Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients

The assessment of kidney function within the first year following transplantation is crucial for predicting long-term graft survival. This study aimed to develop a robust and accurate model using metabolite profiles to predict early long-term outcomes in patient groups at the highest risk of early graft loss. A group of 61 kidney transplant recipients underwent thorough monitoring during a one-year follow-up period, which included a one-week hospital stay and follow-up assessments at three and six months. Based on their 12-month follow-up serum creatinine levels: Group 2 had levels exceeding 1.5 mg/dl, while Group 1 had levels below 1.5 mg/dl. Metabolites were detected by mass spectrometer and first pre-processed. Univariate and multivariate statistical analyses were employed to identify significant differences between the two groups. Nineteen metabolites were found to differ significantly in the 1st week, and seventeen metabolites in the 3rd month (adjusted p-value < 0.05, quality control (QC) < 30, a fold change (FC) > 1.1 or a FC < 0.91, Variable Influence on Projection (VIP) > 1). However, no significant differences were observed in the 6th month. These distinctive metabolites mainly belonged to lipid, fatty acid, and amino acid categories. Ten models were constructed using a backward conditional approach, with the best performance seen in model 5 for Group 2 at the 1st-week mark (AUC 0.900) and model 3 at the 3rd-month mark (AUC 0.924). In conclusion, the models developed in the early stages may offer potential benefits in the management of kidney transplant patients.

Dysregulation of the 3β-hydroxysteroid dehydrogenase type 2 enzyme and steroid hormone biosynthesis in chronic kidney disease

Introduction

Chronic kidney disease (CKD) presents a critical global health challenge, marked by the progressive decline of renal function. This study explores the role of the 3β-hydroxysteroid dehydrogenase type 2 enzyme (HSD3B2) and the steroid hormone biosynthesis pathway in CKD pathogenesis and progression.

Methods

Using an adenine-induced CKD mouse model, we conducted an untargeted metabolomic analysis of plasma samples to identify key metabolite alterations associated with CKD. Immunohistochemistry, Western blotting, and qPCR analyses were performed to confirm HSD3B2 expression in both human and mouse tissues. Additionally, Nephroseq and Human Protein Atlas data were utilized to assess the correlation between HSD3B2 and kidney function. Functional studies were conducted on HK2 cells with HSD3B2 knockdown to evaluate the impact on cell proliferation and apoptosis.

Results

Metabolic characteristics revealed significant shifts in CKD, with 61 metabolites increased and 65 metabolites decreased, highlighting the disruption in steroid hormone biosynthesis pathways influenced by HSD3B2. A detailed examination of seven key metabolites underscored the enzyme's central role. HSD3B2 exhibited a strong correlation with kidney function, supported by data from Nephroseq and the Human Protein Atlas. Immunohistochemistry, Western blotting, and qPCR analyses confirmed a drastic reduction in HSD3B2 expression in CKD-affected kidneys. Suppressed proliferation and increased apoptosis rates in HSD3B2 knocked down HK2 cells further demonstrated the enzyme's significance in regulating renal pathophysiology.

Discussion

These findings underscore the potential of HSD3B2 as a clinical diagnostic and therapeutic target in CKD. While further studies are warranted to fully elucidate the mechanisms, our results provide valuable insights into the intricate interplay between steroid hormone biosynthesis and CKD. This offers a promising avenue for precision medicine approaches and personalized treatment strategies.

Comprehensive mapping of saliva by multiomics in children with idiopathic nephrotic syndrome

AIM

Saliva can reflect an individual's physiological status or susceptibility to systemic disease. However, little attention has been given to salivary analysis in children with idiopathic nephrotic syndrome (INS). We aimed to perform a comprehensive analysis of saliva from INS children.

METHODS

A total of 18 children (9 children with INS and 9 normal controls) were recruited. Saliva was collected from each INS patient in the acute and remission phases. 16S rRNA gene sequencing, widely targeted metabolomics, and 4D-DIA proteomics were performed.

RESULTS

Actinobacteria and Firmicutes were significantly enriched in the pretreatment group compared with the normal control group, while Bacteroidota and Proteobacteria were significantly decreased. A total of 146 metabolites were identified as significantly different between INS children before treatment and normal controls, which covers 17 of 23 categories. KEGG enrichment analysis revealed three significantly enriched pathways, including ascorbate and aldarate metabolism, pentose and glucuronate interconversions, and terpenoid backbone biosynthesis (P < 0.05). A total of 389 differentially expressed proteins were selected between INS children before treatment and normal controls. According to the KEGG and GO enrichment analyses of the KOGs, abnormal ribosome structure and function and humoral immune disorders were the most prominent differences between INS patients and normal controls in the proteomic analysis.

CONCLUSION

Oral microbiota dysbiosis may modulate the metabolic profile of saliva in children with INS. It is hypothesized that children with INS might have "abnormal ribosome structure and function" and "humoral immune disorders".

Revealing novel biomarkers for diagnosing chronic kidney disease in pediatric patients

Pediatric chronic kidney disease (CKD) is a clinical condition characterized by progressive renal function deterioration. CKD diagnosis is based on glomerular filtration rate, but its reliability is limited, especially at the early stages. New potential biomarkers (citrulline (CIT), symmetric dimethylarginine (SDMA), S-adenosylmethionine (SAM), n-butyrylcarnitine (nC4), cis-4-decenoylcarnitine, sphingosine-1-phosphate and bilirubin) in addition to creatinine (CNN) have been proposed for early diagnosis. To verify the clinical value of these biomarkers we performed a comprehensive targeted metabolomics study on a representative cohort of CKD and healthy pediatric patients. Sixty-seven children with CKD and forty-five healthy children have been enrolled in the study. Targeted metabolomics based on liquid chromatography-triple quadrupole mass spectrometry has been used for serum and plasma samples analysis. Univariate data analysis showed statistically significant differences (p < 0.05) in the concentration of CNN, CIT, SDMA, and nC4 among healthy and CKD pediatric patients. The predictive ability of the proposed biomarkers was also confirmed through specificity and sensitivity expressed in Receiver Operating Characteristic curves (AUC = 0.909). In the group of early CKD pediatric patients, AUC of 0.831 was obtained, improving the diagnostic reliability of CNN alone. Moreover, the models built on combined CIT, nC4, SDMA, and CNN allowed to distinguish CKD patients from healthy control regardless of blood matrix type (serum or plasma). Our data demonstrate potential biomarkers in the diagnosis of early CKD stages.

Metabolome evidence of CKDu risks after chronic exposure to simulated Sri Lanka drinking water in zebrafish

It is still a serious public health issue that chronic kidney disease of uncertain etiology (CKDu) in Sri Lanka poses challenges in identification, prevention, and treatment. What environmental factors in drinking water cause kidney damage remains unclear. This study aimed to investigate the risks of various environmental factors that may induce CKDu, including water hardness, fluoride (HF), heavy metals (HM), microcystin-LR (MC-LR), and their combined exposure (HFMM). The research focused on comprehensive metabolome analysis, and correlation with transcriptomic and gut microbiota changes. Results revealed that chronic exposure led to kidney damage and pancreatic toxicity in adult zebrafish. Metabolomics profiling showed significant alterations in biochemical processes, with enriched metabolic pathways of oxidative phosphorylation, folate biosynthesis, arachidonic acid metabolism, FoxO signaling pathway, lysosome, pyruvate metabolism, and purine metabolism. The network analysis revealed significant changes in metabolites associated with renal function and diseases, including 20-Hydroxy-LTE4, PS(18:0/22:2(13Z,16Z)), Neuromedin N, 20-Oxo-Leukotriene E4, and phenol sulfate, which are involved in the fatty acyls and glycerophospholipids class. These metabolites were closely associated with the disrupted gut bacteria of g_ZOR0006, g_Pseudomonas, g_Tsukamurella, g_Cetobacterium, g_Flavobacterium, which belonged to dominant phyla of Firmicutes and Proteobacteria, etc., and differentially expressed genes (DEGs) such as egln3, ca2, jun, slc2a1b, and gls2b in zebrafish. Exploratory omics analyses revealed the shared significantly changed pathways in transcriptome and metabolome like calcium signaling and necroptosis, suggesting potential biomarkers for assessing kidney disease.

A multiplatform metabolomics approach for comprehensive analysis of GIST xenografts with various KIT mutations

Metabolites in biological matrices belong to diverse chemical groups, ranging from non-polar long-chain fatty acids to small polar molecules. The goal of untargeted metabolomic analysis is to measure the highest number of metabolites in the sample. Nevertheless, from an analytical point of view, no single technique can measure such a broad spectrum of analytes. Therefore, we selected a method based on GC-MS and LC-MS with two types of stationary phases for the untargeted profiling of gastrointestinal stromal tumours. The procedure was applied to GIST xenograft samples (n = 71) representing four different mutation models, half of which were treated with imatinib. We aimed to verify the method coverage and advantages of applying each technique. RP-LC-MS measured most metabolites due to a significant fraction of lipid components of the tumour tissue. What is unique and worth noting is that all applied techniques were able to distinguish between different mutation models. However, for detecting imatinib-induced alterations in the GIST metabolome, RP-LC-MS and GC-MS proved to be more relevant than HILIC-LC-MS, resulting in a higher number of significantly changed metabolites in four treated models. Undoubtedly, the inclusion of all mentioned techniques makes the method more comprehensive. Nonetheless, for green chemistry and time and labour saving, we assume that RP-LC-MS and GC-MS analyses are sufficient to cover the global GIST metabolome.

Metabolomic profiles in serum and urine uncover novel biomarkers in children with nephrotic syndrome

BACKGROUND

Nephrotic syndrome is common in children and adults worldwide, and steroid-sensitive nephrotic syndrome (SSNS) accounts for 80%. Aberrant metabolism involvement in early SSNS is sparsely studied, and its pathogenesis remains unclear. Therefore, the goal of this study was to investigate the changes in initiated SSNS patients-related metabolites through serum and urine metabolomics and discover the novel potential metabolites and metabolic pathways.

METHODS

Serum samples (27 SSNS and 56 controls) and urine samples (17 SSNS and 24 controls) were collected. Meanwhile, the non-targeted analyses were performed by ultra-high-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-QTOF-MS) to determine the changes in SSNS. We applied the causal inference model, the DoWhy model, to assess the causal effects of several selected metabolites. An ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to validate hits (D-mannitol, dulcitol, D-sorbitol, XMP, NADPH, NAD, bilirubin, and α-KG-like) in 41 SSNS and 43 controls. In addition, the metabolic pathways were explored.

RESULTS

Compared to urine, the metabolism analysis of serum samples was more clearly discriminated at SSNS. 194 differential serum metabolites and five metabolic pathways were obtained in the SSNS group. Eight differential metabolites were identified by establishing the diagnostic model for SSNS, and four variables had a positive causal effect. After validation by targeted MS, except XMP, others have similar trends like the untargeted metabolic analysis.

CONCLUSION

With untargeted metabolomics analysis and further targeted quantitative analysis, we found seven metabolites may be new biomarkers for risk prediction and early diagnosis for SSNS.

Metabolomics Profiling of Nephrotic Syndrome towards Biomarker Discovery

Nephrotic syndrome (NS) is a kidney illness characterized by excessive proteinuria, hypoalbuminemia, edema, and hyperlipidemia, which may lead to kidney failure and necessitate renal transplantation. End-stage renal disease, cardiovascular issues, and mortality are much more common in those with NS. Therefore, the present study aimed to identify potential new biomarkers associated with the pathogenesis and diagnosis of NS. The liquid chromatography–mass spectrometry (LC–MS) metabolomics approach was applied to profile the metabolome of human serum of patients with NS. A total of 176 metabolites were significantly altered in NS compared to the control. Arginine, proline, and tryptophan metabolism; arginine, phenylalanine, tyrosine, and tryptophan biosynthesis were the most common metabolic pathways dysregulated in NS. Furthermore, alanyl-lysine and isoleucyl-threonine had the highest discrimination between NS and healthy groups. The candidate biomarkers may lead to understanding the possible metabolic alterations associated with NS and serve as potential diagnostic biomarkers.

Comparative metabolomic study of high-flux hemodialysis and high volume online hemodiafiltration in the removal of uremic toxins using 1H NMR spectroscopy

Uremic toxins (UTs) accumulate in the circulation of patients with chronic kidney disease (CKD). High volume hemodiafiltration (HDF) improves clearance of low and medium molecular weight UTs compared to HD. The present study is a post-hoc analysis comparing the metabolomic profile in serum from patients under high flux HD (hf-HD) and HDF in HDFIT, a multicentric randomized controlled trial (RCTs). Per protocol, serum samples were collected pre- and post- dialysis treatments at randomization (baseline) and at the end of the follow up (6 months) and stored in a biorepository. Random (pre- and post-dialysis) samples from nine patients in study arm were selected at baseline and at the end of the follow up. To compare the samples, 26 possibly matching metabolites were identified by a t-test among the four groups using ¹H nuclear magnetic resonance (NMR). To evaluate the comparison between the modalities is a single treatment session, the clearance rates (CRs) of each metabolite were calculated based on pre-dialysis and post-dialysis samples. In addition, to evaluate to effect of UT removal during the trial follow up period, the pre-dialysis metabolite concentrations at the baseline and at 6 months were compared among the two arms of the study. There was no significant difference between in the single session CRs of metabolites when hf-HD and HDF were compared. On the other hand, the comparison between baseline and 6-month (long-term evolution) led to the identification of 16 metabolites that differentiated the hf-HD and the HDF evolutions. Most of these 16 metabolites are involved in several important metabolic pathways, such as metabolism of phenylalanine and biosynthesis of phenylalanine, tyrosine, and tryptophan, which are related to UTs and cardiovascular disease development. Although no difference was observed between hf-HD and HDF samples before and after a single session, concentrations of CKD-relevant metabolites and associated pathologies were stable in the HDF samples, but not in the hf-HD samples, over the six-month period, suggesting that HDF enhances long-term stability.

Advances in the Progression and Prognosis Biomarkers of Chronic Kidney Disease

Chronic kidney disease (CKD) is one of the increasingly serious public health concerns worldwide; the global burden of CKD is increasingly due to high morbidity and mortality. At present, there are three key problems in the clinical treatment and management of CKD. First, the current diagnostic indicators, such as proteinuria and serum creatinine, are greatly interfered by the physiological conditions of patients, and the changes in the indicator level are not synchronized with renal damage. Second, the established diagnosis of suspected CKD still depends on biopsy, which is not suitable for contraindication patients, is also traumatic, and is not sensitive to early progression. Finally, the prognosis of CKD is affected by many factors; hence, it is ineviatble to develop effective biomarkers to predict CKD prognosis and improve the prognosis through early intervention. Accurate progression monitoring and prognosis improvement of CKD are extremely significant for improving the clinical treatment and management of CKD and reducing the social burden. Therefore, biomarkers reported in recent years, which could play important roles in accurate progression monitoring and prognosis improvement of CKD, were concluded and highlighted in this review article that aims to provide a reference for both the construction of CKD precision therapy system and the pharmaceutical research and development.

NMR-based metabolomics associated with chronic kidney disease in humans and animals: a one health perspective

Chronic kidney disease (CKD) is a renal dysfunction that can lead to high rates of mortality and morbidity, particularly when coupled with late diagnosis. CKD has become a major health problem due to its challenging detection at early stages when clear symptoms are yet to be presented. Thus, CKD is likely to be identified when the substantive conditions of the disease are manifest. In order to address the development of the disease and provide necessary treatments at the initial stage, the investigation of new biomarkers and metabolites associated with early detection of CKD are needed. Identified metabolites could be used to confirm the presence of the disease, obtain information on its mechanism and facilitate the development of novel pharmaceutical treatments. Such metabolites may be detected from biofluids and tissues using a range of analytical techniques. There are a number of metabolites that have been identified by mass spectrometry at high sensitivities, whilst the detection of metabolites directly from biofluids using NMR could present a more rapid way to expand our understanding of this disease. This review is focused on NMR-based metabolomics associated with CKD in humans and animals.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets

MOTIVATION

Complex data structures composed of different groups of observations and blocks of variables are increasingly collected in many domains, including metabolomics. Analysing these high-dimensional data constitutes a challenge, and the objective of this article is to present an original multivariate method capable of explicitly taking into account links between data tables when they involve the same observations and/or variables. For that purpose, an extension of standard principal component analysis called NetPCA was developed.

RESULTS

The proposed algorithm was illustrated as an efficient solution for addressing complex multigroup and multiblock datasets. A case study involving the analysis of metabolomic data with different annotation levels and originating from a chronic kidney disease (CKD) study was used to highlight the different aspects and the additional outputs of the method compared to standard PCA. On the one hand, the model parameters allowed an efficient evaluation of each group's influence to be performed. On the other hand, the relative relevance of each block of variables to the model provided decisive information for an objective interpretation of the different metabolic annotation levels.

AVAILABILITY AND IMPLEMENTATION

NetPCA is available as a Python package with NumPy dependencies.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomics analysis of serum in pediatric nephrotic syndrome based on targeted and non-targeted platforms

BACKGROUND AND AIMS

Nephrotic syndrome (NS) is a common pediatric urinary system disease. The aim in this work was to investigate the changes in pediatric NS-related metabolites through serum metabolomics, and explore the new potential metabolites and differential metabolic pathways.

METHODS

Serum samples from 40 pediatric patients with nephrotic syndrome and 40 healthy controls were collected. The targeted and non-targeted metabolomics analyses were performed to determine the metabolic changes in pediatric NS. Based on multivariate statistical analysis and the regression model, the serum potential metabolites were screened and different metabolic pathways were explored.

RESULTS

39 differential metabolites in pediatric NS were obtained based on the metabolomics analysis. 12 differential metabolites (serine, C18: 2 (EFA), C18: 2 (FFA), Isonuatigenin 3- [rhamnosyl- (1- > 2) -glucoside], C18: 4 (EFA), C18: 4 (FFA), caprylic acid, citric acid, methylmalonic acid, caproic acid, canavalioside and uroporphyrin were identified to establish the diagnostic model for pediatric NS. Five metabolic pathways including TCA cycle, amino acid metabolism, bile acid biosynthesis, linoleate metabolism and glyoxylate and dicarboxylate metabolism were the key differential metabolic pathways.

CONCLUSION

These data elucidated the metabolic alterations associated with pediatric NS and suggested a new diagnosis model for monitoring pediatric NS. The current study provides the useful information to bridge the gaps in our understanding of the metabolic alterations associated with pediatric NS and might facilitate the characterization of pediatric NS patients by performing serum metabolomics.

Plasma metabolites associated with chronic kidney disease and renal function in adults from the Baltimore Longitudinal Study of Aging

INTRODUCTION

Chronic kidney disease (CKD) is an important cause of disability and death, but its pathogenesis is poorly understood. Plasma metabolites can provide insights into underlying processes associated with CKD.

OBJECTIVES

To clarify the relationship of plasma metabolites with CKD and renal function in human.

METHODS

We used a targeted metabolomics approach to characterize the relationship of 450 plasma metabolites with CKD and estimated glomerular filtration rate (eGFR) in 616 adults, aged 38-94 years, who participated in the Baltimore Longitudinal Study of Aging.

RESULTS

There were 74 (12.0%) adults with CKD. Carnitine, acetylcarnitine, propionylcarnitine, butyrylcarnitine, trigonelline, trimethylamine N-oxide (TMAO), 1-methylhistidine, citrulline, homoarginine, homocysteine, sarcosine, symmetric dimethylarginine, aspartate, phenylalanine, taurodeoxycholic acid, 3-indolepropionic acid, phosphatidylcholines (PC).aa.C40:2, PC.aa.C40:3, PC.ae.C40:6, triglycerides (TG) 20:4/36:3, TG 20:4/36:4, and choline were associated with higher odds of CKD in multivariable analyses adjusting for potential confounders and using a false discovery rate (FDR) to address multiple testing. Six acylcarnitines, trigonelline, TMAO, 18 amino acids and biogenic amines, taurodeoxycholic acid, hexoses, cholesteryl esters 22:6, dehydroepiandrosterone sulfate, 3-indolepropionic acid, 2 PCs, 17 TGs, and choline were negatively associated with eGFR, and hippuric acid was positively associated with eGFR in multivariable analyses adjusting for potential confounders and using a FDR approach.

CONCLUSION

The metabolites associated with CKD and reduced eGFR suggest that several pathways, such as the urea cycle, the arginine-nitric oxide pathway, the polyamine pathway, and short chain acylcarnitine metabolism are altered in adults with CKD and impaired renal function.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

Evaluation of Different Tandem MS Acquisition Modes to Support Metabolite Annotation in Human Plasma Using Ultra High-Performance Liquid Chromatography High-Resolution Mass Spectrometry for Untargeted Metabolomics

Ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) is a powerful and essential technique for metabolite annotation in untargeted metabolomic applications. The aim of this study was to evaluate the performance of diverse tandem MS (MS/MS) acquisition modes, i.e., all ion fragmentation (AIF) and data-dependent analysis (DDA), with and without ion mobility spectrometry (IM), to annotate metabolites in human plasma. The influence of the LC separation was also evaluated by comparing the performance of MS/MS acquisition in combination with three complementary chromatographic separation modes: reversed-phase chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) with either an amide (aHILIC) or a zwitterionic (zHILIC) stationary phase. RPLC conditions were first chosen to investigate all the tandem MS modes, and we found out that DDA did not provide a significant additional amount of chemical coverage and that cleaner MS/MS spectra can be obtained by performing AIF acquisitions in combination with IM. Finally, we were able to annotate 338 unique metabolites and demonstrated that zHILIC was a powerful complementary approach to both the RPLC and aHILIC chromatographic modes. Moreover, a better analytical throughput was reached for an almost negligible loss of metabolite coverage when IM-AIF and AIF using ramped instead of fixed collision energies were used.

Exploring blood alterations in chronic kidney disease and haemodialysis using metabolomics

Chronic kidney disease (CKD) is characterized by retention of uremic solutes. Compared to patients with non-dialysis dependent CKD, those requiring haemodialysis (HD) have increased morbidity and mortality. We wished to characterise metabolic patterns in CKD compared to HD patients using metabolomics. Prevalent non-HD CKD KDIGO stage 3b-4 and stage 5 HD outpatients were screened at a single tertiary hospital. Various liquid chromatography approaches hyphenated with mass spectrometry were used to identify 278 metabolites. Unsupervised and supervised data analyses were conducted to characterize metabolic patterns. 69 patients were included in the CKD group and 35 in the HD group. Unsupervised data analysis showed clear clustering of CKD, pre-dialysis (preHD) and post-dialysis (postHD) patients. Supervised data analysis revealed qualitative as well as quantitative differences in individual metabolites profiles between CKD, preHD and postHD states. An original metabolomics framework could discriminate between CKD stages and highlight HD effect based on 278 identified metabolites. Significant differences in metabolic patterns between CKD and HD patients were found overall as well as for specific metabolites. Those findings could explain clinical discrepancies between patients requiring HD and those with earlier stage of CKD.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

Combining the advantages of multilevel and orthogonal partial least squares data analysis for longitudinal metabolomics: Application to kidney transplantation

Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a "normal" renal function and possibly also predict rejection. The longitudinal follow-up of those patients with repeated measurements is useful to understand changes and decide whether an intervention is necessary. The time modality, therefore, constitutes a specific dimension in the data structure, requiring dedicated consideration for proper statistical analysis. The handling of specific data structures in metabolomics has received strong interest in recent years. In this work, we demonstrated the recently developed ANOVA multiblock OPLS (AMOPLS) to efficiently analyse longitudinal metabolomic data by considering the intrinsic experimental design. Indeed, AMOPLS combines the advantages of multilevel approaches and OPLS by separating between and within individual variations using dedicated predictive components, while removing most uncorrelated variations in the orthogonal component(s), thus facilitating interpretation. This modelling approach was applied to a clinical cohort study aiming to evaluate the impact of kidney transplantation over time on the plasma metabolic profile of graft patients and donor volunteers. A dataset of 266 plasma metabolites was identified using an LC-MS multiplatform analytical setup. Two separate AMOPLS models were computed: one for the recipient group and one for the donor group. The results highlighted the benefits of transplantation for recipients and the relatively low impacts on blood metabolites of donor volunteers.

Toward a better understanding of chronic kidney disease with complementary chromatographic methods hyphenated with mass spectrometry for improved polar metabolome coverage

The prevalence of chronic kidney disease (CKD) is increasing worldwide. New technical approaches are needed to improve early diagnosis, disease understanding and patient monitoring, and to evaluate new therapies. Metabolomics, as a prime candidate in the field of CKD research, aims to comprehensively analyze the metabolic complexity of biological systems. An extensive analysis of the metabolites contained in biofluids is therefore needed, and the combination of data obtained from multiple analytical platforms constitutes a promising methodological approach. This study presents an original workflow based on complementary chromatographic conditions, reversed-phase and hydrophilic interaction chromatography hyphenated to mass spectrometry to improve the polar metabolome coverage coupled with a univocal metabolite annotation strategy enabling a rapid access to the biological interpretation. This multiplatform workflow was applied in a CKD cohort study to assess plasma metabolic profile modifications related to renal disease. Multivariate analysis of 278 endogenous annotated metabolites enabled patient stratification with respect to CKD stages and helped to generate new biological insights, while also confirming the relevance of tryptophan metabolism pathway in this condition.