Metabolomics in monitoring kidney transplants

Untargeted NMR-based metabolomics analysis of kidney allograft perfusates identifies a signature of delayed graft function

INTRODUCTION

Kidney transplantation (KTx) necessarily conveys an ischemia/reperfusion (I/R) process, which impacts on allograft outcomes. Delayed graft function (DGF) is defined as a non-decrease of serum creatinine by at least 10% daily on 3 consecutive days during the first 7 days post-KTx. DGF significantly conditions both short- and long-term graft outcomes. Still there is a lack of DGF predictive biomarkers.

OBJECTIVES

This study aimed to explore the potential of kidney graft perfusate metabolomics to predict DGF occurrence.

METHODS

49 human perfusates from grafts categorized upon donor type [donation after brain death (DBD)/donation after circulatory death (DCD)] and DGF occurrence and 19 perfusates from a murine model classified upon death type (DBD/DCD) were collected and analyzed by NMR-based metabolomics.

RESULTS

The multivariate analysis of the murine data highlighted significant differences between perfusate metabolomes of DBD versus DCD. These differences were similarly observed in the human perfusates. After correcting for the type of donor, multivariate analysis of human data demonstrated a metabolomics signature that could be correlated with DGF occurrence.

CONCLUSIONS

The metabolome of kidney grafts is influenced by the donor's type in both human and pre-clinical studies and could be correlated with DGF in the human DBD cohort. Thus, metabolomic analysis of perfusate applied prior to KTx may represent a new predictive tool for clinicians in a more personalized management of DGF. Moreover, our data paves the way to better understand the impact of donor's types on the biochemical events occurring between death and the hypothermic storage.

Evaluation of Oxidative Stress and Metabolic Profile in a Preclinical Kidney Transplantation Model According to Different Preservation Modalities

This study addresses a joint nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopy approach to provide a platform for dynamic assessment of kidney viability and metabolism. On porcine kidney models, ROS production, oxidative damage kinetics, and metabolic changes occurring both during the period between organ retrieval and implantation and after kidney graft were examined. The 1H-NMR metabolic profile—valine, alanine, acetate, trimetylamine-N-oxide, glutathione, lactate, and the EPR oxidative stress—resulting from ischemia/reperfusion injury after preservation (8 h) by static cold storage (SCS) and ex vivo machine perfusion (HMP) methods were monitored. The functional recovery after transplantation (14 days) was evaluated by serum creatinine (SCr), oxidative stress (ROS), and damage (thiobarbituric-acid-reactive substances and protein carbonyl enzymatic) assessments. At 8 h of preservation storage, a significantly (p < 0.0001) higher ROS production was measured in the SCS vs. HMP group. Significantly higher concentration data (p < 0.05−0.0001) in HMP vs. SCS for all the monitored metabolites were found as well. The HMP group showed a better function recovery. The comparison of the areas under the SCr curves (AUC) returned a significantly smaller (−12.5 %) AUC in the HMP vs. SCS. EPR-ROS concentration (μmol·g−1) from bioptic kidney tissue samples were significantly lower in HMP vs. SCS. The same result was found for the NMR monitored metabolites: lactate: −59.76%, alanine: −43.17%; valine: −58.56%; and TMAO: −77.96%. No changes were observed in either group under light microscopy. In conclusion, a better and more rapid normalization of oxidative stress and functional recovery after transplantation were observed by HMP utilization.

Metabolomics as a Critical Tool for Studying Clinical Surgery

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented.

The effects of gestational diabetes mellitus with maternal age between 35 and 40 years on the metabolite profiles of plasma and urine

Background

Gestational diabetes mellitus (GDM) is defined as impaired glucose tolerance in pregnancy and without a history of diabetes mellitus. While there are limited metabolomic studies involving advanced maternal age in China, we aim to investigate the metabolomic profiling of plasma and urine in pregnancies complicated with GDM aged at 35–40 years at early and late gestation.

Methods

Twenty normal and 20 GDM pregnant participants (≥ 35 years old) were enlisted from the Complex Lipids in Mothers and Babies (CLIMB) study. Maternal plasma and urine collected at the first and third trimester were detected using gas chromatography-mass spectrometry (GC-MS).

Results

One hundred sixty-five metabolites and 192 metabolites were found in plasma and urine respectively. Urine metabolomic profiles were incapable to distinguish GDM from controls, in comparison, there were 14 and 39 significantly different plasma metabolites between the two groups in first and third trimester respectively. Especially, by integrating seven metabolites including cysteine, malonic acid, alanine, 11,14-eicosadienoic acid, stearic acid, arachidic acid, and 2-methyloctadecanoic acid using multivariant receiver operating characteristic models, we were capable of discriminating GDM from normal pregnancies with an area under curve of 0.928 at first trimester.

Conclusion

This study explores metabolomic profiles between GDM and normal pregnancies at the age of 35–40 years longitudinally. Several compounds have the potential to be biomarkers to predict GDM with advanced maternal age. Moreover, the discordant metabolome profiles between the two groups could be useful to understand the etiology of GDM with advanced maternal age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-04416-5.

Intestinal microbiome and metabolome analyses reveal metabolic disorders in the early stage of renal transplantation

Renal transplantation is the most effective treatment for end-stage renal disease, but the long-term prognosis of organs after transplantation is not ideal. In recent years, the importance of gut microbes and metabolites in the study of disease mechanisms has gradually received attention. However, the coordination between gut microbes and the metabolism of renal transplant patients needs further study. We integrated 16s sequencing and metabolomics data to describe the changes in the serum and fecal metabolites of renal transplant patients. Our data revealed that the gut microbial diversity decreased and the relative abundance of many bacteria, such as Enterococcus and Streptococcus, significantly changed after transplantation. In addition, a large number of amino acids and peptides in serum and feces significantly changed, suggesting an abnormal amino acid metabolism after transplantation. Spearman's correlation analysis revealed the changes in the co-metabolism pattern between gut microbes and the host metabolism after transplantation. Furthermore, Enterococcus was found to be correlated with renal functions and metabolites reflecting renal damage. This study provides potential gut microbes and metabolites impacting renal health, which helps in understanding the renal damage in patients with kidney transplantation.

Improving data quality in liquid chromatography-mass spectrometry metabolomics of human urine

Signal variation is a common drawback in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS), mainly due to the complexity of biological matrices and reduced sample preparation, which results in the accumulation of sample components in the column and the ion source. Here we propose a simple, easy to implement approach to improve data quality in untargeted metabolomics by LC-MS. This approach involves the use of a divert valve to direct the column effluent to waste at the beginning of the chromatographic run and during column cleanup and equilibration, in combination with longer column cleanups in between injections. Our approach was tested using urine samples collected from patients after renal transplantation. Analytical responses were contrasted before and after introducing these modifications by analyzing a batch of untargeted metabolomics data. A significant improvement in peak area repeatability was observed for the quality controls, with relative standard deviations (RSDs) for several metabolites decreasing from ∼60% to ∼10% when our approach was introduced. Similarly, RSDs of peak areas for internal standards improved from ∼40% to ∼10%. Furthermore, calibrant solutions were more consistent after introducing these modifications when comparing peak areas of solutions injected at the beginning and the end of each analytical sequence. Therefore, we recommend the use of a divert valve and extended column cleanup as a powerful strategy to improve data quality in untargeted metabolomics, especially for very complex types of samples where minimum sample preparation is required, such as in this untargeted metabolomics study with urine from renal transplanted patients.

Prognostic value of endogenous and exogenous metabolites in liver transplantation

Liver transplantation has been widely accepted as an effective intervention for end-stage liver diseases and early hepatocellular carcinomas. However, a variety of postoperative complications and adverse reactions have baffled medical staff and patients. Currently, transplantation monitoring relies primarily on nonspecific biochemical tests, whereas diagnosis of multiple complications depends on invasive pathological examination. Therefore, a noninvasive monitoring method with high selectivity and specificity is desperately needed. This review summarized the potential of endogenous small-molecule metabolites as biomarkers for assessing graft function, ischemia-reperfusion injury and liver rejection. Exogenous metabolites, mainly those immunosuppressive agents with high intra- and inter-individual variability, were also discussed for transplantation monitoring.

CYC1, SDHA, UQCRC1, UQCRQ, and SDHB might be important biomarkers in kidney transplant rejection

BACKGROUND

Kidney transplant rejection is considered as a vital factor of kidney transplant failure. Therefore, it's necessary to search for effective biomarkers for kidney transplant surveillance.

METHODS

In this study, we conducted time-series gene expression profiles analysis of samples from kidney transplant patients with different post-transplant days through weighted gene co-expression network analysis (WGCNA). Associations between gene co-expression modules and days post-transplant were determined through spearman rank correlation analysis. Potential kidney transplant rejection-related modules were subjected to gene functional enrichment analysis through clusterProfiler and protein-protein interaction analysis via STRING database.

RESULTS

A total of 11 gene co-expression modules were identified, and the pink module which was mainly involved in "energy derivation by oxidation of organic compounds" and "Huntington disease" showed significant correlation with the phenotypic trait "days post-transplant". CYC1, SDHA, UQCRC1, UQCRQ, and SDHB in the pink module exhibited high scores in the protein-protein interaction network analysis.

CONCLUSIONS

We reported several potential genes may be associated with the kidney transplant rejection, which should provide novel biomarkers for kidney transplant surveillance.

Tubular and Glomerular Biomarkers of Acute Kidney Injury in Newborns

BACKGROUND

Acute Kidney Injury (AKI) is a sudden decrease in kidney function. In the early period, the highest percentage of AKI occurs among newborns hospitalized in the neonatal intensive care units, especially premature neonates. The prognosis of AKI depends on the type and severity of the cause of an injury, the accuracy and the time of diagnosis and treatment. The concentration of serum creatinine is still the main diagnostic test, although it changes in the course of AKI later than glomerular filtration rate GFR. In addition, the reliability of the determination of creatinine level is limited because it depends on many factors. New studies have presented other, more useful laboratory markers of renal function that can be measured in serum and/or in urine.

OBJECTIVE

The aim of the work was to present the latest data about tubular and glomerular biomarkers of acute kidney injury in newborns.

METHODS

We undertook a structured search of bibliographic databases for peer-reviewed research literature by using focused review topics. According to the conceptual framework, the main idea of research literature has been summarized and presented in this study.

RESULTS

The concentrations of some novel biomarkers are higher in serum and/or urine of term and preterm newborns with AKI, especially in the course of perinatal asphyxia.

CONCLUSION

In this systematic review of the literature, we have highlighted the usefulness of biomarkers in predicting tubular and/or glomerular injury in newborns. However, novel biomarkers need to prove their clinical applicability, accuracy, and cost-effectiveness prior to their implementation in clinical practice.

Metabolomics for Investigating Physiological and Pathophysiological Processes

Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.

A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study

Omics data facilitate the gain of novel insights into the pathophysiology of diseases and, consequently, their diagnosis, treatment, and prevention. To this end, omics data are integrated with other data types, e.g., clinical, phenotypic, and demographic parameters of categorical or continuous nature. We exemplify this data integration issue for a chronic kidney disease (CKD) study, comprising complex clinical, demographic, and one-dimensional ¹H nuclear magnetic resonance metabolic variables. Routine analysis screens for associations of single metabolic features with clinical parameters while accounting for confounders typically chosen by expert knowledge. This knowledge can be incomplete or unavailable. We introduce a framework for data integration that intrinsically adjusts for confounding variables. We give its mathematical and algorithmic foundation, provide a state-of-the-art implementation, and evaluate its performance by sanity checks and predictive performance assessment on independent test data. Particularly, we show that discovered associations remain significant after variable adjustment based on expert knowledge. In contrast, we illustrate that associations discovered in routine univariate screening approaches can be biased by incorrect or incomplete expert knowledge. Our data integration approach reveals important associations between CKD comorbidities and metabolites, including novel associations of the plasma metabolite trimethylamine-N-oxide with cardiac arrhythmia and infarction in CKD stage 3 patients.

Urinary metabolomic profiling for noninvasive diagnosis of acute T cell-mediated rejection after kidney transplantation

To improve early renal allograft function, it is important to develop a noninvasive diagnostic method for acute T cell-mediated rejection (TCMR). This study aims to explore potential noninvasive urinary biomarkers to screen for acute TCMR in kidney transplant recipients (KTRs) using untargeted metabolomic profiling. Urinary metabolites, collected from KTRs with stable graft function (STA) or acute TCMR episodes, were analyzed using liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analyses were performed to discriminate differences in urinary metabolites between the two groups. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of potential urinary biomarkers. Statistical analysis revealed the differences in urinary metabolites between the two groups and indicated several statistically significant metabolic features suitable for potential biomarkers. By comparing the retention times and mass fragmentation patterns of the chemicals in metabolite databases, samples, and standards, six of these features were clearly identified. ROC curve analysis showed the best performance of the training set (area under the curve value, 0.926; sensitivity, 90.0%; specificity, 84.6%) using a panel of five potential biomarkers: guanidoacetic acid, methylimidazoleacetic acid, dopamine, 4-guanidinobutyric acid, and L-tryptophan. The diagnostic accuracy of this model was 62.5% for an independent test dataset. LC-MS-based untargeted metabolomic profiling is a promising method to discriminate between acute TCMR and STA groups. Our model, based on a panel of five potential biomarkers, needs to be further validated in larger scale studies.

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.

Metabolomic profiling to characterize acute intestinal ischemia/reperfusion injury

Sepsis and septic shock are the leading causes of death in critically ill patients. Acute intestinal ischemia/reperfusion (AII/R) is an adaptive response to shock. The high mortality rate from AII/R is due to the severity of the disease and, more importantly, the failure of timely diagnosis. The objective of this investigation is to use nuclear magnetic resonance (NMR) analysis to characterize urine metabolomic profile of AII/R injury in a mouse model. Animals were exposed to sham, early (30 min) or late (60 min) acute intestinal ischemia by complete occlusion of the superior mesenteric artery, followed by 2 hrs of reperfusion. Urine was collected and analyzed by NMR spectroscopy. Urinary metabolite concentrations demonstrated that different profiles could be delineated based on the duration of the intestinal ischemia. Metabolites such as allantoin, creatinine, proline, and methylamine could be predictive of AII/R injury. Lactate, currently used for clinical diagnosis, was found not to significantly contribute to the classification model for either early or late ischemia. This study demonstrates that patterns of changes in urinary metabolites are effective at distinguishing AII/R progression in an animal model. This is a proof-of-concept study to further support examination of metabolites in the clinical diagnosis of intestinal ischemia reperfusion injury in patients. The discovery of a fingerprint metabolite profile of AII/R will be a major advancement in the diagnosis, treatment, and prevention of systemic injury in critically ill patients.

Metabolomics and renal disease

PURPOSE OF REVIEW

This review summarizes recent metabolomics studies of renal disease, outlining some of the limitations of the literature to date.

RECENT FINDINGS

The application of metabolomics in nephrology research has expanded from the initial analyses of uremia to include both cross-sectional and longitudinal studies of earlier stages of kidney disease. Although these studies have nominated several potential markers of incident chronic kidney disease (CKD) and CKD progression, a lack of overlap in metabolite coverage has limited the ability to synthesize results across groups. Furthermore, direct examination of renal metabolite handling has underscored the substantial impact kidney function has on these potential markers (and many other circulating metabolites). In experimental studies, metabolomics has been used to identify a signature of decreased mitochondrial function in diabetic nephropathy and a preference for aerobic glucose metabolism in polycystic kidney disease. In each case, these studies have outlined novel therapeutic opportunities. Finally, as a complement to the longstanding interest in renal metabolite clearance, the microbiome has been increasingly recognized as the source of many plasma metabolites, including some with potential functional relevance to CKD and its complications.

SUMMARY

The high-throughput, high-resolution phenotyping enabled by metabolomics technologies has begun to provide insight on renal disease in clinical, physiologic, and experimental contexts.

Computational Models for Transplant Biomarker Discovery

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.

A four-compartment metabolomics analysis of the liver, muscle, serum, and urine response to polytrauma with hemorrhagic shock following carbohydrate prefeed

Objective

Hemorrhagic shock accompanied by injury represents a major physiologic stress. Fasted animals are often used to study hemorrhagic shock (with injury). A fasted state is not guaranteed in the general human population. The objective of this study was to determine if fed animals would exhibit a different metabolic profile in response to hemorrhagic shock with trauma when compared to fasted animals.

Methods

Proton (1H) NMR spectroscopy was used to determine concentrations of metabolites from four different compartments (liver, muscle, serum, urine) taken at defined time points throughout shock/injury and resuscitation. PLS-DA was performed and VIP lists established for baseline, shock and resuscitation (10 metabolites for each compartment at each time interval) on metabolomics data from surviving animals.

Results

Fed status prior to the occurrence of hemorrhagic shock with injury alters the metabolic course of this trauma and potentially affects mortality. The death rate for CPF animals is higher than FS animals (47 vs 28%). The majority of deaths occur post-resuscitation suggesting reperfusion injury. The metabolomics response to shock reflects priorities evident at baseline. FS animals raise the baseline degree of proteolysis to provide additional amino acids for energy production while CPF animals rely on both glucose and, to a lesser extent, amino acids. During early resuscitation levels of metabolites associated with energy production drop, suggesting diminished demand.

Conclusions

Feeding status prior to the occurrence of hemorrhagic shock with injury alters the metabolic course of this trauma and potentially affects mortality. The response to shock reflects metabolic priorities at baseline.

A high-performance liquid chromatography-tandem mass spectrometry-based targeted metabolomics kidney dysfunction marker panel in human urine

BACKGROUND

Previous studies have examined and documented fluctuations in urine metabolites in response to disease processes and drug toxicity affecting glomerular filtration, tubule cell metabolism, reabsorption, oxidative stress, purine degradation, active secretion and kidney amino acylase activity representative of diminished renal function. However, a high-throughput assay that incorporates metabolites that are surrogate markers for such changes into a kidney dysfunction panel has yet to be described.

METHODS

A high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) assay for the quantification of ten metabolites associated with the Krebs cycle, purine degradation, and oxidative stress in human urine was developed and validated. Normal values were assessed in healthy adult (n=120) and pediatric (n=36) individuals. In addition, 9 pediatric renal transplant recipients patients were evaluated before and after initial dosing of the immunosuppressant tacrolimus in a proof-of-concept study.

RESULTS

The assay met all predefined acceptance criteria. The lower limit of quantification ranged from 0.1 to 1000 μmol/l. Inter-day trueness and imprecisions ranged from 91.4-112.9% and 1.5-12.4%, respectively. The total assay run time was 5.5 minutes. Concentrations of glucose, sorbitol, and trimethylamine oxide (TMAO) were elevated in pediatric renal transplant patients (n=9) prior to transplantation as well as before and immediately after initial dosing of tacrolimus. One month post-transplant urine metabolite patterns matched those of healthy children (n=36).

CONCLUSIONS

The LC-MS/MS assay will provide the basis for further large-scale clinical studies to explore these analytes as molecular markers for the patients with renal insufficiency.

Metabolomics in nephrotoxicity

Nephrotoxicity or renal toxicity can be a result of hemodynamic changes, direct injury to cells and tissue, inflammatory tissue injury, and/or obstruction of renal excretion. Nephrotoxicity is frequently induced by a wide spectrum of therapeutic drugs and environ mental pollutants. Knowledge of the complex molecular and pathophysiologic mechanisms leading to nephrotoxicity remains limited, in part, by research that historically focused on single or relatively few risk markers. As such, current kidney injury biomarkers are inadequate in terms of sensitivity and specificity. In contrast, metabolomics enables screening of a vast array of metabolites simultaneously using NMR and MS to assess their role in nephrotoxicity development and progression. A more comprehensive understanding of these biochemical pathways would also provide valuable insight to disease mechanisms critical for drug development and treatment.

Predictive biomarkers of renal allograft failure

BACKGROUND

Survival of renal allografts is limited by chronic allograft deterioration resulting from processes that are difficult to detect in their early stages, when therapeutic interventions would be most effective. Predictive biomarkers from easily accessible specimens, such as blood or urine, might improve early diagnosis of smoldering graft-damaging processes and help with the identification of patients at particularly high risk of sustained injury, thereby helping to tailor therapy and appropriate follow-up screening.

OBJECTIVE

This article reviews recently investigated biomarkers for the prediction of renal allograft failure, outlines the new '-omic' technologies as a potential source for the identification of new predictive biomarkers and judges the practical value of predictive biomarkers at the present timepoint.

METHODS

A literature search was performed using the medical database PubMed. No general restrictions (e.g., year of publication) were applied, but the focus was set on more recently published articles.

CONCLUSION

Despite a large number of interesting studies, none of the investigated candidate biomarkers is robustly established for widespread clinical use or able to replace biopsies for graft assessment.

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.

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.

Metabolomics for early detection of drug-induced kidney injury: review of the current status

The identification of biomarkers of drug-induced kidney injury is an area of intensive focus in drug development. Traditional markers of renal function, including blood urea nitrogen and serum creatinine, are not region-specific and only increase significantly after substantial kidney injury. Therefore, more sensitive markers of kidney injury are needed. The ideal biomarkers will identify nephrotoxicity early in the drug-discovery process, resulting in decreased development costs and safer drugs. Metabolomics, the study of the small biochemicals present in a biological sample, has become a promising player in the nephrotoxicity arena. In this review, we describe the current status of the identification of metabolic biomarkers for drug-induced kidney toxicity screening. Many of these markers have been confirmed across multiple studies and can detect nephrotoxicity earlier than the traditional clinical chemistry and histopathology methods. Upon further validation, such markers will offer clear benefits for the pharmaceutical industry and regulatory agencies.

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.

Genomic and metabolomic advances in the identification of disease and adverse event biomarkers

Incomplete knowledge of tissue pathogenesis is hampering the identification of biomarkers for the appropriate therapeutic targets to prevent or inhibit disease processes, and the prediction and diagnosis of injury due to disease and adverse events of drug therapy. The revolution in genomics and metabolomics, combined with advanced bioinformatics and computational methods for mining such large, complex data sets, are beginning to provide critical insights into tissue injury. Such results will move us closer to the promise of personalized medicine.

Metabolic profiling of kidney and urine in rats with lithium-induced nephrogenic diabetes insipidus by (1)H-NMR-based metabonomics

Lithium (Li) treatment for bipolar affective disorders is associated with a variety of renal side effects. The metabolic response of the kidney to chronic Li treatment has rarely been studied. We applied a novel method of (1)H-nuclear magnetic resonance (NMR)-based metabonomics to integrate metabolic profiling and to identify the changes in the levels of metabolites in the kidney and urine from rats with Li-induced NDI. Metabolic profiles of urine and kidney homogenate [3 different zones (cortex, outer medulla, and inner medulla) or whole kidney] were investigated using high-resolution NMR spectroscopy coupled with pattern recognition methods. The accurate concentrations of metabolites in kidney homogenates and urine were rapidly measured using the target-profiling procedure, and the difference in the levels of metabolites was compared using multivariate analysis, such as principal component analysis and orthogonal partial least squares-discriminant analysis. Major endogenous metabolites for kidney homogenates contained products of glycolysis (glucose, lactate) and amino acids, as well as organic osmolytes (e.g., betaine, myo-inositol, taurine, and glycerophosphocholine). Many metabolites revealed changes in their levels, including decreased levels of organic osmolytes and amino acids in the inner medulla. A number of urinary metabolites were changed in Li-induced NDI, and in particular, elevated urinary levels of acetate, lactate, allantoin, trimethylamine, and creatine could suggest Li-induced renal cell stress or injury. Taken together, metabonomics of kidney tissue and urine based on (1)H-NMR spectroscopy could provide insight into the effects of Li-induced renal effects and cell injury.

Metabonomics: a useful tool for the future surgeon

BACKGROUND

In the past decade or so, a range of technologies have emerged that have shown promise in increasing our understanding of disease processes and progression. These advances are referred to as the "omics" technologies; genomics, transcriptomics, and proteomics. More recently, another "omics" approach has come to the fore: metabonomics, and this technology has the potential for significant clinical impact. Metabonomics refers to the analysis of the metabolome, that is, the metabolic profile of a system. The advantage of studying the metabolome is that the end points of biological events are elucidated.

RESULTS

Although still in its infancy, the metabonomics approach has shown immense promise in areas as diverse as toxicology studies to the discovery of biomarkers of disease. It has also been applied to studies of both renal and hepatic transplants. Metabolome analysis may be conducted on a variety of biological fluids and tissue types and may utilize a number of different technology platforms, mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy being the most popular. In this review, we cover the background to the evolution of metabonomics and its applications with particular emphasis on clinical applications.

CONCLUSIONS

We conclude with the suggestion that metabonomics offers a platform for further biomarker development, drug development, and in the field of medicine.

Proteomics for biomarker discovery in acute kidney injury

Acute kidney injury (AKI), previously referred to as acute renal failure, represents a common and devastating problem in clinical medicine. Despite significant improvements in therapeutics, the mortality and morbidity associated with AKI remain high. A major reason for this is the lack of early markers for AKI, and hence an unacceptable delay in initiating therapy. Fortunately, the application of innovative technologies such as functional genomics and proteomics to human and animal models of AKI has uncovered several novel biomarkers and therapeutic targets. The most promising of these are chronicled in this review. These include the identification of biomarker panels in plasma (neutrophil gelatinase-associated lipocalin and cystatin C) and urine (neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, interleukin-18, cystatin C, alpha1-microglobulin, Fetuin-A, Gro-alpha, and meprin). It is likely that the AKI panels will be useful for timing the initial insult, and assessing the duration and severity of AKI. It is also probable that the AKI panels will distinguish between the various etiologies of AKI and predict clinical outcomes. It will be important in future studies to validate the sensitivity and specificity of these biomarker panels in clinical samples from large cohorts and from multiple clinical situations. Such studies will be facilitated markedly by the development of commercial tools for the reproducible measurement of biomarkers across different laboratories.

Renal transplant rejection markers

Acute rejection is one of the key factors which determine long-term graft function and survival in renal transplant patients. Timely detection and treatment of rejection is therefore, an important goal in the post-transplant surveillance. The standard care with serum creatinine measurements and biopsy upon allograft dysfunction implies that acute rejection is detected in an advanced stage. Therefore, non-invasive monitoring for acute rejection by markers in blood and urine has been tried over the past decades. This review describes the requirements that should be met by non-invasive markers. The experience with single biomarkers and with newer approaches--mRNA expression analysis, metabolomics, and proteomics--will be discussed, including future directions of necessary research.