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Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens

AIM: The aims of this study were to analyze the proteomic differences in renal tissues from patients with diabetes mellitus (DM) and diabetic kidney disease (DKD) and to select sensitive biomarkers for early identification of DKD progression. METHODS: Pressure cycling technology–pulse data-independe...

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Autores principales: Huang, Qinghua, Fei, Xianming, Zhong, Zhaoxian, Zhou, Jieru, Gong, Jianguang, Chen, Yuan, Li, Yiwen, Wu, Xiaohong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714485/
https://www.ncbi.nlm.nih.gov/pubmed/36465646
http://dx.doi.org/10.3389/fendo.2022.995362
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author Huang, Qinghua
Fei, Xianming
Zhong, Zhaoxian
Zhou, Jieru
Gong, Jianguang
Chen, Yuan
Li, Yiwen
Wu, Xiaohong
author_facet Huang, Qinghua
Fei, Xianming
Zhong, Zhaoxian
Zhou, Jieru
Gong, Jianguang
Chen, Yuan
Li, Yiwen
Wu, Xiaohong
author_sort Huang, Qinghua
collection PubMed
description AIM: The aims of this study were to analyze the proteomic differences in renal tissues from patients with diabetes mellitus (DM) and diabetic kidney disease (DKD) and to select sensitive biomarkers for early identification of DKD progression. METHODS: Pressure cycling technology–pulse data-independent acquisition mass spectrometry was employed to investigate protein alterations in 36 formalin-fixed paraffin-embedded specimens. Then, bioinformatics analysis was performed to identify important signaling pathways and key molecules. Finally, the target proteins were validated in 60 blood and 30 urine samples. RESULTS: A total of 52 up- and 311 down-regulated differential proteins were identified as differing among the advanced DKD samples, early DKD samples, and DM controls (adjusted p<0.05). These differentially expressed proteins were mainly involved in ion transport, apoptosis regulation, and the inflammatory response. UniProt database analysis showed that these proteins were mostly enriched in signaling pathways related to metabolism, apoptosis, and inflammation. NBR1 was significantly up-regulated in both early and advanced DKD, with fold changes (FCs) of 175 and 184, respectively (both p<0.01). In addition, VPS37A and ATG4B were significantly down-regulated with DKD progression, with FCs of 0.140 and 0.088, respectively, in advanced DKD and 0.533 and 0.192, respectively, in early DKD compared with the DM control group (both p<0.01). Bioinformatics analysis showed that NBR1, VPS37A, and ATG4B are closely related to autophagy. We also found that serum levels of the three proteins and urine levels of NBR1 decreased with disease progression. Moreover, there was a significant difference in serum VPS37A and ATG4B levels between patients with early and advanced DKD (both p<0.05). The immunohistochemistry assaay exhibited that the three proteins were expressed in renal tubular cells, and NBR1 was also expressed in the cystic wall of renal glomeruli. CONCLUSION: The increase in NBR1 expression and the decrease in ATG4B and VPS37 expression in renal tissue are closely related to inhibition of the autophagy pathway, which may contribute to DKD development or progression. These three proteins may serve as sensitive serum biomarkers for early identification of DKD progression.
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spelling pubmed-97144852022-12-02 Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens Huang, Qinghua Fei, Xianming Zhong, Zhaoxian Zhou, Jieru Gong, Jianguang Chen, Yuan Li, Yiwen Wu, Xiaohong Front Endocrinol (Lausanne) Endocrinology AIM: The aims of this study were to analyze the proteomic differences in renal tissues from patients with diabetes mellitus (DM) and diabetic kidney disease (DKD) and to select sensitive biomarkers for early identification of DKD progression. METHODS: Pressure cycling technology–pulse data-independent acquisition mass spectrometry was employed to investigate protein alterations in 36 formalin-fixed paraffin-embedded specimens. Then, bioinformatics analysis was performed to identify important signaling pathways and key molecules. Finally, the target proteins were validated in 60 blood and 30 urine samples. RESULTS: A total of 52 up- and 311 down-regulated differential proteins were identified as differing among the advanced DKD samples, early DKD samples, and DM controls (adjusted p<0.05). These differentially expressed proteins were mainly involved in ion transport, apoptosis regulation, and the inflammatory response. UniProt database analysis showed that these proteins were mostly enriched in signaling pathways related to metabolism, apoptosis, and inflammation. NBR1 was significantly up-regulated in both early and advanced DKD, with fold changes (FCs) of 175 and 184, respectively (both p<0.01). In addition, VPS37A and ATG4B were significantly down-regulated with DKD progression, with FCs of 0.140 and 0.088, respectively, in advanced DKD and 0.533 and 0.192, respectively, in early DKD compared with the DM control group (both p<0.01). Bioinformatics analysis showed that NBR1, VPS37A, and ATG4B are closely related to autophagy. We also found that serum levels of the three proteins and urine levels of NBR1 decreased with disease progression. Moreover, there was a significant difference in serum VPS37A and ATG4B levels between patients with early and advanced DKD (both p<0.05). The immunohistochemistry assaay exhibited that the three proteins were expressed in renal tubular cells, and NBR1 was also expressed in the cystic wall of renal glomeruli. CONCLUSION: The increase in NBR1 expression and the decrease in ATG4B and VPS37 expression in renal tissue are closely related to inhibition of the autophagy pathway, which may contribute to DKD development or progression. These three proteins may serve as sensitive serum biomarkers for early identification of DKD progression. Frontiers Media S.A. 2022-11-17 /pmc/articles/PMC9714485/ /pubmed/36465646 http://dx.doi.org/10.3389/fendo.2022.995362 Text en Copyright © 2022 Huang, Fei, Zhong, Zhou, Gong, Chen, Li and Wu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Huang, Qinghua
Fei, Xianming
Zhong, Zhaoxian
Zhou, Jieru
Gong, Jianguang
Chen, Yuan
Li, Yiwen
Wu, Xiaohong
Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens
title Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens
title_full Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens
title_fullStr Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens
title_full_unstemmed Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens
title_short Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens
title_sort stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714485/
https://www.ncbi.nlm.nih.gov/pubmed/36465646
http://dx.doi.org/10.3389/fendo.2022.995362
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