Metabolomics and Proteomics in Chronic Kidney Disease and Diabetic Kidney Disease

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Endocrinology and Clinical Metabolic Research".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 6083

Special Issue Editor

1. Department of Epidemiology, IBE, Ludwig-Maximilians-Universität (LMU), Munich, Germany
2. Research Unit Molecular Epidemiology/Deputy Head—Diabetes Research Unit, Institute of Epidemiology, Helmholtz Munich, Germany
Interests: cardiometabolic health; cohort studies; molecular epidemiology; omics

Special Issue Information

Dear Colleagues,

Chronic Kidney Disease (CKD) has become a silent epidemic on the rise, accompanied by the increase in prevalence of associated risk factors such as diabetes, obesity and cardiovascular disease. In particular, Diabetes Kidney Disease (DKD), a common complication of diabetes, accounts for 30-50% of CKD cases, holding the leading cause of end-stage kidney disease and representing an independent risk factor for cardiovascular and all-cause mortality. However, aberrant levels of diagnostic or staging markers of kidney function (such as creatinine, cystatin c, albumin) are only present in late stages of the disease and no targeted therapies exist for CKD beyond the management of traditional cardiorenal risk factors. Therefore, there is an increasing necessity in kidney research to identify novel biomarkers for early kidney impairment (in particular among people with diabetes) and deliver better insight into pathophysiological pathways.

The recent developments of high throughput technologies in metabolomics and proteomics research have open unprecedented avenues in the discovery of new biomarkers for CKD/DKD screening, diagnosis and prognosis. Moreover, new pathophysiological insights are uncovered by integrating genomics or experimental work to these investigations. Assessing the causal directionality of the associations through Mendelian Randomization approaches (for e.g. in population studies) has proven to a be an effective toolset to drive new knowledge. Additionally, extending traditional statistical analyses with Machine Learning tools (including random forest, support vector machines, K -means clustering et cet.) have consistently shown better performance in predicting disease signatures.

In this special issue, we would like to invite articles that investigate proteomics or metabolomics with CKD/DKD development or progression in clinical or cohort studies that make use of all the available toolset of complementary analyses in omics research.

Dr. Jana Nano
Guest Editor

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Keywords

  • proteomis
  • metabolomics
  • chronic kidney disease
  • diabetic kidney disease
  • genomics
  • biomarkers
  • causality
  • machine learning
  • experimental validation

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Published Papers (2 papers)

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Research

18 pages, 2150 KiB  
Article
Expression Profiles of Kidney Mitochondrial Proteome during the Progression of the Unilateral Ureteral Obstruction: Focus on Energy Metabolism Adaptions
by Ariadna Jazmín Ortega-Lozano, Alexis Paulina Jiménez-Uribe, Ana Karina Aranda-Rivera, Leopoldo Gómez-Caudillo, Emmanuel Ríos-Castro, Edilia Tapia, Belen Bellido, Omar Emiliano Aparicio-Trejo, Laura Gabriela Sánchez-Lozada and José Pedraza-Chaverri
Metabolites 2022, 12(10), 936; https://doi.org/10.3390/metabo12100936 - 2 Oct 2022
Cited by 3 | Viewed by 2597
Abstract
Kidney diseases encompass many pathologies, including obstructive nephropathy (ON), a common clinical condition caused by different etiologies such as urolithiasis, prostatic hyperplasia in males, tumors, congenital stenosis, and others. Unilateral ureteral obstruction (UUO) in rodents is an experimental model widely used to explore [...] Read more.
Kidney diseases encompass many pathologies, including obstructive nephropathy (ON), a common clinical condition caused by different etiologies such as urolithiasis, prostatic hyperplasia in males, tumors, congenital stenosis, and others. Unilateral ureteral obstruction (UUO) in rodents is an experimental model widely used to explore the pathophysiology of ON, replicating vascular alterations, tubular atrophy, inflammation, and fibrosis development. In addition, due to the kidney’s high energetic demand, mitochondrial function has gained great attention, as morphological and functional alterations have been demonstrated in kidney diseases. Here we explore the kidney mitochondrial proteome differences during a time course of 7, 14, and 21 days after the UUO in rats, revealing changes in proteins involved in three main metabolic pathways, oxidative phosphorylation (OXPHOS), the tricarboxylic acid cycle (TCA), and the fatty acid (FA) metabolism, all of them related to bioenergetics. Our results provide new insight into the mechanisms involved in metabolic adaptations triggered by the alterations in kidney mitochondrial proteome during the ON. Full article
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19 pages, 45148 KiB  
Article
Interaction between Plasma Metabolomics and Intestinal Microbiome in db/db Mouse, an Animal Model for Study of Type 2 Diabetes and Diabetic Kidney Disease
by Chenhua Wu, Jingjing Fei, Qing Xu, Yingjun Tao, Ziqi Zhou, Yurong Wang, Jie Wu and Harvest F. Gu
Metabolites 2022, 12(9), 775; https://doi.org/10.3390/metabo12090775 - 23 Aug 2022
Cited by 21 | Viewed by 2841
Abstract
Evidence has demonstrated that either metabolites or intestinal microbiota are involved in the pathogenesis of type 2 diabetes (T2D) and diabetic kidney disease (DKD). To explore the interaction between plasma metabolomics and intestinal microbiome in the progress of T2D-DKD, in the current study, [...] Read more.
Evidence has demonstrated that either metabolites or intestinal microbiota are involved in the pathogenesis of type 2 diabetes (T2D) and diabetic kidney disease (DKD). To explore the interaction between plasma metabolomics and intestinal microbiome in the progress of T2D-DKD, in the current study, we analyzed metabolomics in the plasma of db/db mice with liquid chromatography–mass spectrometry and also examined intestinal prokaryotes and entire gut microbiome dysbiosis at the genus level with both 16S rDNA and metagenomic sequencing techniques. We found that Negativibacillus and Rikenella were upregulated, while Akkermansia, Candidatus, Erysipelatoclostridium and Ileibacterium were downregulated in the colon of db/db mice compared with non-diabetic controls. In parallel, a total of 91 metabolites were upregulated, while 23 were downregulated in the plasma of db/db mice. The top five upregulated metabolites included D-arabinose 5-phosphate, estrone 3-sulfate, L-theanine, 3′-aenylic acid and adenosine 5′-monophosphate, and the five most significantly downregulated metabolites were aurohyocholic acid sodium salt, calcium phosphorylcholine chloride, tauro-alpha-muricholic acid sodium salt, galactinol and phosphocholine. These plasma metabolites were interacted with intestinal microbiomes, which are mainly involved in the pathways related to the biosynthesis of unsaturated fatty acids, fatty acid elongation, steroid biosynthesis, and D-arginine and D-ornithine metabolism. In the differential metabolites, N-acetyl-L-ornithine, ornithine and L-kyn could be metabolized by the correspondingly differential ontology genes in the intestinal metagenome. The current study thereby provides evidence for a gut–metabolism–kidney axis in the metabolism of db/db mice, in which the gut microbiome and circulating metabolomics interact, and suggests that information from this axis may contribute to our understanding of T2D and DKD pathogenesis. Full article
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