Assessment of Physiological Rat Kidney Ageing—Implications for the Evaluation of Allograft Quality Prior to Renal Transplantation

Due to organ shortage and rising life expectancy the age of organ donors and recipients is increasing. Reliable biomarkers of organ quality that predict successful long-term transplantation outcomes are poorly defined. The aim of this study was the identification of age-related markers of kidney function that might accurately reflect donor organ quality. Histomorphometric, biochemical and molecular parameters were measured in young (3-month-old) and old (24-month-old) male Sprague Dawley rats. In addition to conventional methods, we used urine metabolomics by NMR spectroscopy and gene expression analysis by quantitative RT-PCR to identify markers of ageing relevant to allograft survival. Beside known markers of kidney ageing like albuminuria, changes in the concentration of urine metabolites such as trimethylamine-N-oxide, trigonelline, 2-oxoglutarate, citrate, hippurate, glutamine, acetoacetate, valine and 1-methyl-histidine were identified in association with ageing. In addition, expression of several genes of the toll-like receptor (TLR) pathway, known for their implication in inflammaging, were upregulated in the kidneys of old rats. This study led to the identification of age-related markers of biological allograft age potentially relevant for allograft survival in the future. Among those, urine metabolites and markers of immunity and inflammation, which are highly relevant to immunosuppression in transplant recipients, are promising and deserve further investigation in humans.

A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease

Identification of chronic kidney disease patients at risk of progressing to end-stage renal disease (ESRD) is essential for treatment decision-making and clinical trial design. Here, we explored whether proton nuclear magnetic resonance (NMR) spectroscopy of blood plasma improves the currently best performing kidney failure risk equation, the so-called Tangri score. Our study cohort comprised 4640 participants from the German Chronic Kidney Disease (GCKD) study, of whom 185 (3.99%) progressed over a mean observation time of 3.70 ± 0.88 years to ESRD requiring either dialysis or transplantation. The original four-variable Tangri risk equation yielded a C statistic of 0.863 (95% CI, 0.831-0.900). Upon inclusion of NMR features by state-of-the-art machine learning methods, the C statistic improved to 0.875 (95% CI, 0.850-0.911), thereby outperforming the Tangri score in 94 out of 100 subsampling rounds. Of the 24 NMR features included in the model, creatinine, high-density lipoprotein, valine, acetyl groups of glycoproteins, and Ca²⁺-EDTA carried the highest weights. In conclusion, proton NMR-based plasma fingerprinting improved markedly the detection of patients at risk of developing ESRD, thus enabling enhanced patient treatment.

Quantification of Metabolites by NMR Spectroscopy in the Presence of Protein

The high reliability of NMR spectroscopy makes it an ideal tool for large-scale metabolomic studies. However, the complexity of biofluids and, in particular, the presence of macromolecules poses a significant challenge. Ultrafiltration and protein precipitation are established means of deproteinization and recovery of free or total metabolite content, but neither is ever complete. In addition, aside from cost and labor, all deproteinization methods constitute an additional source of experimental variation. The Carr-Purcell-Meiboom-Gill (CPMG) echo-train acquisition of NMR spectra obviates the need for prior deproteinization by attenuating signals from macromolecules, but concentration values of metabolites measured in blood plasma will not necessarily reflect total or free metabolite content. Here, in contrast to approaches that propose the determination of individual T₁ and T₂ relaxation times for the computation of correction factors, we demonstrate their determination by spike-in experiments with known amounts of metabolites in pooled samples of the matrix of interest to facilitate the measurement of total metabolite content. Provided that the protein content does not vary too much among individual samples, accurate quantitation of metabolites is feasible. Moreover, samples with significantly deviating protein content may be readily recognized by inclusion of a standard that shows moderate protein binding. It is also shown that urinary proteins when present in high concentrations may effect detection of common urinary metabolites prone to strong protein binding such as tryptophan.

Data Normalization of (1)H NMR Metabolite Fingerprinting Data Sets in the Presence of Unbalanced Metabolite Regulation

Data normalization is an essential step in NMR-based metabolomics. Conducted properly, it improves data quality and removes unwanted biases. The choice of the appropriate normalization method is critical and depends on the inherent properties of the data set in question. In particular, the presence of unbalanced metabolic regulation, where the different specimens and cohorts under investigation do not contain approximately equal shares of up- and down-regulated features, may strongly influence data normalization. Here, we demonstrate the suitability of the Shapiro-Wilk test to detect such unbalanced regulation. Next, employing a Latin-square design consisting of eight metabolites spiked into a urine specimen at eight different known concentrations, we show that commonly used normalization and scaling methods fail to retrieve true metabolite concentrations in the presence of increasing amounts of glucose added to simulate unbalanced regulation. However, by learning the normalization parameters on a subset of nonregulated features only, Linear Baseline Normalization, Probabilistic Quotient Normalization, and Variance Stabilization Normalization were found to account well for different dilutions of the samples without distorting the true spike-in levels even in the presence of marked unbalanced metabolic regulation. Finally, the methods described were applied successfully to a real world example of unbalanced regulation, namely, a set of plasma specimens collected from patients with and without acute kidney injury after cardiac surgery with cardiopulmonary bypass use.