1. Cell Biology
  2. Medicine

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
https://doi.org/10.7554/eLife.70714
Share this article
Cite this article
  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
  2. Download BibTeX
  3. Download .RIS

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

View detailed protocol

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

Request a detailed protocol

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.

https://cdn.elifesciences.org/articles/70714/elife-70714-table3-data1-v1.xlsx

Fluorescent immunohistochemistry

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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

Request a detailed protocol

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
Download asset Open asset
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.

https://cdn.elifesciences.org/articles/70714/elife-70714-fig1-data1-v1.xlsx

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
Download asset Open asset
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
Download asset Open asset
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
Download asset Open asset
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).

Figure 5
Download asset Open asset
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).

Figure 6
Download asset Open asset
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

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).

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.

Metrics

  • 3,908
    views
  • 531
    downloads
  • 39
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Share this article

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

Categories and tags

Research organisms

Further reading

    1. Cell Biology

    Exploring the role of macromolecular crowding and TNFR1 in cell volume control

    Parijat Biswas, Priyanka Roy ... Deepak Kumar Sinha
    Research Article Updated

    The excessive cosolute densities in the intracellular fluid create a physicochemical condition called macromolecular crowding (MMC). Intracellular MMC entropically maintains the biochemical thermodynamic equilibria by favoring associative reactions while hindering transport processes. Rapid cell volume shrinkage during extracellular hypertonicity elevates the MMC and disrupts the equilibria, potentially ushering cell death. Consequently, cells actively counter the hypertonic stress through regulatory volume increase (RVI) and restore the MMC homeostasis. Here, we establish fluorescence anisotropy of EGFP as a reliable tool for studying cellular MMC and explore the spatiotemporal dynamics of MMC during cell volume instabilities under multiple conditions. Our studies reveal that the actin cytoskeleton enforces spatially varying MMC levels inside adhered cells. Within cell populations, MMC is uncorrelated with nuclear DNA content but anti-correlated with the cell spread area. Although different cell lines have statistically similar MMC distributions, their responses to extracellular hypertonicity vary. The intensity of the extracellular hypertonicity determines a cell’s ability for RVI, which correlates with nuclear factor kappa beta (NFkB) activation. Pharmacological inhibition and knockdown experiments reveal that tumor necrosis factor receptor 1 (TNFR1) initiates the hypertonicity-induced NFkB signaling and RVI. At severe hypertonicities, the elevated MMC amplifies cytoplasmic microviscosity and hinders receptor interacting protein kinase 1 (RIPK1) recruitment at the TNFR1 complex, incapacitating the TNFR1-NFkB signaling and consequently, RVI. Together, our studies unveil the involvement of TNFR1-NFkB signaling in modulating RVI and demonstrate the pivotal role of MMC in determining cellular osmoadaptability.

    1. Cell Biology

    The actomyosin system is essential for the integrity of the endosomal system in bloodstream form Trypanosoma brucei

    Fabian Link, Sisco Jung ... Brooke Morriswood
    Research Article

    The actin cytoskeleton is a ubiquitous feature of eukaryotic cells, yet its complexity varies across different taxa. In the parasitic protist Trypanosoma brucei, a rudimentary actomyosin system consisting of one actin gene and two myosin genes has been retained despite significant investment in the microtubule cytoskeleton. The functions of this highly simplified actomyosin system remain unclear, but appear to centre on the endomembrane system. Here, advanced light and electron microscopy imaging techniques, together with biochemical and biophysical assays, were used to explore the relationship between the actomyosin and endomembrane systems. The class I myosin (TbMyo1) had a large cytosolic pool and its ability to translocate actin filaments in vitro was shown here for the first time. TbMyo1 exhibited strong association with the endosomal system and was additionally found on glycosomes. At the endosomal membranes, TbMyo1 colocalised with markers for early and late endosomes (TbRab5A and TbRab7, respectively), but not with the marker associated with recycling endosomes (TbRab11). Actin and myosin were simultaneously visualised for the first time in trypanosomes using an anti-actin chromobody. Disruption of the actomyosin system using the actin-depolymerising drug latrunculin A resulted in a delocalisation of both the actin chromobody signal and an endosomal marker, and was accompanied by a specific loss of endosomal structure. This suggests that the actomyosin system is required for maintaining endosomal integrity in T. brucei.

    1. Cell Biology

    Distinct trafficking routes of polarized and non-polarized membrane cargoes in Aspergillus nidulans

    Georgia Maria Sagia, Xenia Georgiou ... Sofia Dimou
    Research Article Updated

    Membrane proteins are sorted to the plasma membrane via Golgi-dependent trafficking. However, our recent studies challenged the essentiality of Golgi in the biogenesis of specific transporters. Here, we investigate the trafficking mechanisms of membrane proteins by following the localization of the polarized R-SNARE SynA versus the non-polarized transporter UapA, synchronously co-expressed in wild-type or isogenic genetic backgrounds repressible for conventional cargo secretion. In wild-type, the two cargoes dynamically label distinct secretory compartments, highlighted by the finding that, unlike SynA, UapA does not colocalize with the late-Golgi. In line with early partitioning into distinct secretory carriers, the two cargoes collapse in distinct ER-Exit Sites (ERES) in a sec31ts background. Trafficking via distinct cargo-specific carriers is further supported by showing that repression of proteins essential for conventional cargo secretion does not affect UapA trafficking, while blocking SynA secretion. Overall, this work establishes the existence of distinct, cargo-dependent, trafficking mechanisms, initiating at ERES and being differentially dependent on Golgi and SNARE interactions.