Repression of hypoxia-inducible factor-1 contributes to increased mitochondrial reactive oxygen species production in diabetes

  1. Xiaowei Zheng
  2. Sampath Narayanan
  3. Cheng Xu
  4. Sofie Eliasson Angelstig
  5. Jacob Grünler
  6. Allan Zhao
  7. Alessandro Di Toro
  8. Luciano Bernardi
  9. Massimiliano Mazzone
  10. Peter Carmeliet
  11. Marianna Del Sole
  12. Giancarlo Solaini
  13. Elisabete A Forsberg
  14. Ao Zhang
  15. Kerstin Brismar
  16. Tomas A Schiffer
  17. Neda Rajamand Ekberg
  18. Ileana Ruxandra Botusan
  19. Fredrik Palm
  20. Sergiu-Bogdan Catrina  Is a corresponding author
  1. Department of Molecular Medicine and Surgery, Karolinska Institutet, Sweden
  2. Centre for Inherited Cardiovascular Diseases, IRCCS Foundation University Hospital Policlinico San Matteo, Italy
  3. Folkälsan Research Center, Folkälsan Institute of Genetics, University of Helsinki, Finland
  4. Laboratory of Tumor Inflammation and Angiogenesis, Center for Cancer Biology, Vlaams Instituut voor Biotechnologie (VIB); Laboratory of Tumor Inflammation and Angiogenesis, Center for Cancer Biology, Department of Oncology, Katholieke Universiteit (KU) Leuven, Belgium
  5. Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, Katholieke Universiteit (KU) Leuven; Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, Vlaams Instituut voor Biotechnologie (VIB), Belgium
  6. Dipartimento di Biochimica, Università di Bologna, Italy
  7. Department of Medical Cell Biology, Uppsala University, Sweden
  8. Department of Endocrinology and Diabetes, Karolinska University Hospital, Sweden
  9. Center for Diabetes, Academic Specialist Centrum, Sweden

Abstract

Background:

Excessive production of mitochondrial reactive oxygen species (ROS) is a central mechanism for the development of diabetes complications. Recently, hypoxia has been identified to play an additional pathogenic role in diabetes. In this study, we hypothesized that ROS overproduction was secondary to the impaired responses to hypoxia due to the inhibition of hypoxia-inducible factor-1 (HIF-1) by hyperglycemia.

Methods:

The ROS levels were analyzed in the blood of healthy subjects and individuals with type 1 diabetes after exposure to hypoxia. The relation between HIF-1, glucose levels, ROS production and its functional consequences were analyzed in renal mIMCD-3 cells and in kidneys of mouse models of diabetes.

Results:

Exposure to hypoxia increased circulating ROS in subjects with diabetes, but not in subjects without diabetes. High glucose concentrations repressed HIF-1 both in hypoxic cells and in kidneys of animals with diabetes, through a HIF prolyl-hydroxylase (PHD)-dependent mechanism. The impaired HIF-1 signaling contributed to excess production of mitochondrial ROS through increased mitochondrial respiration that was mediated by Pyruvate dehydrogenase kinase 1 (PDK1). The restoration of HIF-1 function attenuated ROS overproduction despite persistent hyperglycemia, and conferred protection against apoptosis and renal injury in diabetes.

Conclusions:

We conclude that the repression of HIF-1 plays a central role in mitochondrial ROS overproduction in diabetes and is a potential therapeutic target for diabetic complications. These findings are timely since the first PHD inhibitor that can activate HIF-1 has been newly approved for clinical use.

Funding:

This work was supported by grants from the Swedish Research Council, Stockholm County Research Council, Stockholm Regional Research Foundation, Bert von Kantzows Foundation, Swedish Society of Medicine, Kung Gustaf V:s och Drottning Victorias Frimurarestifelse, Karolinska Institute’s Research Foundations, Strategic Research Programme in Diabetes, and Erling-Persson Family Foundation for S-B.C.; grants from the Swedish Research Council and Swedish Heart and Lung Foundation for T.A.S.; and ERC consolidator grant for M.M.

Editor's evaluation

The paper is novel, informative, and with interesting translational implications. This paper will be of interest to scientists interested in diabetes and its complications, as well as the wider field of hypoxia biology. It provides evidence to understand why diabetes causes damage to multiple tissues when oxygen supply becomes limited.

https://doi.org/10.7554/eLife.70714.sa0

Introduction

Excessive production of mitochondrial ROS is a key contributor to oxidative stress, which is a major cause of diabetic complications (Charlton et al., 2020; Giacco and Brownlee, 2010). In diabetes, excessive production of ROS in mitochondria is caused by an increased proton gradient across the mitochondrial membrane. This occurs secondary to elevated electron transport chain flux, mainly at complex I and complex III (Nishikawa et al., 2000).

Hypoxia also plays an important role in the development of diabetic complications and is present in both patients with diabetes (Bernardi et al., 2011) and in animal models with diabetes, in all tissues in which complications occur (Catrina, 2014; Catrina and Zheng, 2021). Hypoxia-inducible factor-1 (HIF-1) is a transcription factor central in the cellular response to low oxygen tension (Prabhakar and Semenza, 2015). HIF-1 is a heterodimeric transcription factor composed of two subunits, HIF-1α and HIF-1β, both of which are ubiquitously expressed in mammalian cells. Regulation of HIF-1 function is critically dependent on the degradation of the HIF-1α subunit in normoxia. The molecular basis of its degradation is oxygen-dependent hydroxylation of at least one of the two proline residues by specific Fe2+-, and 2-oxoglutarate-dependent HIF prolyl hydroxylases (PHD 1–3), among which, PHD2 (encoded by Egln1 gene) has the main role. Hydroxylated HIF-1α binds to the von Hippel–Lindau (VHL) tumour suppressor protein, which acts as an E3 ubiquitin ligase and targets HIF-1α for proteasomal degradation. Under hypoxic conditions, HIF-1α is stabilized against degradation, translocates to the nucleus, binds to hypoxic responsive elements (HRE) and activates transcription of a series of genes involved in different processes (i.e. angiogenesis, cell proliferation, survival, and cell metabolism). These processes enable the cell to adapt to reduced oxygen availability (Schödel and Ratcliffe, 2019).

HIF-1, as the key mediator of adaptation to low oxygen tension, contributes to a balance in redox homeostasis by supressing the excessive mitochondrial production of ROS under chronic hypoxia, thereby minimizing potentially deleterious effects (Semenza, 2017). Since HIF-1 stability and function is complexly repressed in diabetes (Catrina and Zheng, 2021), we hypothesized that its repression might contribute to increased ROS. We therefore investigated the impact of glucose levels on ROS production during hypoxia in cells, animal models of diabetes and patients with diabetes, and whether the excessive mitochondrial ROS production in diabetes could be normalized by restoring HIF-1 function.

We found that repressed HIF-1 function secondary to hyperglycemia contributes to an overproduction of mitochondrial ROS with direct pathogenic effects. Consequently, pharmacological or genetic interventions to prevent repression of HIF-1 function normalize mitochondrial production of ROS in diabetes and inhibit the development of nephropathy, in which hypoxia plays an important pathogenic role (Haase, 2017).

Materials and methods

Key Resources Table is in Appendix 1 - key resources table.

Clinical study

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Thirteen non-smoking patients with type 1 diabetes (28.9 ± 7.2 years old; 53.8% male and 46.2% female; HbA1c: 74.4 ± 11.8 mmol/mol (9.0% ± 1.1 %); BMI: 24.3 ± 4.0 kg/m2) and 11 healthy, age-matched controls (30.5 ± 8.5 years old; 54.5% male and 45.5% female; HbA1c: 35.6 ± 2.6 mmol/mol (5.4% ± 0.2 %); BMI: 24.3 ± 4.0 kg/m2) were exposed to intermittent hypoxia for 1 hr, consisting of five hypoxic episodes (13% O2, 6 min) that alternated with normoxic episodes (20.9% O2, 6 min) (Figure 1—figure supplement 1). The subject breathed through a disposable mouthpiece which was connected via an antibacterial filter (Carefusion, Yorba Linda, CA,USA) to a T-tube carrying one-way respiratory valves (Tyco Healthcare, Hamshire, UK). A nose clip assured that respiration occured through the mouth. The air supplied to the subject came from a tube connected to a stopcock (Hans Rudolph, Shawnee, KS, USA) connected to a 60 L Douglas bag (Hans Rudolph) which was continuously filled with hypoxic gas. By turning the stopcock, the air supplied to the subject could be switched from hypoxic to ambient (normoxic) air. The Douglas bag was positioned behind the subject’s bed, so that he/she could not notice when the hypoxic gas or normoxic air was supplied. Blood samples were taken before and immediately after hypoxia exposure. Patients had been diagnosed with diabetes for 10–20 years, showed no signs of peripheral neuropathy and had intact peripheral sensibility when checked with monofilament and vibration tests. The study was approved by the Regional Ethical Review Board in Stockholm, Sweden, and carried out in accordance with the principles of the Declaration of Helsinki. The sample size has been decided according to the experience from previous studies (Duennwald et al., 2013). All participants in the study provided informed consent.

EPR spectroscopy

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ROS levels in the blood were measured using Electron Paramagnetic Resonance (EPR) Spectroscopy (Dikalov et al., 2018). Blood samples were mixed with spin probe 1-hydroxy-3-carboxy-pyrrolidine (CPH, 200 μM) in EPR-grade Krebs HEPES buffer supplemented with 25 mM Deferoxamine (DFX) and 5 mM diethyldithiocarbamate (DETC), and incubated at 37°C for 30 min before being frozen in liquid nitrogen. EPR measurements were carried out using a table-top EPR spectrometer (Noxygen Science Transfer & Diagnostics GmbH, Elzach, Germany). The spectrometer settings were as follows: microwave frequency, 9.752  GHz; modulation frequency, 86  kHz; modulation amplitude, 8.29  G; sweep width, 100.00  G; microwave power, 1.02  mW; number of scans, 15. All data were converted to absolute concentration levels of CP radical (mmol O2-/min/µg) using the standard curve method. All chemicals and reagents for EPR Spectroscopy were obtained from Noxygen Science Transfer & Diagnostics GmbH.

Cell culture

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Mouse Inner Medullary Collecting Duct-3 (mIMCD-3) cells (ATCC CRL-2123; ATCC, USA) were cultured in Dulbecco’s modified Eagle’s medium (DMEM; 5.5 mM glucose) supplemented with 10% heat-inactivated FBS and 100 IU/ml penicillin and streptomycin (Thermo Fisher Scientific). The cells were maintained in a humidified atmosphere with 5% CO2 at 37°C in a cell culture incubator, and were tested negatively for microplasma using MycoAlert PLUS mycoplasma detection kit (LONZA). Cells were cultured under normoxic (21% O2) or hypoxic (1% O2) conditions in Hypoxia Workstation INVIVO2 (Ruskinn).

Nuclear extraction

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To detect HIF-1α, mIMCD-3 cells were cultured in medium containing 5.5 or 30 mM glucose for 24 hours in the absence or presence of DMOG (200 μM), and were exposed to normoxia or hypoxia for 6 hours prior to harvest. The cells were collected and incubated on ice for 10 min in hypotonic buffer containing 10 mM KCl, 1.5 mM MgCl2, 0.2 mM PMSF, 0.5 mM dithiothreitol, and protease inhibitor mix (Complete-Mini; Roche Biochemicals). After the cells were swollen, nuclei were released using a Dounce homogenizer Type B. The nuclei were pelleted and resuspended in a buffer containing 20 mM Tris (pH 7.4), 25% glycerol, 1.5 mM MgCl2, 0.2 mM EDTA, and 0.02 M KCl. Soluble nuclear proteins were released from the nuclei by gentle, drop-wise addition of a buffer containing 20 mM Tris (pH 7.4), 25% glycerol, 1.5 mM MgCl2, 0.2 mM EDTA, and 0.6 M KCl, followed by 30 min of incubation in ice. The nuclear extracts were then centrifuged and dialyzed in dialysis buffer containing 20 mM Tris (pH 7.4), 20% glycerol, 100 mM KCl, 0.2 mM EDTA, 0.2 mM PMSF, 0.5 mM dithiothreitol, and protease inhibitor mix.

Plasmid construction and transfection

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Plasmids encoding an HRE-driven luciferase reporter, Renilla luciferase, GFP, and GFP-HIF-1α were described previously (Zheng et al., 2006). Plasmid pCMV3-FLAG-PDK1 encoding FLAG-tagged human PDK1 was obtained from Sino Biological Inc (Catalog number: HG12312-NF). pCMV3-GFP-FLAG-PDK1 encoding GFP-fused FLAG-tagged PDK1 was generated by subcloning a Hind III-GFP-Hind III fragment from pCMV2-FLAG-GFP into Hind III - digested pCMV3-FLAG-PDK1. Plasmid transfection was performed using Lipofectamine reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol.

HRE-driven luciferase reporter assay

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HIF-1 activity was determined by an HRE-driven luciferase reporter assay. mIMCD-3 cells were transfected with plasmids encoding HRE-driven firefly luciferase and Renilla luciferase using Lipofectamine reagent. Cells were then cultured in media containing normal (5.5 mM) or high (30 mM) glucose concentrations, and were exposed to normoxia or hypoxia for 40 hours. The cells were harvested, and luciferase activity was measured using the Dual Luciferase Assay System (Promega) on the GloMax Luminometer (Promega) according to the manufacturer’s instructions. HRE-driven firefly luciferase activity was normalized to Renilla luciferase activity and expressed as relative luciferase activity.

Cellular apoptosis analysis

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mIMCD3 cells were cultured in media containing normal (5.5 mM) or high (30 mM) glucose concentrations and were exposed to normoxia or hypoxia for 24 hours before analysis. Apoptosis was analyzed using Annexin V-FITC / 7-AAD kit (Beckman Coulter) according to the manufacturer’s protocol. Briefly, the cells were incubated with Annexcin V-FITC and 7-AAD for 15 min in the dark, and then analyzed within 30 min using flow cytometry on a Cyan ADP analyser (Beckman Coulter). The gating scheme is shown in Figure 2—figure supplement 1. Results were expressed as percentage of Annexin V – positive and 7-AAD – negative apoptotic cells.

Determination of caspase 3/7 activity

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mIMCD3 cells were seeded in 96-well plate with 1500 cells/well in duplicates, and were cultured in media containing normal (5.5 mM) or high (30 mM) glucose concentrations and were exposed to normoxia or hypoxia for 24 hr before analysis. Caspase 3/7 activity was evaluated using Caspase-Glo 3/7 assay kit (Promega) on the GloMax Luminometer (Promega) according to the manufacturer’s instructions. The caspase 3/7 activity was finally normalized to the DNA concentration in each well using Quant-iT dsDNA High-Sensitivity Assay Kit (Thermo Fisher Scientific) measured using GloMax Discover Microplate Reader (Promega).

RNA interference

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siRNA for mouse VHL (Flexitube Gene Solution GS22346) was obtained from Qiagen. AllStars negative control siRNA, obtained from Ambion, was used as a control. siRNA was transfected using Lipofectamine RNAiMAX Transfection Reagent (Thermo Fisher Scientific), according to the manufacturer’s protocol. Twenty-four hours after transfection, cells were exposed to 5.5 or 30 mM glucose in normoxia or hypoxia for 24 hr before being harvested.

Detection of mitochondrial ROS levels using flow cytometry

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After 24 hr’ exposure to 5.5 or 30 mM glucose levels in normoxia or hypoxia, mIMCD-3 cells were stained with MitoSOX Red Mitochondrial Superoxide Indicator (Thermo Fisher Scientific). A working concentration of 5 µM was used, and cells were incubated at 37°C for 10 min protected from light. After washing off excess dye, cells were trypsinized and suspended in Krebs HEPES buffer and analysed using flow cytometry on a Cyan ADP analyser (Beckman Coulter). Analysis was performed using FlowJo software, and the gating scheme is shown in Figure 3—figure supplements 1 and 2. Mitochondrial ROS levels were expressed as percentage of MitoSOX Red fluorescence intensity.

Fluorescent immunocytochemistry and confocal microscopy

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mIMCD3 cells were seeded on coverslips and transfected with siRNA or plasmids as desired and were exposed to hypoxia and high glucose levels for 24 hr. The cells were then fixed in 4% Formaldehyde (Sigma) at room temperature (RT) for 15 min. After three washes with Phosphate-buffered Saline (PBS, Sigma), the cells were premeabilized in PBS containing 0.1% Triton-X100 at RT for 10 min. After three washes with PBS, the cells were blocked with PBS containing 5% Bovine Serum Albumin (BSA, Sigma), and incubated with Rabbit polyclonal anti-HIF-1α antibody (GeneTex, Cat No. GTX127309) 1:200 diluted in PBS containing 1% BSA and 0.1% Tween-20 (Sigma) at 4°C over night. After three washes with PBS containing 0.1% Tween-20 (PBS-T) for 5 min each, the cells were incubated with a fluorochrome-conjugated secondary antibody, Goat anti-Rabbit Alexa 594 (ThermoFisher Scientific, A-11037, 1:500 diluted) in PBS containing 1% BSA and 0.1% Tween-20 at RT for 1 hr. The cells were then washed with PBS-T twice and with PBS twice before the cover slips were mounted on the slides using ProLong Gold Antifade Mountant with DAPI (ThermoFisher Scientific). The fluorescent images were captured using a Leica SP8 confocal microscope (Leica Microsystems).

Animals

Diabetic male BKS-Leprdb/db/JOrlRj (Leprdb/db) mice and healthy controls were from Janvier Labs. Characteristics of the mice prior to experiments are summarized in Table 1. Leprdb/db mice with HbA1c levels > 55 mmol/mol or blood glucose >15 mM when HbA1c levels were between 45 and 55 mmol/mol were included in the analysis. Mice were allocated into groups according to their age, HbA1c or blood glucose levels. Mice were injected intraperitoneally (i.p.) with DMOG (320 mg/kg body weight) 4 days and 1 day before sacrifice for the analysis of mitochondrial function. For other analyses, Leprdb/db mice were injected (i.p.) with DMOG (50 mg/kg body weight) every second day for 1 month before sacrifice. Egln1+/- mice and their wild-type (WT) littermates were generated as previously described (Mazzone et al., 2009). Characteristics of the mice prior to experiments are shown in Table 2. Diabetes was induced in male Egln1+/- and WT mice with streptozotocin (STZ) i.p. injections. STZ was administered at 50 mg/kg body weight daily for five consecutive days, and mice were diabetic for at least 6 weeks before sacrifice. Blood glucose before and after STZ injection are shown in Table 3. Mice were exposed to a 12 hr light/dark cycle at 22°C, and were given standard laboratory food and water ad libitum. The sample size was calculated to achieve 30% difference in albuminuria between DMOG or vehicle – treated Leprdb/db mice or between diabetic Egln1+/- and WT mice and was adjusted for each parameter according to preliminary results. The experimental animal procedure was approved by the North Stockholm Ethical Committee for the Care and Use of Laboratory Animals.

Table 1
Characteristics of Leprdb/db (db/db) and control mice prior to experiments.
GroupsWT-ControlDb/db-ControlDb/db-DMOG
Body weight (g)27.44 ± 0.4148.66 ± 1.0050.04 ± 1.08
Blood glucose (mM)7.16 ± 0.3921.27 ± 1.2120.59 ± 1.50
Age (weeks)16 ± 017.25 ± 0.4817.00 ± 0.45
n141616
  1. Data are presented as mean ± SEM. Source data are shown in Table 1—source data 1.

Table 1—source data 1

Characteristics of Leprdb/db and control mice prior to experiments.

https://cdn.elifesciences.org/articles/70714/elife-70714-table1-data1-v1.xlsx
Table 2
Characteristics of Egln1+/- and WT mice prior to experiments.
GroupsWT-ControlEgln1+/--ControlWT-diabeticEgln1+/--diabetic
Start body weight (g)28.02 ± 1.0427.11 ± 0.7928.27 ± 0.7128.64 ± 0.87
Age (weeks)22.46 ± 0.8523.91 ± 0.7222.67 ± 0.8724.57 ± 0.59
n24232421
  1. Data are presented as mean ± SEM. Source data are shown in Table 2—source data 1.

Table 2—source data 1

Characteristics of Egln1+/- and WT mice prior to experiments.

https://cdn.elifesciences.org/articles/70714/elife-70714-table2-data1-v1.xlsx
Table 3
Blood glucose of Egln1+/- and WT mice before and after STZ injection.
GroupsWT-diabeticEgln1+/--diabetic
Blood glucose (mM) before STZ5.11 ± 0.244.18 ± 0.20
Blood glucose (mM) after STZ19.39 ± 1.0417.7 ± 0.81
n2421
  1. Data are presented as mean ± SEM. Source data are shown in Table 3—source data 1.

Table 3—source data 1

Blood glucose of Egln1+/- and WT mice before and after STZ injection.

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Fluorescent immunohistochemistry

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Formalin-fixed, paraffin-embedded kidney tissues were deparaffinized and rehydrated, and antigen retrieval was performed in citrate buffer using a pressure cooker. After washing the slides with PBS-T three times for 3 min each, sections were demarcated with a hydrophobic pen. Sections were blocked with goat serum in PBS for 30 min at RT and then incubated overnight at 4 °C with primary antibodies (HIF-1α antibody, GeneTex, GTX127309, 1:100 diluted; KIM-1 antibody, Novus Biologicals, NBP1-76701, 1:50 diluted). Sections were then washed with PBS-T four times for 5 min each. Sections were incubated for 1 hr at RT in the dark with a fluorochrome-conjugated secondary antibody, Goat anti-Rabbit Alexa fluor 488 or 594 (ThermoFisher Scientific, A-11008 or A-11037, 1:500 diluted). Sections were then washed with PBS-T four times for 5 min each and treated with 0.1% Sudan Black-B solution (Sigma) for 10 min to quench autofluorescence. Sections were counterstained with DAPI for 3 min, and were mounted and stored at 4 °C. Fluorescent images were acquired using a Leica TCS SP5 and SP8 confocal microscope (Leica Microsystems). Image analysis was blinded and performed using Image-Pro Premier v9.2 (Media Cybernetics) software.

To detect hypoxia in mouse kidneys, pimonidazole solution (Hypoxyprobe–1 Omni Kit, Hypoxyprobe, Inc) was i.p. administered to mice at a dosage of 60 mg/kg body weight 90 min prior to tissue harvest. Pimonidazole adducts were detected on kidney sections using a 1:100 diluted RED PE dye-conjugated mouse monoclonal anti-pimonidazole antibody (clone 4.3.11.3) according to the Hypoxyprobe RED PE Kit protocol.

To detect HIF-1α, the above method was modified to incorporate the Tyramide Superboost kit (Thermo Fisher Scientific). Briefly, after antigen retrieval, the sections were blocked with 3% H2O2 to quench endogenous peroxidase activity before blocking with goat serum (both ingredients provided in the kit). Following the PBS-T washes after primary HIF-1α antibody incubation, the sections were incubated with an HRP-conjugated rabbit antibody for 1 hr at RT. Sections were washed rigorously and incubated with tyramide reagent for 10 min at RT in the dark. The reaction was stopped by incubating with the stop solution for 5 min, and samples were washed with PBS-T three times for 3 mins each. The sections were subsequently treated with 0.1% Sudan Black-B solution, counterstained and mounted as mentioned above.

Evaluation of ROS levels in kidney

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ROS levels in kidney tissues were indirectly assessed by evaluating non-enzimatic lipid peroxidation by measuring 4-Hydroxynonenal (4-HNE) protein adduct levels using the OxiSelect HNE Adduct Competitive ELISA kit (STA838, Cell Biolabs) according to the manufacturer’s instruction.

Kidney mitochondrial function

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Mitochondria were isolated from mouse kidneys, and mitochondrial function was determined using high-resolution respirometry (Oxygraph 2 k, Oroboros) as previously described (Schiffer et al., 2018). The analysis was blinded. Briefly, respirometry was performed in respiration medium containing EGTA (0.5 mM), MgCl2 (3 mM), K-lactobionate (60 mM), taurine (20 mM), KH2PO4 (10 mM), HEPES (20 mM), sucrose (110 mM), and fatty-acid-free BSA (1 g/L). Pyruvate (5 mM) and malate (2 mM) were added to measure state two respiration, followed by the addition of ADP (2.5 mM) to measure complex I-mediated maximal respiratory capacity (state three respiration). Complex I + II-mediated maximal oxidative phosphorylation was evaluated after adding succinate (10 mM). LEAK respiration was measured in the presence of pyruvate (5 mM), malate (2 mM), and oligomycin (2.5 μM). Respiration was normalized to mitochondrial protein content, determined spectrophotometrically using the DC Protein Assay kit (Bio-Rad).

RNA purification and quantitative RT-PCR

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Total RNA was extracted from kidney using miRNeasy Mini kit (Qiagen). cDNA was produced using High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific). Quantitative RT-PCR was performed on a 7300 or 7900 Real-Time PCR System (Applied Biosystems) using SYBR Green Master Mix (ThermoFisher Scientific). The average gene expression of β-actin (ACTB) and Hydroxymethylbilane synthase (HMBS) was used as control. Primer sequences are listed in Key Resources Table in Appendix 1 - key resources table.

Protein extraction and western blotting

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Kidney biopsies were homogenized in a buffer containing 50 mM Tris-HCl (pH 7.4), 180 mM NaCl, 0.2% NP-40, 20% glycerol, 0.5 mM phenylmethylsulfonyl fluoride, 5 mM β-mercaptoethanol, and a protease inhibitor mix (Complete-Mini; Roche Biochemicals). Cell lysate was obtained by centrifugation for 30 min at 20,000 g and 4°C. Protein concentrations were determined using the Bradford Protein Assay (Bio-Rad) according to the manufacturer’s protocol. Nuclear extracts and tissue lysate were separated by SDS-PAGE and blotted onto nitrocellulose membranes. Blocking was performed in TBS buffer (50 mM Tris pH 7.4 and 150 mM NaCl) containing 5% nonfat milk, followed by incubation with anti-HIF-1α (1:500, NB100-479; Novus Biologicals), anti-Histone H3 (1:5000, ab1791; Abcam), anti-KIM-1 (1:500, NBP1-76701; Novus Biologicals) or anti-α-tubulin (1:1000, MAB11106; Abnova) antibodies in TBS buffer containing 1% nonfat milk. After several washes, the membranes were incubated with IRDye 800 goat anti-rabbit or IRDye 680 goat anti-mouse secondary antibodies (LI-COR). The membranes were then scanned with Odyssey Clx Imaging System (LI-COR). Quantification of western blots were performed using ImageJ (version 1.53).

TUNEL staining

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Apoptosis in kidneys was detected using the In Situ Cell Death Detection Kit (Sigma Aldrich/Roche). Briefly, formalin-fixed paraffin-embedded sections were deparaffinized, rehydrated and blocked with 3% H2O2 to quench endogenous peroxidase activity. The sections were permeabilized with 0.1% Triton X-100, 0.1% sodium citrate solution and blocked with 3% BSA in PBS; the sections were then incubated with the TUNEL mixture for 1 hr at 37°C. Sections were thoroughly rinsed in PBS, treated with 0.1% Sudan Black-B solution to quench autofluorescence and counterstained with DAPI. Sections were mounted and stored at 4°C. Images were obtained using a Leica TCS SP8 confocal microscope (Leica Microsystems). The images were analyzed using Image-Pro Premier v9.2 software (Media Cybernetics). TUNEL-positive nuclei were counted and expressed as a percentage of the total number of nuclei.

Albuminuria

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Urine was collected from mouse bladders after sacrifice and snap frozen in liquid nitrogen. Urine albumin and creatinine concentrations were evaluated in thawed urine samples using a DCA Vantage Analyzer (Siemens Healthcare GmbH) with the corresponding test cartridges DCA Microalbumin/Creatinine ACR urine test (01443699, Siemens Healthcare GmbH).

Statistical analysis

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All data used for statistical analysis are independent biological replicates. Technical replicates were applied during luciferase reporter, ELISA, caspase 3/7 activity, DNA and protein concentration, and QPCR analysis; and the average of the results from technical replicates is regarded as one biological data. Statistical analysis was performed using GraphPad Prism software. Outliers identified using Grubbs’ test were excluded from analysis. The differences between two groups were analysed using unpaired two-sided Student’s t-test. Multiple comparisons of three or more groups were performed using one-way or two-way ANOVA followed by Bonferroni’s post hoc test or Holm–Sidak’s test, or Brown-Forsythe and Welch ANOVA tests followed by Dunnett T3 multiple comparison test for sample set with unequal standard deviations. p < 0.05 was considered statistically significant. Data are presented as mean ± standard error of the mean (SEM).

Results

Hypoxia increases circulating ROS in patients with diabetes but not in control subjects without diabetes

The effect of hypoxia on ROS production was evaluated in patients with poorly controlled type 1 diabetes (28.9 ± 7.2 years old; HbA1c: 74.4 ± 11.8 mmol/mol) and matched control subjects without diabetes (30.5 ± 8.5 years old; HbA1c: 35.5 ± 2.6 mmol/mol). Participants were exposed to mild and intermittent hypoxia (13% O2) for 1 hr (Figure 1—figure supplement 1), which is known to elicit a clinical response (Duennwald et al., 2013). As shown in Figure 1, ROS levels in peripheral blood were increased by hypoxia in patients with diabetes. However, hypoxia did not change the ROS levels in normoglycemic control subjects.

Figure 1 with 1 supplement see all
Hypoxia increases circulating ROS in patients with diabetes but not in control subjects without diabetes.

Healthy controls (A) and subjects with type 1 diabetes (B) were exposed to intermittent hypoxia for 1 hr. Peripheral blood was taken before (0h) and after (1h) hypoxia exposure. ROS levels were analyzed using Electron Paramagnetic Resonance (EPR) Spectroscopy with CPH spin probes (n = 10–13). Data are represented as mean ± SEM. *, p < 0.05 analysed using unpaired two-sided Student’s t-test. This figure has one figure supplement. Source data are shown in Figure 1—source data 1.

Figure 1—source data 1

ROS levels in blood from patients with diabetes and control subjects.

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High glucose concentrations inhibit HIF-1 signaling through PHD-dependent mechanism and induce apoptosis in hypoxia

Since hypoxia induces ROS in diabetes, and HIF-1 is the central regulator of cellular responses to hypoxia (Prabhakar and Semenza, 2015), we hypothesized that the dysregulated HIF-1 signaling contributes to the ROS overproduction in diabetes. We tested this hypothesis using mouse inner medulla collecting tubular cells (mIMCD-3), given the important pathogenic role of hypoxia in diabetic kidney disease (Palm, 2006). As shown in Figure 2A, the nuclear expression of HIF-1α increased after exposure to hypoxia; however, this effect was attenuated under hyperglycemic conditions. Moreover, HRE-driven luciferase reporter assay showed less HIF activity in hypoxia under hyperglycemic conditions compared with normoglycemic conditions (Figure 2B). High glucose concentrations also increased apoptosis of mIMCD3 cells during hypoxia (Figure 2C). Interestingly, when the cells were exposed to dimethyloxalylglycine (DMOG), a competitive inhibitor of PHD, both HIF-1α expression (Figure 2A) and HIF-1 function (Figure 2D) were increased and apoptosis was inhibited (Figure 2E) under hypoxic and hyperglycemic conditions. These results indicate that high glucose levels inhibit HIF-1 and induce apoptosis in hypoxic mIMCD3 cells through a PHD-dependent mechanism. Moreover, high glucose levels in hypoxia also enhanced caspase-3/7 activity in mIMCD3 cells, which could be inhibited by DMOG treatment (Figure 2F), suggesting that the apoptosis induced by high glucose levels and hypoxia is dependent on caspase-3 and –7.

Figure 2 with 1 supplement see all
High glucose levels inhibit HIF-1 signaling and induce apoptosis, which can be rescued by PHD inhibitor DMOG.

(A) mIMCD-3 cells were cultured in normal (5.5 mM) or high (30 mM) glucose media in the presence of DMOG or vehicle for 24 hr, and were exposed to hypoxia (H) or normoxia (N) for 6 hr before harvest. The nuclear expression of HIF-1α and Histone H3 was measured using western blotting. (B–F) mIMCD-3 cells were exposed to 5.5 or 30 mM glucose levels in normoxia (N) or hypoxia (H) in the presence or absence of DMOG or vehicle for 24 hr. The relative HRE-driven luciferase activity (B and D, n = 6), apoptosis (C and E, n = 4), and the caspase 3/7 activity (F, n = 3–4) were assessed. (G) Caspase 3/7 activity was evaluated in mIMCD-3 cells that were pre-treated with 1 mM NAC or vehicle for 1 hr before exposure to 5.5 or 30 mM glucose levels in normoxia (N) or hypoxia (H) for 24 hr (n = 4). The data under control conditions were considered as 1.0. Data are shown as mean ± SEM. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001 using one-way ANOVA followed by Bonferroni’s post hoc test (B–C, F–G), and unpaired two-sided Student t-test (D–E). This figure has one figure supplement. Source data are shown in Figure 2—source data 1.

Figure 2—source data 1

HRE-driven luciferase activity, apoptosis and caspase 3/7 activity in mIMCD3 cells.

https://cdn.elifesciences.org/articles/70714/elife-70714-fig2-data1-v1.xlsx

We next assessed the role of ROS in mediating apoptosis induced by hyperglycemia under hypoxic conditions, by pretreatment of the mIMCD3 cells with the thiol reducing agent N-acetylcysteine (NAC). As shown in Figure 2G, pretreatment with NAC significantly inhibited the increase of caspase-3/7 activity in mIMCD3 cells exposed to high glucose levels and hypoxia, indicating the role of ROS in the induction of apoptosis in these conditions.

Repression of HIF-1 by high glucose concentrations contributes to increased mitochondrial ROS production in hypoxia

We further investigated the influence of HIF-1 on mitochondrial ROS production in diabetes. Mitochondrial ROS levels were increased in cells exposed to high glucose levels and hypoxia (Figure 3A), which corresponded to impaired HIF-1 activity. Interestingly, HIF-1 activation by DMOG diminished the mitochondrial ROS overproduction induced by high glucose levels in hypoxia (Figure 3A), indicating HIF-1 repression as an important mechanism for increased mitochondrial ROS production in diabetes. This was further confirmed by similar results that were observed when HIF-1 activity was maintained during hyperglycemia in hypoxia by genetic approaches, that is silencing VHL that mediates HIF-1α degradation (Figure 3B–D) or overexpressing HIF-1α (Figure 3E–F). Silencing VHL gene (Figure 3B) in mIMCD3 cells exposed to hypoxia and high glucose levels was followed by an increase of nuclear HIF-1α expression (Figure 3C) and lead to decreased mitochondrial ROS (Figure 3D). Mitochondrial ROS was also decreased in mIMCD3 cells expressing GFP-HIF-1α compared to cells expressing GFP under hypoxic and hyperglycemic conditions (Figure 3E–F). Taken together, these results suggest that mitochondrial ROS overproduction in cells exposed to a combination of hypoxia and hyperglycemia is dependent on the impairment of HIF-1 function and can be attenuated when HIF-1 activity is maintained.

Figure 3 with 2 supplements see all
High glucose levels induce mitochondrial ROS overproduction in hypoxia, which can be rescued by promoting HIF-1 function.

(A) Mitochondrial ROS levels were measured as mitosox intensity in mIMCD-3 cells cultured in normal (5.5 mM) or high (30 mM) glucose media in normoxia (N) or hypoxia (H) for 24 hr in the presence of DMOG or vehicle (n = 5). (B–D) mIMCD-3 cells were transfected with von Hippel–Lindau tumour suppressor (VHL) or control (Ctrl) siRNA, and exposed to hypoxia (H) and 30 mM glucose for 24 hr. VHL gene expression (B, n = 3), endogenous HIF-1α expression (red) and DAPI staining (blue) (C) and mitochondrial ROS levels (D, n = 5) were assessed using quantitative RT-PCR, fluorescent immunocytochemistry and flow cytometry, respectively. (E and F) mIMCD-3 cells were transfected with plasmids encoding GFP or GFP-HIF-1α,and exposed to hypoxia and 30 mM glucose for 24 hr. (E) Expression of GFP and GFP-HIF-1α (green) were detected using confocal microscopy. The nuclear HIF-1α expression was confirmed by immucytochemistry using anti-HIF-1α antibody (red). Nuclei were stained blue with DAPI. (F) Mitochondrial ROS levels are shown (n = 6). The mitosox intensity of cells cultured under control conditions were considered as 100%. Data are shown as mean ± SEM. *, p < 0.05; ***, p < 0.001; ****, p < 0.0001 using one-way ANOVA followed by Bonferroni’s post hoc test (A), and unpaired two-sided Student t-test (B, D and F). This figure has two figure supplements. Source data are shown in Figure 3—source data 1. Scale bar: 50 μm.

Figure 3—source data 1

Mitosox intensity and VHL gene expression in mIMCD3 cells.

https://cdn.elifesciences.org/articles/70714/elife-70714-fig3-data1-v1.xlsx

HIF-1 repression is responsible for excess ROS production in diabetic kidney

To investigate the relevance of HIF-1 modulation on ROS levels in diabetes, we further focused our investigation on the kidney, where low oxygen levels play an important pathogenic role (Palm et al., 2003). ROS levels were higher in the kidney from mouse models of both type 2 diabetes (Leprdb/db mice) and streptozotocin (STZ)-induced type 1 diabetes, as evaluated by 4-Hydroxynonenal (HNE) levels (Figure 4B and D). At the same time, HIF-1 signaling was repressed, as shown by insufficient activation of HIF-1α (Figure 4A and C), despite a profound hypoxic environment indicated by pimonidazole staining (Figure 4—figure supplement 1). This reverse correlation between ROS and HIF-1 activity further supports the hypothesis that the repression of HIF-1 signaling contributes to the ROS overproduction in diabetes.

Figure 4 with 3 supplements see all
Promoting HIF-1 function attenuates renal ROS excess and mitochondrial respiration in mouse models of diabetes.

Kidneys were harvested from wild-type (WT) and Leprdb/db diabetic mice (db/db) that were treated with placebo (vehicle) or DMOG (A–B, E, G), and from non-diabetic control (Ctrl) or diabetic (Db) wild-type (WT) and Egln1+/- mice (C–D, F, H). (A and C) HIF-1α (green), pimonidazole (red, hypoxia marker) and DAPI (blue, nuclear staining) signals were detected by fluorescent immunohistochemistry, and relative HIF-1α expression levels were quantified (A, n = 4–5; C, n = 4–6). Scale bar: 100 μm. (B and D) Renal ROS levels were detected using the OxiSelect HNE adduct competitive ELISA kit (B, n = 7–10; D, n = 5–8). (E and F) Mitochondrial respiratory function was evaluated using high resolution respirometry (E, n = 4–9; F, n = 11–17). (G and H) PDK1 gene expression in kidneys (G, n = 4–9; H, n = 4–6). (I and J) mIMCD-3 cells were transfected with plasmids encoding GFP or GFP-PDK1,and exposed to hypoxia and 30 mM glucose (H30) for 24 hr. (I) Expression of GFP and GFP-HIF-1α (green) and nuclear DAPI staining (blue) were detected using confocal microscopy. Scale bar: 50 μm. (J) Mitochondrial ROS levels are shown (n = 4). Data are shown as mean ± SEM. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001 using one-way ANOVA (A, B, E, G) and two-way ANOVA (C, D, F, H) followed by multi-comparison post hoc tests, and unpaired two-sided Student t-test (J). This figure has three figure supplements. Source data are shown in Figure 4—source data 1.

Figure 4—source data 1

HIF-1α, ROS, and mitochondrial respiration levels in mouse kidneys and PDK1 gene expression and Mitosox intensity in mIMCD3 cells.

https://cdn.elifesciences.org/articles/70714/elife-70714-fig4-data1-v1.xlsx

We therefore sought to assess the influence of promoting HIF-1 function during hyperglycemia on ROS production in these animals. To this end, we inhibited PHD activity, either through pharmacological inhibition, by treatment of the Leprdb/db mice with DMOG or through genetic modification by employing Egln1+/- mice in the STZ-induced model of diabetes. Both methods were able to increase HIF-1α levels (Figure 4A and C) and HIF-1 activity, as assessed by HIF-1 target gene expression, despite persistence of hyperglycemia (Figure 4—figure supplements 2 and 3). Importantly, HIF-1 activation in the kidney was followed by a decrease in renal ROS levels in both Leprdb/db mice and STZ-induced diabetic mice (Figure 4B and D).

Investigation of mitochondrial respiration in the kidneys of both animal models revealed an increase of the complex I- and complex I + II-mediated state three respiration and mitochondrial leak (Figure 4E and F). Promoting HIF-1 activity in the kidney of diabetic animals, by either DMOG treatment or by haplodeficiency of Egln1, was followed by normalization of perturbed mitochondrial respiration (Figure 4E and F). Pyruvate dehydrogenase kinase 1 (PDK1), a direct HIF-1 target gene that inhibits the flux of pyruvate through tricarboxylic acid cycle (TCA) and subsequent mitochondrial respiration (Kim et al., 2006), was down-regulated in diabetic kidneys and could be rescued by HIF-1 activation (Figure 4G and H). These results indicate an important role of PDK1 in mediating the effects of HIF-1 on the regulation of ROS production in diabetic kidney. In order to verify this mechanism, we transfected plasmids encoding GFP or GFP-fused PDK1 (GFP-PDK1) in mIMCD3 cells exposed to high glucose levels in hypoxia (Figure 4I), and assessed the mitochondrial ROS levels using flow cytometry analysis of mitosox intensity in GFP- or GFP-PDK1-positive cells. As shown in Figure 4J, PDK1 overexpression diminished the mitochondrial ROS overproduction in cells exposed to high glucose levels in hypoxia, suggesting that the increased mitochondrial ROS is at least partially mediated by the inhibition of HIF-1 target gene PDK1 in these conditions.

Promoting HIF-1 function reduces renal injury and ameliorates renal dysfunction in mouse models of diabetes

Promoting HIF-1 function in diabetic animals, with its secondary suppression of ROS production, exerted protective effects on kidney function. In both Leprdb/db mice (Figure 5A–C , and G) and mice with STZ-induced diabetes (Figure 5D–F , and H), promoting HIF-1 function prevented typical diabetic kidney lesions, as measured by reduced Kidney Injury Marker-1 (KIM-1) expression (Figure 5A–B , and D–E) and TUNEL staining-assessed apoptosis (Figure 5C and F). This resulted in improved renal function, as demonstrated by decreased albuminuria in both mouse models of diabetes (Figure 5G–H).

Promoting HIF-1 function reduces renal injury and ameliorates renal dysfunction in mouse models of diabetes.

Kidneys were harvested from wild-type (WT) and Leprdb/db diabetic mice (db/db) that were treated with placebo (vehicle) or DMOG (A–C, G), and from non-diabetic control (Ctrl) or diabetic (Db) wild-type (WT) and Egln1+/- (+/-) mice (D–F, H). (A and D) Representative images of KIM-1 (red or green) and DAPI (blue) in kidney that were analysed using fluorescent immunohistochemistry. Quantifications of KIM-1 fluoresent signal are shown in corresponding histogram (A, n = 3–4; D, n = 3–6). (B and E) Representative images of KIM-1 and α-tubulin analyzed by western blotting. (C and F) Apoptotic cells were detected using TUNEL staining, and the percentage of TUNEL-positive cells were quantified (C, n = 4; F: n = 3–5). (G and H) Albuminuria is presented as the ratio of albumin (Alb) to creatinine in mouse urine (G, n = 7–13; H, n = 4–6). Data are shown as mean ± SEM. *, p < 0.05; **, p < 0.01; ***, p < 0.001 analysed using one-way ANOVA (A, C), Brown-Forsythe and Welch ANOVA (G) and two-way ANOVA (D, F, H) followed by multi-comparison test. Source data are shown in Figure 5—source data 1. Scale bar: 100 μm.

Figure 5—source data 1

Evaluation of renal KIM-1 and TUNEL staining and albuminuria of mouse models.

https://cdn.elifesciences.org/articles/70714/elife-70714-fig5-data1-v1.xlsx

Discussion

Excessive mitochondrial ROS production is a central pathogenic contributor to the development of diabetic complications. In addition, excessive ROS stimulate several other deleterious biochemical pathways such as activation of protein kinase C, formation of advanced glycation end-products, polyol pathway flux and overactivity of the hexosamine pathway (Nishikawa et al., 2000). Here, we show that in diabetic models, overproduction of ROS from mitochondria is not due to increased electron transport chain flux secondary to hyperglycemia alone. Impairment of HIF-1 signaling is also a critical mechanism, since promoting HIF-1 activity in diabetic models in vitro and in vivo attenuated ROS production, despite the persistence of hyperglycemia, which prevents the development of oxidative stress-induced kidney injury.

In subjects with diabetes, ROS levels increase after exposure to hypoxia in opposition to control subjects. Acute hypoxia can unmask the impaired HIF-1 signaling that presents in patients with diabetes. However, other ROS sources responsive to acute hypoxia, either from mitochondria (Hernansanz-Agustín et al., 2020; Waypa et al., 2013) or from elsewhere (Weissmann et al., 2000) cannot be excluded. The concentration of oxygen in tissues ranges from 1% to 10%, which continuously activates HIF-1 signaling machinery (Carreau et al., 2011). Therefore, the small decrease in oxygen tension present in patients with diabetes (Bernardi et al., 2017), combined with an impaired HIF-1 activation (Catrina et al., 2004), may contribute to increased ROS levels in tissues associated with diabetic complications.

Indeed, the direct relationship between hyperglycemia-dependent repression of HIF-1 signaling and excess ROS in hypoxia was demonstrated experimentally both in vitro and in vivo in this study. We found that HIF-1 signaling was inhibited by hyperglycemia in tubular cells during hypoxia and in kidneys from mouse models of diabetes, through a PHD-dependent mechanism, which is in accordance with previous observations (Bento and Pereira, 2011; Catrina, 2014). This was followed by increased ROS production in mitochondria, when assessed by a specific mitochondrial probe (Wang et al., 2010), which was not evident under hyperglycemic conditions when HIF-1 function was promoted with different approaches. The relationship between HIF-1 and ROS is bidirectional, with most evidence showing that mitochondrial ROS has a stabilizing effect on HIF-1α (Brunelle et al., 2005; Chandel et al., 1998; Guzy et al., 2005), although the exact mechanisms are still unclear. Several mechanisms in the repressive effects of HIF-1 signaling on mitochondrial ROS production have also been identified (Fukuda et al., 2007; Kim et al., 2006). Our results indicate that the role of HIF-1 on ROS is due to the decreased respiration rate of the mitochondria, since both pharmacological and genetic induction of HIF-1 prevents increased respiration. This effect is at least partially mediated by HIF-1 target gene PDK1, that has been previously shown to inhibit pyruvate dehydrogenase (PDH) activity, leading to reduced flux of lactate through TCA cycle and electron transport chain (Kim et al., 2006).

Along with the cellular systems that mitigate the effect of ROS (e.g. Nrf2) (Jiang et al., 2010), the increased mitochondrial leak noted in diabetes is a pathway aimed at diminishing ROS production by decreasing mitochondrial membrane potential (Echtay et al., 2002; Miwa and Brand, 2003). However, to produce enough ATP, this is followed by increased flux through the electron transport chain. Although HIF-1 activity normally suppresses electron transport chain, this regulation is diminished in diabetes, resulting in an increased oxygen consumption rate and aggravation of cellular hypoxia that contributes to tissue injury. This was confirmed by increased pimonidazole staining in diabetic kidney in this study, as previously observed (Rosenberger et al., 2008). Thus, our results provide evidence for repressed HIF-1 in diabetes as a critical mechanism underlying the vicious cycle between oxidative stress and hypoxia, which is suggested to contribute to kidney injury (Honda et al., 2019).

Indeed, pharmacological or genetic interventions to sustain HIF-1 signaling in diabetes normalized ROS production and had direct consequences on kidney function, despite persistent hyperglycemia. Albuminuria, a typical marker of diabetic nephropathy, was prevented in animal models of either type 1 or type 2 diabetes when HIF-1 signaling was maintained. This is in accordance with the previous reports after exposure to cobalt, which also stabilizes HIF-1α (Ohtomo et al., 2008). The expression of the proximal tubular damage marker KIM-1, which in diabetic nephropathy becomes positive even before detection of albuminuria (Nauta et al., 2011; Nordquist et al., 2015), was not evident when ROS levels were suppressed by promoting HIF signaling in both animal models. This is in agreement with the absence of an increase of KIM-1 in diabetic kidneys where the renal oxygen levels were normalized (Friederich-Persson et al., 2018). Moreover, apoptosis, another classical marker of ROS damage in diabetic nephropathy (Allen et al., 2003), was reduced not only in DMOG-treated mIMCD3 cells exposed to high glucose concentrations in hypoxia but also in DMOG-treated Leprdb/db mice and diabetic Egln1+/- mice. Thus, promoting HIF-1 is a promising therapeutic strategy to prevent or treat even other chronic diabetes complications since excessive production of mitochondrial ROS is a key common driver of diabetic complications (Charlton et al., 2020; Giacco and Brownlee, 2010).

In conclusion, we demonstrate that the PHD-dependent HIF-1 repression induced by high glucose concentrations contributes to excessive production of mitochondrial ROS in diabetes, which is mediated by increased mitochondrial respiration secondary to the inhibition of HIF-1 target gene PDK1 (Figure 6). Promoting HIF-1 function is sufficient to normalize ROS levels during hyperglycemia and protects against diabetic nephropathy, making HIF-1 signaling an attractive therapeutic option for diabetes complications. This is a timely finding, given that the first PHD inhibitor that can activate HIF-1 has been recently approved for clinical use (Chen et al., 2019).

Repression of HIF-1 contributes to increased mitochondrial ROS production in diabetes.

Under non-diabetic conditions (left panel), HIF-1 is induced by hypoxia and activates PDK1 expression which inhibits excess mitochondrial ROS production through inhibition of mitochondrial respiration. However, under diabetic conditions (right panel), HIF-1 is inhibited by high glucose levels through a PHD-dependent mechanism despite hypoxia. This results in decreased expression of PDK1, leading to increased mitochondrial respiration and excessive mitochondrial ROS production which causes tissue damage.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Mus musculus; male)BKS(D)-Leprdb/JOrlRj, Leprdb/db diabetic miceJanvier LabsRRID:MGI:6293869
Strain, strain background (Mus musculus; male)C57BL/6JRj MouseJanvier LabsRRID:MGI:5751862
Strain, strain background (Mus musculus; male)Egln1+/- and wild-type miceOwn colonyPMID:19217150Mazzone et al., 2009
Cell line (Mus musculus)mIMCD-3 cell lineATCCCat#:CRL-2123;RRID:CVCL_0429
Transfected construct (M. musculus)siRNA to mouse VHLQiagenGene Solution siRNA (Cat#: 1027416)Target sequence: TCCGAGATTGATCTACACATA
Transfected construct (M. musculus)AllStars Negative Control siRNAQiagenCat#: 1027280
Antibodyanti-HIF-1alpha (Rabbit polyclonal)GeneTexCat#: GTX127309; RRID:AB_2616089ICC(1:200)IHC(1:100)
Antibodyanti-KIM-1 (Rabbit polyclonal)Novus BiologicalsCat#:NBP1-76701;RRID:AB_11037459IHC(1:50)WB(1:500)
AntibodyGoat anti-Rabbit Secondary Antibody, Alexa Fluor 594Thermo Fisher ScientificCat#:A-11037;RRID:AB_2534095ICC (1:500)IHC (1:500)
AntibodyGoat anti-Rabbit Secondary Antibody, Alexa Fluor 488Thermo Fisher ScientificCat#:A-11008;RRID:AB_143165IHC (1:500)WB (1:500)
Antibodyanti-HIF-1alpha (Rabbit polyclonal)Novus BiologicalsCat#:NB100-479;RRID:AB_10000633WB: 1:500
Antibodyanti-Histone H3 (Rabbit polyclonal)AbcamCat#: ab1791;RRID:AB_302613WB: 1:5,000
Antibodyanti-α-tubulin (mouse monoclonal)AbnovaCat#:MAB11106; RRID:AB_2888691WB:1:1,000
AntibodyIRDye 800 goat anti-rabbit Secondary AntibodyLI_COR BiosciencesCat#:925–32211; RRID:AB_2651127WB:1:20,000
AntibodyIRDye 680 goat anti-mouse Secondary AntibodyLI_COR BiosciencesCat#:925–68070; RRID:AB_2651128WB:1:20,000
Recombinant DNA reagentpCMV3-FLAG-PDK1Sino Biological IncCat#: HG12312-NFPlasmid encoding FLAG-tagged human PDK1
Recombinant DNA reagentpCMV3-GFP-FLAG-PDK1This paperPlasmid encoding GFP-fused FLAG-tagged human PDK1
Sequence-based reagentMouse PDK1_FThis paperPCR primersAGTCCGTTGTCCTTATGAG
Sequence-based reagentMouse PDK1_RThis paperPCR primersCAGAACATCCTTGCCCAG
Sequence-based reagentMouse BNIP3_FThis paperPCR primersAACAGCACTCTGTCTGAGG
Sequence-based reagentMouse BNIP3_RThis paperPCR primersCCGACTTGACCAATCCCA
Sequence-based reagentMouse PGK1_FThis paperPCR primersAGTCCGTTGTCCTTATGAG
Sequence-based reagentMouse PGK1_RThis paperPCR primersCAGAACATCCTTGCCCAG
Sequence-based reagentMouseSDF-1alpha_FThis paperPCR primersGAGAGCCACATCGCCAGAG
Sequence-based reagentMouseSDF-1alpha_RThis paperPCR primersTTTCGGGTCAATGCACACTTG
Sequence-based reagentMouseEgln1_FThis paperPCR primersGGGCAACTACAGGATAAACGG
Sequence-based reagentMouse Egln1_RThis paperPCR primersCTCCACTTACCTTGGCGT
Sequence-based reagentMouse GLUT3_FThis paperPCR primersTCATCTCCATTGTCCTCCAG
Sequence-based reagentMouse GLUT3_RThis paperPCR primersCCAGGAACAGAGAAACTACAG
Sequence-based reagentMouseACTB_FThis paperPCR primersAAGATCAAGATCATTGCTCCTC
Sequence-based reagentMouseACTB_RThis paperPCR primersGGACTCATCGTACTCCTG
Sequence-based reagentMouseHMBS_FThis paperPCR primersCCTGTTCAGCAAGAAGATGGTC
Sequence-based reagentMouseHMBS_RThis paperPCR primersAGAAGTAGGCAGTGGAGTGG
Sequence-based reagentMouseVHL_FThis paperPCR primersCATCACATTGCCAGTGTATACCC
Sequence-based reagentMouseVHL_RThis paperPCR primersGCTGTATGTCCTTCCGCAC
Commercial assay or kitMycoAlert PLUS mycoplasma detection kitLONZACat#:LT07-218
Commercial assay or kitDual-Luciferase Reporter Assay SystemPromegaCat#: E1960
Commercial assay or kitAnnexin V-FITC / 7-AAD kitBeckman CoulterCat#: IM3614
Commercial assay or kitCaspase-Glo 3/7 assay kitPromegaCat#: G8091
Commercial assay or kitQuant-iT dsDNA High-Sensitivity Assay KitThermo Fisher ScientificCat#: Q33120
Commercial assay or kitLipofectamine RNAiMAX Transfection ReagentThermo Fisher ScientificCat#: 13778075
Commercial assay or kitMitoSOX Red Mitochondrial Superoxide Indicator, for live-cell imagingThermo Fisher ScientificCat#:M36008
Commercial assay or kitProLong Gold Antifade Mountant with DAPIThermo Fisher ScientificCat#:P36935
Commercial assay or kitDAPIThermo Fisher ScientificCat#:D1306
Commercial assay or kitHypoxyprobe–1 Omni KitHypoxyprobe, IncCat#:HP1-XXX
Commercial assay or kitTyramide Superboost kitThermo Fisher ScientificCat#:B40943
Commercial assay or kitOxiSelectTM HNE Adduct Competitive ELISA kitCell BiolabsSTA838
Commercial assay or kitDC Protein AssayBIO-RADCat#:5000111
Commercial assay or kitmiRNeasy Mini kitQiagenCat#:217,004
Commercial assay or kitHigh-Capacity cDNA Reverse Transcription KitThermo Fisher ScientificCat#:4368814
Commercial assay or kitSYBR Green Master MixThermo Fisher ScientificCat#:4367659
Commercial assay or kitBradford Protein AssayBIO-RADCat#:5000001
Commercial assay or kitIn Situ Cell Death Detection KitRocheCat#:11684817910RRID:AB_2861314
Commercial assay or kitDCA Microalbumin/Creatinine Urine TestSiemens Healthcare GmbHCat#:01443699
Chemical compound, drugCPH (1-hydroxy-3-carboxy-pyrrolidine)Noxygen Science Transfer & Diagnostics GmbHCat#:NOX-01.1–50 mg
Chemical compound, drugEPR-grade Krebs HEPES bufferNoxygen Science Transfer & Diagnostics GmbHCat#:NOX-7.6.1–500 ml
Chemical compound, drugDeferoxamineNoxygen Science Transfer & Diagnostics GmbHCat#:NOX-09.1–100 mg
Chemical compound, drugDETC (diethyldithiocarbamate)Noxygen Science Transfer & Diagnostics GmbHCat#:NOX-10.1–1 g
Chemical compound, drugDMOG (Dimethyloxalylglycine)Frontier Specialty ChemicalsCat#:D1070
Chemical compound, drugcOmplete, Mini, EDTA-free Protease Inhibitor CocktailRocheCat#: 11836170001
Chemical compound, drugFormaldehyde solutionSigmaCat#: F8775
Chemical compound, drugStreptozotocinSigmaCat#: S0130
Chemical compound, drugSudan Black BSigmaCat#:199,664
Software, algorithmFlowJoFlowJoRRID:SCR_008520
Software, algorithmImage-Pro Premier v9.2Media Cybernetics
Software, algorithmImageJImageJRRID:SCR_003070
Software, algorithmGraphPad PrismGraphPad PrismRRID:SCR_002798
OtherDulbecco’s Modified Eagle’s MediumThermo Fisher Scientific31885–023

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for all the figures and tables.

References

Decision letter

  1. Ernestina Schipani
    Reviewing Editor; University of Pennsylvania, United States
  2. Mone Zaidi
    Senior Editor; Icahn School of Medicine at Mount Sinai, United States
  3. Ernestina Schipani
    Reviewer; University of Pennsylvania, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Repression of Hypoxia-Inducible Factor-1 Contributes to Increased Mitochondrial Reactive Oxygen Species Production in Diabetes" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Ernestina Schipani as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Mone Zaidi as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

The reviewers have listed numerous strengths. Weaknesses were also identified. Please, address concerns and comments point-by-point.

Of note, additional experiments are less important than modifications to current data and text. Reanalysis and modifying text are essential to support the claims of the paper.

Reviewer #1:

In this study, Zheng and colleagues report the novel findings that in diabetic models hyperglycemia suppress HIF1a in a PHD-dependent manner, and this in turn leads to increased mitochondrial ROS and cell death. Both the increased ROS and the cell death are prevented by increasing HIF1a activity either pharmacologically or genetically.

The paper is novel, informative, and with interesting translational implications. The authors used a variety of in vitro and in vivo models for the testing of their hypothesis, with special emphasis on a model of diabetic nephropathy.

However, a few issues need to be addressed in order to strengthen the authors' conclusions and their biological significance.

1. Suppressing HIF1a should also increase mitochondrial oxygen consumption and, thus, intracellular hypoxia. Along those lines, it would be helpful if the authors could show the pimonidazole data per se, in addition to the ratio HIF1a/pimonidazole. Increasing intracellular hypoxia could be an alternative mechanism that may promote cell death upon HIF1a suppression.

2. The authors do not provide unequivocal evidence that the increased mitochondrial ROS are responsible for the cell death upon exposure to hyperglycemia. Along those lines, use of reducing agents such as NAC could be helpful and informative.

3. Is the cell death documented by the authors caspase-3 dependent?

4. All western blot data should be properly quantified.

5. In the various genetic models, inactivation or increased expression of the gene of interest should be properly documented.

Reviewer #2:

The manuscript by Zheng et al. addresses the question of whether abnormal HIF stabilisation in response to hypoxia could be responsible for the increased ROS generation in diabetes, and whether hyperglycaemia could be the driver for the ROS damage and cell death in hypoxia. Diabetes complications across multiple organs are associated with oxidative damage and are accompanied by restricted oxygen delivery due to vascular dysfunction. Thus, understanding how these two phenotypes are linked by diabetes is an important question for advancing our understanding of diabetes complications and to identify novel therapeutic targets.

Strengths

This study uses multiple different techniques to address this question, using cell culture, animal models as well as human samples.

Mechanistic conclusions are based on both genetic and pharmacological approaches, adding strength to the key findings.

This is an excellent continuation of high-quality work conducted by this group in this field. The conclusions have far reaching consequences within the field.

Weaknesses

Although the authors establish a translational pipeline for their findings, from cells to animals to humans, the findings from the human studies could be interpreted in alternative ways to that presented. Cells cultured for 24hrs in hypoxia have sufficient time to activate the HIF transcription factor, its downstream targets and result in a functional outcome for the cell. Humans exposed to hypoxia for 1 hr will not have the same time to induce the HIF-dependent effects, and other mechanisms will be at play. To some extent this is due to the difficulties of performing mechanistic studies in humans, however, care should be taken in not oversimplifying findings and instead highlighting alternative conclusions.

Reactive oxygen species are a group of molecules comprising different species with different reactivities. It is important to be clear which aspects of ROS and their downstream damage are being measured with these methodologies.

Care must be taken to ensure the correct statistical analysis is performed based on the groups assessed, the study design and the scientific question being asked.

My major concern relates to the conclusions drawn from the human studies and their direct relevance to the cell/animal work.

1. Results – human study. This is a really interesting and important finding that the diabetic patients have greater ROS generation in response to hypoxia. However, this is an acute hypoxic response and will not be related to any HIF-related changes in mitochondrial function that form the core of the rest of the paper. These are two independent mechanisms/phenomena. I think this has to be addressed at some point in the paper, that the findings in patients cannot be explained by the mechanism presented in the rest of the manuscript, and are more likely due to other acute hypoxic factors. For example, in the discussion the authors state 'An optimal HIF-1 response during hypoxia, as seen in the control subjects in our study" – this is false – 1 hr is not sufficient to induce a HIF response that has a functional outcome that could have been measured in the blood of the patients.

2. Abstract – nonspecific terminology needs correcting to make the abstract specific to the findings. Terms such as "the dynamics of ROS levels" and "was followed by functional consequences" don't really tell the reader what was found in the paper. Also, please don't use the term "findings are highly significant" as this could relate to statistics.

3. Introduction – paragraph 1 – The final sentence about ROS generation being related to increased glucose availability is not relevant to all tissues – in heart for example there is decreased intracellular glucose availability. Please correct to make this broad statement more tissue specific.

4. Methods – Evaluation of ROS levels in kidney. 4HNE does not detect ROS – it measures oxidative damage as evident by lipid peroxidation. It is incorrect in the methods and results to describe this as ROS levels.

5. Methods – Clinical study – please describe how the patients were given 5 hypoxic episodes – methodological detail is lacking.

6. Statistics – I am confused about some of the choices for statistical analysis. In figure 1 why was an unpaired t-test used when the same patient has been tested at 0 and 4 hrs, this should be a paired t-test. Figure 2 – I'm unclear why a repeated measures ANOVA was used, when these were independent groups of cells, not the same cells measured at different points/conditions (this shouldn't be repeated measures). Figures 4 and 5 – when there are groups with 2 factors varying – genotype (wt vs PHD) and diabetic status (Ctl and db) – this should be a 2-way ANOVA not a one-way ANOVA.

7. Figure 3 – panels B and C – could the authors show HIF blots for these same groups, to compliment the mitosox intensity, so its possible to relate ROS to the degree of HIF stabilisation in these genetic cell models.

8. Figure 4 – could the authors please comment on why respiration in the PHD2 mutant isn't below the WT? One would expect the HIF stabilisation in this model to decrease oxygen consumption.

9. Discussion – 3rd paragraph. The authors have made incorrect conclusions regarding their respiration data. They state that "our results indicate that the role of HIF on ROS is limited to complex I". What the data shows is that when respiring through the ETC that involves all of complexes I, II, III, and IV – with substrate feeding in at the start and oxygen consumed at the end – that there is a HIF dependent effect. They cannot conclude it is at complex I as they haven't independently assessed complex II (independent of complex I), complex III and separately complex IV (which would need to be done with succinate alone, DHQ and TMPD as 3 separate experiments). The data shows the HIF effect resides at some point between Complex I and Complex IV.

10. Table 3 – blood glucose before STZ – is this significantly different?

11. Page 9 – PDK1 only inhibits flux through the TCA is exclusively metabolising glucose – not the case if metabolising amino acids or fatty acids.

Reviewer #3:

Strengths:

1. Oxidative stress and damage is central to pathology of diabetic complications. As mitochondria are a key generator of ROS-mediated damage, the authors nicely connect glucose-dependent HIF1a deficiency with the development of mitochondrial ROS-mediated damage.

2. Approaches to understand and target oxidant stress in diabetic complications are elusive and the authors nicely delineate a PHD2-dependent mechanism by which HIF1a levels decline, subsequently giving rise to renal dysfunction.

3. Notably, improvement of HIF1a levels by PHD2 pharmacologic or genetic inhibition appears to ameliorate renal injury independent of glycemic control. This could be of high translational value as therapies to ameliorate diabetic nephropathy do not exist currently in the clinic.

Weaknesses:

1. The mechanism by which hyperglycemia precipitates PHD2-dependent HIF1a degradation and activation of renal injury is not clear.

2. The induction of mitochondrial damage to elicit mitochondrial ROS and subsequent renal compromise downstream of HIF1a deficiency is proposed to be via PDK1. It would strengthen the paper if the PDK1-dependent mechanism was further solidified.

3. Clinical data on patients with uncontrolled T1D and impaired circulating hypoxia responses are very interesting, but it is unclear how they directly relate to the renal specific findings presented in the remainder of the manuscript.

The study presented by Zheng and colleagues is well written and serves to highlight the important role of ROS in the development of diabetic kidney disease. The studies are interesting, well written, and nicely performed. Further, the translational implications of PHD2 inhibition to potentially ameliorate diabetic nephropathy are highly appealing. However, a central concern is that some of the major observations of this study, while interesting, appear to be consistent with other previous observations in the field. Indeed, several groups have observed a depletion of HIF1a leads to renal demise. Further, PHD2 genetic and pharmacologic inhibition have been shown by several groups to protect against renal injury. Finally, the importance of mitochondrial ROS in diabetic kidney disease is also well known. While this study nicely weaves the concept together, a concern is the results are mostly confirmatory in nature.

Thus, several approaches could be employed to enhance the novelty of the observations within the manuscript.

1. The authors note that glucose reduces HIF1a levels through a PHD2 dependent mechanism, yet in Figure 4 supplement 2, PHD2 levels do not rise in db/db mice. It would substantially improve the manuscript if the mechanisms underlying increased PHD2 activity/HIF1a degradation were reconciled. Is enzymatic activity enhanced? Is it possible that VHL targets HIF1a for degradation more rapidly? Garcia-Pastor et al. (Sci Reports 2019) previously published that glucose inhibits the interaction of HIF1a with Hsp90 in cell lines. Does this occur in primary cells as well?

2. The authors implicate loss of PDK1 activity downstream of HIF1a deficiency as a key mechanism underlying the increase in complex I activity and subsequent mitochondrial ROS. However, reductions in PDK1 gene expression are modest at best. This mechanism, while based upon a highly interesting study from Kim and colleagues in Cell Metabolism, is not well substantiated in the author's study. More conclusive experimentation reconciling this pathway's importance is crucial for the development of the mechanism underlying the authors' model.

3. Increases in mitochondrial ROS in diabetic nephropathy have also been thought to be related to effects on the Nrf2 anti-oxidant response, which is not discussed in this study. It would be helpful to address the potential roles of Nrf2 in the pro-oxidant responses surveyed in their mouse and cell based models.

4. Clinical data on T1D patients, while interesting, is of unclear utility in this manuscript. Do these patients have renal disease? While HbA1c levels are higher in T1D patients versus non-T1D patients, are ROS responses improved in T1D patients with normal glycemic control? Further, as ROS levels are often transient in the blood stream, what are the ambient blood glucose levels during the period of the EPR assays?

https://doi.org/10.7554/eLife.70714.sa1

Author response

Reviewer #1:

In this study, Zheng and colleagues report the novel findings that in diabetic models hyperglycemia suppress HIF1a in a PHD-dependent manner, and this in turn leads to increased mitochondrial ROS and cell death. Both the increased ROS and the cell death are prevented by increasing HIF1a activity either pharmacologically or genetically.

The paper is novel, informative, and with interesting translational implications. The authors used a variety of in vitro and in vivo models for the testing of their hypothesis, with special emphasis on a model of diabetic nephropathy.

However, a few issues need to be addressed in order to strengthen the authors' conclusions and their biological significance.

1. Suppressing HIF1a should also increase mitochondrial oxygen consumption and, thus, intracellular hypoxia. Along those lines, it would be helpful if the authors could show the pimonidazole data per se, in addition to the ratio HIF1a/pimonidazole. Increasing intracellular hypoxia could be an alternative mechanism that may promote cell death upon HIF1a suppression.

The levels of hypoxia evaluated by pimonidazole staining were added as Figure 4-supplement figure 1. It is true that the levels of intracellular hypoxia were increased in diabetic kidneys compared with the animals without diabetes that confirms previous observations (Rosenberger, Khamaisi et al. 2008, PMID: 17914354) and were improved by HIF induction in both animal models. Their potential contribution to tissue injury was added in Discussion (the first paragraph of page 26).

2. The authors do not provide unequivocal evidence that the increased mitochondrial ROS are responsible for the cell death upon exposure to hyperglycemia. Along those lines, use of reducing agents such as NAC could be helpful and informative.

We thank to the reviewer for the constructive suggestion. Since we have found that the cell death induced by high glucose levels in hypoxia is dependent on caspase-3/7 (next question), we have verified the effect of NAC on the caspase-3/7 activity in mIMCD3 cells. As shown in Figure 2G, the caspase-3/7 activity increased in mIMCD3 cells exposed to high glucose concentrations and hypoxia, and was inhibited by NAC pre-treatment. These results indicate that the increased mitochondrial ROS is responsible for cell apoptosis upon exposure to hyperglycemia and hypoxia. This result has been added to the manuscript as Figure 2G.

3. Is the cell death documented by the authors caspase-3 dependent?

The caspase-3/7 activity has been evaluated in mIMCD3 cells exposed to normal (5.5 mM) or high (30 mM) glucose levels in normoxia (N) or hypoxia (H) using Caspase-Glo 3/7 Assay kit from Promega. As shown in Figure 2F, high glucose levels in hypoxia enhanced caspase 3/7 activity in mIMCD3 cells, which could be inhibited by DMOG treatment. These results suggest that the apoptosis induced by high glucose levels in hypoxia is dependent on caspase-3 and -7 and are added to the manuscript as Figure 2F.

4. All western blot data should be properly quantified.

All the western blot data were quantified as suggested. The quantifications have been added below each western blot.

5. In the various genetic models, inactivation or increased expression of the gene of interest should be properly documented.

We thank the reviewer for very good suggestion. The expression of VHL gene in cells transfected with control siRNA and VHL siRNA is shown in Figure 3B, and the endogenous nuclear HIF-1α protein expression was analysed using fluorescent immunocytochemistry (Figure 3C). Expression of GFP and GFP-HIF-1α were verified using confocal microscopy and the nuclear HIF-1α expression in GFP-HIF-1α-expressing cells was further confirmed by immunocytochemistry using anti-HIF-1α antibody (Figure 3E). Egln1 gene expression in healthy and diabetic wild-type and Egln1+/- mice is shown in Figure 4 —figure supplement 3B.

Reviewer #2:

[…]

My major concern relates to the conclusions drawn from the human studies and their direct relevance to the cell/animal work.

1. Results – human study. This is a really interesting and important finding that the diabetic patients have greater ROS generation in response to hypoxia. However, this is an acute hypoxic response and will not be related to any HIF-related changes in mitochondrial function that form the core of the rest of the paper. These are two independent mechanisms/phenomena. I think this has to be addressed at some point in the paper, that the findings in patients cannot be explained by the mechanism presented in the rest of the manuscript, and are more likely due to other acute hypoxic factors. For example, in the discussion the authors state 'An optimal HIF-1 response during hypoxia, as seen in the control subjects in our study" – this is false – 1 hr is not sufficient to induce a HIF response that has a functional outcome that could have been measured in the blood of the patients.

We agree with the reviewer that exposure for 1 hour to hypoxia is not sufficient for the functional outcome of HIF-1-related genes. We believe however that this short exposure to hypoxia unmasks the impaired function of HIF-1 that is already present in the subjects with diabetes before exposure to hypoxia. Our hypothesis is sustained by the findings that the plasma levels of microRNA-210 (which is a target uniquely regulated by HIF-1) are lower in the subjects with diabetes (26.7 ± 14.5 %, P<0.05) than in controls (100 ± 52.5 %) before exposure to hypoxia. However, we cannot exclude other sources of ROS independent of HIF-1 that are activated by short exposure to hypoxia, either from mitochondria (Waypa, Marks et al. 2013. PMID: 23328522) (Hernansanz-Agustin, Choya-Foces et al. 2020. PMID: 32728214), or elsewhere i.e. NADPH oxidases (Weissman, Tadic et al. 2000. PMID: 11000128). This has been added to the Discussion in the manuscript (2nd paragraph of page 24).

2. Abstract – nonspecific terminology needs correcting to make the abstract specific to the findings. Terms such as "the dynamics of ROS levels" and "was followed by functional consequences" don't really tell the reader what was found in the paper. Also, please don't use the term "findings are highly significant" as this could relate to statistics.

We made the changes suggested by the reviewer.

3. Introduction – paragraph 1 – The final sentence about ROS generation being related to increased glucose availability is not relevant to all tissues – in heart for example there is decreased intracellular glucose availability. Please correct to make this broad statement more tissue specific.

The sentence was changed as suggested.

4. Methods – Evaluation of ROS levels in kidney. 4HNE does not detect ROS – it measures oxidative damage as evident by lipid peroxidation. It is incorrect in the methods and results to describe this as ROS levels.

We completely agree with the reviewer and changed the description accordingly.

5. Methods – Clinical study – please describe how the patients were given 5 hypoxic episodes – methodological detail is lacking.

The details of the hypoxia exposures were added (page 7).

6. Statistics – I am confused about some of the choices for statistical analysis. In figure 1 why was an unpaired t-test used when the same patient has been tested at 0 and 4 hrs, this should be a paired t-test. Figure 2 – I'm unclear why a repeated measures ANOVA was used, when these were independent groups of cells, not the same cells measured at different points/conditions (this shouldn't be repeated measures). Figures 4 and 5 – when there are groups with 2 factors varying – genotype (wt vs PHD) and diabetic status (Ctl and db) – this should be a 2-way ANOVA not a one-way ANOVA.

Unpaired t-test was applied in Figure 1 because some samples were missing that precludes the use of paired t-test. We agree that ordinary one-way ANOVA test is appropriate to be used for Figure 2 and changed in consequence. We agree that two-way ANOVA test should be applied in the experiments that have two independent variables (genotype and diabetic status) and we changed accordingly.

7. Figure 3 – panels B and C – could the authors show HIF blots for these same groups, to compliment the mitosox intensity, so its possible to relate ROS to the degree of HIF stabilisation in these genetic cell models.

We agree with the reviewer that it is important to show the HIF-1α expression levels in these experiments. We have therefore documented the nuclear HIF-1α expression in the conditions where ROS levels were decreased. These results have been added to Figure 3.

8. Figure 4 – could the authors please comment on why respiration in the PHD2 mutant isn't below the WT? One would expect the HIF stabilisation in this model to decrease oxygen consumption.

Very interesting question! We believe that non-diabetic condition is a non-challenged state, where the local oxygen levels in kidney are similar between WT and Egln1+/- mice (PHD mutant), as shown by similar pimonidazole staining levels (Figure 4 —figure supplement 1B). Under this condition, HIF-1 signaling is under proper regulation as illustrated by similar HIF-1α (Figure 4C) and HIF-1 target gene e.g. GLUT3 (Figure 4 —figure supplement 3) expression levels, therefore oxygen consumption rate is comparable.

The effects of HIF-1α stabilisation secondary to the genetic manipulation of Egln1 (encoding PHD2) is however uncovered in diabetes, where it maintains an adequate HIF-1 level in opposition to WT mice with diabetes and in consequence to have a lower respiration.

9. Discussion – 3rd paragraph. The authors have made incorrect conclusions regarding their respiration data. They state that "our results indicate that the role of HIF on ROS is limited to complex I". What the data shows is that when respiring through the ETC that involves all of complexes I, II, III, and IV – with substrate feeding in at the start and oxygen consumed at the end – that there is a HIF dependent effect. They cannot conclude it is at complex I as they haven't independently assessed complex II (independent of complex I), complex III and separately complex IV (which would need to be done with succinate alone, DHQ and TMPD as 3 separate experiments). The data shows the HIF effect resides at some point between Complex I and Complex IV.

We thank to the reviewer for the comments. Indeed, the experimental design used does not allow to rule out other mitochondrial ROS generating contributors and we changed the statement in consequence.

10. Table 3 – blood glucose before STZ – is this significantly different?

The blood glucose levels before administration of STZ are not different between wild type mice and Egln1+/- mice using two-way ANOVA test.

11. Page 9 – PDK1 only inhibits flux through the TCA is exclusively metabolising glucose – not the case if metabolising amino acids or fatty acids.

We completely agree with the reviewer and we have made the appropriate correction.

Reviewer #3:

[…]

The study presented by Zheng and colleagues is well written and serves to highlight the important role of ROS in the development of diabetic kidney disease. The studies are interesting, well written, and nicely performed. Further, the translational implications of PHD2 inhibition to potentially ameliorate diabetic nephropathy are highly appealing. However, a central concern is that some of the major observations of this study, while interesting, appear to be consistent with other previous observations in the field. Indeed, several groups have observed a depletion of HIF1a leads to renal demise. Further, PHD2 genetic and pharmacologic inhibition have been shown by several groups to protect against renal injury. Finally, the importance of mitochondrial ROS in diabetic kidney disease is also well known. While this study nicely weaves the concept together, a concern is the results are mostly confirmatory in nature.

Thus, several approaches could be employed to enhance the novelty of the observations within the manuscript.

1. The authors note that glucose reduces HIF1a levels through a PHD2 dependent mechanism, yet in Figure 4 supplement 2, PHD2 levels do not rise in db/db mice. It would substantially improve the manuscript if the mechanisms underlying increased PHD2 activity/HIF1a degradation were reconciled. Is enzymatic activity enhanced? Is it possible that VHL targets HIF1a for degradation more rapidly? Garcia-Pastor et al. (Sci Reports 2019) previously published that glucose inhibits the interaction of HIF1a with Hsp90 in cell lines. Does this occur in primary cells as well?

We thank the reviewer for raising this important question. The mechanisms by which high glucose levels impair HIF-1 expression and function are still incompletely unraveled. We agree with the reviewer that, even though we bring pharmacological and genetic evidence about the importance of PHD-dependent degradation for the HIF-1 repression in diabetes, we did not dissect the specific mechanisms facilitating the PHD2-mediated regulation of HIF-1 in diabetes, a subject that warrants further investigation. Our results have shown that silencing VHL gene can stabilize HIF-1α and reduce mitochondrial ROS levels in cells exposed to high glucose levels and hypoxia (Figure 3B-3D), indicating that VHL contributes to the degradation of HIF-1α in diabetic conditions. We would like to clarify that we have not claimed that the inhibition of HIF-1 in diabetes is specific to the PHD2-dependent HIF-1α degradation. The contribution of other mechanisms to the inhibition of HIF-1 signaling in diabetes needs to be further elucidated.

2. The authors implicate loss of PDK1 activity downstream of HIF1a deficiency as a key mechanism underlying the increase in complex I activity and subsequent mitochondrial ROS. However, reductions in PDK1 gene expression are modest at best. This mechanism, while based upon a highly interesting study from Kim and colleagues in Cell Metabolism, is not well substantiated in the author's study. More conclusive experimentation reconciling this pathway's importance is crucial for the development of the mechanism underlying the authors' model.

We thank the reviewer for the constructive comments. In order to further investigate the role of PDK1, we transfected plasmids encoding GFP or GFP-fused PDK1 (GFP-PDK1) in mIMCD3 cells exposed to high glucose levels and hypoxia (Figure 4I).The mitochondrial ROS levels were assessed by flow cytometry analysis of mitosox intensity in GFP- or GFP-PDK1-positive cells. As shown in Figure 4J, mitochondrial ROS overproduction in cells exposed to high glucose levels and hypoxia was diminished by PDK1 overexpression, suggesting that the increased mitochondrial ROS is at least partially mediated by the inhibition of HIF-1 target gene PDK1 in diabetes. These results have been added to the manuscript (page 22).

3. Increases in mitochondrial ROS in diabetic nephropathy have also been thought to be related to effects on the Nrf2 anti-oxidant response, which is not discussed in this study. It would be helpful to address the potential roles of Nrf2 in the pro-oxidant responses surveyed in their mouse and cell based models.

We thank the reviewer for the suggestion. The potential role of Nrf2 for diabetes nephropathy has been included in the discussion (page 25).

4. Clinical data on T1D patients, while interesting, is of unclear utility in this manuscript. Do these patients have renal disease? While HbA1c levels are higher in T1D patients versus non-T1D patients, are ROS responses improved in T1D patients with normal glycemic control? Further, as ROS levels are often transient in the blood stream, what are the ambient blood glucose levels during the period of the EPR assays?

The patients with T1D were without any detectable complication, including renal disease. The clinical study provides the proof of concept that the subjects with uncontrolled diabetes responded with an increase in ROS levels after exposure to hypoxia, while as ROS levels did not increase in subjects without diabetes.

We agree with the reviewer that a separate investigation on whether improvement of the metabolic control can affect the ROS response to hypoxia warrants further investigation.

In the present study, plasma glucose levels before hypoxia exposure (0h) were significantly higher in patients with diabetes (12.79 ± 6.62 mM) than in control subjects (4.66 ± 0.90 mM), as expected, since only patients with poor glycemic control have been included. After one hour of hypoxia exposure (1h), plasma glucose increased in healthy subjects by 0.64 ± 0.91 mM (P<0.05). This is in line with previous findings (e.g. PMID: 28087818), and may represent a mechanism to protect tissues during hypoxia by increasing fuel supply. Interestingly, plasma glucose levels decreased in patients with diabetes by 3.05 ± 5.03 mM (P<0.05). This opposite response of blood glucose is part of the impaired adaptive responses to hypoxia present in patients with diabetes, that is subject of our ongoing investigation.

https://doi.org/10.7554/eLife.70714.sa2

Article and author information

Author details

  1. Xiaowei Zheng

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Sampath Narayanan and Cheng Xu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2648-1119
  2. Sampath Narayanan

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Xiaowei Zheng and Cheng Xu
    Competing interests
    No competing interests declared
  3. Cheng Xu

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Xiaowei Zheng and Sampath Narayanan
    Competing interests
    No competing interests declared
  4. Sofie Eliasson Angelstig

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Data curation, Formal analysis, Investigation, Validation, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Jacob Grünler

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Data curation, Investigation, Methodology, Validation, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Allan Zhao

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Data curation, Formal analysis, Investigation, Validation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2492-0923
  7. Alessandro Di Toro

    Centre for Inherited Cardiovascular Diseases, IRCCS Foundation University Hospital Policlinico San Matteo, Pavia, Italy
    Contribution
    Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7625-1103
  8. Luciano Bernardi

    Folkälsan Research Center, Folkälsan Institute of Genetics, University of Helsinki, Helsinki, Finland
    Contribution
    Conceptualization, Methodology, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Massimiliano Mazzone

    Laboratory of Tumor Inflammation and Angiogenesis, Center for Cancer Biology, Vlaams Instituut voor Biotechnologie (VIB); Laboratory of Tumor Inflammation and Angiogenesis, Center for Cancer Biology, Department of Oncology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Peter Carmeliet

    Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, Katholieke Universiteit (KU) Leuven; Laboratory of Angiogenesis and Vascular Metabolism, Vesalius Research Center, Vlaams Instituut voor Biotechnologie (VIB), Leuven, Belgium
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7961-1821
  11. Marianna Del Sole

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  12. Giancarlo Solaini

    Dipartimento di Biochimica, Università di Bologna, Bologna, Italy
    Contribution
    Writing – review and editing
    Competing interests
    No competing interests declared
  13. Elisabete A Forsberg

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  14. Ao Zhang

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Present address
    Department of Nephrology, GuangDong Second Traditional Chinese Medicine Hospital, GuangZhou, China
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  15. Kerstin Brismar

    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Conceptualization, Funding acquisition, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  16. Tomas A Schiffer

    Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden
    Contribution
    Data curation, Investigation, Methodology, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  17. Neda Rajamand Ekberg

    1. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    2. Department of Endocrinology and Diabetes, Karolinska University Hospital, Stockholm, Sweden
    3. Center for Diabetes, Academic Specialist Centrum, Stockholm, Sweden
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5597-2593
  18. Ileana Ruxandra Botusan

    1. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    2. Department of Endocrinology and Diabetes, Karolinska University Hospital, Stockholm, Sweden
    3. Center for Diabetes, Academic Specialist Centrum, Stockholm, Sweden
    Contribution
    Investigation, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  19. Fredrik Palm

    Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden
    Contribution
    Conceptualization, Funding acquisition, Resources, Supervision, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Sergiu-Bogdan Catrina
    Competing interests
    No competing interests declared
  20. Sergiu-Bogdan Catrina

    1. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
    2. Department of Endocrinology and Diabetes, Karolinska University Hospital, Stockholm, Sweden
    3. Center for Diabetes, Academic Specialist Centrum, Stockholm, Sweden
    Contribution
    Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Fredrik Palm
    For correspondence
    sergiu-bogdan.catrina@ki.se
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6914-3902

Funding

Vetenskapsrådet

  • Sergiu-Bogdan Catrina

Stockholms Läns Landsting

  • Sergiu-Bogdan Catrina

Stockholm Regional Research Foundation

  • Sergiu-Bogdan Catrina

Bert von Kantzows Foundation

  • Sergiu-Bogdan Catrina

Swedish Society of Medicine

  • Sergiu-Bogdan Catrina

Kung Gustaf V:s och Drottning Victorias Frimurarestifelse

  • Sergiu-Bogdan Catrina

Karolinska Institute's Research Foundations

  • Sergiu-Bogdan Catrina

Strategic Research Programme in Diabetes

  • Sergiu-Bogdan Catrina

Erling-Persson Family Foundation

  • Sergiu-Bogdan Catrina

Vetenskapsrådet (2020-01645)

  • Tomas A Schiffer

Swedish Heart and Lung Foundation (20210431)

  • Tomas A Schiffer

ERC consolidator grant (773208)

  • Massimiliano Mazzone

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank to Valeria Alferova and Anette Landström from Karolinska Institutet, and Natasha Widen, Kajsa Sundqvist and Anette Härström from Karolinska University Hospital for excellent technical assistance.

Ethics

The clinical study was approved by the Regional Ethical Review Board in Stockholm, Sweden, and carried out in accordance with the principles of the Declaration of Helsinki. All participants in the study provided informed consent.

The experimental animal procedure was approved by the North Stockholm Ethical Committee for the Care and Use of Laboratory Animals (ethical permission N250/15, N60/15, and N179/16).

Senior Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Reviewing Editor

  1. Ernestina Schipani, University of Pennsylvania, United States

Reviewer

  1. Ernestina Schipani, University of Pennsylvania, United States

Publication history

  1. Received: May 26, 2021
  2. Preprint posted: June 16, 2021 (view preprint)
  3. Accepted: January 27, 2022
  4. Version of Record published: February 15, 2022 (version 1)

Copyright

© 2022, Zheng et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Xiaowei Zheng
  2. Sampath Narayanan
  3. Cheng Xu
  4. Sofie Eliasson Angelstig
  5. Jacob Grünler
  6. Allan Zhao
  7. Alessandro Di Toro
  8. Luciano Bernardi
  9. Massimiliano Mazzone
  10. Peter Carmeliet
  11. Marianna Del Sole
  12. Giancarlo Solaini
  13. Elisabete A Forsberg
  14. Ao Zhang
  15. Kerstin Brismar
  16. Tomas A Schiffer
  17. Neda Rajamand Ekberg
  18. Ileana Ruxandra Botusan
  19. Fredrik Palm
  20. Sergiu-Bogdan Catrina
(2022)
Repression of hypoxia-inducible factor-1 contributes to increased mitochondrial reactive oxygen species production in diabetes
eLife 11:e70714.
https://doi.org/10.7554/eLife.70714

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    Desiree Schatton et al.
    Research Article

    Proliferating cells undergo metabolic changes in synchrony with cell cycle progression and cell division. Mitochondria provide fuel, metabolites, and ATP during different phases of the cell cycle, however it is not completely understood how mitochondrial function and the cell cycle are coordinated. CLUH is a post-transcriptional regulator of mRNAs encoding mitochondrial proteins involved in oxidative phosphorylation and several metabolic pathways. Here, we show a role of CLUH in regulating the expression of astrin, which is involved in metaphase to anaphase progression, centrosome integrity, and mTORC1 inhibition. We find that CLUH binds both the SPAG5 mRNA and its product astrin, and controls the synthesis and the stability of the full-length astrin-1 isoform. We show that CLUH interacts with astrin-1 specifically during interphase. Astrin-depleted cells show mTORC1 hyperactivation and enhanced anabolism. On the other hand, cells lacking CLUH show decreased astrin levels and increased mTORC1 signaling, but cannot sustain anaplerotic and anabolic pathways. In absence of CLUH, cells fail to grow during G1, and progress faster through the cell cycle, indicating dysregulated matching of growth, metabolism and cell cycling. Our data reveal a role of CLUH in coupling growth signaling pathways and mitochondrial metabolism with cell cycle progression.

    1. Cell Biology
    Dillon Jevon et al.
    Research Article

    A developing understanding suggests that spatial compartmentalisation in pancreatic β cells is critical in controlling insulin secretion. To investigate the mechanisms, we have developed live-cell sub-cellular imaging methods using the mouse organotypic pancreatic slice. We demonstrate that the organotypic pancreatic slice, when compared with isolated islets, preserves intact β cell structure, and enhances glucose dependent Ca2+ responses and insulin secretion. Using the slice technique, we have discovered the essential role of local activation of integrins and the downstream component, focal adhesion kinase, in regulating β cells. Integrins and focal adhesion kinase are exclusively activated at the β cell capillary interface and using in situ and in vitro models we show their activation both positions presynaptic scaffold proteins, like ELKS and liprin, and regulates glucose dependent Ca2+ responses and insulin secretion. We conclude that focal adhesion kinase orchestrates the final steps of glucose dependent insulin secretion within the restricted domain where β cells contact the islet capillaries.