Deep analysis of total serum N-glycome suggests glyco-signatures for phospholipase A2 receptor 1-related idiopathic membranous nephropathy diagnosis.

Idiopathic membranous nephropathy (IMN) is an antibody-mediated and kidney-specific autoimmune disease, with the antigen phospholipase A2 receptor 1 (PLA2R1) accounting for approximately 70% of IMN cases. Although a variety of new podocyte target antigens and their autoantibodies have been identified, they are still of limited diagnostic and therapeutic value due to lack of high specificity and sensitivity. N-glycans play vital roles in renal system and their pathobiological relevance has become increasingly recognized in many kidney diseases, but not fully explored in IMN. To find possible glyco-signatures for PLA2R1-related IMN diagnosis, we herein established a comprehensive workflow for total serum N-glycome analysis based on our recently developed mass spectrometry (MS)-based N-glycan purification method, named Ultrafast Glycoprotein Immobilization for Glycan extraction (UltraGIG). A total of 191 N-glycans were identified from IMN patients, representing the largest N-glycome dataset in IMN. Compared to healthy controls, up-regulation of sialylation and core-fucosylation as well as down-regulation of galactosylation were observed in PLA2R1-positive IMN patients, and up-regulation of hyper-galactosylation was specific for PLA2R1-negative IMN patients. A six-glycan marker panel consisting of H4N3S1, H4N3F1, H6N4S2, H6H5F1S2, H6N5 and H6N6F1S1, was proposed to aid in the accurate diagnosis of PLA2R1-related IMN, which provided new insights into IMN biomarker study. SIGNIFICANCE: PLA2R1-related IMN is a kidney-specific autoimmune disease with a high risk of developing end-stage renal disease (ESRD) and even kidney failure. Current biomarkers are still of limited diagnostic and therapeutic value due to lack of high specificity and sensitivity. An in-depth MS analysis of total serum N-glycome of PLA2R1-related IMN patients was conducted for the first time. We generated the largest dataset of serum N-glycome for IMN to date, and proposed a novel six-glycan marker panel that may help the accurate diagnosis of PLA2R1-related IMN.

Significance of the total renal chronicity score in predicting renal outcome in PLA2R-associated membranous nephropathy.

BACKGROUND: Phospholipase A2 receptor (PLA2R)-associated membranous nephropathy accounts for the majority of membranous nephropathy; however, few studies have determined the prognostic impact and clinical application of renal pathologic change on this disease. METHODS: A retrospective cohort study of 262 patients with PLA2R-associated membranous nephropathy was conducted. The total renal chronicity score calculated according to the degree of glomerulosclerosis, interstitial fibrosis, tubular atrophy, and arteriosclerosis was applied to evaluate renal chronicity. Baseline bias was adjusted by inverse probability weight when assessing the prognostic impact of chronicity, and multiple parameters were used to evaluate the application value of renal chronicity. RESULTS: During a median follow-up of 24.5 months, renal outcome (kidney function deterioration and/or end-stage kidney disease) was observed in 22 (8.40%) patients. Not only did a higher total renal chronicity score independently predict renal outcome [odds ratio (OR): 1.562, 95% confidence interval (CI) 1.073-2.273, P = 0.020], but non-minimal chronicity was also an independent risk factor for renal outcome (OR: 3.170, 95% CI 1.040-9.659, P = 0.042). Moreover, the membranous nephropathy risk classification in the Kidney Disease: Improving Global Outcomes (KDIGO) guideline integrated with non-minimal chronicity showed improvements in categorical net reclassification (0.174, 95% CI 0.012-0.335, P = 0.035), continuous net reclassification (0.462, 95% CI 0.087-0.838, P = 0.016), and integrated discrimination (0.019, 95% CI 0.003-0.035, P = 0.020) compared to the original classification. CONCLUSIONS: Renal chronicity is closely associated with renal outcomes in PLA2R-associated membranous nephropathy, and combining the KDIGO risk classification with chronicity scores may provide a more accurate prognostic prediction.

An online-predictive model of acute kidney injury after pancreatic surgery.

OBJECTIVE: Acute kidney injury(AKI) after pancreatic surgery is associated with increased mortality, longer hospital stays and poor prognosis. This study aims to identify the risk factors and establish an easy-to-use prediction calculator by the nomogram to predict the risk of AKI after pancreatic surgery. METHODS: From January 2016 to June 2018, 1504 patients who underwent pancreatic surgery in our center were included in this retrospective analysis and randomly assigned to primary (1054 patients) and validation (450 patients) cohorts. The independent risk factors of AKI were identified using univariate and multivariate analyses. A risk-predicted nomogram for AKI was developed through multivariate logistic regression analysis in the primary cohort while the nomogram was evaluated in the validation cohort. Nomogram discrimination and calibration were assessed using C-index and calibration curves in the primary and validation cohorts. The clinical utility of the final nomogram was evaluated using decision curve analysis. RESULTS: The overall incidence of AKI after pancreatic surgery was 5.3% (79/1504). Independent risk factors including smoking history, cardiovascular disease, ASA score, baseline eGFR, bilirubin＞2 â€‹mg/dL, undergoing pancreaticoduodenectomy, and intraoperative blood loss＞400 â€‹mL were identified by multivariate analysis. Nomogram revealed moderate discrimination and calibration in estimating the risk of AKI, with an unadjusted C-index of 0.79 (95 %CI, 0.73-0.85). Application of the nomogram in the validation cohort provided moderate discrimination (C-index,0.80 [95% CI, 0.72-0.88]) and good calibration. Besides, the decision curve analysis (DCA) confirmed the clinical usefulness of the nomogram. CONCLUSIONS: An easy-to-use online prediction calculator comprised of preoperative and intraoperative factors was able to individually predict the occurrence risk of AKI among patients with pancreatic surgery, which may help identify reasonable risk judgments and develop proper treatment strategies to a certain extent.

Mass spectrometry-based N-glycosylation analysis in kidney disease.

Kidney disease is a global health concern with an enormous expense. It is estimated that more than 10% of the population worldwide is affected by kidney disease and millions of patients would progress to death prematurely and unnecessarily. Although creatinine detection and renal biopsy are well-established tools for kidney disease diagnosis, they are limited by several inevitable defects. Therefore, diagnostic tools need to be upgraded, especially for the early stage of the disease and possible progression. As one of the most common post-translational modifications of proteins, N-glycosylation plays a vital role in renal structure and function. Deepening research on N-glycosylation in kidney disease provides new insights into the pathophysiology and paves the way for clinical application. In this study, we reviewed recent N-glycosylation studies on several kidney diseases. We also summarized the development of mass spectrometric methods in the field of N-glycoproteomics and N-glycomics.

In-depth profiling of urinary N-glycome in diabetic kidney disease by ultrafast glycoprotein immobilization for glycan extraction (UltraGIG).

Diabetic kidney disease (DKD) is a common and serious kidney-related complication of diabetic mellitus. Although albuminuria and estimated glomerular filtration rate (eGFR) are commonly used diagnostic biomarkers, they have limitations in the diagnosis of DKD due to the lack of specificity and sensitivity. Urinary N-glycans are emerging as novel biomarkers for predicting renal prognosis in DKD. However, most of the current N-glycan profiling methods for DKD were based on lectin affinity enrichment or hydrophilic interaction chromatography (HILIC) with optical detection, which provided limited N-glycome coverage in low throughput. Herein, we developed a novel N-glycan purification method, termed Ultrafast Glycoprotein Immobilization for Glycan extraction (UltraGIG), for in-depth profiling of urinary N-glycome in DKD using mass spectrometry (MS). In UltraGIG, proteins were rapidly (within 40 min) immobilized to resin with high efficiency (over 98%) through NHS-reactive chemistry, and then N-glycans were enzymatically released from the resin by PNGase F. Owing to good efficiency of immobilization of proteins to resin, the subsequent washing steps to remove proteins and other impurities could be easily performed. The UltraGIG showed good selectivity towards glycans (extracting N-glycans from the mixture of IgG and BSA at a 1:100 M ratio) and samples loss was minimized (detection sensitivity at fmol level). A total of 237 N-glycan compositions were identified from urine samples with DKD and healthy controls, which representing the largest data set of N-glycome from DKD. Compared to healthy controls, 3 N-glycans were up-regulated and 14 N-glycans were down-regulated in DKD patients. Collectively, UltraGIG offers a competitive sample purification method for in-depth analysis of urinary N-glycome by MS and provide new insights into biomarker study for the diagnosis of DKD.