Prediction of acute cellular renal allograft rejection by urinary metabolomics using MALDI-FTMS

The impact of methodological choices when developing predictive models using urinary metabolite data

The continuous evolution of metabolomics over the past two decades has stimulated the search for metabolic biomarkers of many diseases. Metabolomic data measured from urinary samples can provide rich information of the biological events triggered by organ rejection in pediatric kidney transplant recipients. With additional validation, metabolic markers can be used to build clinically useful diagnostic tools. However, there are many methodological steps ranging from data processing to modeling that can influence the performance of the resulting metabolomic classifiers. In this study we focus on the comparison of various classification methods that can handle the complex structure of metabolomic data, including regularized classifiers, partial least squares discriminant analysis, and nonlinear classification models. We also examine the effectiveness of a physiological normalization technique widely used in the clinical and biochemical literature but not extensively analyzed and compared in urine metabolomic studies. While the main objective of this work is to interrogate metabolomic data of pediatric kidney transplant recipients to improve the diagnosis of T cell-mediated rejection (TCMR), we also analyze three independent datasets from other disease conditions to investigate the generalizability of our findings.

New Insights from Metabolomics in Pediatric Renal Diseases

Renal diseases in childhood form a spectrum of different conditions with potential long-term consequences. Given that, a great effort has been made by researchers to identify candidate biomarkers that are able to influence diagnosis and prognosis, in particular by using omics techniques (e.g., metabolomics, lipidomics, genomics, and transcriptomics). Over the past decades, metabolomics has added a promising number of ‘new’ biomarkers to the ‘old’ group through better physiopathological knowledge, paving the way for insightful perspectives on the management of different renal diseases. We aimed to summarize the most recent omics evidence in the main renal pediatric diseases (including acute renal injury, kidney transplantation, chronic kidney disease, renal dysplasia, vesicoureteral reflux, and lithiasis) in this narrative review.

Urinary oxylipin signature as biomarkers to monitor the allograft function during the first six months post-renal transplantation

Oxylipins such as isoprostanes (IsoPs), prostaglandins (PGs) and thromboxanes (TXs) are lipid mediators derived from the oxidation of polyunsaturated fatty acids, which regulate the magnitude of oxidative stress and inflammation processes and play an important role in pathophysiological processes in the kidney. A total of 36 oxylipins were analyzed by UHPLC-QqQ-MS/MS in the urine of 41 renal recipients from cadaveric donors of the Nephrology Unit of the University Hospital Virgen de la Arrixaca during the first six months after renal transplantation, in order to investigate several candidate oxylipins as more accurate and predictive biomarkers in renal transplantation than classical biological variables. A decrease in nine PGs, mostly from the AA-D pathway (p < 0.05) and one IsoP: 15-keto-15-F_2t-IsoP (p < 0.001) was observed. Moreover, two PGs (2,3-dinor-11β-PGF_2α and 17-trans-PGF_3α) increased between five days and six months after renal transplantation (p < 0.05). In addition, when kidney function improved, a positive correlation between oxylipin levels and the excretion of urine proteins was observed. These results suggest that oxylipins could be useful markers for monitoring renal function in the post-renal transplantation period. These findings could be of utility not only for the development of strategies for long-term preservation of graft function, but also for innovative and alternative therapies -using oxylipins as predictive markers-to avoid organ rejection.

A novel approach reveals that HLA class 1 single antigen bead-signatures provide a means of high-accuracy pre-transplant risk assessment of acute cellular rejection in renal transplantation

BACKGROUND

Acute cellular rejection (ACR) is associated with complications after kidney transplantation, such as graft dysfunction and graft loss. Early risk assessment is therefore critical for the improvement of transplantation outcomes. In this work, we retrospectively analyzed a pre-transplant HLA antigen bead assay data set that was acquired by the e:KID consortium as part of a systems medicine approach.

RESULTS

The data set included single antigen bead (SAB) reactivity profiles of 52 low-risk graft recipients (negative complement dependent cytotoxicity crossmatch, PRA < 30%) who showed detectable pre-transplant anti-HLA 1 antibodies. To assess whether the reactivity profiles provide a means for ACR risk assessment, we established a novel approach which differs from standard approaches in two aspects: the use of quantitative continuous data and the use of a multiparameter classification method. Remarkably, it achieved significant prediction of the 38 graft recipients who experienced ACR with a balanced accuracy of 82.7% (sensitivity = 76.5%, specificity = 88.9%).

CONCLUSIONS

The resultant classifier achieved one of the highest prediction accuracies in the literature for pre-transplant risk assessment of ACR. Importantly, it can facilitate risk assessment in non-sensitized patients who lack donor-specific antibodies. As the classifier is based on continuous data and includes weak signals, our results emphasize that not only strong but also weak binding interactions of antibodies and HLA 1 antigens contain predictive information.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00724022 . Retrospectively registered July 2008.

Nanoparticle microarray for high-throughput microbiome metabolomics using matrix-assisted laser desorption ionization mass spectrometry

A high-throughput matrix-assisted laser desorption/ionization mass spectrometry (MALDI)-MS-based metabolomics platform was developed using a pre-fabricated microarray of nanoparticles and organic matrices. Selected organic matrices, inorganic nanoparticle (NP) suspensions, and sputter coated metal NPs, as well as various additives, were tested for metabolomics analysis of the turkey gut microbiome. Four NPs and one organic matrix were selected as the optimal matrix set: α-cyano-4-hydroycinnamic acid, Fe₃O₄ and Au NPs in positive ion mode with 10 mM sodium acetate, and Cu and Ag NPs in negative ion mode with no additive. Using this set of five matrices, over two thousand unique metabolite features were reproducibly detected across intestinal samples from turkeys fed a diet amended with therapeutic or sub-therapeutic antibiotics (200 g/ton or 50 g/ton bacitracin methylene disalicylate (BMD), respectively), or non-amended feed. Among the thousands of unique features, 56 of them were chemically identified using MALDI-MS/MS, with the help of in-parallel liquid chromatography (LC)-MS/MS analysis. Lastly, as a proof of concept application, this protocol was applied to 52 turkey cecal samples at three different time points from the antibiotic feed trial. Statistical analysis indicated variations in the metabolome of turkeys with different ages or treatments. Graphical abstract ᅟ.

Gene Expression Studies and Targeted Metabolomics Reveal Disturbed Serine, Methionine, and Tyrosine Metabolism in Early Hypertensive Nephrosclerosis

Introduction

Hypertensive nephrosclerosis is among the leading causes of end-stage renal disease, but its pathophysiology is poorly understood. We wanted to explore early metabolic changes using gene expression and targeted metabolomics analysis.

Methods

We analyzed gene expression in kidneys biopsied from 20 patients with nephrosclerosis and 31 healthy controls with an Affymetrix array. Thirty-one amino acids were measured by liquid chromatography coupled with mass spectrometry (LC-MS) in urine samples from 62 patients with clinical hypertensive nephrosclerosis and 33 age- and sex-matched healthy controls, and major findings were confirmed in an independent cohort of 45 cases and 15 controls.

Results

Amino acid catabolism and synthesis were strongly underexpressed in hypertensive nephrosclerosis (13- and 7-fold, respectively), and these patients also showed gene expression patterns indicating decreased fatty acid oxidation (12-fold) and increased interferon gamma (10-fold) and cellular defense response (8-fold). Metabolomics analysis revealed significant distribution differences in 11 amino acids in hypertensive nephrosclerosis, among them tyrosine, phenylalanine, dopamine, homocysteine, and serine, with 30% to 70% lower urine excretion. These findings were replicated in the independent cohort. Integrated gene-metabolite pathway analysis showed perturbations of renal dopamine biosynthesis. There were also significant differences in homocysteine/methionine homeostasis and the serine pathway, which have strong influence on 1-carbon metabolism. Several of these disturbances could be interconnected through reduced regeneration of tetrahydrofolate and tetrahydrobiopterin.

Conclusion

Early hypertensive nephrosclerosis showed perturbations of intrarenal biosynthesis of dopamine, which regulates natriuresis and blood pressure. There were also disturbances in serine/glycine and methionine/homocysteine metabolism, which may contribute to endothelial dysfunction, atherosclerosis, and renal fibrosis.

Non-invasive staging of chronic kidney allograft damage using urine metabolomic profiling

Chronic kidney allograft damage is characterized by IFTA and GS. We sought to identify urinary metabolite signatures associated with severity of IFTA and GS in pediatric kidney transplant recipients. Urine samples (n = 396) from 60 pediatric transplant recipients were obtained at the time of kidney biopsy and assayed for 133 metabolites by mass spectrometry. Metabolite profiles were quantified via PLS-DA. We used mixed-effects regression to identify laboratory and clinical predictors of histopathology. Urine samples (n = 174) without rejection or AKI were divided into training/validation sets (75:25%). Metabolite classifiers trained on IFTA severity and %GS showed strong statistical correlation (r = .73, P < .001 and r = .72; P < .001, respectively) and remained significant on the validation sets. Regression analysis identified additional clinical features that improved prediction: months post-transplant (GS, IFTA); and proteinuria, GFR, and age (GS only). Addition of clinical variables improved performance of the %GS classifier (AUC = 0.9; 95% CI = 0.85-0.96) but not for IFTA (AUC = 0.82; 95% CI = 0.71-0.92). Despite the presence of potentially confounding phenotypes, these findings were further validated in samples withheld for rejection or AKI. We identify urine metabolite classifiers for IFTA and GS, which may prove useful for non-invasive assessment of histopathological damage.

Targeted metabolomic analysis of 33 amino acids and biogenic amines in human urine by ion-pairing HPLC-MS/MS: Biomarkers for tacrolimus nephrotoxicity after renal transplantation

Calcineurin inhibitor nephrotoxicity, especially for the widely used tacrolimus, has become a major concern in post-transplant immunosuppression. Multiparametric amino acid metabolomics is useful for biomarker identification of tacrolimus nephrotoxicity, for which specific quantitative methods are highlighted as a premise. This article presents a targeted metabolomic assay to quantify 33 amino acids and biogenic amines in human urine by high-performance liquid chromatography coupled with tandem mass spectrometry. Chromatographic separation was carried out on an Agilent Zorbax SB-C₁₈ column (3.0 × 150 mm, 5 μm) with addition of an ion-pairing agent in the mobile phase, and MS/MS detection was achieved in both the positive and negative multiple reaction monitoring modes. Good correlation coefficients (r² > 0.98) were obtained for most analytes. Intra- and inter-day precision, stability, carryover and incurred sample reanalysis met with the acceptance criteria of the guidance of the US Food and Drug Administration. Analysis on urine from healthy volunteers and renal transplantation patients with tacrolimus nephrotoxicity confirmed symmetric dimethylarginine and serine as biomarkers for kidney injury, with AUC values of 0.95 and 0.81 in receiver operating characteristic analysis, respectively. Additionally, symmetric dimethylarginine exhibited a tight correlation with serum creatinine, and was therefore indicative of renal function. The targeted metabolomic assay was time and cost prohibitive for amino acid analysis in human urine, facilitating the biomarker identification of tacrolimus nephrotoxicity.

Urinary Metabolomics for Noninvasive Detection of Antibody-Mediated Rejection in Children After Kidney Transplantation

BACKGROUND

Biomarkers are needed that identify patients with antibody-mediated rejection (AMR). The goal of this study was to evaluate the utility of urinary metabolomics for early noninvasive detection of AMR in pediatric kidney transplant recipients.

METHODS

Urine samples (n = 396) from a prospective, observational cohort of 59 renal transplant patients with surveillance or indication biopsies were assayed for 133 unique metabolites by quantitative mass spectrometry. Samples were classified according to Banff criteria for AMR and partial least squares discriminant analysis was used to identify associated changes in metabolite patterns by creating a composite index based on all 133 metabolites.

RESULTS

Urine samples of patients with (n = 40) and without AMR (n = 278) were analyzed and a classifier for AMR was identified (area under receiver operating characteristic curve = 0.84; 95% confidence interval, 0.77-0.91; P = 0.006). Application of the classifier to "indeterminate" samples (samples that partially fulfilled Banff criteria for AMR; n = 65) yielded an AMR score of 0.19 ± 0.15, intermediate between scores for AMR and No AMR (0.28 ± 0.14 and 0.10 ± 0.13 respectively, P ≤ 0.001). The AMR score was associated with the presence of donor-specific antibodies, biopsy indication, Banff ct, t, ah and cg scores, and retained accuracy when applied to subclinical cases (creatinine, <25% increase from baseline) or had minimal or no transplant glomerulopathy (Banff cg0-1). Exploratory classifiers that segregated samples based on concurrent T cell-mediated rejection (TCMR) identified overlapping metabolite signatures between AMR and TCMR, suggesting similar pathophysiology of tissue injury.

CONCLUSIONS

These preliminary findings identify a urine metabolic classifier for AMR. Independent validation is needed to verify its utility for accurate, noninvasive AMR detection.

The Use of Genomics and Pathway Analysis in Our Understanding and Prediction of Clinical Renal Transplant Injury

The development and application of high-throughput molecular profiling have transformed the study of human diseases. The problem of handling large, complex data sets has been facilitated by advances in complex computational analysis. In this review, the recent literature regarding the application of transcriptional genomic information to renal transplantation, with specific reference to acute rejection, acute kidney injury in allografts, chronic allograft injury, and tolerance is discussed, as is the current published data regarding other "omics" strategies-proteomics, metabolomics, and the microRNA transcriptome. These data have shed new light on our understanding of the pathogenesis of specific disease conditions after renal transplantation, but their utility as a biomarker of disease has been hampered by study design and sample size. This review aims to highlight the opportunities and obstacles that exist with genomics and other related technologies to better understand and predict renal allograft outcome.

Metabolomic Profiling in Individuals with a Failing Kidney Allograft

BACKGROUND

Alteration of certain metabolites may play a role in the pathophysiology of renal allograft disease.

METHODS

To explore metabolomic abnormalities in individuals with a failing kidney allograft, we analyzed by liquid chromatography-mass spectrometry (LC-MS/MS; for ex vivo profiling of serum and urine) and two dimensional correlated spectroscopy (2D COSY; for in vivo study of the kidney graft) 40 subjects with varying degrees of chronic allograft dysfunction stratified by tertiles of glomerular filtration rate (GFR; T1, T2, T3). Ten healthy non-allograft individuals were chosen as controls.

RESULTS

LC-MS/MS analysis revealed a dose-response association between GFR and serum concentration of tryptophan, glutamine, dimethylarginine isomers (asymmetric [A]DMA and symmetric [S]DMA) and short-chain acylcarnitines (C4 and C12), (test for trend: T1-T3 = p<0.05; p = 0.01; p<0.001; p = 0.01; p = 0.01; p<0.05, respectively). The same association was found between GFR and urinary levels of histidine, DOPA, dopamine, carnosine, SDMA and ADMA (test for trend: T1-T3 = p<0.05; p<0.01; p = 0.001; p<0.05; p = 0.001; p<0.001; p<0.01, respectively). In vivo 2D COSY of the kidney allograft revealed significant reduction in the parenchymal content of choline, creatine, taurine and threonine (all: p<0.05) in individuals with lower GFR levels.

CONCLUSIONS

We report an association between renal function and altered metabolomic profile in renal transplant individuals with different degrees of kidney graft function.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.

Blood, urine and faecal metabolite profiles in the study of adult renal disease

Chronic kidney disease (CKD) is a major public health burden and to date traditional biomarkers of renal function (such as serum creatinine and cystatin C) are unable to identify at-risk individuals before the disease process is well under way. To help preventive strategies and maximize the potential for effective interventions, it is important to characterise the molecular changes that take place in the development of renal damage. Metabolomics is a promising tool to identify markers of renal disease since the kidneys are involved in the handling of major biochemical classes of metabolites. These metabolite levels capture a snap-shot of the metabolic profile of the individual, allowing for the potential identification of early biomarkers, and the monitoring of real-time kidney function. In this review, we describe the current status of the identification of blood/urine/faecal metabolic biomarkers in different entities of kidney diseases including: acute kidney injury, chronic kidney disease, renal transplant, diabetic nephropathy and other disorders.

High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.

Urinary metabolomics for noninvasive detection of borderline and acute T cell-mediated rejection in children after kidney transplantation

The goal of this study was to evaluate the utility of urinary metabolomics for noninvasive diagnosis of T cell-mediated rejection (TCMR) in pediatric kidney transplant recipients. Urine samples (n = 277) from 57 patients with surveillance or indication kidney biopsies were assayed for 134 unique metabolites by quantitative mass spectrometry. Samples without TCMR (n = 183) were compared to borderline tubulitis (n = 54) and TCMR (n = 30). Partial least squares discriminant analysis identified distinct classifiers for TCMR (area under receiver operating characteristic curve [AUC] = 0.892; 95% confidence interval [CI] 0.827-0.957) and borderline tubulitis (AUC = 0.836; 95% CI 0.781-0.892), respectively. Application of the TCMR classifier to borderline tubulitis samples yielded a discriminant score (-0.47 ± 0.33) mid-way between TCMR (-0.20 ± 0.34) and No TCMR (-0.80 ± 0.32) (p < 0.001 for all comparisons). Discriminant scoring for combined borderline/TCMR versus No TCMR (AUC = 0.900; 95% CI 0.859-0.940) applied to a validation cohort robustly distinguished between samples with (-0.08 ± 0.52) and without (-0.65 ± 0.54, p < 0.001) borderline/TCMR (p < 0.001). The TCMR discriminant score was driven by histological t-score, ct-score, donor-specific antibody and biopsy indication, and was unaffected by renal function, interstitial or microcirculatory inflammation, interstitial fibrosis or pyuria. These preliminary findings suggest that urinary metabolomics is a sensitive, specific and noninvasive tool for TCMR identification that is superior to serum creatinine, with minimal confounding by other allograft injury processes.

Proteomics and metabolomics in renal transplantation-quo vadis?

The improvement of long-term transplant organ and patient survival remains a critical challenge following kidney transplantation. Proteomics and biochemical profiling (metabolomics) may allow for the detection of early changes in cell signal transduction regulation and biochemistry with high sensitivity and specificity. Hence, these analytical strategies hold the promise to detect and monitor disease processes and drug effects before histopathological and pathophysiological changes occur. In addition, they will identify enriched populations and enable individualized drug therapy. However, proteomics and metabolomics have not yet lived up to such high expectations. Renal transplant patients are highly complex, making it difficult to establish cause-effect relationships between surrogate markers and disease processes. Appropriate study design, adequate sample handling, storage and processing, quality and reproducibility of bioanalytical multi-analyte assays, data analysis and interpretation, mechanistic verification, and clinical qualification (=establishment of sensitivity and specificity in adequately powered prospective clinical trials) are important factors for the success of molecular marker discovery and development in renal transplantation. However, a newly developed and appropriately qualified molecular marker can only be successful if it is realistic that it can be implemented in a clinical setting. The development of combinatorial markers with supporting software tools is an attractive goal.

Metabolomics of human intestinal transplant rejection

Surveillance endoscopy with biopsy is the standard method to monitor intestinal transplant recipients but it is invasive, costly and prone to sampling error. Early noninvasive biomarkers of intestinal rejection are needed. In this pilot study we applied metabolomics to characterize the metabolomic profile of intestinal allograft rejection. Fifty-six samples of ileostomy fluid or stool from 11 rejection and 45 nonrejection episodes were analyzed by ultraperformance liquid chromatography in conjunction with Quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS). The data were acquired in duplicate for each sample in positive ionization mode and preprocessed using XCMS (Scripps) followed by multivariate data analysis. We detected a total of 2541 metabolites in the positive ionization mode (mass 50-850 Daltons). A significant interclass separation was found between rejection and nonrejection. The proinflammatory mediator leukotriene E4 was the metabolite with the highest fold change in the rejection group compared to nonrejection. Water-soluble vitamins B2, B5, B6, and taurocholate were also detected with high fold change in rejection. The metabolomic profile of rejection was more heterogeneous than nonrejection. Although larger studies are needed, metabolomics appears to be a promising tool to characterize the pathophysiologic mechanisms involved in intestinal allograft rejection and potentially to identify noninvasive biomarkers.

Analysis of the differentially expressed low molecular weight peptides in human serum via an N-terminal isotope labeling technique combining nano-liquid chromatography/matrix-assisted laser desorption/ionization mass spectrometry

RATIONALE

Peptidomics analysis of human serum is challenging due to the low abundance of serum peptides and interference from the complex matrix. This study analyzed the differentially expressed (DE) low molecular weight peptides in human serum integrating a DMPITC-based N-terminal isotope labeling technique with nano-liquid chromatography and matrix-assisted laser desorption/ionization mass spectrometry (nano-LC/MALDI-MS).

METHODS

The workflow introduced a [d(6)]-4,6-dimethoxypyrimidine-2-isothiocyanate (DMPITC)-labeled mixture of aliquots from test samples as the internal standard. The spiked [d(0)]-DMPITC-labeled samples were separated by nano-LC then spotted on the MALDI target. Both quantitative and qualitative studies for serum peptides were achieved based on the isotope-labeled peaks.

RESULTS

The DMPITC labeling technique combined with nano-LC/MALDI-MS not only minimized the errors in peptide quantitation, but also allowed convenient recognition of the labeled peptides due to the 6 Da mass difference. The data showed that the entire research procedure as well as the subsequent data analysis method were effective, reproducible, and sensitive for the analysis of DE serum peptides.

CONCLUSIONS

This study successfully established a research model for DE serum peptides using DMPITC-based N-terminal isotope labeling and nano-LC/MALDI-MS. Application of the DMPITC-based N-terminal labeling technique is expected to provide a promising tool for the investigation of peptides in vivo, especially for the analysis of DE peptides under different biological conditions.

A pilot metabolic profiling study in serum of patients with chronic kidney disease based on (1) H-NMR-spectroscopy

BACKGROUND

Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. The metabolomics with a highly multiplexed and efficient manner is a challenging goal in nephrology.

METHODS

A (1) H-NMR based metabolomics approach was applied to establish a human CKD serum metabolic profile. Serum samples were obtained from CKD patients with four stages (N= 80) and healthy controls (N= 28). The data acquired by CMPG spectrum were further processed by pattern recognition (PR) analysis. Principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was capable of clustering the disease groups and establishing disease-specific metabolites profile.

RESULTS

The classification models could grade CKD patients with considerably high value of Q(2) and R(2) . The significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis (glucose, lactate), amino acids (valine, alanine, glutamate, glycine), organic osmolytes (betaine, myo-inositol, taurine, glycerophosphcholine), and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100%.

CONCLUSION

The study illustrated that serum metabolic profile was altered in response to renal dysfunction and the progression of CKD. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages.

Urinary metabolomics in monitoring acute tubular injury of renal allografts: a preliminary report

Acute tubular injury (ATI) is very common in biopsy specimens from renal allografts that suffer from delayed graft function (DGF) or dysfunction. Currently there are few reports on investigating small molecule metabolites in urine samples from transplant recipients as a noninvasive method to predict the ATI of renal allografts instead of an allograft biopsy. In our study matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS) was used to analyze small molecule metabolites in urine samples from renal transplant recipients with biopsy-proven slight ATI or moderate ATI or acute tubular necrosis (ATN). To evaluate the ATI-specific value of those small molecules, we applied the Principal Component Analysis (PCA) program. Mass spectra data were imported into the PCA, where loading graphs were constructed to express the constituents of the urine samples. Slight ATI, moderate ATI, or ATN of renal allografts were separated obviously in the loading graph. The position of urine samples in the graph may reflect the tubular injury status of allografts. A farther apart point from the original site may mean the allograft suffered from more severe ATI (even ATN), and vice versa. Detection of small molecule metabolites in urine samples of recipients through MALDI-FTMS may offer a promising noninvasive, high throughput, rapid tool to predict ATI/ATN of renal allografts.

Metabonomics study of the acute graft rejection in rat renal transplantation using reversed-phase liquid chromatography and hydrophilic interaction chromatography coupled with mass spectrometry

Acute graft rejection is one of the most common and serious post complications in renal transplantation, noninvasive diagnosis of acute graft rejection is essential for reducing risk of surgery and timely treatment. In this study, a non-targeted metabonomics approach based on ultra performance liquid chromatography (UPLC) coupled with quadrupole time-of-flight mass spectrometry (MS) is used to investigate the effect of acute graft rejection in rat renal transplantation on metabolism. To collect more metabolite information both hydrophilic interaction chromatography and reversed-phase liquid chromatography were used. Using the partial least squares-discriminant analysis, we found that the change of metabonome in a sham-operated group and a non-graft rejection group had a similar trend, while that of the acute graft rejection group was clearly different. Several discriminating metabolites of the acute graft rejection were identified, including creatinine, phosphatidyl-cholines, lyso-phosphatidylcholines, carnitine C16:0, free fatty acids and indoxyl sulfate etc. These discriminating metabolites suggested that acute graft rejection in renal transplantation can lead to the accumulation of creatinine in the body, and also the abnormal metabolism of phospholipids. These findings are useful to understand the mechanisms of the rejection, it also means that a UPLC-MS metabonomic approach is a suitable tool to investigate the metabolic abnormality in the acute graft rejection in renal transplantation.

Biomarkers of acute kidney injury

Acute kidney injury (AKI) is a common problem in both the inpatient and outpatient setting and often results from drug toxicities. Traditional methods of identifying AKI, through measurement of blood urea nitrogen and serum creatinine, are problematic in that they are slow to detect decreases in glomerular filtration rate (GFR) and are influenced by a variety of factors that are not related to GFR changes. The problems inherent in a creatinine-based diagnosis of AKI have impeded the development of proper therapeutics in AKI and posed problems in evaluating nephrotoxicity of drugs and other chemical exposures. In recent years, a number of new biomarkers of AKI with more favorable test characteristics than creatinine have been identified and studied in a variety of experimental and clinical settings. This review will consider the most well-established biomarkers and appraise the literature, with particular attention given to the use of biomarkers in identifying toxin-mediated AKI.

Biomarkers of immunosuppressant organ toxicity after transplantation: status, concepts and misconceptions

INTRODUCTION

A major challenge in transplantation is improving long-term organ transplant and patient survival. Immunosuppressants protect the transplant organ from alloimmune reactions, but sometimes also exhibit limiting side effects. The key to improving long-term outcome following transplantation is the selection of the correct immunosuppressive regimen for an individual patient for minimizing toxicity while maintaining immunosuppressive efficacy.

AREAS COVERED

Proteomics and metabolomics have the potential to develop sensitive and specific diagnostic tools for monitoring early changes in cell signal transduction, regulation and biochemical pathways. Here, we review the steps required for the development of molecular markers from discovery, mechanistic and clinical qualification to regulatory approval, and present a critical discussion of the current status of molecular marker development as relevant for the management and individualization of immunosuppressive drug regimens.

EXPERT OPINION

Although metabolomics and proteomics-based studies have yielded several candidate molecular markers, most published studies are poorly designed, statistically underpowered and/or often have not gone beyond the discovery stage. Most molecular marker candidates are still at an early stage. Due to the high complexity of and the resources required for diagnostic marker development, initiatives and consortia organized and supported by funding agencies and regulatory agencies will be critical.

Mass spectrometry-based technologies for high-throughput metabolomics

The metabolome is composed of a vast number of small-molecule metabolites that exhibit a diversity of physical and chemical properties and exist over a wide dynamic range in biological samples. Multiple analytical techniques, used in a complementary manner, are required to achieve high coverage of the metabolome. MS is playing a central role in metabolomics research. Herein, we present a brief overview of the MS-based technologies employed for high-throughput metabolomics. These technologies range from chromatography-MS techniques, such as GC-MS and LC-MS, to chromatography-free techniques, such as direct infusion, matrix-assisted and matrix-free laser desorption/ionization, imaging and some new ambient ionization approaches. Chemoinformatics and bioinformatics tools are widely available to facilitate successful metabolomics studies by turning the complex metabolomics data into biological information through streamlined data processing, analysis and interpretation.

Molecular diagnostics in transplantation

The past few decades are characterized by an explosive evolution of genetics and molecular cell biology. Advances in chemistry and engineering have enabled increased data throughput, permitting the study of complete sets of molecules with increasing speed and accuracy using techniques such as genomics, transcriptomics, proteomics, and metabolomics. Prediction of long-term outcomes in transplantation is hampered by the absence of sufficiently robust biomarkers and a lack of adequate insight into the mechanisms of acute and chronic alloimmune injury and the adaptive mechanisms of immunological quiescence that may support transplantation tolerance. Here, we discuss some of the great opportunities that molecular diagnostic tools have to offer both basic scientists and translational researchers for bench-to-bedside clinical application in transplantation medicine, with special focus on genomics and genome-wide association studies, epigenetics (DNA methylation and histone modifications), gene expression studies and transcriptomics (including microRNA and small interfering RNA studies), proteomics and peptidomics, antibodyomics, metabolomics, chemical genomics and functional imaging with nanoparticles. We address the challenges and opportunities associated with the newer high-throughput sequencing technologies, especially in the field of bioinformatics and biostatistics, and demonstrate the importance of integrative approaches. Although this Review focuses on transplantation research and clinical transplantation, the concepts addressed are valid for all translational research.

Detecting adaptive immunity: applications in transplantation monitoring

In recent decades, continuous improvements in immunosuppressive therapy have led to a significant increase in kidney allograft survival. Despite innovative developments and improvements in immunosuppression, chronic allograft injury and late graft loss still remain major causes of morbidity and mortality. In clinical practice, long-term immunosuppression is adapted and fine-tuned according to drug levels, kidney function, and biopsy results. As an invasive procedure, indication biopsy still represents an indispensible diagnostic gold standard. However, in an effort to further improve outcomes on the basis of individualized treatment, there is an urgent need for noninvasive assays, as well as biomarkers, to more accurately monitor allogeneic responses and predict the risk of acute and chronic allograft rejection. This article discusses strategies for immune monitoring of T-cell responsiveness and humoral alloreactivity. Furthermore, new microarray and gene profiling data are highlighted, which may identify hyporesponsive transplant recipients who could benefit from a reduction or even withdrawal of immunosuppression. Finally, supplementary transplant risk assessment markers, such as soluble CD30 and urinary effector molecule analysis, are discussed as promising new tools. Recent developments and improvements in test principles to monitor and predict allograft immunity are encouraging and may herald the transition of present empiric immunosuppression to individualized immunosuppressive treatment. Nonetheless, before implementation of immune monitoring in routine clinical practice, there is still a need for prospective trials designed to clarify the actual diagnostic potential of individual test systems in a therapeutic context.

Multivariate classification of urine metabolome profiles for breast cancer diagnosis

Background

Diagnosis techniques using urine are non-invasive, inexpensive, and easy to perform in clinical settings. The metabolites in urine, as the end products of cellular processes, are closely linked to phenotypes. Therefore, urine metabolome is very useful in marker discoveries and clinical applications. However, only univariate methods have been used in classification studies using urine metabolome. Since multiple genes or proteins would be involved in developments of complex diseases such as breast cancer, multiple compounds including metabolites would be related with the complex diseases, and multivariate methods would be needed to identify those multiple metabolite markers. Moreover, because combinatorial effects among the markers can seriously affect disease developments and there also exist individual differences in genetic makeup or heterogeneity in cancer progressions, single marker is not enough to identify cancers.

Results

We proposed classification models using multivariate classification techniques and developed an analysis procedure for classification studies using metabolome data. Through this strategy, we identified five potential urinary biomarkers for breast cancer with high accuracy, among which the four biomarker candidates were not identifiable by only univariate methods. We also proposed potential diagnosis rules to help in clinical decision making. Besides, we showed that combinatorial effects among multiple biomarkers can enhance discriminative power for breast cancer.

Conclusions

In this study, we successfully showed that multivariate classifications are needed to precisely diagnose breast cancer. After further validation with independent cohorts and experimental confirmation, these marker candidates will likely lead to clinically applicable assays for earlier diagnoses of breast cancer.

Proteomic profiling of renal allograft rejection in serum using magnetic bead-based sample fractionation and MALDI-TOF MS

Proteomics is one of the emerging techniques for biomarker discovery. Biomarkers can be used for early noninvasive diagnosis and prognosis of diseases and treatment efficacy evaluation. In the present study, the well-established research systems of ClinProt Micro solution incorporated unique magnetic bead sample preparation technology, which, based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), have become very successful in bioinformatics due to its outstanding performance and reproducibility for discovery disease-related biomarker. We collected fasting blood samples from patients with biopsy-confirmed acute renal allograft rejection (n = 12), chronic rejection (n = 12), stable graft function (n = 12) and also from healthy volunteers (n = 13) to study serum peptidome patterns. Specimens were purified with magnetic bead-based weak cation exchange chromatography and analyzed with a MALDI-TOF mass spectrometer. The results indicated that 18 differential peptide peaks were selected as potential biomarkers of acute renal allograft rejection, and 6 differential peptide peaks were selected as potential biomarkers of chronic rejection. A Quick Classifier Algorithm was used to set up the classification models for acute and chronic renal allograft rejection. The algorithm models recognize 82.64% of acute rejection and 98.96% of chronic rejection episodes, respectively. We were able to identify serum protein fingerprints in small sample sizes of recipients with renal allograft rejection and establish the models for diagnosis of renal allograft rejection. This preliminary study demonstrated that proteomics is an emerging tool for early diagnosis of renal allograft rejection and helps us to better understand the pathogenesis of disease process.

A new NMR-based metabolomics approach for the diagnosis of biliary tract cancer

BACKGROUND & AIMS

Biliary tract cancer is highly lethal at presentation, with increasing mortality worldwide. Current diagnostic measures employing multiple criteria such as imaging, cytology, and serum tumor markers are not satisfactory, and a new diagnostic tool is needed. Because bile is a cognate metabolite-rich bio-fluid in the biliary ductal system, we tested a new metabolomic approach to develop an effective diagnostic tool.

METHODS

Biles were collected prospectively from patients with cancer (n=17) or benign biliary tract diseases (n=21) with percutaneous or endoscopic methods. Nuclear magnetic resonance spectra (NMR) of these biles were analyzed using orthogonal partial least square discriminant analysis (OPLS-DA).

RESULTS

The metabolomic 2-D score plot showed good separation between cancer and benign groups. The contributing NMR signals were analyzed using a statistical TOCSY approach. The diagnostic performance assessed by leave-one-out analysis exhibited 88% sensitivity and 81% specificity, better than the conventional markers (CEA, CA19-9, and bile cytology).

CONCLUSION

The NMR-based metabolomics approach provides good performance in discriminating cancer and benign biliary duct diseases. The excellent predictability of the method suggests that it can, at least, augment the currently available diagnostic approaches.

Metabolomics: moving to the clinic

Assessment of a biological system by means of global and non-targeted metabolite profiling--metabolomics or metabonomics--provides the investigator with molecular information that is close to the phenotype in question in the sense that metabolites are an ultimate product of gene, mRNA, and protein activity. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers which could assist diagnosis, provide therapy guidance, and evaluate response to therapy for particular diseases. Also, within the fields of drug discovery, drug toxicology, and personalized pharmacology, metabolomics is emerging as a powerful tool. This review seeks to update the reader on analytical strategies, biomarker findings, and implications of metabolomics for the clinic. Particular attention is paid to recent biomarkers found related to neurological, cardiovascular, and cancer diseases. Moreover, the impact of metabolomics in the drug discovery and development process is examined.

Characterizations of urinary sediments precipitated after freezing and their effects on urinary protein and chemical analyses

One of the obstacles in analyzing frozen urine samples is the formation of uncharacterized precipitates. Frequently, these precipitates are discarded before analysis. Some laboratory data may be erroneous if these precipitates contain important compounds. In the present study, we examined urinary sediments precipitated after overnight storage at -20 degrees C. Although cells and debris were removed before freezing, the precipitates remained, whereas storing the centrifuged urine overnight at 4 degrees C did not result in precipitate formation. There were no significant differences observed among 10 healthy individuals (5 men and 5 women). EDTA (5 mM) could efficiently reduce the amount of precipitates to approximately 25% of the initial amount. The addition of exogenous CaCl2, but not sodium oxalate and NaCl, significantly increased the amount of precipitates in a dose-dependent manner. Linear regression analysis revealed a significant correlation between endogenous urinary calcium level and the amount of precipitates (r = 0.894; P < 0.001). Urine pH also had some effects on the type and amount of precipitates. These precipitates were composed mainly of calcium oxalate dihydrate and amorphous calcium crystals. The results also showed that these precipitates could deplete urinary proteins and calcium ions (23.6 +/- 1.1% decrease). Therefore, these freezer-induced urinary sediments significantly affect protein analysis and measurement of calcium levels in the urine. However, vigorous shaking of the sample at room temperature could redissolve these precipitates. Our data strongly indicate that these freezer-induced precipitates must be taken into account when the frozen urine samples are analyzed.