Gut microbiota-derived indole-3-propionic acid alleviates diabetic kidney disease through its mitochondrial protective effect via reducing ubiquitination mediated-degradation of SIRT1 - PubMed (original) (raw)

doi: 10.1016/j.jare.2024.08.018. Epub 2024 Aug 13.

Man Guo 2, Qi Wu 3, Xiaozhen Tan 4, Chunxia Jiang 1, Fangyuan Teng 4, Jiao Chen 2, Fanjie Zhang 5, Xiumei Ma 6, Xinyue Li 7, Junling Gu 8, Wei Huang 2, Chunxiang Zhang 9, Betty Yuen-Kwan Law 10, Yang Long 11, Yong Xu 12

Affiliations

Gut microbiota-derived indole-3-propionic acid alleviates diabetic kidney disease through its mitochondrial protective effect via reducing ubiquitination mediated-degradation of SIRT1

Yan Zeng et al. J Adv Res. 2025 Jul.

Abstract

Introduction: Gut microbes and their metabolites play crucial roles in the pathogenesis of diabetic kidney disease (DKD). However, which one and how specific gut-derived metabolites affect the progression of DKD remain largely unknown.

Objectives: This study aimed to investigate the potential roles of indole-3-propionic acid (IPA), a microbial metabolite of tryptophan, in DKD.

Methods: Metagenomic sequencing was performed to analyze the microbiome structure in DKD. Metabolomics screening and validation were conducted to identify characteristic metabolites associated with DKD. The protective effect of IPA on DKD glomerular endothelial cells (GECs) was assessed through in vivo and in vitro experiments. Further validation via western blot, immunoprecipitation, gene knockout, and site-directed mutation elucidated the mechanism of IPA on mitochondrial injury.

Results: Alterations in gut microbial community structure and dysregulated tryptophan metabolism were evident in DKD mice. Serum IPA levels were significantly reduced in DKD patients and correlated with fasting blood glucose, HbA1c, urine albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR). IPA supplementation ameliorated albuminuria, bolstered the integrity of the glomerular filtration barrier, and mitigated mitochondrial impairments in GECs. Mechanistically, IPA hindered SIRT1 phosphorylation-mediated ubiquitin-proteasome degradation, restoring SIRT1's role in promoting PGC-1α deacetylation and nuclear translocation, thereby upregulating genes associated with mitochondrial biosynthesis and antioxidant defense.

Conclusion: Our findings underscore the potential of the microbial metabolite IPA to attenuate DKD progression, offering novel insights and potential therapeutic strategies for its management.

Keywords: Diabetic kidney disease; Glomerular endothelial cells; Indole-3-propionic acid; Mitochondria; Oxidative stress; SIRT1.

Copyright © 2024. Published by Elsevier B.V.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

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Graphical abstract

Fig. 1

Fig. 1

Altered gut microbial community structure and metabolic pathways in DKD mice. (A) Multivariate statistical analyses (PCA, PCoA, and NMDS) of gut microbiome from DKD and control mice. These analyses were used to assess differences in microbial composition. PCA shows separation along the first principal component (PC1), PCoA illustrates clustering patterns based on Bray-Curtis distances, and NMDS visualizes community dissimilarities with stress values indicating goodness of fit. (B) Alpha and beta diversity analyses of the gut microbiota in DKD and control mice. Alpha diversity metrics (e.g., Chao1 index) were used to evaluate species richness, while beta diversity was assessed using Bray-Curtis dissimilarity indices. Statistical significance was determined using the Wilcoxon rank-sum test. (C, D) Community bar plot and Circos plot analyses at the phylum level, displaying the relative abundance and distribution of bacterial phyla in individual samples from DKD and control mice. The bar plot shows the proportional differences in microbial community structure between groups, while the Circos plot highlights the relative abundance and distribution of various phyla within the gut microbiota of both groups. (E) Heatmap of the top 50 most abundant phyla in DKD and control mice, showing relative abundance across individual samples. Clustering analysis visualizes similarities and differences in microbial composition between the groups. (F) KEGG pathway analysis comparing the metabolic functions of gut microbiota in DKD and control mice, with statistical significance assessed using the Wilcoxon rank-sum test. (G) Multivariate statistical analyses (PCA, PCoA, and NMDS) of the tryptophan metabolism pathway (KEGG ko00380) in gut microbiota from DKD and control mice. (H) Pearson correlation analysis showing the top 50 bacterial species significantly associated with serum IPA levels. (I) Redundancy analysis illustrating the relationship between gut microbiota composition and three explanatory variables: blood glucose levels, UACR, and IPA levels, with statistical significance determined using permutation tests. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Fig. 2

Fig. 2

Dysregulation of tryptophan metabolism in DKD mice. (A) Schematic representation of the untargeted metabolomics workflow for serum samples from DKD mice and controls. (B, C) Multivariate statistical analyses (PCA, OPLS-DA, and PLS-DA) showing distinct clustering within each group and clear discrimination between DKD mice and controls in both ion modes. (D, E) Heat maps and (F, G) metabolic profiles of differential metabolites (OPLS-DA: VIP-value > 1, _P_-value < 0.05), with 123 metabolites significantly changed in the negative ion mode and 42 in the positive ion mode compared to controls. (H) Principal metabolic pathways of dietary tryptophan in the body. (I) Heatmaps of all identified tryptophan metabolites, with each row representing a different metabolite and each column a sample. The color intensity indicates the expression level, with red indicating upregulation and blue indicating downregulation. (J) Chord diagram showing correlation coefficients (r) between all differential metabolites and UACR. (K) Scatter plot displaying the correlation coefficients (r) and significance levels (_P_-value) for various indole derivatives with UACR. Each dot represents an indole derivative, with the dot size indicating the correlation strength (larger dots represent stronger correlations) and color intensity representing the _P_-value (darker colors indicate more significant correlations). * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Fig. 2

Dysregulation of tryptophan metabolism in DKD mice. (A) Schematic representation of the untargeted metabolomics workflow for serum samples from DKD mice and controls. (B, C) Multivariate statistical analyses (PCA, OPLS-DA, and PLS-DA) showing distinct clustering within each group and clear discrimination between DKD mice and controls in both ion modes. (D, E) Heat maps and (F, G) metabolic profiles of differential metabolites (OPLS-DA: VIP-value > 1, _P_-value < 0.05), with 123 metabolites significantly changed in the negative ion mode and 42 in the positive ion mode compared to controls. (H) Principal metabolic pathways of dietary tryptophan in the body. (I) Heatmaps of all identified tryptophan metabolites, with each row representing a different metabolite and each column a sample. The color intensity indicates the expression level, with red indicating upregulation and blue indicating downregulation. (J) Chord diagram showing correlation coefficients (r) between all differential metabolites and UACR. (K) Scatter plot displaying the correlation coefficients (r) and significance levels (_P_-value) for various indole derivatives with UACR. Each dot represents an indole derivative, with the dot size indicating the correlation strength (larger dots represent stronger correlations) and color intensity representing the _P_-value (darker colors indicate more significant correlations). * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Fig. 3

Impaired serum IPA levels in DKD patients correlate significantly with fasting blood glucose, HbA1c, UACR, and eGFR. (A) Representative images of H&E staining, Masson staining, and PAS staining showing the pathological renal structure and deposited collagenous matrix in DKD patients. Scale bar: 200 μm and 50 μm, respectively. (B) Pearson correlation analyses display the correlation coefficients (r) and significance levels (_P_-value) for tryptophan metabolites with fasting blood glucose, HbA1c, eGFR, and UACR. Each dot represents an indole derivative, with the size of the dot indicating the strength of the correlation (larger dots represent stronger correlations) and the color intensity representing the _P_-value (darker colors indicate more significant correlations). (C) Serum IPA concentrations in DKD (n = 14), T2DM (n = 14), and healthy subjects (n = 9). Pearson correlation analyses assessed the associations of circulating IPA levels with (D) fasting blood glucose, (E) HbA1c, (F) UACR, and (G) eGFR. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001.

Fig. 4

Fig. 4

IPA alleviates albuminuria and improves the integrity of the glomerular filtration barrier in DKD mice. (A) Experimental protocol outlining the animal study. (B) Serum IPA levels after 12 weeks of intragastric IPA administration. Changes in (C) random blood glucose and (D) body weight across different groups. (E) UACR levels of mice in different groups after 12 weeks of intragastric IPA administration. Renal morphology assessed by (F) H&E staining and (G) Masson staining showing glomerular and cortical interstitial changes. Scale bar, 200 μm. (H) TEM images illustrating ultrastructural alterations in the glomerular filtration barrier. Scale bar, 2 μm. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001.

Fig. 5

Fig. 5

IPA ameliorates alterations in mitochondrial morphology, structure, and function in GECs of DKD mice. (A) Representative TEM images revealing mitochondrial morphological and structural changes in GECs, with blue arrows indicating GEC mitochondria. Scale bar, 1 μm. Quantitative analysis of (B) mitochondrial density and (C) volume based on electron microscope images of GEC mitochondria. (D) ATP content and (E) MDA levels in the kidney cortex of mice. In GECs stimulated with 30 mM glucose, (F) DCHF-DA staining indicated changes in ROS levels, and (G) the fluorescent probe JC-1 demonstrated alterations in mitochondrial membrane potential. Scale bar, 200 μm. Quantification of (H) ATP levels and (I) MDA contents under different interventions in GECs. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

Fig. 6

IPA promotes mitochondrial biogenesis and antioxidant defense by facilitating PGC-1α deacetylation and nuclear translocation. Protein levels of PGC-1α (in whole cell lysate, WCL), SOD2, mtTFA, and PGC-1α (in nucleus) in (A–C) the renal cortex tissue of mice and in (D–F) GECs. Representative images of PGC-1α immunofluorescence in (G) kidney sections and (H) GECs. Scale bar, 50 μm. (I, J) Immunoprecipitation of Ac-lysine with PGC-1α in GECs. Endogenous PGC-1α was immunoprecipitated using anti-PGC-1α, and the immunoprecipitates were analyzed with anti-Ac-lysine. (K, L) Protein levels of PGC-1α, SOD2, and mtTFA in GECs with the intervention of ZLN005, a PGC-1α agonist, and SR-18292, a PGC-1α inhibitor. (M) ROS levels, (N) mitochondrial membrane potential, (O) ATP levels, and (P) MDA contents of GECs with the intervention of SR-18292. Scale bar, 200 μm. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001.

Fig. 6

Fig. 6

IPA promotes mitochondrial biogenesis and antioxidant defense by facilitating PGC-1α deacetylation and nuclear translocation. Protein levels of PGC-1α (in whole cell lysate, WCL), SOD2, mtTFA, and PGC-1α (in nucleus) in (A–C) the renal cortex tissue of mice and in (D–F) GECs. Representative images of PGC-1α immunofluorescence in (G) kidney sections and (H) GECs. Scale bar, 50 μm. (I, J) Immunoprecipitation of Ac-lysine with PGC-1α in GECs. Endogenous PGC-1α was immunoprecipitated using anti-PGC-1α, and the immunoprecipitates were analyzed with anti-Ac-lysine. (K, L) Protein levels of PGC-1α, SOD2, and mtTFA in GECs with the intervention of ZLN005, a PGC-1α agonist, and SR-18292, a PGC-1α inhibitor. (M) ROS levels, (N) mitochondrial membrane potential, (O) ATP levels, and (P) MDA contents of GECs with the intervention of SR-18292. Scale bar, 200 μm. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001.

Fig. 7

Fig. 7

IPA upregulates SIRT1 expression and mediates mitochondrial biogenesis and antioxidant defense through the SIRT1-regulated PGC-1α-SOD2/mtTFA signaling pathway. Protein levels of SIRT1 in the (A, C) renal cortex tissue of mice and in (B, D) GECs. (E–H) Protein levels of PGC-1α (in the nucleus), SIRT1, PGC-1α (in whole cell lysate, WCL), SOD2, and mtTFA in GECs with the intervention of SRT 1720, an agonist of SIRT1, and EX 527, an inhibitor of SIRT1. To evaluate the impact of SIRT1 deficiency on IPA's protective effect, SIRT1 heterozygous (SIRT1+/-) mice were utilized in (I-N), and (O-V) GECs were derived from wild type (WT) and SIRT1+/- mice. (I) UACR levels in mice from various groups after 12 weeks of intragastric IPA administration. (J–L) Protein levels of PGC-1α (in the nucleus), SIRT1, SOD2, and mtTFA, and (M) the ATP and (N) MDA content in the renal cortex tissue of mice in different groups. (O-R) Protein levels of PGC-1α (in the nucleus), SIRT1, SOD2, and mtTFA in GECs. (S) The ATP levels, (T) MDA content, (U) ROS levels (V), and mitochondrial membrane potential in GECs. Scale bar, 200 μm. (W) Representative images of immunohistochemistry analysis of SIRT1, PGC-1α, SOD2, and mtTFA on kidney biopsy specimens from both healthy subjects and DKD patients. Scale bar, 250 μm. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001.

Fig. 7

Fig. 7

IPA upregulates SIRT1 expression and mediates mitochondrial biogenesis and antioxidant defense through the SIRT1-regulated PGC-1α-SOD2/mtTFA signaling pathway. Protein levels of SIRT1 in the (A, C) renal cortex tissue of mice and in (B, D) GECs. (E–H) Protein levels of PGC-1α (in the nucleus), SIRT1, PGC-1α (in whole cell lysate, WCL), SOD2, and mtTFA in GECs with the intervention of SRT 1720, an agonist of SIRT1, and EX 527, an inhibitor of SIRT1. To evaluate the impact of SIRT1 deficiency on IPA's protective effect, SIRT1 heterozygous (SIRT1+/-) mice were utilized in (I-N), and (O-V) GECs were derived from wild type (WT) and SIRT1+/- mice. (I) UACR levels in mice from various groups after 12 weeks of intragastric IPA administration. (J–L) Protein levels of PGC-1α (in the nucleus), SIRT1, SOD2, and mtTFA, and (M) the ATP and (N) MDA content in the renal cortex tissue of mice in different groups. (O-R) Protein levels of PGC-1α (in the nucleus), SIRT1, SOD2, and mtTFA in GECs. (S) The ATP levels, (T) MDA content, (U) ROS levels (V), and mitochondrial membrane potential in GECs. Scale bar, 200 μm. (W) Representative images of immunohistochemistry analysis of SIRT1, PGC-1α, SOD2, and mtTFA on kidney biopsy specimens from both healthy subjects and DKD patients. Scale bar, 250 μm. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001.

Fig. 8

Fig. 8

IPA elevated SIRT1 levels by preventing its ubiquitin–proteasome degradation. (A, B) SIRT1 mRNA expression in the kidney cortex tissue of mice and in GECs. (C, D) SIRT1 protein expression under the intervention of the protein synthesis inhibitor cycloheximide (CHX). (E, F) SIRT1 protein expression with the intervention of MG132, a proteasome inhibitor, and Bafilomycin A1, an autophagy inhibitor. (G, I, J) Polyubiquitin and SIRT1 protein expression in whole cell lysates (WCL) of GECs. (H, K) Immunoprecipitation of polyubiquitin with SIRT1 in GECs. Endogenous SIRT1 was immunoprecipitated using anti-SIRT1, and the immunoprecipitates were analyzed with anti-ubiquitin. (L, N) Protein expression of SIRT1, MDM2, Smurf2, CUL4, and COP1 in WCL of GECs. (M, O) Immunoprecipitation of MDM2, Smurf2, CUL4, and COP1 with SIRT1 in GECs. Endogenous SIRT1 was immunoprecipitated using anti-SIRT1, and the immunoprecipitates were analyzed with anti-MDM1, anti-Smurf2, anti-CUL4, and anti-COP1. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P < 0.001, **** P < 0.0001.

Fig. 8

Fig. 8

IPA elevated SIRT1 levels by preventing its ubiquitin–proteasome degradation. (A, B) SIRT1 mRNA expression in the kidney cortex tissue of mice and in GECs. (C, D) SIRT1 protein expression under the intervention of the protein synthesis inhibitor cycloheximide (CHX). (E, F) SIRT1 protein expression with the intervention of MG132, a proteasome inhibitor, and Bafilomycin A1, an autophagy inhibitor. (G, I, J) Polyubiquitin and SIRT1 protein expression in whole cell lysates (WCL) of GECs. (H, K) Immunoprecipitation of polyubiquitin with SIRT1 in GECs. Endogenous SIRT1 was immunoprecipitated using anti-SIRT1, and the immunoprecipitates were analyzed with anti-ubiquitin. (L, N) Protein expression of SIRT1, MDM2, Smurf2, CUL4, and COP1 in WCL of GECs. (M, O) Immunoprecipitation of MDM2, Smurf2, CUL4, and COP1 with SIRT1 in GECs. Endogenous SIRT1 was immunoprecipitated using anti-SIRT1, and the immunoprecipitates were analyzed with anti-MDM1, anti-Smurf2, anti-CUL4, and anti-COP1. Data are presented as mean ± SD; * P< 0.05, ** P< 0.01, *** P < 0.001, **** P < 0.0001.

Fig. 9

Fig. 9

IPA suppresses phosphorylation-induced ubiquitination and proteasome-dependent degradation of SIRT1. (A–D) Expression of pSIRT1 (Ser46) protein in the kidney cortex tissue of mice and in GECs. (E) SIRT1 mutational plasmid constructed by substituting Ser46 with alanine (Ala-46, A46) and aspartate (Asp-46, D46). (F–J) Protein expression of polyubiquitin, pSIRT1 (Ser46), and SIRT1 in whole cell lysates (WCL) of GECs. Immunoprecipitation of polyubiquitin with SIRT1 in GECs. Endogenous SIRT1 was immunoprecipitated using anti-SIRT1, and the immunoprecipitates were analyzed with anti-ubiquitin. Data are presented as mean ± SD; * P < 0.05, ** P< 0.01, *** P< 0.001, **** P< 0.0001.

References

    1. Anders H.J., Huber T.B., Isermann B., Schiffer M. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol. 2018;14(6):361–377. - PubMed
    1. Fox C.S., Matsushita K., Woodward M., Bilo H.J., Chalmers J., Heerspink H.J., et al. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis. Lancet. 2012;380(9854):1662–1673. - PMC - PubMed
    1. Papademetriou V., Lovato L., Doumas M., Nylen E., Mottl A., Cohen R.M., et al. Chronic kidney disease and intensive glycemic control increase cardiovascular risk in patients with type 2 diabetes. Kidney Int. 2015;87(3):649–659. - PubMed
    1. Qin J., Li R., Raes J., Arumugam M., Burgdorf K.S., Manichanh C., et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59–65. - PMC - PubMed
    1. Shine E.E., Crawford J.M. Molecules from the Microbiome. Annu Rev Biochem. 2021;90:789–815. - PubMed

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