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

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.

Consistency of metabolite associations with measured glomerular filtration rate in children and adults

Background

There is interest in identifying novel filtration markers that lead to more accurate GFR estimates than current markers (creatinine and cystatin C) and are more consistent across demographic groups. We hypothesize that large-scale metabolomics can identify serum metabolites that are strongly influenced by glomerular filtration rate (GFR) and are more consistent across demographic variables than creatinine, which would be promising filtration markers for future investigation.

Methods

We evaluated the consistency of associations between measured GFR (mGFR) and 887 common, known metabolites quantified by an untargeted chromatography- and spectroscopy-based metabolomics platform (Metabolon) performed on frozen blood samples from 580 participants in Chronic Kidney Disease in Children (CKiD), 674 participants in Modification of Diet in Renal Disease (MDRD) Study and 962 participants in African American Study of Kidney Disease and Hypertension (AASK). We evaluated metabolite-mGFR correlation association with metabolite class, molecular weight, assay platform and measurement coefficient of variation (CV). Among metabolites with strong negative correlations with mGFR (r < -0.5), we assessed additional variation by age (height in children), sex, race and body mass index (BMI).

Results

A total of 561 metabolites (63%) were negatively correlated with mGFR. Correlations with mGFR were highly consistent across study, sex, race and BMI categories (correlation of metabolite-mGFR correlations between 0.88 and 0.95). Amino acids, carbohydrates and nucleotides were more often negatively correlated with mGFR compared with lipids, but there was no association with metabolite molecular weight, liquid chromatography/mass spectrometry platform and measurement CV. Among 114 metabolites with strong negative associations with mGFR (r < -0.5), 27 were consistently not associated with age (height in children), sex or race.

Conclusions

The majority of metabolite-mGFR correlations were negative and consistent across sex, race, BMI and study. Metabolites with consistent strong negative correlations with mGFR and non-association with demographic variables may represent candidate markers to improve estimation of GFR.

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.

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.

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.

Association Between Graft Function and Urine CXCL10 and Acylcarnitines Levels in Kidney Transplant Recipients

OBJECTIVE

To evaluate post-transplantation graft functions noninvasively by using urine C-X-C motif chemokine 10 (CXCL10) and metabolome analysis.

METHODS

The 65 living-donor kidney-transplant recipients in our cohort underwent renal biopsy to investigate possible graft dysfunction. The patients were divided into 2 groups, according to pathology reports: chronic allograft dysfunction (CAD; n = 18) and antibody-mediated/humoral allograft rejection (AMR; n = 16). The control group was composed of renal transplant recipients with stable health (n = 33). We performed serum creatinine, blood urea nitrogen (BUN), cystatin C, urine protein, CXCL10, and metabolome analyses on specimens from the patients.

RESULTS

BUN, creatinine, cystatin C, urine protein, leucine + isoleucine, citrulline, and free/acetyl/propionyl carnitine levels were significantly higher in patients with CAD and AMR, compared with the control individuals. CXCL10 levels were significantly elevated in patients with AMR, compared with patients with CAD and controls. CXCL10 (AUC = 0.771) and cystatin C (AUC = 0.746) were significantly higher in the AMR group, compared with the CAD group (P<.02).

CONCLUSIONS

CXCL10 and metabolome analyzes are useful for evaluation of graft functions. Also, CXCL10 might be useful as a supplementary noninvasive screening test for diagnosis of allograft rejection.

Peritransplant Cardiometabolic and Mitochondrial Function: The Missing Piece in Donor Heart Dysfunction and Graft Failure

Primary graft dysfunction is an important cause of morbidity and mortality after cardiac transplantation. Donor brain stem death (BSD) is a significant contributor to donor heart dysfunction and primary graft dysfunction. There remain substantial gaps in the mechanistic understanding of peritransplant cardiac dysfunction. One of these gaps is cardiac metabolism and metabolic function. The healthy heart is an "omnivore," capable of utilizing multiple sources of nutrients to fuel its enormous energetic demand. When this fails, metabolic inflexibility leads to myocardial dysfunction. Data have hinted at metabolic disturbance in the BSD donor and subsequent heart transplantation; however, there is limited evidence demonstrating specific metabolic or mitochondrial dysfunction. This review will examine the literature surrounding cardiometabolic and mitochondrial function in the BSD donor, organ preservation, and subsequent cardiac transplantation. A more comprehensive understanding of this subject may then help to identify important cardioprotective strategies to improve the number and quality of donor hearts.

Yield and utility of surveillance kidney biopsies in pediatric kidney transplant recipients at various time points post-transplant

BACKGROUND

Due to a lack of consensus on SB for pediatric kidney transplant recipients, we evaluated the yield and clinical utility of SB findings at various time points post-transplant.

METHODS

Patients transplanted at a single institution between 2014 and 2020 with at least one SB at 1.5, 3, 6, 12, and 24 months post-transplant were included. Additional biopsies were done for indication (IB). TCMR was classified by Banff criteria (score ≥i1t1).

RESULTS

Forty-seven patients had 142 biopsies (SB = 113, IB = 29); 19 (40.4%) of whom experienced at least one TCMR episode in the first-year post-transplant. The greatest SB yield of any pathologic abnormality was at 6 months (57.1%; P < .001). Six months also had the highest yield for TCMR (42.9%), compared with 3.3%, 20.8%, 15.0%, and 9.1% at 1.5, 3, 12 months, and 24 months, respectively (P = .003). SB instigated intensification of immunosuppression (28.3% cases), reduction of immunosuppression (2.7% cases), and other non-immunosuppressant changes (1.8% cases). The 6-month SB led to the greatest number of changes in management (53.6%), compared with 1.5, 3, 12, and 24 months (13.3, 20.8, 25.0, and 36.4%, respectively; P = .012). There were no major biopsy-related complications.

CONCLUSIONS

SBs identify an important burden of subclinical rejection and other pathology leading to changes in clinical management. The greatest yield was at 6 months, whereas the least utility was at the 1.5 months. Selection of SB timing may be tailored such that the optimal yield is balanced against the procedural risk.

Noninvasive biomarkers in monitoring kidney allograft health

PURPOSE OF REVIEW

A key aspect of posttransplant management is to identify and treat graft injury before it becomes irreversible. The gold-standard for detection is histology, but biopsy is uncomfortable for the patient and carries a risk of complications. Detection of changes at a molecular level may preempt histological injury, and thereby identify injury earlier.

RECENT FINDINGS

Indicators of immune system activation, such as candidate chemokines CXCL9 and CXCL10, and by-products of neutrophil activity, have been related to acute rejection and early allograft function. Transcriptomic studies of multiple-gene panels have identified candidate combinations that have proven very promising in risk-stratification and prediction of acute rejection, as well as diagnosis of both T-cell-mediated and antibody-mediated rejection. Serum and urine cell-free DNA is also a promising area of investigation, particularly in antibody-mediated rejection.

SUMMARY

Noninvasive, rapid, and accurate tests for risk-prediction and diagnosis in renal transplant allografts are urgently required. The ideal candidate is one that can be measured in either urine or blood, is cheap, and is both sensitive and specific for the condition of interest. Numerous strategies have been proposed, with varying degrees of clinical and preclinical success. A few that meet the essential criteria have been evaluated; a few have made it as far as clinical testing.

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.

Metabolomic identification of novel diagnostic biomarkers in ectopic pregnancy

INTRODUCTION

Ectopic pregnancy (EP) is a potentially life-threatening condition and early diagnosis still remains a challenge, causing a delay in management leading to tubal rupture.

OBJECTIVES

To identify putative plasma biomarkers for the detection of tubal EP and elucidate altered biochemical pathways in EP compared to intrauterine pregnancies.

METHODS

This case-control study included prospective recruitment of 39 tubal EP cases and 89 early intrauterine pregnancy controls. Plasma samples were biochemically profiled using proton nuclear magnetic resonance spectroscopy (¹H NMR). To avoid over-fitting, datasets were randomly divided into a discovery group (26 cases vs 60 controls) and a test group (13 cases and 29 controls). Logistic regression models were developed in the discovery group and validated in the independent test group. Area under the receiver operating characteristics curve (AUC), 95% confidence interval (CI), sensitivity, and specificity values were calculated.

RESULTS

In total 13 of 43 (30.3%) metabolite concentrations were significantly altered in EP plasma (p < 0.05). Metabolomic profiling yielded significant separation between EP and controls (p < 0.05). Independent validation of a two-metabolite model consisting of lactate and acetate, achieved an AUC (95% CI) = 0.935 (0.843-1.000) with a sensitivity of 92.3% and specificity of 96.6%. The second metabolite model (D-glucose, pyruvate, acetoacetate) performed well with an AUC (95% CI) = 0.822 (0.657-0.988) and a sensitivity of 84.6% and specificity of 86.2%.

CONCLUSION

We report novel metabolomic biomarkers with a high accuracy for the detection of EP. Accurate biomarkers could potentially result in improved early diagnosis of tubal EP cases.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

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.