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

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

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

Non-invasive biomarkers of acute rejection in pediatric kidney transplantation: New targets and strategies

Kidney transplantation is the preferred treatment for pediatric end-stage renal disease. However, pediatric recipients face unique challenges due to their prolonged need for kidney function to accommodate growth and development. The continual changes in the immune microenvironment during childhood development and the heightened risk of complications from long-term use of immunosuppressive drugs. The overwhelming majority of children may require more than one kidney transplant in their lifetime. Acute rejection (AR) stands as the primary cause of kidney transplant failure in children. While pathologic biopsy remains the "gold standard" for diagnosing renal rejection, its invasive nature raises concerns regarding potential functional impairment and the psychological impact on children due to repeated procedures. In this review, we outline the current research status of novel biomarkers associated with AR in urine and blood after pediatric kidney transplantation. These biomarkers exhibit superior diagnostic and prognostic performance compared to conventional ones, with the added advantages of being less invasive and highly reproducible for long-term graft monitoring. We also integrate the limitations of these novel biomarkers and propose a refined monitoring model to optimize the management of AR in pediatric kidney transplantation.

Immune monitoring in pediatric kidney transplant

BACKGROUND

Long-term outcomes in pediatric kidney transplantation remain suboptimal, largely related to chronic rejection. Creatinine is a late marker of renal injury, and more sensitive, early markers of allograft injury are an active area of current research.

METHODS

This is an educational review summarizing existing strategies for monitoring for rejection in kidney transplant recipients.

RESULTS

We summarize supporting currently available clinical tests, including surveillance biopsy, donor specific antibodies, and donor-derived cell free DNA, as well as the potential limitations of these studies. In addition, we review the current avenues of active research, including transcriptomics, proteomics, metabolomics, and torque tenovirus levels.

CONCLUSION

Advancing the use of noninvasive immune monitoring will depend on well-designed multicenter trials that include patients with stable graft function, include biopsy results on all patients, and can demonstrate both association with a patient-relevant clinical endpoint such as graft survival or change in glomerular filtration rate and a potential timepoint for intervention.

Optimizing the approach to monitoring allograft inflammation using serial urinary CXCL10/creatinine testing in pediatric kidney transplant recipients

BACKGROUND

Urinary CXCL10/creatinine (uCXCL10/Cr) is proposed as an effective biomarker of subclinical rejection in pediatric kidney transplant recipients. This study objective was to model implementation in the clinical setting.

METHODS

Banked urine samples at a single center were tested for uCXCL10/Cr to validate published thresholds for rejection diagnosis (>80% specificity). The positive predictive value (PPV) for rejection diagnosis for uCXCL10/Cr-indicated biopsy was modeled with first-positive versus two-test-positive approaches, with accounting for changes associated with urinary tract infection (UTI), BK and CMV viremia, and subsequent recovery.

RESULTS

Seventy patients aged 10.5 ± 5.6 years at transplant (60% male) had n = 726 urine samples with n = 236 associated biopsies (no rejection = 167, borderline = 51, and Banff 1A = 18). A threshold of 12 ng/mmol was validated for Banff 1A versus no-rejection diagnosis (AUC = 0.74, 95% CI = 0.57-0.92). The first-positive test approach (n = 69) did not resolve a clinical diagnosis in 38 cases (55%), whereas the two-test approach resolved a clinical diagnosis in the majority as BK (n = 17/60, 28%), CMV (n = 4/60, 7%), UTI (n = 8/60, 13%), clinical rejection (n = 5/60, 8%), and transient elevation (n = 18, 30%). In those without a resolved clinical diagnosis, PPV from biopsy for subclinical rejection is 24% and 71% (p = .017), for first-test versus two-test models, respectively. After rejection treatment, uCXCL10/Cr level changes were all concordant with change in it-score. Sustained uCXCL10/Cr after CMV and BK viremia resolution was associated with later acute rejection.

CONCLUSIONS

Urinary CXCL10/Cr reliably identifies kidney allograft inflammation. These data support a two-test approach to reliably exclude other clinically identifiable sources of inflammation, for kidney biopsy indication to rule out subclinical rejection.

Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint

Kidney transplantation is an essential medical procedure that significantly enhances the survival rates and quality of life for patients with end-stage kidney disease. However, despite advancements in immunosuppressive therapies, allograft rejection remains a leading cause of organ loss. Notably, predictions of cellular rejection processes primarily rely on biopsy analysis, which is not routinely performed due to its invasive nature. The present work evaluates if the serum proteomic fingerprint, as acquired by Fourier Transform Infrared (FTIR) spectroscopy, can predict cellular rejection processes. We analyzed 28 serum samples, corresponding to 17 without cellular rejection processes and 11 associated with cellular rejection processes, as based on biopsy analyses. The leave-one-out-cross validation procedure of a Naïve Bayes model enabled the prediction of cellular rejection processes with high sensitivity and specificity (AUC > 0.984). The serum proteomic profile was obtained in a high-throughput mode and based on a simple, rapid, and economical procedure, making it suitable for routine analyses and large-scale studies. Consequently, the current method presents a high potential to predict cellular rejection processes translatable to clinical scenarios, and that should continue to be explored.

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

Rejection markers in kidney transplantation: do new technologies help children?

Recent insights in allorecognition and graft rejection mechanisms revealed a more complex picture than originally considered, involving multiple pathways of both adaptive and innate immune response, supplied by efficient inflammatory synergies. Current pillars of transplant monitoring are serum creatinine, proteinuria, and drug blood levels, which are considered as traditional markers, due to consolidated experience, low cost, and widespread availability. The most diffuse immunological biomarkers are donor-specific antibodies, which are included in routine post-transplant monitoring in many centers, although with some reproducibility issues and interpretation difficulties. Confirmed abnormalities in these traditional biomarkers raise the suspicion for rejection and guide the indication for graft biopsy, which is still considered the gold standard for rejection monitoring. Rapidly evolving new "omic" technologies have led to the identification of several novel biomarkers, which may change the landscape of transplant monitoring should their potential be confirmed. Among them, urinary chemokines and measurement of cell-free DNA of donor origin are perhaps the most promising. However, at the moment, these approaches remain highly expensive and cost-prohibitive in most settings, with limited clinical applicability; approachable costs upon technology investments would speed their integration. In addition, transcriptomics, metabolomics, proteomics, and the study of blood and urinary extracellular vesicles have the potential for early identification of subclinical rejection with high sensitivity and specificity, good reproducibility, and for gaining predictive value in an affordable cost setting. In the near future, information derived from these new biomarkers is expected to integrate traditional tools in routine use, allowing identification of rejection prior to clinical manifestations and timely therapeutic intervention. This review will discuss traditional, novel, and invasive and non-invasive biomarkers, underlining their strengths, limitations, and present or future applications in children.

Noninvasive testing for mycophenolate exposure in children with renal transplant using urinary metabolomics

BACKGROUND

Despite the common use of mycophenolate in pediatric renal transplantation, lack of effective therapeuic drug monitoring increases uncertainty over optimal drug exposure and risk for adverse reactions. This study aims to develop a novel urine test to estimate MPA exposure based using metabolomics.

METHODS

Urine samples obtained on the same day of MPA pharmacokinetic testing from two prospective cohorts of pediatric kidney transplant recipients were assayed for 133 unique metabolites by mass spectrometry. Partial least squares (PLS) discriminate analysis was used to develop a top 10 urinary metabolite classifier that estimates MPA exposure. An independent cohort was used to test pharmacodynamic validity for allograft inflammation (urinary CXCL10 levels) and eGFR ratio (12mo/1mo eGFR) at 1 year.

RESULTS

Fifty-two urine samples from separate children (36.5% female, 12.0 ± 5.3 years at transplant) were evaluated at 1.6 ± 2.5 years post-transplant. Using all detected metabolites (n = 90), the classifier exhibited strong association with MPA AUC by principal component regression (r = 0.56, p < .001) and PLS (r = 0.75, p < .001). A practical classifier (top 10 metabolites; r = 0.64, p < .001) retained similar accuracy after cross-validation (LOOCV; r = 0.52, p < .001). When applied to an independent cohort (n = 97 patients, 1053 samples), estimated mean MPA exposure over Year 1 was inversely associated with mean urinary CXCL10:Cr (r = -0.28, 95% CI -0.45, -0.08) and exhibited a trend for association with eGFR ratio (r = 0.35, p = .07), over the same time period.

CONCLUSIONS

This urinary metabolite classifier can estimate MPA exposure and correlates with allograft inflammation. Future studies with larger samples are required to validate and evaluate its clinical application.

Metabolomic Profiling of Plasma, Urine, and Saliva of Kidney Transplantation Recipients

Kidney biopsy is commonly used to diagnose kidney transplant dysfunction after transplantation. Therefore, the development of minimally invasive and quantitative methods to evaluate kidney function in transplant recipients is necessary. Here, we used capillary electrophoresis-mass spectrometry to analyze the biofluids collected from transplant recipients with impaired (Group I, n = 31) and stable (Group S, n = 19) kidney function and from donors (Group D, n = 9). Metabolomics analyses identified and quantified 97 metabolites in plasma, 133 metabolites in urine, and 108 metabolites in saliva. Multivariate analyses revealed apparent differences in the metabolomic profiles of the three groups. In plasma samples, arginine biosynthesis and purine metabolism between the I and S Groups differed. In addition, considerable differences in metabolomic profiles were observed between samples collected from participants with T cell-mediated rejection (TCR), antibody-mediated rejection, and other kidney disorders (KD). The metabolomic profiles in the three types of biofluids showed different patterns between TCR and KD, wherein 3-indoxyl sulfate showed a significant increase in TCR consistently in both plasma and urine samples. These results suggest that each biofluid has different metabolite features to evaluate kidney function after transplantation and that 3-indoxyl sulfate could predict acute rejection.

Urinary metabolomics to develop predictors for pediatric acute kidney injury

BACKGROUND

Acute kidney injury (AKI) is characterized by an abrupt decline in glomerular filtration rate (GFR). We sought to identify separate early urinary metabolomic signatures at AKI onset (with-AKI) and prior to onset of functional impairment (pre-AKI).

METHODS

Pre-AKI (n=15), AKI (n=22), and respective controls (n=30) from two prospective PICU cohort studies provided urine samples which were analyzed by GC-MS and DI-MS mass spectrometry (193 metabolites). The cohort (n=58) was 8.7±6.4 years old and 66% male. AKI patients had longer PICU stays, higher PRISM scores, vasopressors requirement, and respiratory diagnosis and less commonly had trauma or post-operative diagnosis. Urine was collected within 2-3 days after admission and daily until day 5 or 14.

RESULTS

The metabolite classifiers for pre-AKI samples (1.5±1.1 days prior to AKI onset) had a cross-validated area under receiver operator curve (AUC)=0.93 (95%CI 0.85-1.0); with-AKI samples had an AUC=0.94 (95%CI 0.87-1.0). A parsimonious pre-AKI classifier with 13 metabolites was similarly robust (AUC=0.96, 95%CI 0.89-1.0). Both classifiers were similar and showed modest correlation of high-ranking metabolites (tau=0.47, p<0.001).

CONCLUSIONS

This exploratory study demonstrates the potential of a urine metabolite classifier to detect AKI-risk in pediatric populations earlier than the current standard of diagnosis with the need for external validation. A higher resolution version of the Graphical abstract is available as Supplementary information with inner reference to ESM for GA.

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.

Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection

Kidney transplantation is the preferred treatment for patients with end-stage kidney disease, because it prolongs survival and improves quality of life. Allograft biopsy is the gold standard for diagnosing allograft rejection. However, it is invasive and reactive, and continuous monitoring is unrealistic. Various biomarkers for diagnosing allograft rejection have been developed over the last two decades based on omics technologies to overcome these limitations. Omics technologies are based on a holistic view of the molecules that constitute an individual. They include genomics, transcriptomics, proteomics, and metabolomics. The omics approach has dramatically accelerated biomarker discovery and enhanced our understanding of multifactorial biological processes in the field of transplantation. However, clinical application of omics-based biomarkers is limited by several issues. First, no large-scale prospective randomized controlled trial has been conducted to compare omics-based biomarkers with traditional biomarkers for rejection. Second, given the variety and complexity of injuries that a kidney allograft may experience, it is likely that no single omics approach will suffice to predict rejection or outcome. Therefore, integrated methods using multiomics technologies are needed. Herein, we introduce omics technologies and review the latest literature on omics biomarkers predictive of allograft rejection in kidney transplant recipients.

Associations between the Maternal Exposome and Metabolome during Pregnancy

BACKGROUND

Maternal exposure to environmental chemicals during pregnancy can influence various maternal and offspring health parameters. Modification of maternal metabolism by environmental exposure may be an important pathway for these impacts. However, there is limited evidence regarding exposure to a wide array of chemicals and the metabolome during pregnancy.

OBJECTIVES

We investigated the relationship between the urinary exposome and metabolome during pregnancy.

METHODS

Urine samples were collected in the first and third trimesters from 1,024 pregnant women recruited in prenatal clinics in Jiangsu Province, China. The exposome was analyzed using the first trimester sample with ultra-high performance liquid chromatography-high resolution accurate mass spectrometry (UHPLC-HRMS) and inductively coupled plasma mass spectrometry. The metabolome was analyzed using the third trimester sample with UHPLC-HRMS. We evaluated associations between each of 106 exposures in the first trimester with 139 metabolites in the third trimester.

RESULTS

We identified 1,245 significant associations (p<3.39×10-6, Bonferroni correction) between chemical exposures and maternal metabolism during pregnancy. Among elements, the largest number of the significant metabolic associations were observed for magnesium, and among organic compounds, for 4-tert-octylphenol. We used exposome-metabolome associations to explore mechanisms underlying published associations between prenatal chemical exposures and offspring health outcomes. This integration of the literature with our results suggests that reported associations between 10 analytes and birth weight, gestational age, fat deposition, neurobehavioral development, immunological disorders, and hypertension may be partially mediated by metabolites associated with these exposures.

DISCUSSION

This high-dimensional analysis of the urinary exposome and metabolome identified many associations between chemical exposures and maternal metabolism during pregnancy. Integration of these associations with the literature on health outcomes of exposure suggests that environmental modulation of the maternal metabolome may play a role in the association between prenatal exposure on pregnancy and child health outcomes. https://doi.org/10.1289/EHP9745.

Urinary metabolomic profiling from spontaneous tolerant kidney transplanted recipients shows enrichment in tryptophan-derived metabolites

Background

Operational tolerance is the holy grail in solid organ transplantation. Previous reports showed that the urinary compartment of operationally tolerant recipients harbor a specific and unique profile. We hypothesized that spontaneous tolerant kidney transplanted recipients (KTR) would have a specific urinary metabolomic profile associated to operational tolerance.

Methods

We performed metabolomic profiling on urine samples from healthy volunteers, stable KTR under standard and minimal immunosuppression and spontaneous tolerant KTR using liquid chromatography in tandem with mass spectrometry. Supervised and unsupervised multivariate computational analyses were used to highlight urinary metabolomic profile and metabolite identification thanks to workflow4metabolomic platform.

Findings

The urinary metabolome was composed of approximately 2700 metabolites. Raw unsupervised clustering allowed us to separate healthy volunteers and tolerant KTR from others. We confirmed by two methods a specific urinary metabolomic signature in tolerant KTR mainly driven by kynurenic acid independent of immunosuppressive drugs, serum creatinine and gender.

Interpretation

Kynurenic acid and tryptamine enrichment allowed the identification of putative pathways and metabolites associated with operational tolerance like IDO, GRP35 and AhR and indole alkaloids.

Funding

This study was supported by the ANR, IRSRPL and CHU de Nantes.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

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 Biomarkers for Kidney Allograft Injury

The current standard of serum creatinine and biopsy to monitor allograft health has many limitations. The most significant drawback of the current standard is the lack of sensitivity and specificity to allograft injuries, which are diagnosed only after significant damage to the allograft. Thus, it is of critical need to identify a biomarker that is sensitive and specific to the early detection of allograft injuries. Urine, as the direct renal ultrafiltrate that can be obtained noninvasively, directly reflects intrarenal processes in the allograft at greater accuracy than analysis of peripheral blood. We review transcriptomic, metabolomic, genomic, and proteomic discovery-based approaches to identifying urinary biomarkers for the noninvasive detection of allograft injuries, as well as the use of urine cell-free DNA in the QSant urine assay as a sensitive surrogate for the renal allograft biopsy for rejection diagnosis.

A Canadian Study of Cisplatin Metabolomics and Nephrotoxicity (ACCENT): A Clinical Research Protocol

Background:

Cisplatin, a chemotherapy used to treat solid tumors, causes acute kidney injury (AKI), a known risk factor for chronic kidney disease and mortality. AKI diagnosis relies on biomarkers which are only measurable after kidney damage has occurred and functional impairment is apparent; this prevents timely AKI diagnosis and treatment. Metabolomics seeks to identify metabolite patterns involved in cell tissue metabolism related to disease or patient factors. The A Canadian study of Cisplatin mEtabolomics and NephroToxicity (ACCENT) team was established to harness the power of metabolomics to identify novel biomarkers that predict risk and discriminate for presence of cisplatin nephrotoxicity, so that early intervention strategies to mitigate onset and severity of AKI can be implemented.

Objective:

Describe the design and methods of the ACCENT study which aims to identify and validate metabolomic profiles in urine and serum associated with risk for cisplatin-mediated nephrotoxicity in children and adults.

Design:

Observational prospective cohort study.

Setting:

Six Canadian oncology centers (3 pediatric, 1 adult and 2 both).

Patients:

Three hundred adults and 300 children planned to receive cisplatin therapy.

Measurements:

During two cisplatin infusion cycles, serum and urine will be measured for creatinine and electrolytes to ascertain AKI. Many patient and disease variables will be collected prospectively at baseline and throughout therapy. Metabolomic analyses of serum and urine will be done using mass spectrometry. An untargeted metabolomics approach will be used to analyze serum and urine samples before and after cisplatin infusions to identify candidate biomarkers of cisplatin AKI. Candidate metabolites will be validated using an independent cohort.

Methods:

Patients will be recruited before their first cycle of cisplatin. Blood and urine will be collected at specified time points before and after cisplatin during the first infusion and an infusion later during cancer treatment. The primary outcome is AKI, defined using a traditional serum creatinine-based definition and an electrolyte abnormality-based definition. Chart review 3 months after cisplatin therapy end will be conducted to document kidney health and survival.

Limitations:

It may not be possible to adjust for all measured and unmeasured confounders when evaluating prediction of AKI using metabolite profiles. Collection of data across multiple sites will be a challenge.

Conclusions:

ACCENT is the largest study of children and adults treated with cisplatin and aims to reimagine the current model for AKI diagnoses using metabolomics. The identification of biomarkers predicting and detecting AKI in children and adults treated with cisplatin can greatly inform future clinical investigations and practices.

Emerging monitoring technologies in kidney transplantation

Non-invasive technologies to monitor kidney allograft health utilizing high-throughput assays of blood and urine specimens are emerging out of the research realm and slowly becoming part of everyday clinical practice. HLA epitope analysis and eplet mismatch score determination promise a more refined approach to the pre-transplant recipient-donor HLA matching that may lead to reduced rejection risk. High-resolution HLA typing and multiplex single antigen bead assays are identifying potential new offending HLA antibody subtypes. There is increasing recognition of the deleterious role non-HLA antibodies play in post-transplant outcomes. Donor-derived cell-free DNA detected by next-generation sequencing is a promising biomarker for kidney transplant rejection. Multi-omics techniques are shedding light on discrete genomic, transcriptomic, proteomic, and metabolomic signatures that correlate with and predict allograft outcomes. Over the next decade, a comprehensive approach to optimize kidney matching and monitor transplant recipients for acute and chronic graft dysfunction will likely involve a combination of those emerging technologies summarized in this review.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

A Noninvasive Urine Metabolome Panel as Potential Biomarkers for Diagnosis of T Cell-Mediated Renal Transplant Rejection

Acute T cell-mediated rejection (TCMR) is a major complication after renal transplantation. TCMR diagnosis is very challenging and currently depends on invasive renal biopsy and nonspecific markers such as serum creatinine. A noninvasive metabolomics panel could allow early diagnosis and improved accuracy and specificity. We report, in this study, on urine metabolome changes in renal transplant recipients diagnosed with TCMR, with a view to future metabolomics-based diagnostics in transplant medicine. We performed urine metabolomic analyses in three study groups: (1) 7 kidney transplant recipients with acute TCMR, (2) 15 kidney transplant recipients without rejection but with impaired kidney function, and (3) 6 kidney transplant recipients with stable renal function, using ¹H-nuclear magnetic resonance. Multivariate modeling of metabolites suggested a diagnostic panel where the diagnostic accuracy of each metabolite was calculated by receiver operating characteristic curve analysis. The impaired metabolic pathways associated with TCMR were identified by pathway analysis. In all, a panel of nine differential metabolites encompassing nicotinamide adenine dinucleotide, 1-methylnicotinamide, cholesterol sulfate, gamma-aminobutyric acid (GABA), nicotinic acid, nicotinamide adenine dinucleotide phosphate, proline, spermidine, and alpha-hydroxyhippuric acid were identified as novel potential metabolite biomarkers of TCMR. Proline, spermidine, and GABA had the highest area under the curve (>0.7) and were overrepresented in the TCMR group. Nicotinate and nicotinamide metabolism was the most important pathway in TCMR. These findings call for clinical validation in larger study samples and suggest that urinary metabolomics warrants future consideration as a noninvasive research tool for TCMR diagnostic innovation.

Tryptophan Metabolism via Kynurenine Pathway: Role in Solid Organ Transplantation

Solid organ transplantation is a gold standard treatment for patients suffering from an end-stage organ disease. Patient and graft survival have vastly improved during the last couple of decades; however, the field of transplantation still encounters several unique challenges, such as a shortage of transplantable organs and increasing pool of extended criteria donor (ECD) organs, which are extremely prone to ischemia-reperfusion injury (IRI), risk of graft rejection and challenges in immune regulation. Moreover, accurate and specific biomarkers, which can timely predict allograft dysfunction and/or rejection, are lacking. The essential amino acid tryptophan and, especially, its metabolites via the kynurenine pathway has been widely studied as a contributor and a therapeutic target in various diseases, such as neuropsychiatric, autoimmune disorders, allergies, infections and malignancies. The tryptophan-kynurenine pathway has also gained interest in solid organ transplantation and a variety of experimental studies investigating its role both in IRI and immune regulation after allograft implantation was first published. In this review, the current evidence regarding the role of tryptophan and its metabolites in solid organ transplantation is presented, giving insights into molecular mechanisms and into therapeutic and diagnostic/prognostic possibilities.

Targeted Urine Metabolomics for Monitoring Renal Allograft Injury and Immunosuppression in Pediatric Patients

Despite new advancements in surgical tools and therapies, exposure to immunosuppressive drugs related to non-immune and immune injuries can cause slow deterioration and premature failure of organ transplants. Diagnosis of these injuries by non-invasive urine monitoring would be a significant clinical advancement for patient management, especially in pediatric cohorts. We investigated the metabolomic profiles of biopsy matched urine samples from 310 unique kidney transplant recipients using gas chromatography-mass spectrometry (GC-MS). Focused metabolite panels were identified that could detect biopsy confirmed acute rejection with 92.9% sensitivity and 96.3% specificity (11 metabolites) and could differentiate BK viral nephritis (BKVN) from acute rejection with 88.9% sensitivity and 94.8% specificity (4 metabolites). Overall, targeted metabolomic analyses of biopsy-matched urine samples enabled the generation of refined metabolite panels that non-invasively detect graft injury phenotypes with high confidence. These urine biomarkers can be rapidly assessed for non-invasive diagnosis of specific transplant injuries, opening the window for precision transplant medicine.

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.

A urinary metabolite constellation to detect acute rejection in kidney allografts

BACKGROUND

To validate a novel method for post-transplant surveillance to detect kidney allograft rejection via a characteristic constellation of the urine metabolites alanine, citrate, lactate, and urea investigated by nuclear magnetic resonance (NMR) spectroscopy a first prospective, observational study was performed.

METHODS

Within the UMBRELLA study 986 urine specimens were collected from 109 consecutively enrolled renal transplant recipients, and metabolite constellations were analyzed. A metabolite rejection score was calculated and compared to histopathological results of corresponding indication and protocol allograft biopsies (n = 206).

FINDINGS

The metabolite constellation was found to be a useful biomarker to non-invasively detect acute allograft rejection (AUC = 0.75; 95% confidence interval (CI) 0.68-0.83; based on 46 cases and 520 control samples). Combined analysis of the metabolite rejection score and the estimated glomerular filtration rate (eGFR) at the time of urine sampling further improved the overall test performance significantly (AUC = 0.84; 95% CI 0.76-0.91; based on 42 cases and 468 controls). Regarding the time course analysis in patients without rejection episodes the test results remained well below a diagnostic threshold associated with high risk of acute rejection. In other cases, a marked increase above this threshold indicated acute allograft rejection already six to ten days before diagnostic renal biopsies were performed.

INTERPRETATION

A combination of an NMR-based urine metabolite analysis and eGFR is promising as a non-invasive test for post-transplant surveillance and to support decision making whether renal allografts need histopathological evaluation.

LC-MS-based metabolomics analysis to identify meprin-β-associated changes in kidney tissue from mice with STZ-induced type 1 diabetes and diabetic kidney injury

Meprin metalloproteases have been implicated in the pathophysiology of diabetic kidney disease (DKD). Single-nucleotide polymorphisms in the meprin-β gene have been associated with DKD in Pima Indians, a Native American ethnic group with an extremely high prevalence of DKD. In African American men with diabetes, urinary meprin excretion positively correlated with the severity of kidney injury. In mice, meprin activity decreased at the onset of diabetic kidney injury. Several studies have identified meprin targets in the kidney. However, it is not known how proteolytic processing of the targets by meprins impacts the metabolite milieu in kidneys. In the present study, global metabolomics analysis identified differentiating metabolites in kidney tissues from wild-type and meprin-β knockout mice with streptozotocin (STZ)-induced type 1 diabetes. Kidney tissues were harvested at 8 wk post-STZ and analyzed by hydrophilic interaction liquid chromatography ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Principal component analysis identified >200 peaks associated with diabetes. Meprin expression-associated metabolites with strong variable importance of projection scores were indoxyl sulfate, N-γ-l-glutamyl-l-aspartic acid, N-methyl-4-pyridone-3-carboxamide, inosine, and cis-5-decenedioic acid. N-methyl-4-pyridone-3-carboxamide has been previously implicated in kidney injury, and its isomers, 4-PY and 2-PY, are markers of peroxisome proliferation and inflammation that correlate with creatinine clearance and glucose tolerance. Meprin deficiency-associated differentiating metabolites with high variable importance of projection scores were cortisol, hydroxymethoxyphenylcarboxylic acid-O-sulfate, and isovaleryalanine. The data suggest that meprin-β activity enhances diabetic kidney injury in part by altering the metabolite balance in kidneys, favoring high levels of uremic toxins such as indoxyl sulfate and N-methyl-pyridone-carboxamide.

Non-invasive differentiation of non-rejection kidney injury from acute rejection in pediatric renal transplant recipients

Acute kidney injury (AKI) is a major concern in pediatric kidney transplant recipients, where non-alloimmune causes must be distinguished from rejection. We sought to identify a urinary metabolite signature associated with non-rejection kidney injury (NRKI) in pediatric kidney transplant recipients. Urine samples (n = 396) from 60 pediatric transplant participants were obtained at time of kidney biopsy and quantitatively assayed for 133 metabolites by mass spectrometry. Metabolite profiles were analyzed via projection on latent structures discriminant analysis. Mixed-effects regression identified laboratory and clinical predictors of NRKI and distinguished NRKI from T cell-mediated rejection (CMR), antibody-mediated rejection (AMR), and mixed CMR/AMR. Urine samples (n = 199) without rejection were split into NRKI (n = 26; ΔSCr ≥25%), pre-NRKI (n = 35; ΔSCr ≥10% and <25%), and no NRKI (n = 138; ΔSCr <10%) groups. The NRKI discriminant score (dscore) distinguished between NRKI and no NRKI (AUC = 0.86; 95% CI = 0.79-0.94), confirmed by leave-one-out cross-validation (AUC = 0.79; 95% CI = 0.68-0.89). The NRKI dscore also distinguished between NRKI and pre-NRKI (AUC = 0.82; 95% CI = 0.71-0.93). In a linear mixed-effects regression model to account for repeated measures, the NRKI dscore was independent of concurrent rejection, but there was a non-statistical trend for higher dscores with rejection severity. A second exploratory classifier developed to distinguish NRKI from clinical rejection had similar test characteristics (AUC = 0.81, 95% CI = 0.70-0.92, confirmed by LOOCV). This study demonstrates the potential of a urine metabolite classifier to detect NRKI in pediatric kidney transplant patients and non-invasively discriminate NRKI from rejection.

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.

NMR for sample quality assessment in metabolomics

The EU Framework 7 project SPIDIA was the occasion for development of NMR approaches to evaluate the impact of different pre-analytical treatments on the quality of biological samples dedicated to metabolomics. Systematic simulation of different pre-analytical procedures was performed on urine and blood serum and plasma. Here we review the key aspects of these studies that have led to the development of CEN technical specifications, to be translated into ISO/IS in the course of the EU Horizon 2020 project SPIDIA4P. Inspired by the SPIDIA results, follow-up research was performed, extending the analysis to different sample types and to the different effects of long-term storage. The latter activity was in conjunction with the local European da Vinci Biobank. These results (which partially contributed to the ANNEX of CEN/TS 16945"MOLECULAR IN VITRO DIAGNOSTIC EXAMINATIONS - SPECIFICATIONS FOR PRE-EXAMINATION PROCESSES FOR METABOLOMICS IN URINE, VENOUS BLOOD SERUM AND PLASMA") are presented in detail.

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.

The prognostic value of urinary chemokines at 6 months after pediatric kidney transplantation

Pediatric kidney transplantation is lifesaving, but long-term allograft survival is still limited by injury processes mediated by alloimmune inflammation that may otherwise be clinically silent. Chemokines associated with alloimmune inflammation may offer prognostic value early post-transplant by identifying patients at increased risk of poor graft outcomes. We conducted a single-center prospective cohort study of consecutive pediatric kidney transplant recipients (<19 years). Urinary CCL2 and CXCL10 measured at 6 months post-transplant were evaluated for association with long-term eGFR decline, allograft survival, and concomitant acute cellular rejection histology. Thirty-eight patients with a mean age of 12.4 ± 4.6 years were evaluated. Urinary CCL2 was associated with eGFR decline until 6 months (ρ -0.43; P < .01), but not at later time points. Urinary CXCL10 was associated with eGFR decline at 36 months (ρ -0.49; P < .01), risk of 50% eGFR decline (HR = 1.04; P = .02), risk of allograft loss (HR = 1.05; P = .01), borderline rejection or rejection episodes 6-12 months post-transplant (r .41; P = .02), and Banff i + t score (r .47, P < .01). CCL2 and CXCL10 were also correlated with one another (ρ 0.54; P < .01). CCL2 and CXCL10 provide differing, but complementary, information that may be useful for early non-invasive prognostic testing in pediatric kidney transplant recipients.

Evolution of renal function and urinary biomarker indicators of inflammation on serial kidney biopsies in pediatric kidney transplant recipients with and without rejection

Urinary CXCL10 and metabolites are biomarkers independently associated with TCMR. We sought to test whether these biomarkers fluctuate in association with histological severity of TCMR over short time frames. Forty-nine pairs of renal biopsies obtained 1-3 months apart from 40 pediatric renal transplant recipients were each scored for TCMR acuity score (i + t; Banff criteria). Urinary CXCL10:Cr and TCMR MDS were obtained at each biopsy and were tested for association with changes between biopsies in acuity, estimated GFR (ΔeGFR), and 12-month ΔeGFR. Sequential biopsies were obtained 1.8 ± 0.8 months apart. Biopsy 1 was usually obtained under protocol (75%), and 62% percent had evidence of TCMR. Using each biopsy pair for comparison, ΔeGFR did not predict change in acuity. By contrast, change in acuity was significantly correlated with change in urinary CXCL10:Cr (ρ 0.45, P = .003) and MDS (ρ 0.29, P = .04) between biopsies. The 12-month ΔeGFR was not predicted by TCMR acuity or CXCL10:Cr at Biopsy 2; however, an inverse correlation was seen with urinary MDS (ρ -0.35; P = .02). Changes in eGFR correlate poorly with evolving TCMR acuity on histology. Urinary biomarkers may be superior for non-invasive monitoring of rejection, including histological response to therapy, and may be prognostic for medium-term function.

Novel non-invasive biomarkers diagnostic of acute rejection in renal transplant recipients: A systematic review

BACKGROUND AND OBJECTIVES

Acute rejection is a significant complication detrimental to kidney transplant function. Current accepted means of diagnosis is percutaneous renal biopsy, a costly and invasive procedure. There is an urgent need to detect and validate non-invasive biomarkers capable of replacing the biopsy.

DESIGN, SETTING, PARTICIPANTS AND MEASUREMENTS

Comprehensive literature searches of Medline, EMBASE and Cochrane Central Register of Controlled Trials databases were performed. Eligible studies were included as per inclusion criteria and assessed for quality using the GRADE quality of evidence tool. Outcomes evaluated included biomarker diagnostic performance, number of patients/samples, mean age and gender ratio, immunosuppression regime, in addition to clinical applications of the biomarker(s) tested. PRISMA guidelines were followed. Where possible, statistical analysis of comparative performance data was performed.

RESULTS

23 studies were included in this review, including 19 adult, 3 paediatric and 1 mixed studies. A total of 2858 participants and 50 candidate non-invasive tests were identified. Sensitivity, specificity and area under the curve performance values ranged 36%-100%, 30%-100% and 0.55-0.98, respectively.

CONCLUSIONS

Although larger, more robust multi-centre validation studies are needed before non-invasive biomarkers can replace the biopsy, numerous candidate tests have demonstrated significant promise for various facets of postoperative management. Suggested uses include: ruling out patients with a low risk of acute rejection to avoid the need for biopsy, non-invasive testing where the biopsy is contraindicated and a prompt diagnosis is needed, and integration into a serial blood monitoring protocol in conjunction with serum creatinine.

Biomarkers and Pharmacogenomics in Kidney Transplantation

This review is focused on present and future biomarkers, along with pharmacogenomics used in clinical practice for kidney transplantation. It aims to highlight biomarkers that could potentially be used to improve kidney transplant early and long-term graft survival, but also potentially patient co-morbidity. Future directions for improving outcomes are discussed, which include immune tolerance and personalising immunosuppression regimens.

Practice Patterns in the Treatment and Monitoring of Acute T Cell-Mediated Kidney Graft Rejection in Canada

Background

One of the goals of the Canadian National Transplant Research Program (CNTRP) is to develop novel therapies for acute rejection that could positively affect graft outcomes with greater efficacy or less toxicity. To develop innovative management strategies for kidney graft rejection, new modalities need to be compared with current clinical practices. However, there are no standardized practices concerning the management of acute T cell-mediated rejection (TCMR).

Objectives

To describe clinicians' practice patterns in the diagnosis, treatment, and monitoring of acute TCMR in Canada.

Design

Survey.

Setting Patients/Participants

Canadian transplant nephrologists and transplant surgeons involved in the management of acute TCMR.

Methods and Measurements

We developed an anonymous, web-based survey consisting of questions related to the diagnosis, treatment, and monitoring of TCMR. The survey was disseminated on 3 occasions between June and October 2016 through the Canadian Society of Transplantation (CST) kidney group electronic mailing list.

Results

Forty-seven respondents, mostly transplant nephrologists (97%), originating from at least 18 of the 25 Canadian centers offering adult or pediatric kidney transplantation, participated in the study. Surveillance biopsies were used by 28% of respondents to screen for kidney graft rejection. High-dose steroids were used by most of the respondents to treat clinical and subclinical Banff grade 1A and 1B rejections. Nine percent (95% confidence interval [CI]: 1-17) of practitioners used lymphocyte-depleting agents as the first-line approach for the treatment of Banff grade 1B acute rejection. Eighteen percent (95% CI: 7-29) and 36% (95% CI: 8-65) of respondents reported that they would not use high-dose steroids for treating clinical and subclinical borderline rejections, respectively. Seventy percent (95% CI: 54-83) of respondents answered that there was no indication to assess histological response to treatment independent of the change in kidney function.

Limitations

The limitations of this study are its limited sample size and the low representation of pediatric specialists.

Conclusions

There is heterogeneity regarding the use of surveillance biopsies, treatment of borderline rejection, and modalities to monitor treatment response among transplant physicians. Our results illustrate the current state of practice patterns across Canada and can be used to inform the design of future trials.

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.

Effects of Shu Gan Jian Pi formula on rats with carbon tetrachloride‑induced liver fibrosis using serum metabonomics based on gas chromatography‑time of flight mass spectrometry

Liver fibrosis is a common stage in the majority of chronic liver diseases, regardless of the etiology, and its progression may lead to hepatic cirrhosis or hepatocellular carcinoma. Metabolomics, a powerful approach in systems biology, is a discipline used to qualitatively and quantitatively analyze the small molecule metabolites of cells at specific times and under certain conditions. The present study aimed to investigate serum metabolic changes following Shu Gan Jian Pi formula (SGJPF) treatment of carbon tetrachloride (CCl₄)-induced liver fibrosis in rats using gas chromatography-time of flight mass spectrometry (GC-TOFMS). In addition, the potential mechanisms were explored. Rat liver fibrosis was induced by twice-weekly subcutaneous CCl₄ injection for 12 continuous weeks. During the same period, the SGJPF group received 16.2 g/kg body weight SGJPF, diluted in water, once a day for 12 weeks. Rats in the control and model groups received oral administration of the same volume of saline solution. Serum samples from the control, model and SGJPF groups were collected after 12 weeks of treatment, and metabolic profile alterations were analyzed by GC-TOF/MS. Metabolic profile analysis indicated that clustering differed between the three groups and the following 12 metabolites were detected in the serum of all three groups: Isoleucine; L-malic acid; D-erythro-sphingosine; putrescine; malonic acid; 3,6-anhydro-D-galactose, α-ketoglutaric acid; ornithine; glucose; hippuric acid; tetrahydrocorticosterone; and fucose. The results demonstrated that SGJPF treatment mitigated the effects of CCl₄-induced liver fibrosis on biomarker levels, thus indicating that SGJPF may have a therapeutic effect on CCl₄-induced liver fibrosis in rats. The mechanism may involve the regulation of energy, amino acid, sphingolipid, cytochrome P450, glucose and water-electrolyte metabolism.

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.

The nephrologist of tomorrow: towards a kidney-omic future

Omics refers to the collective technologies used to explore the roles and relationships of the various types of molecules that make up the phenotype of an organism. Systems biology is a scientific discipline that endeavours to quantify all of the molecular elements of a biological system. Therefore, it reflects the knowledge acquired by omics in a meaningful manner by providing insights into functional pathways and regulatory networks underlying different diseases. The recent advances in biotechnological platforms and statistical tools to analyse such complex data have enabled scientists to connect the experimentally observed correlations to the underlying biochemical and pathological processes. We discuss in this review the current knowledge of different omics technologies in kidney diseases, specifically in the field of pediatric nephrology, including biomarker discovery, defining as yet unrecognized biologic therapeutic targets and linking omics to relevant standard indices and clinical outcomes. We also provide here a unique perspective on the field, taking advantage of the experience gained by the large-scale European research initiative called "Systems Biology towards Novel Chronic Kidney Disease Diagnosis and Treatment" (SysKid). Based on the integrative framework of Systems biology, SysKid demonstrated how omics are powerful yet complex tools to unravel the consequences of diabetes and hypertension on kidney function.

Changing Paradigms in the Management of Rejection in Kidney Transplantation: Evolving From Protocol-Based Care to the Era of P4 Medicine

PURPOSE OF REVIEW

P4 medicine denotes an evolving field of medicine encompassing predictive, preventive, personalized, and participatory medicine. Using the example of kidney allograft rejection because of donor-recipient incompatibility in human leukocyte antigens, this review outlines P4 medicine's relevance to the various stages of the kidney transplant cycle.

SOURCES OF INFORMATION

A search for English articles was conducted in Medline via OvidSP (up to August 18, 2016) using a combination of subject headings (MeSH) and free text in titles, abstracts, and author keywords for the concepts kidney transplantation and P4 medicine. The electronic database search was expanded further on particular subject headings.

FINDINGS

Available histocompatibility methods exemplify current applications of the predictive and preventive domains of P4 medicine in kidney transplant recipients' care. Pharmacogenomics are discussed as means to facilitate personalized immunosuppression regimens and promotion of active patient participation as a means to improve adherence.

LIMITATIONS

For simplicity, this review focuses on rejection. P4 medicine, however, should more broadly address health concerns in kidney transplant recipients, including competing outcomes such as infections, malignancies, and cardiovascular disease. This review highlights how biomarkers to evaluate these competing outcomes warrant validation and standardization prior to their incorporation into clinical practice.

IMPLICATIONS

Consideration of all 4 domains of the P4 medicine framework when caring for and/or studying kidney transplant recipients has the potential of increasing therapeutic efficiency, minimizing adverse effects, decreasing health care costs, and maximizing wellness. Technologies to gauge immune competency, immunosuppression requirements, and early/reversible immune-mediated injuries are required to optimize kidney transplant care.

Moving Biomarkers toward Clinical Implementation in Kidney Transplantation

Long-term kidney transplant outcomes remain suboptimal, delineating an unmet medical need. Although current immunosuppressive therapy in kidney transplant recipients is effective, dosing is conventionally adjusted empirically on the basis of time after transplant or altered in response to detection of kidney dysfunction, histologic evidence of allograft damage, or infection. Such strategies tend to detect allograft rejection after significant injury has already occurred, fail to detect chronic subclinical inflammation that can negatively affect graft survival, and ignore specific risks and immune mechanisms that differentially contribute to allograft damage among transplant recipients. Assays and biomarkers that reliably quantify and/or predict the risk of allograft injury have the potential to overcome these deficits and thereby, aid clinicians in optimizing immunosuppressive regimens. Herein, we review the data on candidate biomarkers that we contend have the highest potential to become clinically useful surrogates in kidney transplant recipients, including functional T cell assays, urinary gene and protein assays, peripheral blood cell gene expression profiles, and allograft gene expression profiles. We identify barriers to clinical biomarker adoption in the transplant field and suggest strategies for moving biomarker-based individualization of transplant care from a research hypothesis to clinical implementation.

An overview of renal metabolomics

The high-throughput, high-resolution phenotyping enabled by metabolomics has been applied increasingly to a variety of questions in nephrology research. This article provides an overview of current metabolomics methodologies and nomenclature, citing specific considerations in sample preparation, metabolite measurement, and data analysis that investigators should understand when examining the literature or designing a study. Furthermore, we review several notable findings that have emerged in the literature that both highlight some of the limitations of current profiling approaches, as well as outline specific strengths unique to metabolomics. More specifically, we review data on the following: (i) tryptophan metabolites and chronic kidney disease onset, illustrating the interpretation of metabolite data in the context of established biochemical pathways; (ii) trimethylamine-N-oxide and cardiovascular disease in chronic kidney disease, illustrating the integration of exogenous and endogenous inputs to the blood metabolome; and (iii) renal mitochondrial function in diabetic kidney disease and acute kidney injury, illustrating the potential for rapid translation of metabolite data for diagnostic or therapeutic aims. Finally, we review future directions, including the need to better characterize interperson and intraperson variation in the metabolome, pool existing data sets to identify the most robust signals, and capitalize on the discovery potential of emerging nontargeted methods.

Biomarkers in Transplantation--Proteomics and Metabolomics

Modern multianalyte "omics" technologies allow for the identification of molecular signatures that confer significantly more information than measurement of a single parameter as typically used in current medical diagnostics. Proteomics and metabolomics bioanalytical assays capture a large set of proteins and metabolites in body fluids, cells, or tissues and, complementing genomics, assess the phenome. Proteomics and metabolomics contribute to the development of novel predictive clinical biomarkers in transplantation in 2 ways: they can be used to generate a diagnostic fingerprint or they can be used to discover individual proteins and metabolites of diagnostic potential. Much fewer metabolomics than proteomics biomarker studies in transplant patients have been reported, and, in contrast to proteomics discovery studies, new lead metabolite markers have yet to emerge. Most clinical proteomics studies have been discovery studies. Several of these studies have assessed diagnostic sensitivity and specificity. Nevertheless, none of these newly discovered protein biomarkers have yet been implemented in clinical decision making in transplantation. The currently most advanced markers discovered in proteomics studies in transplant patients are the chemokines CXCL-9 and CXCL-10, which have successfully been validated in larger multicenter trials in kidney transplant patients. These chemokines can be measured using standard immunoassay platforms, which should facilitate clinical implementation. Based on the published evidence, it is reasonable to expect that these chemokine markers can help guiding and individualizing immunosuppressive regimens, may be able to predict acute and chronic T-cell-mediated and antibody-mediated rejection, and may be useful tools for risk stratification of kidney transplant patients.