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Altered proximal tubular cell glucose metabolism during acute kidney injury is associated with mortality

Nature Metabolism volume 2, pages 732–743 (2020)Cite this article

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Abstract

Acute kidney injury (AKI) is strongly associated with mortality, independently of its cause. The kidney contributes to up to 40% of systemic glucose production by gluconeogenesis during fasting and under stress conditions. Whether kidney gluconeogenesis is impaired during AKI and how this might influence systemic metabolism remain unknown. Here we show that glucose production and lactate clearance are impaired during human and experimental AKI by using renal arteriovenous catheterization in patients, lactate tolerance testing in mice and glucose isotope labelling in rats. Single-cell transcriptomics reveal that gluconeogenesis is impaired in proximal tubule cells during AKI. In a retrospective cohort of critically ill patients, we demonstrate that altered glucose metabolism during AKI is a major determinant of systemic glucose and lactate levels and is strongly associated with mortality. Thiamine supplementation increases lactate clearance without modifying renal function in mice with AKI, enhances glucose production by renal tubular cells ex vivo and is associated with reduced mortality and improvement of the metabolic pattern in a retrospective cohort of critically ill patients with AKI. This study highlights an unappreciated systemic role of renal glucose and lactate metabolism under stress conditions, delineates general mechanisms of AKI-associated mortality and introduces a potential intervention targeting metabolism for a highly prevalent clinical condition with limited therapeutic options.

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Fig. 1: Renal glucose production and lactate clearance are impaired during human AKI.
Fig. 2: Renal gluconeogenesis is impaired in experimental AKI, resulting in systemic metabolic alterations.
Fig. 3: Metabolic adaptation to AKI is cell specific and predictive of impaired renal evolution.
Fig. 4: Renal metabolic impairment during AKI is strongly associated with mortality in ICU patients.
Fig. 5: Thiamine may rescue PT cell metabolism during AKI.

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Data availability

RNA-seq data for human kidney biopsies are available at GEO (GSE126805). Mouse RNA-seq data are available at GEO (GSE52004) and ref. 50 and as a supplementary table in ref. 18. snRNA-seq data are available at GEO (GSE151167). Clinical data are available from the corresponding authors upon reasonable request. Raw data are included in this published article (and its Supplementary Information files). Source data are provided with this paper.

Code availability

Custom code will be made available on gitlab.unige.ch following publication.

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Acknowledgements

This study was funded by the Swiss National Science Foundation (grant nos. PP00P3 157454 to S.S. and 167773 to P.E.C.), by a STARTER grant (no. RS03-25) to S.S. and D.L. from the HUG private foundation (the foundation of Geneva University Hospitals and the University of Geneva’s Faculty of Medicine) and by The Eli and Edythe Broad Foundation (to P.C. and A.P.M.). P.G. was supported by the ATIP Avenir programme. We thank G. Alvarado, A. Ransick and A. Kim for technical support.

Author information

Author notes

  1. These authors contributed equally: Pietro E Cippà and Sophie de Seigneux.

Authors and Affiliations

  1. Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, Geneva, Switzerland

    David Legouis, Anna Faivre, Thomas Verissimo, Lena Berchtold, Marylise Fernandez, Pierre-Yves Martin & Sophie de Seigneux

  2. Division of Intensive Care, University Hospital of Geneva, Geneva, Switzerland

    David Legouis & Jérôme Pugin

  3. Department of Anaesthesiology, Intensive Care Medicine, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden

    Sven-Erick Ricksten

  4. Division of Endocrinology, Diabetes, Hypertension and Nutrition, Department of Internal Medicine Specialties, University Hospital of Geneva, Geneva, Switzerland

    Karim Gariani

  5. French National Institute of Health and Medical Research UMR_S1155, Rare and Common Kidney Diseases, Matrix Remodeling and Repair; AP-HP, Tenon Hospital, Renal Intensive Care Unit, Sorbonne Université, Paris, France

    Charles Verney, Pierre Galichon, Sandrine Placier & Alexandre Hertig

  6. Service of Nephrology, University Hospitals of Leuven, Leuven, Belgium

    Lena Berchtold & Maarten Naesens

  7. Department of Cell Biology and Metabolism, University of Geneva, Geneva, Switzerland

    Eric Feraille

  8. Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA

    Kari Koppitch, Andrew P. McMahon & Pietro E. Cippà

  9. Service of Nephrology, Department of Internal Medicine Specialties, University Hospital of Geneva, Geneva, Switzerland

    Pierre-Yves Martin & Sophie de Seigneux

  10. Division of Nephrology, Ente Ospedaliero Cantonale, Lugano, Switzerland

    Pietro E. Cippà

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Contributions

D.L. and S.S. conceived and designed the project. S.-E.R. performed human renal catheterization. D.L., A.F., S.P., C.V., K.G., L.B., M.F., T.V. and K.K. performed experiments. D.L., P.-Y.M. and J.P. generated and analysed ICU datasets. P.E.C. and A.P.M conducted snRNA-seq experiments and analysis. M.N. and P.E.C. provided RNA-seq data from kidney allograft recipients and D.L. performed analyses. S.S., D.L. and P.E.C. performed data interpretation. P.G., A.H. and E.F. validated data interpretation.

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Correspondence to David Legouis.

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Extended data

Extended Data Fig. 1 Kidney gluconeogenesis is downregulated in AKI.

a, Unprocessed gels of representative western blot analysis for FBP1, PCK1, HK1 and PKM. Experiment was repeated twice with similar results b, Coomassie staining of the representative western blots. c, Liver mRNA expression of genes involved in gluconeogenesis (Fbp1, Pck1) and glycolysis (Hk1, Pkm) in sham versus IRI mice at 48 hours of reperfusion (IRI n=9 mice, sham n=8 mice). Data were shown as boxplots (median, interquartile, 2 and 95th percentiles) and compared with Wilcoxon two tailed test.

Source data

Extended Data Fig. 2 Validation of the snRNAseq analysis in the IRI model.

(a-c): Validation of the Neph-GFP mouse. Immunofluorescence from a representative example of a Neph-GFP mouse kidney showing GFP+ nuclei in Ltl+ PT cells and Umod+ TAL cells, but GFP- nuclei in Aqp2+ collecting duct cells. One representative example of >3 similar experiments is shown (d-j). A mouse model of moderate IRI to study tubular repair. d, Serum creatinine values measured 48h after IRI, with different ischemia times, as indicated. n=3 in sham and IRI 15 min, n=7 in IRI 10 min. Barplots represent mean and errorbars standard error of the mean. e, BUN values measure 48h and 28 days after IRI (15 minutes ischemia). N=5-12 mice, n=6 in sham, n= 12 in 48h, n=5 in 28d. Barplots represent mean and errorbars standard error of the mean. f, qPCR on renal tissue isolated at different time points after IRI with different ischemia times, as indicated. N= 6 in sham, n= 10 in IRI 10 min 48h, n= 6 in IRI 15 min 48h and n= 6 in IRI 15 min 28d. Data were compared using two tailed Wilcoxon test. Barplots represent mean and errorbars standard error of the mean. (g-j) One representative examples of >3 similar experiments is shown. Conventional H&E staining (g,i) and immunofluorescence (h,j) on kidney sections obtained 2 and 28 days after 15 min IRI showing the presence of damaged tubular cell at the cortico-medullary junction (S3 region of the PT). 28 days after IRI most tubular cells recovered, but still retained EDU (injected 48h after IRI), but some flattened cells and damaged tubules persisted (f).

Extended Data Fig. 3 snRNAseq analysis after IRI.

a, TSNE analysis on merged datasets including GFP+ nuclei at different time points after IRI as indicated. The line delineates proximal tubule (PT) cells. S1 and S3 indicate the corresponding segments of the PT. n=11,274 single cell transcriptomes from GFP+ cells obtained at different time points after injury (merged datasets); one representative example of 3 similar experiments is shown. b, Feature plots of representative genes, typically expressed by proximal tubule cells (Slc34a1), S1 cells (Slc5a12), S3 cells (Cyp7b1) and injured proximal tubule cells (Fth1). c, SWNE plot including PT nuclei isolated at different time points after IRI as presented in Fig. 2. The colors indicate the expression level of representative genes as indicated.

Extended Data Fig. 4 Upstream regulation of gluconeogenesis.

a, TMM normalized cpm of HNFA4 and PPARA genes in allograft kidney biopsies obtained at the end of the transplantation from brain-stem death donor (IRI group, n=25 patients) and 3 or 12 months after transplantation in patients with recovery renal graft status (control group, n=17 patients). b, relation between HNFA4 and PPARA genes with gluconeogenesis genes FBP1, PCK1 PCK2 and PC. BMI, body mass index. c, SWNE plots including PT nuclei isolated at different time points after IRI and highlighting the separation of PT cells in the early phase after IRI from steady-state condition. Representative regulators genes expressed in normal PT cells are embedded. The colours of the dots corresponds to the time point of harvesting after IRI. Violin plot and boxplot (with mean, IQR, 1st and 95th percentiles) are shown. Differential expression analyses were performed using genewise negative binomial model with quasi-likelihood test. Relations between genes expression were modelised using linear model. d, Heatmap showing the expression level of the pathways involved in cellular stress allograft kidney biopsies obtained at the end of the transplantation from brain-stem death donor (red color) and 3 or 12 months after transplantation in patients with recovery renal graft status (blue color). Lighter blue represents a downregulation whereas darker blue represents an upregulation of the pathway. e, Circular representation of Pearson correlation coefficient between each pathway and FBP1 (top) and HAVCR (bottom) expression in kidney biopsy sampled after reperfusion (n=42 patients). Blue triangles represent a positive, whereas red triangles represent a negative correlation. Levels of significant corresponds to transparency level. Violin plot and boxplot (with mean, IQR, 1st and 95th percentiles) are shown. Differential expression analyses were performed using genewise negative binomial model with quasi-likelihood test. Relation between genes expression was modelised using linear model. P-values for Pearson correlations were calculated using Fisher’s asymptotic test.

Extended Data Fig. 5 Impaired gluconeogenesis is associated to long term outcomes.

a, FBP1, PCK1, PCK2, HK1, PFKP and PKM genes expression data from RNAseq performed 3 and 12 months after transplantation and classified as recovery (n=23 patients), early transition to CKD (n=22 patients) and CKD (n=27 patients) using machine learning computational approach13.Relation between glycolytic and gluconeogenic gene expression and one-year renal function in kidney allograft recipients. Differential expression analyses were performed using genewise negative binomial model with quasi likelihood test with Benjamini-Hochberg correction for multiple test. Data are represented as violin plots and boxplot (mean, interquartile, 1st and 95th percentiles). b, Relation between one-year eGFR and FBP1, PC, PKM and PFKP gene expression at 3 different time points (during the transplantation, 3 and 12 months after n=38 patients for each time) in kidney allograft recipients. Each dot represents the coefficient (±95% CI) of the robust linear model fitting the relation between gene expression levels and one-year eGFR. Positive value indicates a positive association. c, Relation between FPB1 (top panel), PKM (bottom panel) genes expression at 3 months after transplantation and one-year GFR estimated by the CKD-EPI equation (n=38 patients). The regression was fitted using a robust linear model and p-values were calculated with a robust F-test.

Extended Data Fig. 6 Metabolic pattern in ICU patients.

a, Urinary glucose levels during human AKI according to KDIGO stage represented as violin plots with errors bars representing the interquartile range. Number of patients in each condition are shown in the figure. Groups were compared using ANOVA. b, Metabolism patterns definition: Scatter plot showing all the glucose and lactate values recorded in the ICU datasets (n=661’51). Five metabolism status were defined : baseline (lactate levels below median and with glucose levels between the 25th and the 50th percentile) ; impaired metabolism (lactate levels above the median with glucose level below the 75th percentile) ; isolated low glucose level (lactate levels below median with glucose levels above the 75th percentile) ; isolated high glucose level (lactate levels below median with glucose levels below the 25th percentile) and stress response (lactate levels above median and glucose levels above the 75th percentile). c, Flow chart of propensity score matching for AKI. d, Balance of variables for AKI before and after matching: Relative standardized differences between AKI and non-AKI patients, before and after propensity score matching for each variable included in the propensity score. A relative standardised difference less than 10% was considered to support the assumption of balance between groups. e, ICU mortality according to KDIGO and impaired metabolism status: Cumulative incidence curves for ICU mortality in the cohort of ICU patients matched using a propensity score for AKI comparing 4 groups (with or without impaired metabolism pattern and with or without AKI) stratified for KDIGO. f, Relation between proportion of metabolism status classified as impaired metabolism, for each allograft kidney recipients during the ICU stay and the post-operative day 15 serum creatinine levels, assessed by robust linear model (n=275) and the p-values were calculated from a robust F-test.

Extended Data Fig. 7 Thiamine administration in ICU patients.

a, Flow chart of propensity score matching for thiamine supplementation. b, Balance of variables for thiamine supplementation before and after matching: Relative standardized differences between patients receiving or not thiamine supplementation, with or without AKI, before and after propensity score matching for each variable included in the propensity score. A relative standardized difference less than 10% was considered to support the assumption of balance between groups. c, Cumulative incidence curves for ICU mortality in the non-AKI patients matched according to their thiamine supplementation status (n=263 patients in thiamine and n=526 in the non-thiamine groups respectively). Comparisons were performed with multivariate conditional logistic regression stratified on matched patients and generalized linear mixed model. d, Stacked barplots showing metabolism patterns over time in ICU patients free of AKI and receiving (n=263 patients) or not (n=526 patients) thiamine supplementation according to a propensity score matching. Comparisons were performed with multivariate conditional logistic regression stratified on matched patients and generalised linear mixed model. P-values were calculated from two tailed Wald test.

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Raw image for PCK1 and FBP1 proteins. Raw image for HK1 and PKM proteins.

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Legouis, D., Ricksten, SE., Faivre, A. et al. Altered proximal tubular cell glucose metabolism during acute kidney injury is associated with mortality. Nat Metab 2, 732–743 (2020). https://doi.org/10.1038/s42255-020-0238-1

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