Distinct metabolic profile of primary focal segmental glomerulosclerosis revealed by NMR-based metabolomics

The current use of proteomics and metabolomics in glomerulonephritis: a systematic literature review

BACKGROUND

Glomerulonephritis inherently leads to the development of chronic kidney disease. It is the second most common diagnosis in patients requiring renal replacement therapy in the United Kingdom. Metabolomics and proteomics can characterise, identify and quantify an individual's protein and metabolite make-up. These techniques have been optimised and can be performed on samples including kidney tissue, blood and urine. Utilising omic techniques in nephrology can uncover disease pathophysiology and transform the diagnostics and treatment options for glomerulonephritis.

OBJECTIVES

To evaluate the utility of metabolomics and proteomics using mass spectrometry and nuclear magnetic resonance in glomerulonephritis.

METHODS

The systematic review was registered on PROSPERO (CRD42023442092). Standard and extensive Cochrane search methods were used. The latest search date was March 2023. Participants were of any age with a histological diagnosis of glomerulonephritis. Descriptive analysis was performed, and data presented in tabular form. An area under the curve or p-value was presented for potential biomarkers discovered.

RESULTS

Twenty-seven studies were included (metabolomics (n = 9)), and (proteomics (n = 18)) with 1818 participants. The samples analysed were urine (n = 19) blood (n = 4) and biopsy (n = 6). The typical outcome themes were potential biomarkers, disease phenotype, risk of progression and treatment response.

CONCLUSION

This review shows the potential of metabolomic and proteomic analysis to discover new disease biomarkers that may influence diagnostics and disease management. Further larger-scale research is required to establish the validity of the study outcomes, including the several proposed biomarkers.

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".

A systematic review of metabolomic findings in adult and pediatric renal disease

Chronic kidney disease (CKD) affects over 0.5 billion people worldwide across their lifetimes. Despite a growingly ageing world population, an increase in all-age prevalence of kidney disease persists. Adult-onset forms of kidney disease often result from lifestyle-modifiable metabolic illnesses such as type 2 diabetes. Pediatric and adolescent forms of renal disease are primarily caused by morphological abnormalities of the kidney, as well as immunological, infectious and inherited metabolic disorders. Alterations in energy metabolism are observed in CKD of varying causes, albeit the molecular mechanisms underlying pathology are unclear. A systematic indexing of metabolites identified in plasma and urine of patients with kidney disease alongside disease enrichment analysis uncovered inborn errors of metabolism as a framework that links features of adult and pediatric kidney disease. The relationship of genetics and metabolism in kidney disease could be classified into three distinct landscapes: (i) Normal genotypes that develop renal damage because of lifestyle and / or comorbidities; (ii) Heterozygous genetic variants and polymorphisms that result in unique metabotypes that may predispose to the development of kidney disease via synergistic heterozygosity, and (iii) Homozygous genetic variants that cause renal impairment by perturbing metabolism, as found in children with monogenic inborn errors of metabolism. Interest in the identification of early biomarkers of onset and progression of CKD has grown steadily in the last years, though it has not translated into clinical routine yet. This systematic review indexes findings of differential concentration of metabolites and energy pathway dysregulation in kidney disease and appraises their potential use as biomarkers.

Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ - 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.

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.

LC-MS/MS based metabolomics and proteomics reveal candidate biomarkers and molecular mechanism of early IgA nephropathy

BACKGROUND

Immunoglobulin A nephropathy (IgAN), a globally common primary chronic glomerulopathy, is one of the leading causes of end-stage renal disease. However, the underlying mechanisms of IgAN have yet to be demonstrated. There were no adequate and reliable plasma biomarkers for clinical diagnosis, especially at the early stage. In the present study, integrative proteomics and metabolomics were aimed at exploring the mechanism of IgAN and identifying potential biomarkers.

METHODS

Plasma from IgAN and healthy individuals were collected and analyzed in a randomized controlled manner. Data-independent acquisition quantification proteomics and mass spectrometry based untargeted metabolomics techniques were used to profile the differentially expressed proteins (DEPs) and differentially abundant metabolites (DAMs) between two groups and identify potential biomarkers for IgAN from health at the early stage. Disease-related pathways were screened out by clustering and function enrichment analyses of DEPs and DAMs. And the potential biomarkers for IgAN were identified through the machine learning approach. Additionally, an independent cohort was used to validate the priority candidates by enzyme-linked immunosorbent assay (ELISA).

RESULTS

Proteomic and metabolomic analyses of IgAN plasma showed that the complement and the immune system were activated, while the energy and amino acid metabolism were disordered in the IgAN patients. PRKAR2A, IL6ST, SOS1, and palmitoleic acid have been identified as potential biomarkers. Based on the AUC value for the training and test sets, the classification performance was 0.994 and 0.977, respectively. The AUC of the external validation of the four biomarkers was 0.91.

CONCLUSION

In this study, we combined proteomics and metabolomics techniques to analyze the plasma of IgAN patients and healthy individuals, constructing a biomarker panel, which could provide new insights and provide potential novel molecular diagnoses for IgAN.

Nuclear Magnetic Resonance Metabolomic Profiling and Urine Chemistries in Incident Kidney Stone Formers Compared with Controls

BACKGROUND

The urine metabolites and chemistries that contribute to kidney stone formation are not fully understood. This study examined differences between the urine metabolic and chemistries profiles of first-time stone formers and controls.

METHODS

High-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was performed in 24-hour urine samples from a prospective cohort of 418 first-time symptomatic kidney stone formers and 440 controls. In total, 48 NMR-quantified metabolites in addition to 12 standard urine chemistries were assayed. Analysis of covariance was used to determine the association of stone former status with urine metabolites or chemistries after adjusting for age and sex and correcting for the false discovery rate. Gradient-boosted machine methods with nested cross-validation were applied to predict stone former status.

RESULTS

Among the standard urine chemistries, stone formers had lower urine oxalate and potassium and higher urine calcium, phosphate, and creatinine. Among NMR urine metabolites, stone formers had lower hippuric acid, trigonelline, 2-furoylglycine, imidazole, and citrate and higher creatine and alanine. A cross-validated model using urine chemistries, age, and sex yielded a mean AUC of 0.76 (95% CI, 0.73 to 0.79). A cross-validated model using urine chemistries, NMR-quantified metabolites, age, and sex did not meaningfully improve the discrimination (mean AUC, 0.78; 95% CI, 0.75 to 0.81). In this combined model, among the top ten discriminating features, four were urine chemistries and six NMR-quantified metabolites.

CONCLUSIONS

Although NMR-quantified metabolites did not improve discrimination, several urine metabolic profiles were identified that may improve understanding of kidney stone pathogenesis.

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.

Membranous nephropathy: Systems biology-based novel mechanism and traditional Chinese medicine therapy

Membranous nephropathy (MN) is a renal-limited non-inflammatory autoimmune disease in the glomerulus, which is the second or third main cause of end-stage kidney diseases in patients with primary glomerulonephritis. Substantial achievements have increased our understanding of the aetiology and pathogenesis of murine and human MN. The identification of nephritogenic autoantibodies against neutral endopeptidase, phospholipase A₂ receptor (PLA₂R) and thrombospondin type-1 domain-containing 7A (THSD7A) antigens provide more specific concept-driven intervention strategies for treatments by specific B cell-targeting monoclonal antibodies to inhibit antibody production and antibody-antigen immune complex deposition. Furthermore, additional antibody specificities for antigens have been discovered, but their pathogenic effects are uncertain. Although anti-PLA₂R and anti-THSD7A antibodies as a diagnostic marker is widely used in MN patients, many questions including autoimmune response development, antigenic epitopes, and podocyte damage signalling pathways remain unresolved. This review describes the current available evidence regarding both established and novel molecular mechanisms based on systems biology approaches (gut microbiota, long non-coding RNAs, metabolite biomarkers and DNA methylation) in MN, with an emphasis on clinical findings. This review further summarizes the applications of traditional Chinese medicines such as Tripterygium wilfordii and Astragalus membranaceus for MN treatment. Lastly, this review considers how the identification of novel antibodies/antigens and unresolved questions and future challenges reveal the pathogenesis of MN.

In Vitro Diagnostic Examination and Prognosis Surveillance by Hierarchical Heterojunction-Assisted Metabolic Analysis

High-throughput metabolic analysis based on laser desorption/ionization mass spectrometry exhibits broad prospects in the field of large-scale precise medicine, for which the assisted ionization ability of the matrix becomes a determining step. In this work, the gold-decorated hierarchical metal oxide heterojunctions (dubbed Au/HMOHs) are proposed as a matrix for extracting urine metabolic fingerprints (UMFs) of primary nephrotic syndrome (PNS). The hierarchical heterojunctions are simply derived from metal-organic framework (MOF)-on-MOF hybrids, and the native built-in electric field from heterojunctions plus the extra Au decoration provides remarkable ionization efficiency, attaining high-quality UMFs. These UMFs are employed to realize precise diagnosis, subtype classification, and effective prognosis evaluation of PNS by appropriate machine learning, all with 100% accurate ratios. Moreover, a high-confidence marker panel for PNS diagnosis is constructed. Interestingly, all panel metabolite markers present obviously uniform downregulation in PNS compared to healthy controls, shedding light on mechanism exploration and pathway analysis. This work drives the application of metabolomics toward precision medicine.

Balancing the Equation: A Natural History of Trimethylamine and Trimethylamine-N-oxide

Trimethylamine (TMA) and its N-oxide (TMAO) are ubiquitous in prokaryote and eukaryote organisms as well as in the environment, reflecting their fundamental importance in evolutionary biology, and their diverse biochemical functions. Both metabolites have multiple biological roles including cell-signaling. Much attention has focused on the significance of serum and urinary TMAO in cardiovascular disease risk, yet this is only one of the many facets of a deeper TMA-TMAO partnership that reflects the significance of these metabolites in multiple biological processes spanning animals, plants, bacteria, and fungi. We report on analytical methods for measuring TMA and TMAO and attempt to critically synthesize and map the global functions of TMA and TMAO in a systems biology framework.

Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C

Assessment of renal function relies on the estimation of the glomerular filtration rate (eGFR). Existing eGFR equations, usually based on serum levels of creatinine and/or cystatin C, are not uniformly accurate across patient populations. In the present study, we expanded a recent proof-of-concept approach to optimize an eGFR equation targeting the adult population with and without chronic kidney disease (CKD), based on a nuclear magnetic resonance spectroscopy (NMR) derived 'metabolite constellation' (GFRNMR). A total of 1855 serum samples were partitioned into development, internal validation and external validation datasets. The new GFRNMR equation used serum myo-inositol, valine, creatinine and cystatin C plus age and sex. GFRNMR had a lower bias to tracer measured GFR (mGFR) than existing eGFR equations, with a median bias (95% confidence interval [CI]) of 0.0 (-1.0; 1.0) mL/min/1.73 m2 for GFRNMR vs. -6.0 (-7.0; -5.0) mL/min/1.73 m2 for the Chronic Kidney Disease Epidemiology Collaboration equation that combines creatinine and cystatin C (CKD-EPI2012) (p < 0.0001). Accuracy (95% CI) within 15% of mGFR (1-P15) was 38.8% (34.3; 42.5) for GFRNMR vs. 47.3% (43.2; 51.5) for CKD-EPI2012 (p < 0.010). Thus, GFRNMR holds promise as an alternative way to assess eGFR with superior accuracy in adult patients with and without CKD.

Biomarkers in pediatric glomerulonephritis and nephrotic syndrome

Glomerular diseases are often chronic or recurring and thus associated with a tremendous physical, psychological, and economic burden. Their etiologies are often unknown, and their pathogeneses are frequently poorly understood. The diagnoses and management of these diseases are therefore based on clinical features, traditional laboratory markers, and, often, kidney pathology. However, the clinical presentation can be highly variable, the kidney pathology may not establish a definitive diagnosis, and the therapeutic responses and resulting clinical outcomes are often unpredictable. To try to address these challenges, significant research efforts have been made over the last decade to identify potential biomarkers that can help clinicians optimize the diagnosis and prognosis at clinical presentation, as well as help predict long-term outcomes. Unfortunately, these efforts have to date only identified a single biomarker for glomerular disease that has been fully validated and developed for widespread clinical use (anti-PLA2R antibodies to diagnose membranous nephropathy). In this manuscript, we review the definitions and development of biomarkers, as well as the current knowledge on both historical and novel candidate biomarkers of glomerular disease, with an emphasis on those associated with idiopathic nephrotic syndrome.

Serum Myo-Inositol, Dimethyl Sulfone, and Valine in Combination with Creatinine Allow Accurate Assessment of Renal Insufficiency-A Proof of Concept

Evaluation of renal dysfunction includes estimation of glomerular filtration rate (eGFR) as the initial step and subsequent laboratory testing. We hypothesized that combined analysis of serum creatinine, myo-inositol, dimethyl sulfone, and valine would allow both assessment of renal dysfunction and precise GFR estimation. Bio-banked sera were analyzed using nuclear magnetic resonance spectroscopy (NMR). The metabolites were combined into a metabolite constellation (GFRNMR) using n = 95 training samples and tested in n = 189 independent samples. Tracer-measured GFR (mGFR) served as a reference. GFRNMR was compared to eGFR based on serum creatinine (eGFRCrea and eGFREKFC), cystatin C (eGFRCys-C), and their combination (eGFRCrea-Cys-C) when available. The renal biomarkers provided insights into individual renal and metabolic dysfunction profiles in selected mGFR-matched patients with otherwise homogenous clinical etiology. GFRNMR correlated better with mGFR (Pearson correlation coefficient r = 0.84 vs. 0.79 and 0.80). Overall percentages of eGFR values within 30% of mGFR for GFRNMR matched or exceeded those for eGFRCrea and eGFREKFC (81% vs. 64% and 74%), eGFRCys-C (81% vs. 72%), and eGFRCrea-Cys-C (81% vs. 81%). GFRNMR was independent of patients' age and sex. The metabolite-based NMR approach combined metabolic characterization of renal dysfunction with precise GFR estimation in pediatric and adult patients in a single analytical step.

Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer

Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix™) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the Rametrix^TM screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix™ screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control Surine^TM. Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the Rametrix^TM screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, Rametrix^TM was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve Rametrix^TM models and demonstrate clinical relevance, this study demonstrates the ability of the Rametrix^TM screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Metabolomics

INTRODUCTION

Nephrotic syndrome (NS) is a kidney disease that affects both children and adults. Glucocorticoids have been the primary therapy for >60 years but are ineffective in approximately 20% of children and approximately 50% of adult patients. Unfortunately, patients with steroid-resistant NS (SRNS; vs. steroid-sensitive NS [SSNS]) are at high risk for both glucocorticoid-induced side effects and disease progression.

METHODS

We performed proton nuclear magnetic resonance (1H NMR) metabolomic analyses on plasma samples (n = 86) from 45 patients with NS (30 SSNS and 15 SRNS) obtained at initial disease presentation before glucocorticoid initiation and after approximately 7 weeks of glucocorticoid therapy to identify candidate biomarkers able to either predict SRNS before treatment or define critical molecular pathways/targets regulating steroid resistance.

RESULTS

Stepwise logistic regression models identified creatinine concentration and glutamine concentration (odds ratio [OR]: 1.01; 95% confidence interval [CI]: 0.99-1.02) as 2 candidate biomarkers predictive of SRNS, and malonate concentration (OR: 0.94; 95% CI: 0.89-1.00) as a third candidate predictive biomarker using a similar model (only in children >3 years). In addition, paired-sample analyses identified several candidate biomarkers with the potential to identify mechanistic molecular pathways/targets that regulate clinical steroid resistance, including lipoproteins, adipate, pyruvate, creatine, glucose, tyrosine, valine, glutamine, and sn-glycero-3-phosphcholine.

CONCLUSION

Metabolomic analyses of serial plasma samples from children with SSNS and SRNS identified elevated creatinine and glutamine concentrations, and reduced malonate concentrations, as auspicious candidate biomarkers to predict SRNS at disease onset in pediatric NS, as well as additional candidate biomarkers with the potential to identify mechanistic molecular pathways that may regulate clinical steroid resistance.

Chronic kidney disease: a review of proteomic and metabolomic approaches to membranous glomerulonephritis, focal segmental glomerulosclerosis, and IgA nephropathy biomarkers

Chronic Kidney Disease (CKD) is a global health problem annually affecting millions of people around the world. It is a comprehensive syndrome, and various factors may contribute to its occurrence. In this study, it was attempted to provide an accurate definition of chronic kidney disease; followed by focusing and discussing on molecular pathogenesis, novel diagnosis approaches based on biomarkers, recent effective antigens and new therapeutic procedures related to high-risk chronic kidney disease such as membranous glomerulonephritis, focal segmental glomerulosclerosis, and IgA nephropathy, which may lead to end-stage renal diseases. Additionally, a considerable number of metabolites and proteins that have previously been discovered and recommended as potential biomarkers of various CKD_s using ‘-omics-’ technologies, proteomics, and metabolomics were reviewed.

Omics studies for comprehensive understanding of immunoglobulin A nephropathy: state-of-the-art and future directions

Immunoglobulin A nephropathy (IgAN) is the most common worldwide primary glomerulonephritis with a strong autoimmune component. The disease shows variability in both clinical phenotypes and endpoints and can be potentially subdivided into more homogeneous subtypes through the identification of specific molecular biomarkers. This review focuses on the role of omics in driving the identification of potential molecular subtypes of the disease through the integration of multilevel data from genomics, transcriptomics, epigenomics, proteomics and metabolomics. First, the identification of molecular biomarkers, including mapping of the full spectrum of common and rare IgAN risk alleles, could permit a more precise stratification of IgAN patients. Second, the analysis of transcriptomic patterns and their modulation by epigenetic factors like microRNAs has the potential to increase our understanding in the pathogenic mechanisms of the disease. Third, the specificity of urinary proteomic and metabolomic signatures and the understanding of their functional relevance may contribute to the development of new non-invasive biomarkers for a better molecular characterization of the renal damage and its follow-up. All these approaches can give information for targeted therapeutic decisions and will support novel clinical decision making. In conclusion, we offer a framework of omic studies and outline barriers and potential solutions that should be used for improving the diagnosis and treatment of the disease. The ongoing decade is exploiting novel high-throughput molecular technologies and computational analyses for improving the diagnosis (precision nephrology) and treatment (personalized therapy) of the IgAN subtypes.

Urinary myo-inositol is associated with the clinical outcome in focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) have similar initial histological findings; however, their prognoses are distinct. Therefore, it is of great importance to discriminate FSGS from MCD in the early phase of disease and predict clinical prognosis. A discovery set of 184 urine samples (61 healthy control, 80 MCD, and 43 FSGS) and a validation set of 61 urine samples (12 healthy control, 26 MCD, and 23 FSGS) were collected at the time of kidney biopsy. Metabolic profiles were examined using nuclear magnetic resonance spectroscopy. Of 70 urinary metabolites, myo-inositol was significantly higher in FSGS patients than in control patients (discovery set, 2.34-fold, P < 0.001; validation set, 2.35-fold, P = 0.008) and MCD patients (discovery set, 2.48-fold, P = 0.002; validation set, 1.69-fold, P = 0.042). Myo-inositol showed an inverse relationship with the initial estimated glomerular filtration rate (eGFR) and was associated with the plasma level of soluble urokinase-type plasminogen activator receptor in FSGS patients. Myo-inositol treatment ameliorated the decreased expression of ZO-1 and synaptopodin in an in vitro FSGS model, and as myo-inositol increased, myo-inositol oxygenase tissue expression decreased proportionally to eGFR. Furthermore, urinary myo-inositol exhibited an increase in the power to discriminate FSGS patients, and its addition could better predict the response to initial treatment. In conclusion, urinary myo-inositol may be an important indicator in the diagnosis and treatment of FSGS patients.

Differences in Serum Amino Acid Phenotypes Among Patients with Diabetic Nephropathy, Hypertensive Nephropathy, and Chronic Nephritis

Background

We assessed levels of circulating amino acids in different etiologies of chronic kidney disease (CKD) and the association of amino acids with risk factors of CKD progression.

Material/Methods

High-performance liquid chromatography-based analysis was used to determine amino acid profiles in patients with diabetic nephropathy (DN, n=20), hypertensive nephropathy (HN, n=26), and chronic nephritis (CN, n=33), and in healthy controls (HC, n=25).

Results

All 3 types of CKD patients displayed decreased serum levels of serine, glycine, GABA, and tryptophan compared with healthy controls. Moreover, serine and tryptophan were positively correlated with glucose in DN cohorts. Total cholesterol was positively correlated with tryptophan levels in the DN cohort and negatively correlated with serine levels in the CN cohort. In the HN cohort, glycine was negatively correlated with triglyceride levels, and systolic blood pressure (SBP) was negatively correlated with GABA levels.

Conclusions

Patients with different etiologies of CKD have significantly different amino acids profiles, and this indicates specific supplementary nutritional needs in CKD patients.

TMA, A Forgotten Uremic Toxin, but Not TMAO, Is Involved in Cardiovascular Pathology

Trimethylamine-N-oxide (TMAO) has been suggested as a marker and mediator of cardiovascular diseases. However, data are contradictory, and the mechanisms are obscure. Strikingly, the role of the TMAO precursor trimethylamine (TMA) has not drawn attention in cardiovascular studies even though toxic effects of TMA were proposed several decades ago. We assessed plasma TMA and TMAO levels in healthy humans (HH) and cardiovascular patients qualified for aortic valve replacement (CP). The cytotoxicity of TMA and TMAO in rat cardiomyocytes was evaluated using an MTT test. The effects of TMA and TMAO on albumin and lactate dehydrogenase (LDH) were assessed using fluorescence correlation spectroscopy. In comparison to HH, CP had a two-fold higher plasma TMA (p < 0.001) and a trend towards higher plasma TMAO (p = 0.07). In CP plasma, TMA was inversely correlated with an estimated glomerular filtration rate (eGFR, p = 0.002). TMA but not TMAO reduced cardiomyocytes viability. Incubation with TMA but not TMAO resulted in the degradation of the protein structure of LDH and albumin. In conclusion, CP show increased plasma TMA, which is inversely correlated with eGFR. TMA but not TMAO exerts negative effects on cardiomyocytes, likely due to its disturbing effect on proteins. Therefore, TMA but not TMAO may be a toxin and a marker of cardiovascular risk.

Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease

Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.

Risk factors analysis for hyperuricemic nephropathy among CKD stages 3-4 patients: an epidemiological study of hyperuricemia in CKD stages 3-4 patients in Ningbo, China

Objective: Uric acid (UA) is a risk marker of CKD and SUA level in CKD 3–4 patients closely correlates with hyperuricemic nephropathy (HN) morbidity. This study was designed to evaluate the risk factors for HN in CKD 3–4 patients.

Methods: The 461 CKD 3–4 patients were recruited and all patients were divided into three groups (24 h UUA normal, underexeret, and overproduct type groups) according to the 24 h UUA level after receiving low purine food for five days. Clinical and biochemical characteristics of CKD patients were collected for the logistic regression analysis. Correlation analysis of the mRNA relative expression level of hUAT and hURAT1 with serum UA (SUA) level also was evaluated.

Results: There were significant increases in characteristics including average age, waist-to-height ratio (WHR), SUA levels, HN ratio, TG/HDL ratio, body mass index (BMI), blood pressure (BP), uNgal/Cr. ratio, and uKim-1/Cr. ratio in overproduct type group in comparison with the other two groups. Logistic regression analysis showed SUA, CHO, uKim-1/Cr. ratio and uNgal/Cr. ratio were independent and multiple risk factors for HN. Moreover, hUAT and hURAT1 mRNA relative expression levels were significantly correlated with SUA level in the underexeret type CKD 3–4 patients.

Conclusions: These results showed SUA and other characteristics contributed to HN morbidity in CKD 3–4 patients.

Trace Amines and Their Receptors

Trace amines are endogenous compounds classically regarded as comprising β-phenylethyalmine, p-tyramine, tryptamine, p-octopamine, and some of their metabolites. They are also abundant in common foodstuffs and can be produced and degraded by the constitutive microbiota. The ability to use trace amines has arisen at least twice during evolution, with distinct receptor families present in invertebrates and vertebrates. The term "trace amine" was coined to reflect the low tissue levels in mammals; however, invertebrates have relatively high levels where they function like mammalian adrenergic systems, involved in "fight-or-flight" responses. Vertebrates express a family of receptors termed trace amine-associated receptors (TAARs). Humans possess six functional isoforms (TAAR1, TAAR2, TAAR5, TAAR6, TAAR8, and TAAR9), whereas some fish species express over 100. With the exception of TAAR1, TAARs are expressed in olfactory epithelium neurons, where they detect diverse ethological signals including predators, spoiled food, migratory cues, and pheromones. Outside the olfactory system, TAAR1 is the most thoroughly studied and has both central and peripheral roles. In the brain, TAAR1 acts as a rheostat of dopaminergic, glutamatergic, and serotonergic neurotransmission and has been identified as a novel therapeutic target for schizophrenia, depression, and addiction. In the periphery, TAAR1 regulates nutrient-induced hormone secretion, suggesting its potential as a novel therapeutic target for diabetes and obesity. TAAR1 may also regulate immune responses by regulating leukocyte differentiation and activation. This article provides a comprehensive review of the current state of knowledge of the evolution, physiologic functions, pharmacology, molecular mechanisms, and therapeutic potential of trace amines and their receptors in vertebrates and invertebrates.

A highly sensitive liquid chromatography electrospray ionization mass spectrometry method for quantification of TMA, TMAO and creatinine in mouse urine

Our method describes the quantification in mouse urine of trimethylamine (TMA), trimethylamine N-oxide (TMAO) and creatinine. The method combines derivatization of TMA, with ethyl bromoacetate, and LC chromatographic separation on an ACE C₁₈ column. The effluent was continuously electrosprayed into the linear ion trap mass spectrometer (LTQ), which operated in selective ion monitoring (SIM) modes set for targeted analytes and their internal standards (IS). All validation parameters were within acceptable ranges of analytical method validation guidelines. Intra- and inter-day assay precision and accuracy coefficients of variation were <3.1%, and recoveries for TMA and TMAO were 97–104%. The method developed uses a two-step procedure. Firstly, TMA and TMAO are analyzed without a purification step using a 5-min gradient cap-LC- SIMs analysis, then creatinine is analyzed using the same experimental conditions. The method is robust, highly sensitive, reproducible and has the high-throughput capability of detecting TMA, TMAO and creatinine at on-column concentrations as low as 28 pg/mL, 115 pg/mL and 1 ng/mL, respectively. The method is suitable for analysis of TMA, TMAO and creatinine in both male and female mouse urine.

The key benefits of the method are:

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The small sample volume of urine required, which overcomes the difficulties of collecting sufficient volumes of urine at defined times.

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No sample pre-treatment is necessary.

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The quantification of TMA, TMAO and creatinine using the same cap-LC-MS method.

A comprehensive narrative review of diagnostic biomarkers in human primary membranous nephropathy

Membranous nephropathy (MN) is relatively major cause of nephrotic syndrome in adults which is recognized as an organ-specific autoimmune disease. The etiology of most cases is idiopathic, whereas the secondary MN is caused by systemic autoimmune diseases, infections, medications and malignancies. The idiopathic disease is developed by the formation of sub-epithelial immune complex deposits most likely due to binding the circulating auto-antibodies to intrinsic antigen on podocytes. The major auto antibody is the anti-phospholipase A2 receptor (anti-PLA2R), however, it is not enough sensitive. Several attempts for diagnostic biomarker identification by modern analytical technologies have been devoted recently. This article reviews the biomarker candidates for primary type of MN that are detected by different approaches on human subjects.

1 H NMR-based metabolomics study for identifying urinary biomarkers and perturbed metabolic pathways associated with severity of IgA nephropathy: a pilot study

The severity of IgA nephropathy (IgAN), the most common primary glomerulonephritis, is judged on the basis of histologic and clinical features. A limited number of studies have considered molecular signature of IgAN for this issue, and no reliable biomarkers have been presented non-invasively for use in patient evaluations. This study aims to identify metabolite markers excreted in the urine and impaired pathways that are associated with a known marker of severity (proteinuria) to predict mild and severe stages of IgAN. Urine samples were analysed using nuclear magnetic resonance from biopsy-proven IgAN patients at mild and severe stages. Multivariate statistical analysis and pathway analysis were performed. The most changed metabolites were acetoacetate, hypotaurine, homocysteine, L-kynurenine and phenylalanine. Nine metabolites were positively correlated with proteinuria, including mesaconic acid, trans-cinnamic acid, fumaric acid, 5-thymidylic acid, anthranilic acid, indole, deoxyguanosine triphosphate, 13-cis-retinoic acid and nicotinamide riboside, while three metabolites were negatively correlated with proteinuria including acetoacetate, hypotaurine and hexanal. 'Phenylalanine metabolism' was the most significant pathway which was impaired in severe stage in comparison to mild stage of IgAN. This study indicates that nuclear magnetic resonance is a versatile technique that is capable of detecting metabolite biomarkers in combination with advanced multivariate statistical analysis. Copyright © 2017 John Wiley & Sons, Ltd.

Metabolomics for clinical use and research in chronic kidney disease

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.

1 H NMR-based metabolomics exploring urinary biomarkers correlated with proteinuria in focal segmental glomerulosclerosis: a pilot study

Focal segmental glomerulosclerosis (FSGS) is a common glomerulonephritis, and its rates of occurrence are increasing worldwide. Proteinuria is a clinical defining feature of FSGS which correlates with the severity of podocyte injury in patients with nephrotic-range protein excretion. Metabolite biomarkers corresponding with the level of proteinuria could be considered as non-invasive complementary prognostic factors to proteinuria. The urine samples of 15 patients (n = 6 women and n = 9 men) with biopsy-proven FSGS were collected and subjected to nuclear magnetic resonance (NMR) analysis for metabolite profiling. Multivariate statistical analyses, including principal component analysis and orthogonal projection to latent structure discriminant analysis, were applied to construct a predictive model based on patients with proteinuria >3000 mg/day and <3000 mg/day. In addition, random forest was performed to predict differential metabolites, and pathway analysis was performed to find the defective pathways responsible for proteinuria. Ten metabolites, significant in both statistical methods (orthogonal projection to latent structure discriminant analysis and random forest), were considered as prognostic biomarkers for FSGS: citrulline, dimethylamine, proline, acetoacetate, alpha-ketoisovaleric acid, valine, isobutyrate, D-Palmitylcarnitine, histidine, and N-methylnicotinamide. Pathway analysis revealed impairment of the branched-chain amino acid degradation pathways in patients with massive proteinuria. This study shows that metabolomics can reveal the molecular changes corresponding with disease progression in patients with FSGS and provide a new insight for pathogenic pathways. Copyright © 2016 John Wiley & Sons, Ltd.

Trimethylamine and Trimethylamine N-Oxide, a Flavin-Containing Monooxygenase 3 (FMO3)-Mediated Host-Microbiome Metabolic Axis Implicated in Health and Disease

Flavin-containing monooxygenase 3 (FMO3) is known primarily as an enzyme involved in the metabolism of therapeutic drugs. On a daily basis, however, we are exposed to one of the most abundant substrates of the enzyme trimethylamine (TMA), which is released from various dietary components by the action of gut bacteria. FMO3 converts the odorous TMA to nonodorous TMA N-oxide (TMAO), which is excreted in urine. Impaired FMO3 activity gives rise to the inherited disorder primary trimethylaminuria (TMAU). Affected individuals cannot produce TMAO and, consequently, excrete large amounts of TMA. A dysbiosis in gut bacteria can give rise to secondary TMAU. Recently, there has been much interest in FMO3 and its catalytic product, TMAO, because TMAO has been implicated in various conditions affecting health, including cardiovascular disease, reverse cholesterol transport, and glucose and lipid homeostasis. In this review, we consider the dietary components that can give rise to TMA, the gut bacteria involved in the production of TMA from dietary precursors, the metabolic reactions by which bacteria produce and use TMA, and the enzymes that catalyze the reactions. Also included is information on bacteria that produce TMA in the oral cavity and vagina, two key microbiome niches that can influence health. Finally, we discuss the importance of the TMA/TMAO microbiome-host axis in health and disease, considering factors that affect bacterial production and host metabolism of TMA, the involvement of TMAO and FMO3 in disease, and the implications of the host-microbiome axis for management of TMAU.

Systematic biomarker discovery and coordinative validation for different primary nephrotic syndromes using gas chromatography-mass spectrometry

The goal of this study is to identify systematic biomarker panel for primary nephrotic syndromes from urine samples by applying a non-target metabolite profiling, and to validate their utility in independent sampling and analysis by multiplex statistical approaches. Nephrotic syndrome (NS) is a nonspecific kidney disorder, which is mostly represented by minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and membranous glomerulonephritis (MGN). Since urine metabolites may mirror disease-specific functional perturbations in kidney injury, we examined urine samples for distinctive metabolic changes to identify biomarkers for clinical applications. We developed unbiased multi-component covarianced models from a discovery set with 48 samples (12 healthy controls, 12 MCD, 12 FSGS, and 12 MGN). To extensively validate their diagnostic potential, new batch from 54 patients with primary NS were independently examined a year after. In the independent validation set, the model including citric acid, pyruvic acid, fructose, ethanolamine, and cysteine effectively discriminated each NS using receiver operating characteristic (ROC) analysis except MCD-MGN comparison; nonetheless an additional metabolite multi-composite greatly improved the discrimination power between MCD and MGN. Finally, we proposed the re-constructed metabolic network distinctively dysregulated by the different NSs that may deepen comprehensive understanding of the disease mechanistic, and help the enhanced identification of NS and therapeutic plans for future.

The complex metabolism of trimethylamine in humans: endogenous and exogenous sources

Trimethylamine (TMA) is a tertiary amine with a characteristic fishy odour. It is synthesised from dietary constituents, including choline, L-carnitine, betaine and lecithin by the action of microbial enzymes during both healthy and diseased conditions in humans. Trimethylaminuria (TMAU) is a disease typified by its association with the characteristic fishy odour because of decreased TMA metabolism and excessive TMA excretion. Besides TMAU, a number of other diseases are associated with abnormal levels of TMA, including renal disorders, cancer, obesity, diabetes, cardiovascular diseases and neuropsychiatric disorders. Aside from its role in pathobiology, TMA is a precursor of trimethylamine-N-oxide that has been associated with an increased risk of athero-thrombogenesis. Additionally, TMA is a major air pollutant originating from vehicular exhaust, food waste and animal husbandry industry. The adverse effects of TMA need to be monitored given its ubiquitous presence in air and easy absorption through human skin. In this review, we highlight multifaceted attributes of TMA with an emphasis on its physiological, pathological and environmental impacts. We propose a clinical surveillance of human TMA levels that can fully assess its role as a potential marker of microbial dysbiosis-based diseases.

A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis

Focal Segmental Glomerulosclerosis (FSGS) is a type of nephrotic syndrome which accounts for 20 and 40 % of such cases in children and adults, respectively. The high prevalence of FSGS makes it the most common primary glomerular disorder causing end-stage renal disease. Although the pathogenesis of this disorder has been widely investigated, the exact mechanism underlying this disease is still to be discovered. Current therapies seek to stop the progression of FSGS and often fail to cure the patients since progression to end-stage renal failure is usually inevitable. In the present work, we use a kidney-specific metabolic network model to study FSGS. The model was obtained by merging two previously published kidney-specific metabolic network models. The validity of the new model was checked by comparing the inactivating reaction genes identified in silico to the list of kidney disease implicated genes. To model the disease state, we used a complete list of FSGS metabolic biomarkers extracted from transcriptome and proteome profiling of patients as well as genetic deficiencies known to cause FSGS. We observed that some specific pathways including chondroitin sulfate degradation, eicosanoid metabolism, keratan sulfate biosynthesis, vitamin B6 metabolism, and amino acid metabolism tend to show variations in FSGS model compared to healthy kidney. Furthermore, we computationally searched for the potential drug targets that can revert the diseased metabolic state to the healthy state. Interestingly, only one drug target, N-acetylgalactosaminidase, was found whose inhibition could alter cellular metabolism towards healthy state.

Lack of CD2AP disrupts Glut4 trafficking and attenuates glucose uptake in podocytes

The adapter protein CD2-associated protein (CD2AP) functions in various signaling and vesicle trafficking pathways, including endosomal sorting and/or trafficking and degradation pathways. Here, we investigated the role of CD2AP in insulin-dependent glucose transporter 4 (Glut4, also known as SLC2A4) trafficking and glucose uptake. Glucose uptake was attenuated in CD2AP(-/-) podocytes compared with wild-type podocytes in the basal state, and CD2AP(-/-) podocytes failed to increase glucose uptake in response to insulin. Live-cell imaging revealed dynamic trafficking of HA-Glut4-GFP in wild-type podocytes, whereas in CD2AP(-/-) podocytes, HA-Glut4-GFP clustered perinuclearly. In subcellular membrane fractionations, CD2AP co-fractionated with Glut4, IRAP (also known as LNPEP) and sortilin, constituents of Glut4 storage vesicles (GSVs). We further found that CD2AP forms a complex with GGA2, a clathrin adaptor, which sorts Glut4 to GSVs, suggesting a role for CD2AP in this process. We also found that CD2AP forms a complex with clathrin and connects clathrin to actin in the perinuclear region. Furthermore, clathrin recycling back to trans-Golgi membranes from the vesicular fraction containing GSVs was defective in the absence of CD2AP. This leads to reduced insulin-stimulated trafficking of GSVs and attenuated glucose uptake into CD2AP(-/-) podocytes.

Volatile Organic Metabolites Identify Patients with Mesangial Proliferative Glomerulonephritis, IgA Nephropathy and Normal Controls

Urinary volatile organic compounds (VOCs) analysis for kidney diseases has attracted a large amount of scientific interest recently, and urinary metabolite analysis has already been applied to many diseases. Urine was collected from 15 mesangial proliferative glomerulonephritis (MsPGN) patients, 21 IgA nephropathy (IgAN) patients and 15 healthy controls. Solid phase microextraction-chromatography- mass spectrometry (SPME-GC-MS) was used to analyse the urinary metabolites. The statistical methods principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLSDA) were performed to process the final data. Five metabolites were significantly greater in the group of MsPGN patients than in the normal control group (P < 0.05) while three metabolites were found at increased levels in the group of IgAN patients compared with the normal controls (P < 0.05). In addition, five metabolites were significantly increased in the group of IgAN patients compared with the MsPGN patients (P < 0.05). These five metabolites may be specific biomarkers for distinguishing between MsPGN and IgAN. The analysis of urinary VOCs appears to have potential clinical applications as a diagnostic tool.