Karyomegalic interstitial nephritis in a renal allograft.

Karyomegalic interstitial nephritis (KIN) is a rare renal interstitial disease entity characterized by large tubular nuclei, accompanied by interstitial inflammation, tubular atrophy, and interstitial fibrosis. Approximately 50 cases of KIN have been described in the native kidney. In this case study, we describe the first case of KIN in a kidney allograft. A 41-year-old man presented with declining kidney function and a serum creatinine of 2.7 mg/dL. The native kidney biopsy showed large pleomorphic nuclei in the proximal and distal tubular epithelial cells, which was associated with interstitial inflammation, and extensive interstitial fibrosis and tubular atrophy. Immunohistochemistry for cytomegalovirus, adenovirus, and simian virus 40 were negative. A diagnosis of KIN was rendered. The patient received a living-related kidney transplant from his sister. At 4-, 12-, and 24-months posttransplant, protocol allograft biopsies showed KIN with large pleomorphic nuclei in the proximal and distal tubules with mild interstitial inflammation, minimal tubular atrophy, and interstitial fibrosis. At 24.7 months of follow-up, the patient has stable renal function with a serum creatinine of 1.6 mg/dL. The KIN may represent recurrent KIN or donor-associated KIN. Recognition of this rare disease entity is important as it can be mistaken for a viral infection.

Tree species richness predicted using a spatial environmental model including forest area and frost frequency, eastern USA.

Assessing geographic patterns of species richness is essential to develop biological conservation as well as to understand the processes that shape these patterns. We aim to improve geographic prediction of tree species richness (TSR) across eastern USA by using: 1) gridded point-sample data rather than spatially generalized range maps for the TSR outcome variable, 2) new predictor variables (forest area FA; mean frost day frequency MFDF) and 3) regression models that account for spatial autocorrelation. TSR was estimated in 50 km by 50 km grids using Forest Inventory and Analysis (FIA) point-sample data. Eighteen environmental predictor variables were employed, with the most effective set selected by a LASSO that reduced multicollinearity. Those predictors were then employed in Generalized linear models (GLMs), and in Eigenvector spatial filtering (ESF) models that accounted for spatial autocorrelation. Models were evaluated by model fit statistics, spatial patterns of TSR predictions, and spatial autocorrelation. Our results showed gridded TSR was best-predicted by the ESF model that used, in descending order of influence: precipitation seasonality, mean precipitation in the driest quarter, FA, and MFDF. ESF models, by accounting for spatial autocorrelation, outperformed GLMs regardless of the predictors employed, as indicated by percent deviance explained and spatial autocorrelation of residuals. Small regions with low TSR, such as the Midwest prairie peninsula, were successfully predicted by ESF models, but not by GLMs or other studies. Gridded TSR in Florida was only correctly predicted by the ESF model with FA and MFDF, and was over-predicted by all other models.

Coding variants in nephrin (NPHS1) and susceptibility to nephropathy in African Americans.

BACKGROUND AND OBJECTIVES: Presumed genetic risk for diabetic and nondiabetic end stage renal disease is strong in African Americans. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Exome sequencing data from African Americans with type 2 diabetic end stage renal disease and nondiabetic, non-nephropathy controls in the T2D-GENES study (Discovery, n=529 patients and n=535 controls) were evaluated, focusing on missense variants in NPHS1. Associated variants were then evaluated in independent type 2 diabetic end stage renal disease (Replication, n=1305 patients and n=760 controls), nondiabetic end stage renal disease (n=1705), and type 2 diabetes-only, non-nephropathy samples (n=503). All participants were recruited from dialysis facilities and internal medicine clinics across the southeastern United States from 1991 to present. Additional NPHS1 missense variants were identified from exome sequencing resources, genotyped, and sequence kernel association testing was then performed. RESULTS: Initial analysis identified rs35238405 (T233A; minor allele frequency=0.0096) as associated with type 2 diabetic end stage renal disease (adjustment for admixture P=0.042; adjustment for admixture+APOL1 P=0.080; odds ratio, 2.89 and 2.36, respectively); with replication in independent type 2 diabetic end stage renal disease samples (P=0.018; odds ratio, 4.30) and nondiabetic end stage renal disease samples (P=0.016; odds ratio, 4.48). In a combined analysis (all patients with end stage renal disease versus all controls), T233A was associated with all-cause end stage renal disease (P=0.0038; odds ratio, 2.82; n=3270 patients and n=1187 controls). A P-value of <0.001 was obtained after adjustment for admixture and APOL1 in sequence kernel association testing. Two additional variants (H800R and Y1174H) were nominally associated with protection from end stage renal disease (P=0.036; odds ratio, 0.44; P=0.0084; odds ratio, 0.040, respectively) in the locus-wide single-variant association tests. CONCLUSIONS: Coding variants in NPHS1 are associated with both risk for and protection from common forms of nephropathy in African Americans.

Phospholipase A2 receptor staining in pediatric idiopathic membranous glomerulopathy.

BACKGROUND: Membranous glomerulopathy, though typically a disease of adults, does occur in children. Antiphospholipase A2 receptor (PLA2R) autoantibodies have recently been implicated as a causative agent in most cases of adult primary (idiopathic) membranous glomerulopathy. PLA2R staining of renal biopsies in two recent large case series of adults with primary membranous glomerulopathy showed a sensitivity of approximately 75 % for detecting primary membranous glomerulopathy. To our knowledge, this is the largest study of its kind to assess PLA2R staining in a pediatric population. METHODS: Forty-one consecutive cases of pediatric membranous glomerulopathy were identified from our database, and clinical follow-up was performed to confirm primary membranous glomerulopathy. Twenty-two patients met inclusion criteria and are the subject of this report. RESULTS: Granular, capillary loop immunofluorescence staining for immunoglobulin G (IgG) was present in 100 % of patients, and C3 staining was present in 77 %. PLA2R staining was identified in ten patients, providing a sensitivity of 45 % [confidence interval (CI) 25-67 %]. Bovine serum albumin staining was performed in all PLA2R-negative cases and showed no positivity. Morphologic findings associated with negative PLA2R staining included segmental membranous lesions, mesangial and subendothelial deposits, C1q and "full-house" staining, and lower-stage lesions by electron microscopy. At 38 months' average follow-up, all patients were still considered as having primary membranous glomerulopathy, with none developing a clinically detectable secondary etiology. CONCLUSIONS: PLA2R staining sensitivity is much lower in the pediatric than the adult primary membranous glomerulopathy population. This finding suggests a more diverse and currently incompletely described set of etiologies for this disease in this group.

An assessment of the optimal scale for monitoring of MODIS and FIA NPP across the eastern USA.

Robust monitoring of carbon sequestration by forests requires the use of multiple data sources analyzed at a common scale. To that end, model-based Moderate Resolution Imaging Spectroradiometer (MODIS) and field-based Forest Inventory and Analysis (FIA) data of net primary productivity (NPP) were compared at increasing levels of spatial aggregation across the eastern USA. A total of 52,167 FIA plots and colocated MODIS forest cover NPP pixels were analyzed using a hexagonal tiling system. A protocol was developed to assess the optimal scale as an optimal size of landscape patches at which to map spatially explicit estimates of MODIS and FIA NPP. The optimal mapping resolution (hereafter referred to as optimal scale) is determined using spatially scaled z-statistics as the tradeoff between increased spatial agreement as measured by Pearson's correlation coefficient and decreased details of coverage as measured by the number of hexagons. Spatial sensitivity was also assessed using land cover assessment and forest homogeneity using spatially scaled z-statistics. Pearson correlations indicate that MODIS and FIA NPP are most highly correlated when using large hexagons, while z-statistics indicate an optimal scale at an intermediate hexagon size of 390 km(2). This optimal scale had more spatial detail than was obtained for larger hexagons and greater spatial agreement than was obtained for smaller hexagons. The z-statistics for land cover assessment and forest homogeneity also indicated an optimal scale of 390 km(2).