Allograft biopsies: studying them for all they're worth

Metabolomic Profiling in Individuals with a Failing Kidney Allograft

BACKGROUND

Alteration of certain metabolites may play a role in the pathophysiology of renal allograft disease.

METHODS

To explore metabolomic abnormalities in individuals with a failing kidney allograft, we analyzed by liquid chromatography-mass spectrometry (LC-MS/MS; for ex vivo profiling of serum and urine) and two dimensional correlated spectroscopy (2D COSY; for in vivo study of the kidney graft) 40 subjects with varying degrees of chronic allograft dysfunction stratified by tertiles of glomerular filtration rate (GFR; T1, T2, T3). Ten healthy non-allograft individuals were chosen as controls.

RESULTS

LC-MS/MS analysis revealed a dose-response association between GFR and serum concentration of tryptophan, glutamine, dimethylarginine isomers (asymmetric [A]DMA and symmetric [S]DMA) and short-chain acylcarnitines (C4 and C12), (test for trend: T1-T3 = p<0.05; p = 0.01; p<0.001; p = 0.01; p = 0.01; p<0.05, respectively). The same association was found between GFR and urinary levels of histidine, DOPA, dopamine, carnosine, SDMA and ADMA (test for trend: T1-T3 = p<0.05; p<0.01; p = 0.001; p<0.05; p = 0.001; p<0.001; p<0.01, respectively). In vivo 2D COSY of the kidney allograft revealed significant reduction in the parenchymal content of choline, creatine, taurine and threonine (all: p<0.05) in individuals with lower GFR levels.

CONCLUSIONS

We report an association between renal function and altered metabolomic profile in renal transplant individuals with different degrees of kidney graft function.

Post-transplant nuclear renal scans correlate with renal injury biomarkers and early allograft outcomes

BACKGROUND

Clinical- and histopathology-based scores are limited predictors of allograft outcome. In addition, more objective markers of early transplant function are needed to identify and validate biomarkers and predictive scores. We evaluated existing scores and transcriptome biomarkers of kidney injury as predictors of early transplant function measured by renal scan.

METHODS

Clinical, histopathologic and transcriptome data were collected in 143 consecutive kidney transplant recipients. A post-operative renal scan was performed within 48 h. Prediction scores for early outcomes were calculated.

RESULTS

Patients were stratified into three groups by renal scan: normal, mild-to-moderate or severe dysfunction. Kidneys with severe dysfunction were more often from deceased donors (P < 0.001), had greater HLA antigen mismatches (P < 0.001), were transplanted into older recipients (P = 0.040), had lower urine output during the first 8 h (P < 0.001), higher Day 7 serum creatinine (P < 0.001) and higher incidence of delayed graft function (P < 0.001). Clinical- and pathology-based scores did not discriminate between scan groups. In contrast, the overall transcriptome (P < 0.001) and transcripts of preselected acute kidney injury (AKI) genes were significantly different between the groups, with kidney injury molecule 1 (P = 0.001) and neutrophil gelatinase-associated lipocalin (P = 0.002) being most highly expressed and genes associated with glutathione metabolism (GSTA1, 3 and 4) most down-regulated in kidneys with subsequent severe dysfunction.

CONCLUSIONS

Renal scans reflect early transplant function and allow for a more objective assessment of scores predicting early outcome and for identification of biomarkers. The study shows that transcript levels of AKI genes correlate better with renal scans than clinical- or histopathology-based scores.

Assessment of kidney organ quality and prediction of outcome at time of transplantation

The critical importance of donor organ quality, i.e., number of surviving nephrons, ability to withstand injury, and capacity for repair in determining short- and long-term outcomes is becoming increasingly clear. This review provides an overview of studies to assess donor kidney quality and subsequent transplant outcomes based on clinical pathology and transcriptome-based variables available at time of transplantation. Prediction scores using clinical variables function when applied to large data sets but perform poorly for the individual patient. Histopathology findings in pre-implantation or post-reperfusion biopsies help to assess structural integrity of the donor kidney, provide information on pre-existing donor disease, and can serve as a baseline for tracking changes over time. However, more validated approaches of analysis and prospective studies are needed to reduce the number of discarded organs, improve allocation, and allow prediction of outcomes. Molecular profiling detects changes not seen by morphology or captured by clinical markers. In particular, molecular profiles provide a quantitative measurement of inflammatory burden or immune activation and reflect coordinated changes in pathways associated with injury and repair. However, description of transcriptome patterns is not an end in itself. The identification of predictive gene sets and the application to an individualized patient management needs the integration of clinical and pathology-based variables, as well as more objective reference markers of transplant function, post-transplant events, and long-term outcomes.