Specific in situ inflammatory states associate with progression to renal failure in lupus nephritis

Abstract

BACKGROUND. In human lupus nephritis (LN), tubulointerstitial inflammation (TII) on biopsy predicts progression to endstage renal disease (ESRD). However, only about half of patients with moderate-to-severe TII develop ESRD. We hypothesized that this heterogeneity in outcome reflects different underlying inflammatory states. Therefore, we interrogated renal biopsies from LN longitudinal and cross-sectional cohorts.METHODS. Data were acquired using conventional and highly multiplexed confocal microscopy. To accurately segment cells across whole biopsies, and to understand their spatial relationships, we developed computational pipelines by training and implementing several deep-learning models and other computer vision techniques.RESULTS. High B cell densities were associated with protection from ESRD. In contrast, high densities of CD8 + , γδ, and other CD4 -CD8 -T cells were associated with both acute renal failure and progression to ESRD. B cells were often organized into large periglomerular neighborhoods with Tfh cells, while CD4 -T cells formed small neighborhoods in the tubulointerstitium, with frequency that predicted progression to ESRD.CONCLUSION. These data reveal that specific in situ inflammatory states are associated with refractory and progressive renal disease.

Altered Circulating Follicular T Helper Cell Subsets and Follicular T Regulatory Cells Are Indicators of a Derailed B Cell Response in Lupus, Which Could Be Modified by Targeting IL-21R

“…Blood T FH 17-like cells seem to share many features with genuine Th17 cells, including their IL-17 and IL-21 cytokine, RORγt transcription factor, as well as CCR6 chemokine receptor expression. There are several investigations that observed the recruitment of circulating CCR6 + Th17 or cT FH cells in response to chemokine ligand production in kidney tissues [ 55 , 56 , 57 , 58 , 59 ]. In a murine lupus model, the inhibition of a multifunctional serine/threonine kinase, namely, calcium/calmodulin-dependent protein kinase IV (CaMK4), led to the decrease in CCR6 and CCL20 expression, as well as to the amelioration of glomerular injury, which highlighted the role of the CCR6/CCL20 axis in SLE [ 60 ].…”

Anthocyanin improves kidney function in diabetic kidney disease by regulating amino acid metabolism

“…Black soybean peel (Anhui, China) was prepared as raw materials for C3G. The crude extraction of anthocyanin was according to the methods as described in previous research [ 15 ]. Following, C3G was seperated and purified from the crude extraction by ultra performance liquid chromatography-tandem mass spectrometry.…”

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

“…Machine learning can also be used to evaluate histologic findings [28–31,32 ▪▪ ,33]. As with MR image analysis, the use of the machine learning paradigm to evaluate histology could lead to better standardization of diagnoses, and, additionally, allow for the recognition or incorporation of features that were previously not considered in lupus nephritis or other lupus tissue pathologies.…”

“…Many of the recent studies using machine learning to evaluate lupus nephritis kidney biopsies focused on automating and thereby standardizing the classification of features from glomerular lesions biopsy [28–31]. However, tubulointerstitial inflammation, not glomerular inflammation, is a known predictor for end-stage renal disease (ESRD), but not all patients with this feature progress [32 ▪▪ ]. As such, Abraham et al [32 ▪▪ ] employed confocal microscopy and machine learning to determine whether cellular compositions (T, B, myeloid dendritic, and plasmacytoid dendritic cells) in kidney regions could predict progression to ESRD.…”

“…However, tubulointerstitial inflammation, not glomerular inflammation, is a known predictor for end-stage renal disease (ESRD), but not all patients with this feature progress [32 ▪▪ ]. As such, Abraham et al [32 ▪▪ ] employed confocal microscopy and machine learning to determine whether cellular compositions (T, B, myeloid dendritic, and plasmacytoid dendritic cells) in kidney regions could predict progression to ESRD. The machine learning algorithm was built to identify and classify the immune cell types, and then these frequencies were correlated with clinical features.…”