Exacerbating effects of circadian rhythm disruption on the systemic lupus erythematosus.

OBJECTIVE: Circadian rhythm disruption (CRD) has been associated with inflammation and immune disorders, but its role in SLE progression is unclear. We aimed to investigate the impact of circadian rhythms on immune function and inflammation and their contribution to SLE progression to lupus nephritis (LN). METHODS: This study retrospectively analysed the clinical characteristics and transcriptional profiles of 373 samples using bioinformatics and machine-learning methods. A flare risk score (FRS) was established to predict overall disease progression for patients with lupus. Mendelian randomisation was used to analyse the causal relationship between CRD and SLE progression. RESULTS: Abnormalities in the circadian pathway were detected in patients with SLE, and lower enrichment levels suggested a disease state (normalised enrichment score=0.6714, p=0.0062). The disruption of circadian rhythms was found to be closely linked to lupus flares, with the FRS showing a strong ability to predict disease progression (area under the curve (AUC) of 5-year prediction: 0.76). The accuracy of disease prediction was improved by using a prognostic nomogram based on FRS (AUC=0.77). Additionally, Mendelian randomisation analysis revealed an inverse causal relationship between CRD and SLE (OR 0.6284 (95% CI 0.3630 to 1.0881), p=0.0485) and a positive causal relationship with glomerular disorders (OR 0.0337 (95% CI 1.634e-3 to 6.934e-1), p=0.0280). CONCLUSION: Our study reveals that genetic characteristics arising from CRD can serve as biomarkers for predicting the exacerbation of SLE. This highlights the crucial impact of CRD on the progression of lupus.

Mesenteric lymph nodes: a critical site for the up-regulatory effect of hUC-MSCs on Treg cells by producing TGF-ß1 in colitis treatment.

BACKGROUND: Mesenchymal stem cells (MSCs) demonstrate a wide range of therapeutic capabilities in the treatment of inflammatory bowel disease (IBD). The intraperitoneal injection of MSCs has exhibited superior therapeutic efficacy on IBD than intravenous injection. Nevertheless, the precise in vivo distribution of MSCs and their biological consequences following intraperitoneal injection remain inadequately understood. Additional studies are required to explore the correlation between MSCs distribution and their biological effects. METHODS: First, the distribution of human umbilical cord MSCs (hUC-MSCs) and the numbers of Treg and Th17 cells in mesenteric lymph nodes (MLNs) were analyzed after intraperitoneal injection of hUC-MSCs. Subsequently, the investigation focused on the levels of transforming growth factor beta1 (TGF-ß1), a key cytokine to the biology of both Treg and Th17 cells, in tissues of mice with colitis, particularly in MLNs. The study also delved into the impact of hUC-MSCs therapy on Treg cell counts in MLNs, as well as the consequence of TGFB1 knockdown hUC-MSCs on the differentiation of Treg cells and the treatment of IBD. RESULTS: The therapeutic effectiveness of intraperitoneally administered hUC-MSCs in the treatment of colitis was found to be significant, which was closely related to their quick migration to MLNs and secretion of TGF-ß1. The abundance of hUC-MSCs in MLNs of colitis mice is much higher than that in other organs even the inflamed sites of colon. Intraperitoneal injection of hUC-MSCs led to a significant increase in the number of Treg cells and a decrease in Th17 cells especially in MLNs. Furthermore, the concentration of TGF-ß1, the key cytokine for Treg differentiation, were also found to be significantly elevated in MLNs after hUC-MSCs treatment. Knockdown of TGFB1 in hUC-MSCs resulted in a noticeable reduction of Treg cells in MLNs and the eventually failure of hUC-MSCs therapy in colitis. CONCLUSIONS: MLNs may be a critical site for the regulatory effect of hUC-MSCs on Treg/Th17 cells and the therapeutic effect on colitis. TGF-ß1 derived from hUC-MSCs promotes local Treg differentiation in MLNs. This study will provide new ideas for the development of MSC-based therapeutic strategies in IBD patients.

Deep Learning-Based Model Significantly Improves Diagnostic Performance for Assessing Renal Histopathology in Lupus Glomerulonephritis.

Background: Assessment of glomerular lesions and structures plays an essential role in understanding the pathological diagnosis of glomerulonephritis and prognostic evaluation of many kidney diseases. Renal pathophysiological assessment requires novel high-throughput tools to conduct quantitative, unbiased, and reproducible analyses representing a central readout. Deep learning may be an effective tool for glomerulonephritis pathological analysis. Methods: We developed a murine renal pathological system (MRPS) model to objectify the pathological evaluation via the deep learning method on whole-slide image (WSI) segmentation and feature extraction. A convolutional neural network model was used for accurate segmentation of glomeruli and glomerular cells of periodic acid-Schiff-stained kidney tissue from healthy and lupus nephritis mice. To achieve a quantitative evaluation, we subsequently filtered five independent predictors as image biomarkers from all features and developed a formula for the scoring model. Results: Perimeter, shape factor, minimum internal diameter, minimum caliper diameter, and number of objects were identified as independent predictors and were included in the establishment of the MRPS. The MRPS showed a positive correlation with renal score (r = 0.480, p < 0.001) and obtained great diagnostic performance in discriminating different score bands (Obuchowski index, 0.842 [95% confidence interval: 0.759, 0.925]), with an area under the curve of 0.78-0.98, sensitivity of 58-93%, specificity of 72-100%, and accuracy of 74-94%. Conclusion: Our MRPS for quantitative assessment of renal WSIs from MRL/lpr lupus nephritis mice enables accurate histopathological analyses with high reproducibility, which may serve as a useful tool for glomerulonephritis diagnosis and prognosis evaluation.