
Ingelheim and Alexion. TT has received a research grant from JT. Enarodustat(JTZ-951) was provided by JT, Tokyo, Japan. EAS and HRU are coinventors ofpatents owned by RIKEN. Part of this study was done in collaboration with
Olympus Corporation and kind software supports by Bitplane. The other author
declared no competing interests.

ACKNOWLEDGMENTS

We are grateful to Dr. Shuhei Yao and Dr. Shinji Tanaka for valuable
discussions. We also thank Dr. Tsuyoshi Inoue, Dr. Satoru Fukuda, and
Ms. Kahoru Amitani for their technical support. This work was partially
carried out under the support of Isotope Science Center, theKidney International (2020) 97, 934–950

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University of Tokyo. This work was supported by Grant-in-Aid forJapan Society for the Promotion of Science (JSPS) Research Fellow(JSPS KAKENHI grant 19J11928 to SH), Grant-in-Aid for ScientiﬁcResearch (B) (JSPS KAKENHI grant 18H02824 to MN), Grant-in-Aid for
Scientiﬁc Research (C) (JSPS KAKENHI grant 17K09688 to TT), andGrant-in-Aid for Scientiﬁc Research on Innovative Areas (KAKENHI
grant 26111003 to MN).

SUPPLEMENTARY MATERIAL
Supplementary Methods.
Figure S1. Additional parameters of Mito Stress Test and Glycolytic
Rate Assay. (A) The results of maximal respiration, spare respiratory
capacity and ATP production measured by Mito Stress Test are shown(n ¼ 36 for each group, ****P < 0.0001). (B) Compensatory glycolysis
measured by Glycolytic Rate Assay is shown (n ¼ 36 for each group,****P < 0.0001). All data are expressed as mean  SD.
Figure S2. Mito Stress Test with another siRNA (#2) for hypoxiainducible factor-1 (HIF-1). (A) The experiments using another siHIF-1(#2) showed the same results as with siHIF-1 (#1) illustrated in
Figure 2. O2 consumption rates (OCR) were measured in real time
under basal conditions and in response to indicated mitochondrial
inhibitors (oligomycin, FCCP, and Rot/AA). (B) HIF-1 knockdown
reversed decreases in OCR parameters (basal respiration, maximal
respiration, spare respiratory capacity and ATP production) induced
by enarodustat (n ¼ 18 for each group, ****P < 0.0001). All data areexpressed as mean  SD.
Figure S3. The absolute concentration of 116 metabolites related toenergy metabolism in renal cortical tissue. The absoluteconcentrations of 116 energy-related-metabolites in renal corticaltissue of rats’ experiments were shown. All data are expressed asmean  SD.
Figure S4. Additional data from streptozotocin-induced diabetic rats.
(A) Malondialdehyde (MDA) levels in renal cortical tissue are shown;
MDA is a lipid peroxidation marker that indicates the degree ofoxidative stress. MDA level was increased in diabetic renal corticaltissue and enarodustat reversed it. (B) Quantitative polymerase chain
reaction (qPCR) data of pyruvate kinase muscle isozyme M2 (PKM2)
are shown; enarodustat signiﬁcantly increased the level of PKM2mRNA in diabetic renal tissue. For multiplex comparisons, 1-way
analysis of variance (ANOVA) was applied, followed by the Tukey
multiple comparisons test, if appropriate (*P < 0.05, **P < 0.01,***P < 0.001). All data are expressed as mean  SD.
Figure S5. Pathway enrichment analysis using genes correlated withurinary albumin levels in alloxan-induced mouse model. We extracted
the genes whose expression levels correlated with urinary albumin
levels (Pearson correlation coefﬁcient: jrj > 0.5) from the microarraydata in alloxan induced diabetic mouse model (257 probes; 232
genes). The results of pathway enrichment analysis using theextracted genes are shown. Genes related to the mitochondrial
membrane and respiratory electron transport (highlighted in red)
were upregulated.
Figure S6. Urinary glucose levels in rat and mouse animal models
of diabetes. (A) Urinary glucose levels of streptozotocin (STZ)induced diabetic rats are shown. (B) Urinary glucose levels ofalloxan-induced diabetic mice are shown. Enarodustat did not alter
urinary glucose levels. For multiplex comparisons, 1-way analysis ofvariance (ANOVA) was applied, followed by the Tukey multiple
comparisons test, if appropriate (****P < 0.0001). All data areexpressed as mean  SD.
Table S1. The absolute concentrations of 116 energy-relatedmetabolites in renal cortical tissue. The absolute concentrations of
116 energy-related-metabolites in renal cortical tissue of rats’ experiments are shown. N.D., not detected.
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Table S2. Metabolites for metabolite set enrichment analysis (MSEA)
between group A (sham) and B (DKD). Metabolites were selected bypartial least squares discriminant analysis (PLS-DA) variable
importance in projection (VIP) score $ 1.
Table S3. Metabolites for metabolite set enrichment analysis (MSEA)
between group B (DKD) and C (DKD þ enarodustat). Metabolites wereselected by partial least squares discriminant analysis (PLS-DA)
variable importance in projection (VIP) score $ 1.
Supplementary material is linked to the online version of the paper at
www.kidney-international.org.

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