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

Early Metabolomic Profiling as a Predictor of Renal Function Six Months After Kidney Transplantation

BACKGROUND

Kidney transplantation is the therapy of choice for patients with advanced chronic kidney disease; however, predicting graft outcomes remains a significant challenge. Early identification of reliable biomarkers could enhance post-transplant management and improve long-term outcomes. This study aimed to identify metabolomic biomarkers within the first week after kidney transplantation that predict renal function at six months.

METHODS

We conducted a prospective study involving 50 adult patients who received deceased donor kidney transplants. Plasma samples collected one week after transplant were analyzed using liquid chromatography-mass spectrometry in a semi-targeted metabolomic approach. A Partial Least Squares-Discriminant Analysis (PLS-DA) model identified metabolites associated with serum creatinine > 1.5 mg/dL at six months. Metabolites were selected based on a Variable Importance in Projection (VIP) score > 1.5, which was used to optimize model performance.

RESULTS

The PLS-DA model demonstrated strong predictive performance with an area under the curve (AUC) of 0.958. The metabolites negatively associated with serum creatinine > 1.5 mg/dL were 3-methylindole, guaiacol, histidine, 3-indolepropionic acid, and α-lipoic acid. Conversely, the metabolites positively associated with worse kidney graft outcomes included homocarnosine, 5-methylcytosine, xanthosine, choline, phenylalanine, kynurenic acid, and L-kynurenine.

CONCLUSIONS

Early metabolomic profiling after transplantation shows promise in predicting renal function. Identifying metabolites with antioxidant and anti-inflammatory properties, as well as those that are harmful and could be targeted therapeutically, underscores their potential clinical significance. The link between several metabolites and the tryptophan pathway suggests that further specific evaluation of this pathway is warranted. These biomarkers can enhance patient management and graft survival.

Perspective: metabolomics has the potential to change the landscape of kidney transplantation diagnostics

Kidney transplantation is the most efficient renal replacement therapy. Current diagnostics for monitoring graft health are either invasive or lack precision. Metabolomics is an emerging discipline focused on the analysis of the small molecules involved in metabolism. Given the kidneys' central role in metabolic homeostasis and previous observations of altered metabolites correlating with restricted kidney graft function, metabolomics is highly promising for the discovery of novel biomarkers and the development of novel diagnostics. In this perspective, we summarize the known metabolic roles for the kidney, discuss biomarkers of graft health and immune status emerging from metabolomics research, and provide our perspective on how these and future findings can be integrated in clinical practice to enable precision diagnostics.

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

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.

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.

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.

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.

Antibody-mediated rejection after kidney transplantation in children; therapy challenges and future potential treatments

Antibody‐mediated rejection (AMR) remains one of the most critical problems in renal transplantation, with a significant impact on patient and graft survival. In the United States, no treatment has received FDA approval jet. Studies about treatments of AMR remain controversial, limited by the absence of a gold standard and the difficulty in creating large, multi‐center studies. These limitations emerge even more in pediatric transplantation because of the limited number of pediatric studies and the occasional use of some therapies with unknown and poorly documented side effects. The lack of recommendations and the unsharp definition of different forms of AMR contribute to the challenging management of the therapy by pediatric nephrologists. In an attempt to help clinicians involved in the care of renal transplanted children affected by an AMR, we rely on the latest recommendations of the Transplantation Society (TTS) for the classification and treatment of AMR to describe treatments available today and potential new treatments with a particular focus on the pediatric population.

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.

A Prospective Multicenter Trial to Evaluate Urinary Metabolomics for Non-invasive Detection of Renal Allograft Rejection (PARASOL): Study Protocol and Patient Recruitment

Background: In an earlier monocentric study, we have developed a novel non-invasive test system for the prediction of renal allograft rejection, based on the detection of a specific urine metabolite constellation. To further validate our results in a large real-world patient cohort, we designed a multicentric observational prospective study (PARASOL) including six independent European transplant centers. This article describes the study protocol and characteristics of recruited better patients as subjects. Methods: Within the PARASOL study, urine samples were taken from renal transplant recipients when kidney biopsies were performed. According to the Banff classification, urine samples were assigned to a case group (renal allograft rejection), a control group (normal renal histology), or an additional group (kidney damage other than rejection). Results: Between June 2017 and March 2020, 972 transplant recipients were included in the trial (1,230 urine samples and matched biopsies, respectively). Overall, 237 samples (19.3%) were assigned to the case group, 541 (44.0%) to the control group, and 452 (36.7%) samples to the additional group. About 65.9% were obtained from male patients, the mean age of transplant recipients participating in the study was 53.7 ± 13.8 years. The most frequently used immunosuppressive drugs were tacrolimus (92.8%), mycophenolate mofetil (88.0%), and steroids (79.3%). Antihypertensives and antidiabetics were used in 88.0 and 27.4% of the patients, respectively. Approximately 20.9% of patients showed the presence of circulating donor-specific anti-HLA IgG antibodies at time of biopsy. Most of the samples (51.1%) were collected within the first 6 months after transplantation, 48.0% were protocol biopsies, followed by event-driven (43.6%), and follow-up biopsies (8.5%). Over time the proportion of biopsies classified into the categories Banff 4 (T-cell-mediated rejection [TCMR]) and Banff 1 (normal tissue) decreased whereas Banff 2 (antibody-mediated rejection [ABMR]) and Banff 5I (mild interstitial fibrosis and tubular atrophy) increased to 84.2 and 74.5%, respectively, after 4 years post transplantation. Patients with rejection showed worse kidney function than patients without rejection. Conclusion: The clinical characteristics of subjects recruited indicate a patient cohort typical for routine renal transplantation all over Europe. A typical shift from T-cellular early rejections episodes to later antibody mediated allograft damage over time after renal transplantation further strengthens the usefulness of our cohort for the evaluation of novel biomarkers for allograft damage.

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.

Design and Methods of the Validating Injury to the Renal Transplant Using Urinary Signatures (VIRTUUS) Study in Children

Lack of noninvasive diagnostic and prognostic biomarkers to reliably detect early allograft injury poses a major hindrance to long-term allograft survival in pediatric kidney transplant recipients.

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.

Tackling Chronic Kidney Transplant Rejection: Challenges and Promises

Despite advances in post-transplant management, the long-term survival rate of kidney grafts and patients has not improved as approximately forty percent of transplants fails within ten years after transplantation. Both immunologic and non-immunologic factors contribute to late allograft loss. Chronic kidney transplant rejection (CKTR) is often clinically silent yet progressive allogeneic immune process that leads to cumulative graft injury, deterioration of graft function. Chronic active T cell mediated rejection (TCMR) and chronic active antibody-mediated rejection (ABMR) are classified as two principal subtypes of CKTR. While significant improvements have been made towards a better understanding of cellular and molecular mechanisms and diagnostic classifications of CKTR, lack of early detection, differential diagnosis and effective therapies continue to pose major challenges for long-term management. Recent development of high throughput cellular and molecular biotechnologies has allowed rapid development of new biomarkers associated with chronic renal injury, which not only provide insight into pathogenesis of chronic rejection but also allow for early detection. In parallel, several novel therapeutic strategies have emerged which may hold great promise for improvement of long-term graft and patient survival. With a brief overview of current understanding of pathogenesis, standard diagnosis and challenges in the context of CKTR, this mini-review aims to provide updates and insights into the latest development of promising novel biomarkers for diagnosis and novel therapeutic interventions to prevent and treat CKTR.

Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review

OBJECTIVE

This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making.

BACKGROUND

Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants.

METHODS

PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods.

RESULTS

Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses.

CONCLUSIONS

Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.

Urinary Biomarkers for Diagnosis and Prediction of Acute Kidney Allograft Rejection: A Systematic Review

Noninvasive tools for diagnosis or prediction of acute kidney allograft rejection have been extensively investigated in recent years. Biochemical and molecular analyses of blood and urine provide a liquid biopsy that could offer new possibilities for rejection prevention, monitoring, and therefore, treatment. Nevertheless, these tools are not yet available for routine use in clinical practice. In this systematic review, MEDLINE was searched for articles assessing urinary biomarkers for diagnosis or prediction of kidney allograft acute rejection published in the last five years (from 1 January 2015 to 31 May 2020). This review follows the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Articles providing targeted or unbiased urine sample analysis for the diagnosis or prediction of both acute cellular and antibody-mediated kidney allograft rejection were included, analyzed, and graded for methodological quality with a particular focus on study design and diagnostic test accuracy measures. Urinary C-X-C motif chemokine ligands were the most promising and frequently studied biomarkers. The combination of precise diagnostic reference in training sets with accurate validation in real-life cohorts provided the most relevant results and exciting groundwork for future studies.

Advances in Detection of Kidney Transplant Injury

Early detection of graft injury after kidney transplantation is key to maintaining long-term good graft function. Graft injury could be due to a multitude of factors including ischaemia reperfusion injury, cell or antibody-mediated rejection, progressive interstitial fibrosis and tubular atrophy, infections and toxicity from the immunosuppressive drugs themselves. The current gold standard for assessing renal graft dysfunction is renal biopsy. However, biopsy is usually late when triggered by a change in serum creatinine and of limited utility in diagnosis of early injury when histological changes are equivocal. Therefore, there is a need for timely, objective and non-invasive diagnostic techniques with good early predictive value to determine graft injury and provide precision in titrating immunosuppression. We review potential novel plasma and urine biomarkers that offer sensitive new strategies for early detection and provide major insights into mechanisms of graft injury. This is a rapidly expanding field, but it is likely that a combination of biomarkers will be required to provide adequate sensitivity and specificity for detecting graft injury.

Combining the advantages of multilevel and orthogonal partial least squares data analysis for longitudinal metabolomics: Application to kidney transplantation

Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a "normal" renal function and possibly also predict rejection. The longitudinal follow-up of those patients with repeated measurements is useful to understand changes and decide whether an intervention is necessary. The time modality, therefore, constitutes a specific dimension in the data structure, requiring dedicated consideration for proper statistical analysis. The handling of specific data structures in metabolomics has received strong interest in recent years. In this work, we demonstrated the recently developed ANOVA multiblock OPLS (AMOPLS) to efficiently analyse longitudinal metabolomic data by considering the intrinsic experimental design. Indeed, AMOPLS combines the advantages of multilevel approaches and OPLS by separating between and within individual variations using dedicated predictive components, while removing most uncorrelated variations in the orthogonal component(s), thus facilitating interpretation. This modelling approach was applied to a clinical cohort study aiming to evaluate the impact of kidney transplantation over time on the plasma metabolic profile of graft patients and donor volunteers. A dataset of 266 plasma metabolites was identified using an LC-MS multiplatform analytical setup. Two separate AMOPLS models were computed: one for the recipient group and one for the donor group. The results highlighted the benefits of transplantation for recipients and the relatively low impacts on blood metabolites of donor volunteers.

Development and validation of a peripheral blood mRNA assay for the assessment of antibody-mediated kidney allograft rejection: A multicentre, prospective study

Background

Antibody-mediated rejection, a leading cause of renal allograft graft failure, is diagnosed by histological assessment of invasive allograft biopsies. Accurate non-invasive biomarkers are not available.

Methods

In the multicentre, prospective BIOMARGIN study, blood samples were prospectively collected at time of renal allograft biopsies between June 2011 and August 2016 and analyzed in three phases. The discovery and derivation phases of the study (N = 117 and N = 183 respectively) followed a case-control design and included whole genome transcriptomics and targeted mRNA expression analysis to construct and lock a multigene model. The primary end point was the diagnostic accuracy of the locked multigene assay for antibody-mediated rejection in a third validation cohort of serially collected blood samples (N = 387). This trial is registered with ClinicalTrials.gov, number NCT02832661.

Findings

We identified and locked an 8-gene assay (CXCL10, FCGR1A, FCGR1B, GBP1, GBP4, IL15, KLRC1, TIMP1) in blood samples from the discovery and derivation phases for discrimination between cases with (N = 49) and without (N = 134) antibody-mediated rejection. In the validation cohort, this 8-gene assay discriminated between cases with (N = 41) and without antibody-mediated rejection (N = 346) with good diagnostic accuracy (ROC AUC 79·9%; 95% CI 72·6 to 87·2, p < 0·0001). The diagnostic accuracy of the 8-gene assay was retained both at time of stable graft function and of graft dysfunction, within the first year and also later after transplantation. The 8-gene assay is correlated with microvascular inflammation and transplant glomerulopathy, but not with the histological lesions of T-cell mediated rejection.

Interpretation

We identified and validated a novel 8-gene expression assay that can be used for non-invasive diagnosis of antibody-mediated rejection.

Funding

The Seventh Framework Programme (FP7) of the European Commission.

Follicular Helper T Cell Derived Exosomes Promote B Cell Proliferation and Differentiation in Antibody-Mediated Rejection after Renal Transplantation

Follicular helper T cells (Tfh cells) are closely related to the occurrence and development of antibody-mediated rejection (AMR) after renal transplantation. Exosomes play a key role in the rejection after organ transplantation. However, whether Tfh-derived exosomes are involved in AMR has not been reported. We collected peripheral blood from 42 kidney transplant patients and found no significant differences in CD4+CXCR5+ and CD4+CXCR5+CXCR3+CCR6-exosomes between AMR and non-AMR groups, whereas the proportion of CD4+CXCR5+CXCR3-exosomes was significantly higher in AMR group than that in non-AMR group; CTLA-4 expression of CD4+CXCR5+exosomes was significantly lower in AMR group than that in non-AMR group. HLA-G expression was not significantly different between two groups. We further separated CD4+CXCR5+cells from patients by magnetic beads. Coculture experiments showed that Tfh cell-derived exosomes in AMR patients significantly promoted B cell proliferation and differentiation, compared with non-AMR group, the percentage of B cells and plasma cells increased by 87.52% and 110.2%, respectively. In conclusion, our study found that Tfh cell-derived exosomes could promote the proliferation and differentiation of B cells and they may play an important role in the development of AMR after renal transplantation.

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.

An Improved Method for Measuring Absolute Metabolite Concentrations in Small Biofluid or Tissue Samples

The measurement of absolute metabolite concentrations in small samples remains a significant analytical challenge. This is particularly the case when the sample volume is only a few microliters or less and cannot be determined accurately via direct measurement. We previously developed volume determination with two standards (VDTS) as a method to address this challenge for biofluids. As a proof-of-principle, we applied VDTS to NMR spectra of polar metabolites in the hemolymph (blood) of the tiny yet powerful genetic model Drosophila melanogaster. This showed that VDTS calculation of absolute metabolite concentrations in fed versus starved Drosophila larvae is more accurate than methods utilizing normalization to total spectral signal. Here, we introduce paired VDTS (pVDTS), an improved VDTS method for biofluids and solid tissues that implements the statistical power of paired control and experimental replicates. pVDTS utilizes new equations that also include a correction for dilution errors introduced by the variable surface wetness of solid samples. We then show that metabolite concentrations in Drosophila larvae are more precisely determined and logically consistent using pVDTS than using the original VDTS method. The refined pVDTS workflow described in this study is applicable to a wide range of different tissues and biofluids.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

Defining the phenotype of antibody-mediated rejection in kidney transplantation: Advances in diagnosis of antibody injury

The diagnostic criteria for antibody-mediated rejection (ABMR) are constantly evolving in light of the evidence. Inclusion of C4d-negative ABMR has been one of the major advances in the Banff Classification in recent years. Currently Banff 2015 classification requires evidence of donor specific antibodies (DSA), interaction between DSA and the endothelium, and acute tissue injury (in the form of microvasculature injury (MVI); acute thrombotic microangiopathy; or acute tubular injury in the absence of other apparent cause). In this article we review not only the ABMR phenotypes acknowledged in the most recent Banff classification, but also the phenotypes related to novel pathogenic antibodies (non-HLA DSA, antibody isoforms and subclasses, complement-binding functionality) and molecular diagnostic tools (gene transcripts, metabolites, small proteins, cytokines, and donor-derived cell-free DNA). These novel tools are also being considered for the prognosis and monitoring of treatment response. We propose that improved classification of ABMR based on underlying pathogenic mechanisms and outcomes will be an important step in identifying patient-centered therapies to extend graft survival.