WNT16 as a promising biomarker for systemic lupus erythematosus and its role in regulating cell proliferation and apoptosis.

OBJECTIVES: To investigate the expression and function of WNT16, a member of the WNT family protein, in the context of systemic lupus erythematosus (SLE). METHODS: WNT16 expression was assessed in peripheral blood mononuclear cells (PBMCs) from 35 SLE patients and 25 healthy individuals using quantitative polymerase chain reaction. Additionally, serum WNT16 protein levels were quantified via enzyme-linked immunosorbent assay in 162 SLE patients, 96 healthy controls (HC), and disease controls comprised 154 individuals with rheumatoid arthritis (RA) and Sjögren's syndrome (SS). We investigated the associations between WNT16 protein levels and clinical manifestations, laboratory indices, and disease activity in SLE patients. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic potential of serum WNT16 for SLE. Furthermore, we performed a knockdown assay on Jeko-1 cells and assessed cell proliferation and apoptosis using Cell Counting Kit-8 and flow cytometry. RESULTS: WNT16 mRNA in SLE patients' PBMCs were significantly lower than those in HC. Furthermore, serum WNT16 in SLE patients were markedly reduced compared to HC, RA, and SS cohorts. ROC curve analysis indicated that plasma WNT16 levels could serve as a potential biomarker for SLE identification (AUC=0.809, SLE vs. HC; AUC=0.760, SLE vs. RA; AUC=0.710, SLE vs. SS). Notably, a weak positive correlation was observed between WNT16 protein and both alkaline phosphatase and lymphocyte percentages. Conversely, a weak negative correlation existed between WNT16 and low-density lipoprotein, neutrophil percentage, and the incidence of pleurisy and disease activity. Additionally, our study confirmed that WNT16 knockdown impairs cell proliferation and enhances apoptosis. CONCLUSIONS: Serum WNT16 levels effectively differentiate SLE patients from healthy controls and individuals with other autoimmune disorders. WNT16 serves as a potential biomarker with high sensitivity. The diminished expression of WNT16 in SLE may have a significant role in its pathogenesis through the regulation of cell proliferation and apoptosis.

Machine learning models predicts risk of proliferative lupus nephritis.

Objective: This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe. Methods: This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers. Results: Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN. Conclusion: This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.

Manipulation of an α-glucosidase in the industrial glucoamylase-producing Aspergillus niger strain O1 to decrease non-fermentable sugars production and increase glucoamylase activity.

Dextrose equivalent of glucose from starch hydrolysis is a critical index for starch-hydrolysis industry. Improving glucose yield and decreasing the non]-fermentable sugars which caused by transglycosylation activity of the enzymes during the starch saccharification is an important direction. In this study, we identified two key α-glucosidases responsible for producing non-fermentable sugars in an industrial glucoamylase-producing strain Aspergillus niger O1. The results showed the transglycosylation product panose was decreased by more than 88.0% in agdA/agdB double knock-out strains than strain O1. Additionally, the B-P1 domain of agdB was found accountable as starch hydrolysis activity only, and B-P1 overexpression in ΔAΔB-21 significantly increased glucoamylase activity whereas keeping the glucoamylase cocktail low transglycosylation activity. The total amounts of the transglycosylation products isomaltose and panose were significantly decreased in final strain B-P1-3 by 40.7% and 44.5%, respectively. The application of engineered strains will decrease the cost and add the value of product for starch biorefinery.

IKBKE and BANK1 Polymorphisms and Clinical Characteristics in Chinese Women with Systemic Lupus Erythematosus.

BACKGROUND: Defects in apoptotic cell clearance is a pathogenic factor in systemic lupus erythematosus (SLE). This study screened potential pathogenic single nucleotide polymorphisms (SNPs) related to anti-apoptosis from an SLE family and explored their contribution to SLE susceptibility in Chinese women. METHODS: Four SNPs (IKBKE rs15672, BANK1 rs12640056, BANK1 rs6842661, and NFKBIA rs1957106) with potential SLE susceptibility were analyzed for clinical characteristics between 567 patients with SLE and 345 healthy control subjects. RESULTS: IKBKE rs15672 G/A and BANK1 rs12640056C/T polymorphisms were associated with SLE susceptibility (rs15672 A vs G, P = 0.028, OR = 1.25, 95% CI = 1.02-1.52; rs12640056 T vs C, P = 0.015, OR = 0.78, 95% CI = 0.64-0.95, respectively). In addition, patients with AA+GA genotypes of IKBKE rs15672 had higher positive rates of anti-SSB antibodies (q = 0.008) and lower positive rates of anti-RIB antibodies (q = 0.024) than those with the GG genotype. There were no significant differences in BANK1 rs12640056 between different genotypes and clinical characteristics. CONCLUSION: IKBKE rs15672 G/A and BANK1 rs12640056C/T polymorphisms are associated with susceptibility to SLE in Chinese women. This highlights the important role of these two SNPs in this disease and suggests that multiple genes from these pathways are candidates for functional studies and therapeutic targets.