Proteomics and Metabolomics in Kidney Disease, including Insights into Etiology, Treatment, and Prevention

Clinical Journal of the American Society of Nephrology, Journal Year: 2019, Volume and Issue: 15(3), P. 404 - 411

Published: Oct. 21, 2019

In this review of the application proteomics and metabolomics to kidney disease research, we key concepts, highlight illustrative examples, outline future directions. The proteome metabolome reflect influence environmental exposures in addition genetic coding. Circulating levels proteins metabolites are dynamic modifiable, thus amenable therapeutic targeting. Design analytic considerations studies should be tailored investigator’s goals. For identification clinical biomarkers, adjustment for all potential confounding variables, particularly GFR, strict significance thresholds warranted. However, approach has obscure biologic signals can overly conservative given high degree intercorrelation within metabolome. Mass spectrometry, often coupled up-front chromatographic separation techniques, is a major workhorse both metabolomics. High-throughput antibody- aptamer-based proteomic platforms have emerged as additional, powerful approaches assay proteome. As breadth coverage these methodologies continues expand, machine learning tools pathway analyses help select molecules greatest interest categorize them distinct themes. Studies date already made substantial effect, example elucidating target antigens membranous nephropathy, identifying signature urinary peptides that adds prognostic information albumin CKD, implicating circulating inflammatory mediators diabetic demonstrating role microbiome uremic milieu, highlighting bioenergetics modifiable factor AKI. Additional required replicate expand on findings independent cohorts. Further, more work needed understand longitudinal trajectory protein metabolite markers, perform transomics merged datasets, incorporate tissue–based investigation.

Language: Английский

---

Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease

Nature Reviews Nephrology, Journal Year: 2021, Volume and Issue: 18(1), P. 38 - 55

Published: Oct. 6, 2021

Language: Английский

---

Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease

Chinese Chemical Letters, Journal Year: 2024, Volume and Issue: 35(11), P. 109627 - 109627

Published: Feb. 9, 2024

Language: Английский

---

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

Journal of the American Society of Nephrology, Journal Year: 2022, Volume and Issue: 33(2), P. 375 - 386

Published: Jan. 11, 2022

Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding pediatric CKD causes. We sought identify signatures in based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).

Language: Английский

---

Metabolite profiling of CKD progression in the chronic renal insufficiency cohort study

JCI Insight, Journal Year: 2022, Volume and Issue: 7(20)

Published: Sept. 1, 2022

BACKGROUNDMetabolomic profiling in individuals with chronic kidney disease (CKD) has the potential to identify novel biomarkers and provide insight into pathogenesis.METHODSWe examined association between blood metabolites CKD progression, defined as subsequent development of end-stage renal (ESRD) or estimated glomerular filtrate rate (eGFR) halving, 1,773 participants Chronic Renal Insufficiency Cohort (CRIC) study, 962 African-American Study Kidney Disease Hypertension (AASK), 5,305 Atherosclerosis Risk Communities (ARIC) study.RESULTSIn CRIC, more than half measured were associated progression minimally adjusted Cox proportional hazards models, but number strength associations markedly attenuated by serial adjustment for covariates, particularly eGFR. Ten significantly fully models CRIC; 3 these also significant AASK ARIC, highlighting markers filtration (pseudouridine), histamine metabolism (methylimidazoleacetate), azotemia (homocitrulline). Our findings highlight N-acetylserine a marker tubular function, observed CRIC ARIC.CONCLUSIONOur demonstrate application metabolomics causal pathways progression.FUNDINGThis study was supported NIH (U01 DK106981, U01 DK106982, DK085689, R01 DK108803, DK124399).

Language: Английский

---

Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

American Journal of Kidney Diseases, Journal Year: 2024, Volume and Issue: 84(1), P. 49 - 61.e1

Published: Jan. 23, 2024

Language: Английский

---

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes

Kidney International, Journal Year: 2019, Volume and Issue: 96(6), P. 1381 - 1388

Published: Aug. 30, 2019

Language: Английский

---

An untargeted metabolomics study of blood pressure: findings from the Bogalusa Heart Study

Journal of Hypertension, Journal Year: 2020, Volume and Issue: 38(7), P. 1302 - 1311

Published: Jan. 30, 2020

Objective: To identify novel and confirm previously reported metabolites associated with SBP, DBP, hypertension in a biracial sample of Bogalusa Heart Study (BHS) participants. Methods: We employed untargeted, ultra-high performance liquid chromatography tandem mass spectroscopy metabolomics profiling among 1249 BHS participants (427 African-Americans 822 whites) BP covariable data collected during the 2013 to 2016 visit cycle. A total 1202 were tested for associations continuous binary phenotypes using multiple linear logistic regression models, respectively, overall race-stratified analyses. Results: 24 robustly BP, achieving Bonferroni-corrected P less than 4.16 × 10 −5 analysis consistent effect sizes across race groups. The identified included three amino acid nucleotide from histidine, pyrimidine, or tryptophan metabolism sub-pathways, seven cofactor vitamin xenobiotic ascorbate aldarate metabolism, bacterial/fungal, chemical, food component lipid eicosanoid, phosphatidylcholine, phosphatidylethanolamine, sphingolipid four still unnamed metabolites. Six described confirmed by our study (Bonferroni-corrected < 4.95 −4 directions studies). Furthermore, demonstrated 5.92-fold, 4.77-fold, 4.54-fold enrichment nominally significant signals ( = 3.08 −10 , 5.93 −8 2.30 respectively). Conclusion: In aggregate, provides new information about potential molecular mechanisms underlying regulation. also demonstrate reproducibility findings studies despite differences populations metabolite methods.

Language: Английский

---

Mapping the metabolomic and lipidomic changes in the bleomycin model of pulmonary fibrosis in young and aged mice

Disease Models & Mechanisms, Journal Year: 2021, Volume and Issue: 15(1)

Published: Nov. 30, 2021

Alterations in metabolic pathways were recently recognized as potential underlying drivers of idiopathic pulmonary fibrosis (IPF), translating into novel therapeutic targets. However, knowledge and lipid regulation fibrotic lungs is limited. To comprehensively characterize perturbations the bleomycin mouse model IPF, we analyzed metabolome lipidome by mass spectrometry. We identified increased tissue turnover repair, evident enhanced breakdown proteins, nucleic acids lipids extracellular matrix turnover. Energy production was upregulated, including glycolysis, tricarboxylic acid cycle, glutaminolysis, lactate fatty oxidation. Higher eicosanoid synthesis indicated inflammatory processes. Because risk IPF increases with age, investigated how age influences metabolomic lipidomic changes bleomycin-induced model. Surprisingly, except for cytidine, did not detect any significantly differential metabolites or between old young bleomycin-treated lungs. Together, that reflect higher energy demand, proliferation, remodeling, collagen deposition inflammation, which might serve to improve diagnostic options lung diseases future.

Language: Английский

---

Per- and Polyfluoroalkyl Substances Concentrations are Associated with an Unfavorable Cardio-Metabolic Risk Profile: Findings from Two Population-Based Cohort Studies

Exposure and Health, Journal Year: 2024, Volume and Issue: 16(5), P. 1251 - 1262

Published: Feb. 6, 2024

Abstract Per- and polyfluoroalkyl substances (PFAS) are widely used persistent chemicals, leading to ubiquitous exposure. Although high PFAS levels have been associated with an adverse cardiovascular risk profile, the distribution of relations cardio-metabolic markers in general population not fully characterized. We assessed association between blood perfluorooctaneic acid (PFOA), perfluorooctane sulfonic (PFOS), perfluorohexanesulfonic (PFHxS) a range lipoproteins metabolites as well clinical lipid measurements. data from participants Netherlands Epidemiology Obesity study (NEO) ( n = 584) Rhineland Study 1962), jointly spanning age 30 89 years. were measured Metabolon HD4 platform, lipoprotein metabolite profiles using Nightingale’s nuclear magnetic resonance-spectroscopy mainly comprised markers. Using linear regression analyses, we quantified age-, sex-, education-adjusted associations PFOA, PFOS, PFHxS measurements 224 metabolites. Higher PFAS, particularly PFOS PFHxS, higher concentrations total lipid, cholesterol phospholipid content most HDL, IDL, LDL, VLDL subclasses. The effect sizes age-dependent for majority associations, deleterious effects being generally stronger people below compared those above median age. Our observation that even low unfavorable calls further critical regulation substances.

Language: Английский

---