Urine metabolomics insight into acute kidney injury point to oxidative stress disruptions in energy generation and H2S availability

Martin‐Lorenzo, Marta; González-Calero, Laura; Ramos-Barron, M.A.; Sanchez-Niño, María Dolores; Gómez-Alamillo, C.; García‐Segura, Juan M.; Arias, Manuel; Vivanco, Fernando; Alvarez-Llamas, Gloria

doi:10.1007/s00109-017-1594-5

Cited by 32 publications

(25 citation statements)

References 42 publications

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“…Referring to the daily intraindividual dynamics in the urinary microbiome of an nRTX patient recovering from AKI, we hypothesize that also these shifts depended on metabolite and electrolyte excretion in urine during the recovery from AKI, as it has already been well documented in recent literature that the urinary metabolome experiences changes dependent on kidney function [49,50]. This is consistent with our metabolome analysis of this patient.…”

Section: Discussionsupporting

confidence: 90%

The urinary microbiome shows different bacterial genera in renal transplant recipients and non-transplant patients at time of acute kidney injury – a pilot study

et al. 2020

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Background: In the past urine was considered sterile. Through the introduction of next generation sequencing, it has become clear that a urinary microbiome exists. Acute kidney injury (AKI) represents a major threat to kidney transplant recipients. Remarkable changes in the urinary metabolome occur during AKI, which may influence the urinary microbiome. To our knowledge, this is the first study that examines the urinary microbiome in renal transplant recipients (RTX) and non-transplant recipients (nRTX) at time of AKI.Methods: In this cross-sectional pilot-study the urinary microbiome of 21 RTX and 9 nRTX with AKI was examined. Clean catch morning urine samples were obtained from all patients on the first day of AKI diagnosis. AKI was defined according to KDIGO guidelines. Urinary microbiota and the urinary metabolome during AKI were assessed in one patient. 16S rRNA sequencing was performed. Sequences were processed using UPARSE-pipeline for operational taxonomic units (OTU) and taxon finding. Results: We successfully extracted and sequenced bacterial DNA from 100% of the urine samples. All 30 patients revealed at least 106,138 reads. 319 OTU and 211 different genera were identified. The microbiotic diversity richness in the RTX group was no different from the nRTX group. Eighteen genera were solely present in nRTX and 7 in RTX. Conclusions: The urinary microbiome at time of AKI showed different bacterial genera in RTX compared to nRTX. The nRTX group exhibited no different diversity to the RTX group. Irrespective of the status of a previous renal transplantation, the urinary microbiome comprised > 210 different genera. An intraindividual change in microbiota diversity and richness was observed in one study patient during recovery from AKI.

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Section: Discussionsupporting

confidence: 90%

The urinary microbiome shows different bacterial genera in renal transplant recipients and non-transplant patients at time of acute kidney injury – a pilot study

et al. 2020

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“…Variables at VIP (variable influence on projection) with a value larger than 1.0 and fold change larger than 1.2 were selected and further input into Welch's t -test to test the meaning of each feature. The areas under the receiver operating characteristic curves (ROC) were employed to assess the significance of the biomarkers [ 23 ]. In general, only t R – m/z pairs with VIP > 1, fold change > 1.2 and p < 0.05 were considered as the potential biomarkers.…”

Section: Methodsmentioning

confidence: 99%

Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MS ^E mode

Yang

et al. 2018

R. Soc. open sci.

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In this work, an untargeted metabolomic method based on ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) in MSE (E represents collision energy) mode was exploited to determine the dynamic metabolic alterations in the plasma of male C57BL/6 mice during the onset and development of lung carcinoma. Plasma samples were collected from control and model mice (male C57BL/6 mice experimentally inoculated with the Lewis lung carcinoma cells) at 7 and 14 days post-inoculation (DPI). As a result, 15 dysregulated metabolites, including cholesterol sulphate, tiglylcarnitine, 1-palmitoylglycerophosphoinositol, 2-stearoylglycerophosphoinositol, stearoylcarnitine, PC(20:2(11Z,14Z)/16:0), PC(22:4(7Z,10Z,13Z,16Z)/14:0), PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:0), PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0), 12,20-Dioxo-leukotriene B4, sphingosine 1-phosphate(d19:1-P), sphingomyelin(d18:0/16:1(9Z)), lysoPC(16:0), lysoPC(18:0) and lysoPC(20:4(5Z,8Z,11Z,14Z)), were identified in the plasma of model mice with xenografts at both 7 and 14 DPI. All the altered metabolites associated with the onset and development of lung carcinoma were involved in the metabolism of glycerophospholipid, fatty acid, sphingolipid and arachidonic acid. The feasible utility of these endogenous biomarkers as potential diagnostic indicators was validated through receiver operating characteristic curve analysis. Collectively, these findings provide a systematic view of metabolic changes linked to the onset and development of lung carcinoma.

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“…Metabonomics research can screen differential markers and examine metabolic pathways and mechanisms. For the metabonomics sample selection, blood and urine are typically used in human tests because of the convenience and noninvasiveness of sampling [6,7].…”

Section: Introductionmentioning

confidence: 99%

Changes of Differential Urinary Metabolites after High‐Intensive Training in Teenage Football Players

Chen

Liu

Yang³

et al. 2020

BioMed Research International

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Objective. The mechanism underlying the fatigue of football players is closely related to the energy depletion and accumulation of metabolites; the present study tries to explore the metabolic mechanism in teenage football players during exercise-induced fatigue. Methods. 12 teenage football players were subjected to three groups of combined training by using a cycle ergometer, with the subjective Rating of Perceived Exertion (RPE) as a fatigue criterion. The following indicators were measured in each group after training: maximum oxygen uptake (VO2max), anaerobic power, and average anaerobic power. Urine samples were collected before and after the training. Gas chromatography-mass spectrometry (GC-MS) was performed for the metabonomics analysis of the samples. The metabolism data was analyzed by using principal component analysis (PCA) and orthogonal partial least squares analysis (OPLS-DA), through the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to confirm the potential differences between metabolites, and the MetPA database was used to analyze the related metabolic pathways. Results. There was no significant difference between the maximal oxygen uptakes among the three groups. Compared with group 1, the maximum and average anaerobic power in group 3 significantly decreased (p<0.05) at the end of training. GC-MS detected 635 metabolites in the urine samples. Through PCA, OPLS-DA analysis, and KEGG matching, 25 different metabolites (3↑22↓) that met the conditions were finally selected. These different metabolites belonged to 5 metabolic pathways: glycine-serine-threonine metabolism, citrate cycle, tyrosine metabolism, nitrogen metabolism, and glycerophospholipid metabolism. Conclusions. During the combined exercise of aerobic and anaerobic metabolism, teenage football players show a significant decrease in anaerobic capacity after fatigue. The metabolic mechanism of exercise fatigue was related to disorders in amino acid and energy metabolism.

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Urine metabolomics insight into acute kidney injury point to oxidative stress disruptions in energy generation and H2S availability

Cited by 32 publications

References 42 publications

The urinary microbiome shows different bacterial genera in renal transplant recipients and non-transplant patients at time of acute kidney injury – a pilot study

The urinary microbiome shows different bacterial genera in renal transplant recipients and non-transplant patients at time of acute kidney injury – a pilot study

Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MS ^E mode

Changes of Differential Urinary Metabolites after High‐Intensive Training in Teenage Football Players

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Urine metabolomics insight into acute kidney injury point to oxidative stress disruptions in energy generation and H2S availability

Cited by 32 publications

References 42 publications

The urinary microbiome shows different bacterial genera in renal transplant recipients and non-transplant patients at time of acute kidney injury – a pilot study

The urinary microbiome shows different bacterial genera in renal transplant recipients and non-transplant patients at time of acute kidney injury – a pilot study

Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MS E mode

Changes of Differential Urinary Metabolites after High‐Intensive Training in Teenage Football Players

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Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MS ^E mode