Clinical significance of proteinuria at posttransplant year 1 in kidney transplantation

A Partial Picture of the Single-Cell Transcriptomics of Human IgA Nephropathy

The molecular mechanisms underlying renal damage of IgA nephropathy (IgAN) remain incompletely defined. Here, single-cell RNA sequencing (scRNA-seq) was applied to kidney biopsies from IgAN and control subjects to define the transcriptomic landscape at single-cell resolution. We presented a comprehensive scRNA-seq analysis of human renal biopsies from IgAN. We showed for the first time that IgAN mesangial cells displayed increased expression of several novel genes including MALAT1, GADD45B, SOX4, and EDIL3, which were related to cell proliferation and matrix accumulation. The overexpressed genes in tubule cells of IgAN were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling, and NOD-like receptor signaling. Furthermore, we compared the results of 4 IgAN patients with the published scRNA-Seq data of healthy kidney tissues of three human donors in order to further validate the findings in our study. The results also verified that the overexpressed genes in tubule cells from IgAN patients were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling, and NOD-like receptor signaling. The receptor-ligand crosstalk analysis revealed potential interactions between mesangial cells and other cells in IgAN. IgAN patients with overt proteinuria displayed elevated genes participating in several signaling pathways compared with microproteinuria group. It needs to be mentioned that based on number of mesangial cells and other kidney cells analyzed in this study, the results of our study are preliminary and needs to be confirmed on larger number of cells from larger number of patients and controls in future studies. Therefore, these results offer new insight into pathogenesis and identify new therapeutic targets for IgAN.

Albuminuria in kidney transplant recipients is associated with increased urinary serine proteases and activation of the epithelial sodium channel

Albuminuria predicts adverse renal outcome in kidney transplant recipients. The present study addressed the hypothesis that albuminuria is associated with increased urine serine proteases with the ability to activate the epithelial sodium channel (ENaC) and with greater extracellular volume and higher blood pressure. In a cross-sectional design, kidney transplant recipients with ( n = 18) and without ( n = 19) albuminuria were included for office blood pressure measurements, estimation of volume status by bioimpedance, and collection of spot urine and plasma samples. Urine was analyzed for serine proteases and for the ability to activate ENaC current in vitro. Urine exosome protein was immunoblotted for prostasin and γ-ENaC protein. In the present study, it was found that, compared with nonalbuminuria (8.8 mg/g creatinine), albuminuric (1,722 mg/g creatinine) kidney transplant recipients had a higher systolic and diastolic blood pressure, despite receiving significantly more antihypertensives, and a greater urinary total plasminogen, active plasmin, active urokinase-type plasminogen activator, and prostasin protein abundance, which correlated significantly with u-albumin. Fluid overload correlated with systolic blood pressure, urinary albumin/creatinine, and plasminogen/creatinine. Urine from albuminuric kidney transplant recipients evoked a greater amiloride- and aprotinin-sensitive inward current in single collecting duct cells (murine cell line M1). γENaC subunits at 50 and 75 kDa showed increased abundance in urine exosomes from albuminuric kidney transplant recipients when compared with controls. These findings show that albuminuria in kidney transplant recipients is associated with hypertension, ability of urine to proteolytically activate ENaC current, and increased abundance of γENaC. ENaC activity could contribute to hypertension and adverse outcome in posttransplant proteinuria.

Derivation of a Predictive Model for Graft Loss Following Acute Kidney Injury in Kidney Transplant Recipients

Background:

Acute kidney injury (AKI) is common in the kidney transplant population.

Objective:

To derive a multivariable survival model that predicts time to graft loss following AKI.

Design:

Retrospective cohort study using health care administrative and laboratory databases.

Setting:

Southwestern Ontario (1999-2013) and Ottawa, Ontario, Canada (1996-2013).

Patients:

We included first-time kidney only transplant recipients who had a hospitalization with AKI 6 months or greater following transplant.

Measurements:

AKI was defined using the Acute Kidney Injury Network criteria (stage 1 or greater). The first episode of AKI was included in the analysis. Graft loss was defined by return to dialysis or repeat kidney transplant.

Methods:

We performed a competing risk survival regression analysis using the Fine and Gray method and modified the model into a simple point system. Graft loss with death as a competing event was the primary outcome of interest.

Results:

A total of 315 kidney transplant recipients who had a hospitalization with AKI 6 months or greater following transplant were included. The median (interquartile range) follow-up time was 6.7 (3.3-10.3) years. Graft loss occurred in 27.6% of the cohort. The final model included 6 variables associated with an increased risk of graft loss: younger age, increased severity of AKI, failure to recover from AKI, lower baseline estimated glomerular filtration rate, increased time from kidney transplant to AKI admission, and receipt of a kidney from a deceased donor. The risk score had a concordance probability of 0.75 (95% confidence interval [CI], 0.69-0.82). The predicted 5-year risk of graft loss fell within the 95% CI of the observed risk more than 95% of the time.

Limitations:

The CIs of the estimates were wide, and model overfitting is possible due to the limited sample size; the risk score requires validation to determine its clinical utility.

Conclusions:

Our prognostic risk score uses commonly available information to predict the risk of graft loss in kidney transplant patients hospitalized with AKI. If validated, this predictive model will allow clinicians to identify high-risk patients who may benefit from closer follow-up or targeted enrollment in future intervention trials designed to improve outcomes.