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Basic Research
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Lipoxins Regulate the Early Growth Response–1 Network and Reverse Diabetic Kidney Disease

Eoin P. Brennan, Muthukumar Mohan, Aaron McClelland, Christos Tikellis, Mark Ziemann, Antony Kaspi, Stephen P. Gray, Raelene Pickering, Sih Min Tan, Syed Tasadaque Ali-Shah, Patrick J. Guiry, Assam El-Osta, Karin Jandeleit-Dahm, Mark E. Cooper, Catherine Godson and Phillip Kantharidis
JASN February 2018, ASN.2017101112; DOI: https://doi.org/10.1681/ASN.2017101112
Eoin P. Brennan
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
2University College Dublin Diabetes Complications Research Centre, UCD Conway Institute of Biomolecular and Biomedical Research, UCD School of Medicine and Medical Sciences, and
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Muthukumar Mohan
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
3Department of Diabetes and
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Aaron McClelland
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
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Christos Tikellis
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
3Department of Diabetes and
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Mark Ziemann
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
4Epigenetics in Human Health and Disease Laboratory, Department of Diabetes, Central Clinical School, Monash University, Clayton, Victoria, Australia
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Antony Kaspi
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
4Epigenetics in Human Health and Disease Laboratory, Department of Diabetes, Central Clinical School, Monash University, Clayton, Victoria, Australia
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Stephen P. Gray
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
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Raelene Pickering
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
3Department of Diabetes and
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Sih Min Tan
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
3Department of Diabetes and
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Syed Tasadaque Ali-Shah
5Centre for Synthesis and Chemical Biology, UCD School of Chemistry and Chemical Biology, University College Dublin, Dublin, Ireland; and
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Patrick J. Guiry
5Centre for Synthesis and Chemical Biology, UCD School of Chemistry and Chemical Biology, University College Dublin, Dublin, Ireland; and
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Assam El-Osta
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
4Epigenetics in Human Health and Disease Laboratory, Department of Diabetes, Central Clinical School, Monash University, Clayton, Victoria, Australia
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Karin Jandeleit-Dahm
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
3Department of Diabetes and
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Mark E. Cooper
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
3Department of Diabetes and
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Catherine Godson
2University College Dublin Diabetes Complications Research Centre, UCD Conway Institute of Biomolecular and Biomedical Research, UCD School of Medicine and Medical Sciences, and
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Phillip Kantharidis
1Juvenile Diabetes Research Foundation Danielle Alberti Memorial Centre for Diabetes Complications, Diabetes Division, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia;
3Department of Diabetes and
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    Figure 1.

    LXs attenuate DKD. (A) Structure of endogenous LXA4 and Benzo-LXA4 and study design overview. ApoE−/− mice were rendered diabetic by STZ, and a subgroup of diabetic and nondiabetic ApoE−/− mice were administered ethanol (0.1%), LXA4 (5 μg/kg), or Benzo-LXA4 (1.7 μg/kg) twice weekly ip from weeks 1 to 10 (10-week prevention study) or weeks 1 to 20 (20-week prevention study). (B and C) Paraffin-embedded kidney sections of 20-week diabetic and control ApoE−/− mice administered ethanol (0.1%), LXA4, or Benzo-LXA4 from weeks 1 to 20 were stained by periodic acid–Schiff stain. Quantification of the glomerulosclerosis injury index is shown in the bar graph as mean±SEM (n=8–10/group; *P<0.05;**P<0.01). (D) Gene expression analysis of markers of kidney damage in kidney cortex tissue isolated from 10-week diabetic and nondiabetic ApoE−/− mice administered vehicle, LXA4, or Benzo-LXA4. Expression was normalized to 18S for gene expression analysis (n=8–10, ±SEM; *P≤0.05 versus ApoE−/−+vehicle; φP≤0.05 versus Diabetic ApoE−/−+Vehicle). KO, knockout; qPCR, quantitative polymerase chain reaction.

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    Figure 2.

    LXs reverse established DKD. (A) Study design overview. ApoE−/− mice were rendered diabetic by STZ and diabetes was allowed to progress for 10 weeks, after which a subgroup of diabetic and nondiabetic ApoE−/− mice were administered ethanol (0.1%), LXA4 (5 μg/kg), or Benzo-LXA4 (1.7 μg/kg) twice weekly ip from weeks 10 to 16. (B and C) Paraffin-embedded kidney sections of 16-week intervention study diabetic and control ApoE−/− mice administered ethanol (0.1%), LXA4, or Benzo-LXA4 from weeks 10 to 16 were stained by periodic acid–Schiff stain. Quantification of the glomerulosclerosis injury index is shown in the bar graph as mean±SEM (n=8–10/group; *P<0.05; **P<0.01; ***P<0.001). (D and E) Immunohistochemical staining of collagen IV in glomeruli of paraffin-embedded kidney sections of 16-week intervention study diabetic and control ApoE−/− mice administered ethanol (0.1%), LXA4, or Benzo-LXA4 from weeks 10 to 16. Quantification of staining is shown in the bar graph as mean±SEM (n=8–10/group; *P<0.05). Gene expression analysis of markers of (F) kidney fibrosis and (G) inflammation in kidney cortex tissue isolated from 16-week diabetic and nondiabetic ApoE−/− mice administered vehicle, LXA4, or Benzo-LXA4 from weeks 10 to 16. Expression was normalized to 18S for gene expression analysis (n=8–10, ±SEM; *P≤0.05 versus ApoE−/−+vehicle; φP≤0.05 versus Diabetic ApoE−/−+Vehicle). KO, knockout; qPCR, quantitative polymerase chain reaction.

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    Figure 3.

    Transcriptome profiling identifies the networks associated with kidney disease in the diabetic ApoE−/− mouse model. (A) Venn diagram indicating number of significant differentially expressed transcripts (FDR P value <0.05) between diabetic and control ApoE−/− mice administered ethanol (0.1%), LXA4, or Benzo-LXA4 from weeks 1 to 10. (B) Renal expression levels of fn1, col4a1, grem1, vcam1, cyp2d12, trp53inp1, sulf2, mgmt, cdkn1a, and eda2r genes in control and diabetic mice administered ethanol (0.1%) (n=5–6 per group; FDR P value <0.05 for all transcripts). (C) Upstream regulators expected to be increased or decreased between control and diabetic ApoE−/− mice administered ethanol. Regulators were identified using the IPA regulation Z-score algorithm according to gene expression changes. A positive or negative Z-score value indicates that a function is predicted to be increased (red color) or decreased (blue color), respectively. Corresponding −Log2 P values indicate whether there is a statistically significant overlap between the dataset genes and genes that are known to be regulated by the upstream regulator. All regulators represented on the graph are significantly enriched (Z-score ≥2 or ≤−2; P<0.05). GPCRs, G-protein coupled receptors; vs, versus.

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    Figure 4.

    Analysis of renal transcriptome responses identifies LX-regulated transcriptional networks. (A and B) Upstream regulator analysis of transcriptome data identifies regulators predicted to be changed between diabetic ApoE−/− mice administered ethanol versus (A) LXA4 or (B) Benzo-LXA4 treatment from weeks 1 to 10. Regulators were identified using the IPA regulation Z-score algorithm according to gene expression changes. A positive or negative Z-score value indicates that a function is predicted to be increased (red color) or decreased (blue color), respectively. Corresponding −Log2 P values indicate whether there is a statistically significant overlap between the dataset genes and genes that are known to be regulated by the upstream regulator. All regulators represented on the graph are significantly enriched (Z-score ≥2 or ≤−2; P<0.05). (C and D) Heatmaps of normalized gene expression indicating transcripts displaying significant differential expression (FDR P value <0.05) between diabetic mice administered ethanol (0.1%) versus LXA4 or Benzo-LXA4 from weeks 1 to 10. Expression levels range from blue (low expression) to red (high expression). (E) Box plots indicating expression of genes regulated by both LXA4 and Benzo-LXA4 (egr1, adamtsl3, ngef, lamb3, grem1, and nr4a1). Transcript abundance across all treatment group is shown (*FDR P value <0.05). ID, identifier.

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    Figure 5.

    LXA4 regulates the Egr-1 transcriptional network in renal epithelial cells. (A) Heatmap of over-represented transcription factor binding sites (TFBSs) in promoters of genes identified to be differentially expressed between nondiabetic and diabetic ApoE−/− mice administered ethanol, and also between diabetic mice ApoE−/− mice administered ethanol versus LXA4 or Benzo-LXA4. Enriched TFBSs were identified using the Genomatix Z-score algorithm, with a positive Z-score (red color) indicative of TFBS enrichment. All regulators represented on the graph are significantly enriched in at least one comparison (Z-score ≥2; P<0.05). (B) Egr-1 gene expression levels in human renal proximal tubule epithelial cells (HK-2) after TNF-α (1 ng/ml), TGFβ (5 ng/ml), and LXA4 treatment (0.1 nM; 30 minutes). (C) Gene expression of Egr-1 after siRNA treatment targeting Egr-1 in HK-2 cells. (D) Gene expression of markers of TNF-α signaling activation in HK-2 cells after TNF-α treatment (1 ng/ml; 24 hours) in the presence/absence of nontargeting scrambled siRNA or Egr-1 siRNA. (E) TNF-α gene expression levels in HK-2 cells after TNF-α (1 ng/ml) and LXA4 (0.1 nM) treatment. (F) Gene expression of markers of TGFβ signaling activation in HK-2 cells after TGFβ treatment (5 ng/ml; 24 hours) in the presence/absence of nontargeting scrambled siRNA or Egr-1 siRNA. (G) Representative western blot and corresponding densitometry analysis of protein expression levels of markers of TGFβ signaling activation in HK-2 cells after TGFβ treatment (5 ng/ml; 24 hours) in the presence/absence of nontargeting scrambled siRNA or Egr-1 siRNA. Expression was normalized to Gapdh for gene expression analysis and β-actin for protein expression (n=3–4, ±SEM). *P≤0.05. qPCR, quantitative polymerase chain reaction; siRNA, short-interfering RNA; vs, versus.

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    Table 1.

    Metabolic data for ApoE−/− mice with and without diabetes treated with LXA4 or Benzo-LXA4

    ApoE KO+VehicleApoE KO+LXA4ApoE KO+Benzo-LXA4
    NondiabeticDiabeticNondiabeticDiabeticNondiabeticDiabetic
    10 wk progression study: LXA4 or Benzo-LXA4 treatment, weeks 1–10
     Plasma glucose, mmol/L10.7±0.428.2±1.2a10.5±0.327.5±1.2a11.1±1.329.2±0.8a
     Glycated Hb, %4.8±0.110.0±0.9a4.7±0.110.1±0.8a4.4±0.19.7±0.8a
     Systolic BP, mm Hg108.0±0.31109.0±0.73108.0±0.21108.0±0.69109.0±0.76109.0±0.27
     Body weight, g30.2±0.524.2±0.6a29.7±0.623.8±0.6a29.9±0.324.9±0.7a
     Kidney weight, g0.19±0.0030.21±0.006a0.19±0.0060.20±0.0060.20±0.0060.20±0.006
     Kidney/body weight, %0.6±0.010.9±0.02a0.6±0.020.8±0.03a,b0.7±0.020.8±0.02a,b
     Urinary albumin, µg/24 h11.3±1.533.8±4.1a12.3±3.321.3±3.7a,b9.1±2.227.1±6.7a
     Creatinine clearance, ml/min0.21±0.060.26±0.020.15±0.04c0.19±0.04b0.12±0.02c0.14±0.02b
     Creatinine clearance, ml/min per m214.1±2.331.2±2.6a16.5±4.419.1±2.5b12.6±1.915.7±2.3b
    20 wk progression study: LXA4 or Benzo-LXA4 treatment, weeks 1–20
     Plasma glucose, mmol/L10.1±0.228.7±0.7a10.1±0.230.0±0.6a10.0±0.129.4±0.7a
     Glycated Hb, %4.3±0.111.0±0.7a4.6±0.111.9±0.7a4.7±0.110.3±0.9a
     Systolic BP, mm Hg108.0±0.38109.0±0.55108.0±0.21107.0±0.66108.0±73109.0±0.27
     Body weight, g29.9±0.525.8±0.8a30.6±0.723.8±0.9a31.0±0.624.7±0.8a
     Kidney weight, g0.19±0.0050.21±0.006a0.20±0.0060.23±0.008a0.20±0.0050.22±0.005a
     Kidney/body weight, %0.6±0.010.8±0.03a0.6±0.010.9±0.03a0.6±0.010.8±0.03a
     Urinary albumin, µg/24 h17.6±2.884.0±20.9a14.0±2.858.5±11.0a18.1±2.833.7±4.8a,b
     Creatinine clearance, ml/min0.15±0.020.20±0.02a0.16±0.030.19±0.020.18±0.020.33±0.07a,b
     Creatinine clearance, ml/min per m215.8±2.324.9±1.9a16.5±3.121.0±2.719.7±1.938.4±7.4a
    16 wk intervention study: LXA4 or Benzo-LXA4 treatment, weeks 10–16
     Plasma glucose, mmol/L10.1±0.231.4±0.7a9.8±0.129.4±0.9a9.7±0.231.8±0.4a
     Glycated Hb, %4.6±0.1311.8±0.5a4.4±0.111.5±0.7a4.7±0.210.7±1.0a
     Systolic BP, mm Hg109.0±0.61109.0±0.71110.0±0.83109.0±0.44109.0±0.72108.0±0.71
     Body weight, g32.9±1.023.4±0.9a31.5±0.623.7±0.6a31.9±0.324.3±0.9a
     Kidney weight, g0.21±0.0080.20±0.0080.21±0.0070.21±0.0060.21±0.0060.20±0.007
     Kidney/body weight, %0.6±0.030.9±0.02a0.7±0.020.9±0.04a0.6±0.010.8±0.03a
     Urinary albumin, µg/24 h13.9±1.425.1±2.1a15.2±1.917.3±2.4b15.1±1.621.3±2.9a
    • Data are shown as mean±SEM. n=8–12 per group. KO, knockout; Hb, haemoglobin.

    • ↵a ANOVA P<0.05 versus nondiabetic.

    • ↵b ANOVA P<0.05 versus diabetic ApoE KO+Vehicle.

    • ↵c ANOVA P<0.05 versus nondiabetic ApoE KO+Vehicle.

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Journal of the American Society of Nephrology: 33 (6)
Journal of the American Society of Nephrology
Vol. 33, Issue 6
June 2022
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Lipoxins Regulate the Early Growth Response–1 Network and Reverse Diabetic Kidney Disease
Eoin P. Brennan, Muthukumar Mohan, Aaron McClelland, Christos Tikellis, Mark Ziemann, Antony Kaspi, Stephen P. Gray, Raelene Pickering, Sih Min Tan, Syed Tasadaque Ali-Shah, Patrick J. Guiry, Assam El-Osta, Karin Jandeleit-Dahm, Mark E. Cooper, Catherine Godson, Phillip Kantharidis
JASN Feb 2018, ASN.2017101112; DOI: 10.1681/ASN.2017101112

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Lipoxins Regulate the Early Growth Response–1 Network and Reverse Diabetic Kidney Disease
Eoin P. Brennan, Muthukumar Mohan, Aaron McClelland, Christos Tikellis, Mark Ziemann, Antony Kaspi, Stephen P. Gray, Raelene Pickering, Sih Min Tan, Syed Tasadaque Ali-Shah, Patrick J. Guiry, Assam El-Osta, Karin Jandeleit-Dahm, Mark E. Cooper, Catherine Godson, Phillip Kantharidis
JASN Feb 2018, ASN.2017101112; DOI: 10.1681/ASN.2017101112
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