An updated advancement of bifunctional IL-27 in inflammatory autoimmune diseases.

Interleukin-27 (IL-27) is a member of the IL-12 family. The gene encoding IL-27 is located at chromosome 16p11. IL-27 is considered as a heterodimeric cytokine, which consists of Epstein-Barr virus (EBV)-induced gene 3 (Ebi3) and IL-27p28. Based on the function of IL-27, it binds to receptor IL-27rα or gp130 and then regulates downstream cascade. To date, findings show that the expression of IL-27 is abnormal in different inflammatory autoimmune diseases (including systemic lupus erythematosus, rheumatoid arthritis, Sjogren syndrome, Behcet's disease, inflammatory bowel disease, multiple sclerosis, systemic sclerosis, type 1 diabetes, Vogt-Koyanagi-Harada, and ankylosing spondylitis). Moreover, in vivo and in vitro studies demonstrated that IL-27 is significantly in3volved in the development of these diseases by regulating innate and adaptive immune responses, playing either an anti-inflammatory or a pro-inflammatory role. In this review, we comprehensively summarized information about IL-27 and autoimmunity based on available evidence. It is hoped that targeting IL-27 will hold great promise in the treatment of inflammatory autoimmune disorders in the future.

Prediction model for developing neuropsychiatric systemic lupus erythematosus in lupus patients.

OBJECTIVE: This study aimed to construct a predictive model for assessing the risk of development of neuropsychiatric systemic lupus erythematosus (NPSLE) among patients with SLE based on clinical, laboratory, and meteorological data. METHODS: A total of 2232 SLE patients were included and were randomly assigned into training and validation sets. Variables such as clinical and laboratory data and local meteorological data were screened by univariate and least absolute shrinkage and selection operator (LASSO) logistic regression modelling. After 10-fold cross-validation, the predictive model was built by multivariate logistic regression, and a nomogram was constructed to visualize the risk of NPSLE. The efficacy and accuracy of the model were assessed by receiver operating characteristic (ROC) curve and calibration curve analysis. Net clinical benefit was assessed by decision curve analysis. RESULTS: Variables that were included in the predictive model were anti-dsDNA, anti-SSA, lymphocyte count, hematocrit, erythrocyte sedimentation rate, pre-albumin, retinol binding protein, creatine kinase isoenzyme MB, Nterminal brain natriuretic peptide precursor, creatinine, indirect bilirubin, fibrinogen, hypersensitive C-reactive protein, CO, and mild contamination. The nomogram showed a broad prediction spectrum; the area under the curve (AUC) was 0.895 (0.858-0.931) for the training set and 0.849 (0.783-0.916) for the validation set. CONCLUSION: The model exhibits good predictive performance and will confer clinical benefit in NPSLE risk calculation. Key Points • Clinical, laboratory, and meteorological data were incorporated into a predictive model for neuropsychiatric systemic lupus erythematosus (NPSLE) in SLE patients. • Anti-dsDNA, anti-SSA, LYM, HCT, ESR, hsCRP, IBIL, PA, RBP, CO, Fib, NT-proBNP, Crea, CO, and mild contamination are predictors of the development of NPSLE and may have potential for research. • The nomogram has good predictive performance and clinical value and can be used to guide clinical diagnosis and treatment.

Relationship between CD274 gene polymorphism and systemic lupus erythematosus risk in a Chinese Han population.

OBJECTIVE: Relationship between surface antigen differentiation cluster 274 (CD274) gene polymorphism and systemic lupus erythematosus (SLE) risk is limited. This study aims to discuss whether in a Chinese Han population, CD274 gene polymorphisms may relate to SLE susceptibility. METHODS: Three hundred and ten SLE patients and 390 healthy controls were included in this case-control study. Using the Kompetitive Allele-Specific PCR (KASP) approach, five single nucleotide polymorphisms (SNPs), including rs2890658, rs4143815, rs822339, rs2282055, and rs2297137, were genotyped for CD274 gene polymorphisms. Correlation between the polymorphisms and clinical, laboratory features in SLE patients were discussed. RESULTS: Frequency of C allele was substantially lower in SLE patients than in healthy controls (p = .015), and CC genotype was significantly negatively related to developing SLE at locus rs4143815 (p = .013). At locus rs822339, frequency of GA genotype was higher than that of the healthy controls (p = .006). At locus rs2282055, frequency of GG genotype was lower than that of healthy controls (p = .024). According to subgroup analysis, the CD274 gene polymorphisms rs2890658, rs4143815, rs822339, rs2282055, and rs2297137 were partly linked to some clinical symptoms of SLE patients, such as Complement 4 (C4), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR). CONCLUSION: CD274 gene polymorphisms may be susceptible to SLE in the Chinese Han people.

Synergistic effects of BTN3A1, SHP2, CD274, and STAT3 gene polymorphisms on the risk of systemic lupus erythematosus: a multifactorial dimensional reduction analysis.

OBJECTIVE: Systemic lupus erythematosus is a complex autoimmune disorder, and evidence supports the significance of genetic polymorphisms in SLE genetic susceptibility. The aim of this study was to assess the effects of BTN3A1 (butyrophilin 3A1), SHP2 (Src homology-2 containing protein tyrosine phosphatase), CD274 (programmed cell death 1 ligand 1), and STAT3 (signal transducer-activator of transcription 3) gene interactions on SLE risk. MATERIALS AND METHODS: Two hundred and ninety patients diagnosed with SLE and 370 healthy controls were recruited. A multifactor dimensionality reduction (MDR) approach was used to determine the epistasis among single nucleotide polymorphisms (SNPs) on the BTN3A1 (rs742090), SHP2 (rs58116261), CD174 (rs702275), and STAT3 (rs8078731) genes. The best risk prediction model was identified in terms of precision and cross-validation consistency. RESULTS: Allele A and genotype AA were negatively related to genetic susceptibility of SLE for BTN3A1 rs742090 (OR = 0.788 (0.625-0.993), P = 0.044; OR = 0.604 (0.372-0.981), P = 0.040). For STAT3 rs8078731, allele A and genotype AA were positively related to the risk of SLE (OR = 1.307 (1.032-1.654), P = 0.026; OR = 1.752 (1.020-3.010), P = 0.041). MDR analysis revealed the most significant interaction between BTN3A1 rs742090 and SHP2 rs58116261. The best risk prediction model was a combination of BTN3A1 rs742090, SHP2 rs58116261, and STAT3 rs8078731 (accuracy = 0.5866, consistency = 10/10, OR = 1.9870 (1.5964-2.4731), P = 0.001). CONCLUSION: These data indicate that risk prediction models formed by gene interactions (BTN3A1, SHP2, STAT3) can identify susceptible populations of SLE. Key Points • BTN3A1 rs742090 polymorphism was a protective factor for systemic lupus erythematosus, while STAT3 rs8078731 polymorphism was a risk factor. • There was a strong synergistic effect of BTN3A1 rs742090 and SHP2 rs58116261, and interaction among BTN3A1 rs742090, SHP2 rs58116261, and STAT3 rs8078731 constructed the best model to show association with SLE risk.

Neuropeptide Y, a potential marker for lupus, promotes lupus development.

OBJECTIVE: Relationship between neuropeptide Y (NPY) serum levels, NPY genetic mutation with systemic lupus erythematosus (SLE) pathogenesis is yet to be clarified, and role of NPY in development of SLE needs elucidation. METHOD: This study included 460 SLE patients, 472 non-SLE cases, 500 healthy volunteers. Serum NPY, matrix metalloproteinase-1 (MMP-1) and MMP-8 levels were tested by ELISA. Genotyping 7 NPY single nucleotides polymorphisms (SNPs) (rs5573, rs5574, rs16129, rs16138, rs16140, rs16147, rs16478) was obtained by Kompetitive Allele-Specific PCR (KASP) method. Pristane-induced lupus mice were treated with NPY-Y1 receptor antagonist, and histological analysis, serological changes of the mice were evaluated. RESULTS: NPY serum concentrations were significantly increased in SLE patients when compared to that in healthy volunteers, non-SLE cases. Rs5573 G allele, rs16129 T allele, rs16147 G allele frequencies were significantly different between SLE cases and healthy controls. Rs5574 TT + TC genotypes were related to levels of IgG, C3, C4 and erythrocyte sedimentation rate, and rs16138 GG + GC genotypes correlated with SLE cases with anti-double-stranded deoxyribonucleic acid antibody (anti-dsDNA) (+). Serum MMP-1, MMP-8 concentrations were higher in SLE patients, and NPY levels were significantly related to MMP-1, MMP-8 levels. After treatment of lupus mice with NPY-Y1 receptor antagonist, damage of liver, spleen and kidney was alleviated, production of autoantibodies (anti-nuclear antibody (ANA), total IgG, anti-dsDNA) and MMP-1, MMP-8 was down-regulated, and differentiation of CD3+, CD8+ T cells, B cells, monocytes, macrophages, T helper 1 (Th1), Th2, Th17 cells was reversed. CONCLUSION: NPY may be a biomarker for lupus, which may promote occurrence and development of lupus.

Biology of endophilin and it's role in disease.

Endophilin is an evolutionarily conserved family of protein that involves in a range of intracellular membrane dynamics. This family consists of five isoforms, which are distributed in various tissues. Recent studies have shown that Endophilin regulates diseases pathogenesis, including neurodegenerative diseases, tumors, cardiovascular diseases, and autoimmune diseases. In vivo, it regulates different biological functions such as vesicle endocytosis, mitochondrial morphological changes, apoptosis and autophagosome formation. Functional studies confirmed the role of Endophilin in development and progression of these diseases. In this study, we have comprehensively discussed the complex function of Endophilin and how the family contributes to diseases development. It is hoped that this study will provide new ideas for targeting Endophilin in diseases.

Association between endothelin-1 and systemic lupus erythematosus: insights from a case-control study.

Systemic lupus erythematosus (SLE) is a chronic rheumatic disorder. Endothelin-1, a vasoconstrictor, belongs to the endothelin family and is associated with vascular-related damages. To date, association between ET-1 and pathogenesis of SLE remains unclear. This case-control study was carried out by 314 SLE, 252 non-SLE diseases patients and 500 healthy controls. Serum ET-1, CCN3, IL-28B levels were detected by ELISA, and ET-1 gene polymorphisms (rs5369, rs5370, rs1476046, rs2070699, rs2071942, rs2071943, rs3087459, rs4145451, rs6458155, rs9369217) were genotyped with Kompetitive Allele-Specific PCR. SLE patients had high levels of ET-1, which were correlated with some clinical, laboratory features. Serum CCN3, IL-28B levels were higher in SLE patients, and ET-1 levels were positively correlated with the two cytokines. Rs5370, rs1476046, rs2070699, rs2071942, rs2071943, rs3087459, rs6458155 and rs2070699 were associated with SLE risk. Rs2070699 (T, TT) was related to SLE patients with alopecia. Rs5370 (T, TT, TG), rs1476046 (G,GA), rs2071942 (G,GA) and rs2071943 (G,GA) were associated with SLE patients with pericarditis, pyuria and fever manifestation, respectively. Rs3087459 (CC) and rs9369217 (TC) were related to SLE patients with positive anti-SSB antibody. Rs5369 (AA) was associated with IgG and CRP levels in SLE patients. In conclusion, elevated serum ET-1 in SLE patients may be a potential disease marker, and its gene polymorphisms were related to SLE susceptibility.

Systemic lupus erythematosus with high disease activity identification based on machine learning.

OBJECTIVE: Clinical evaluation of systemic lupus erythematosus (SLE) disease activity is limited and inconsistent, and high disease activity significantly, seriously impacts on SLE patients. This study aims to generate a machine learning model to identify SLE patients with high disease activity. METHOD: A total of 1014 SLE patients with low disease activity and 453 SLE patients with high disease activity were included. A total of 94 clinical, laboratory data and 17 meteorological indicators were collected. After data preprocessing, we use mutual information and multisurf to evaluate and select the importance of features. The selected features are used for machine learning modeling. Performance of the model is evaluated and verified by a series of binary classification indicators. RESULTS: We screened out hematuria, proteinuria, pyuria, low complement, precipitation, sunlight and other features for model construction by integrated feature selection. After hyperparameter optimization, the LGB has the best performance (ROC: AUC = 0.930; PRC: AUC = 0.911, APS = 0.913; balance accuracy: 0.856), and the worst is the naive bayes (ROC: AUC = 0.849; PRC: AUC = 0.719, APS = 0.714; balance accuracy: 0.705). Finally, the selection of features has good consistency in the composite feature importance bar plot. CONCLUSION: We identify SLE patients with high disease activity by a simple machine learning pipeline, especially the LGB model based on the characteristics of proteinuria, hematuria, pyuria and other feathers screened out by collective feature selection.

Association of STAT3 gene polymorphisms with systemic lupus erythematosus in a Chinese Han population.

OBJECTIVE: Evidence supports the important role of STAT3 in SLE; however, association between STAT3 gene polymorphisms and SLE risk needs discussion. METHODS: Three hundred SLE patients and 380 healthy controls from Chinese Han population were included. DNA is extracted from peripheral blood mononuclear cells and the clinical characteristics of patients are collected. STAT3 gene polymorphisms (rs6503695, rs744166, rs9912773, and rs12601982) were genotyped by the Kompetitive Allele-Specific PCR (KASP) method. SPSS 26.0 was utilized to analyze the genetic susceptibility of SLE and STAT3 gene polymorphisms. RESULTS: Frequencies of genotypes CT, TT, and TT+CT were significantly lower in SLE patients compared with those in healthy controls with respect to rs6503695 (p = .007, p < .001, p = .001). Frequencies of rs744166 genotypes AG, AA, and AA+AG were decreased in SLE patients as compared to those in healthy controls (p = .034, p = .006, p = .009). The recessive models (CC vs GG+GC) for rs9912773 and (AA vs GG+GA) for rs12601982 were significantly related to SLE patients (p = .014, p = .035). Moreover, allele C of rs6503695 was related to optic nerve damage in SLE patients (p = .036). rs744166 allele G was correlated with positive rash and albuminuria in SLE patients (p = .006, p = .014). For rs9912773, SLE patients carrying genotype GG had higher serum C3 and C4 levels compared to genotype GC+CC (p = .029, p = .028). The rs12601982 allele G was strongly associated with positive hypocomplementemia in SLE patients (p = .034). SLE patients carrying genotypes GG, GC, and CC had different SLEDAI score for rs12601982 (GG vs GC vs CC, p = .003). CONCLUSION: STAT3 gene polymorphisms associated with SLE susceptibility.

Exploring machine learning methods for predicting systemic lupus erythematosus with herpes.

OBJECTIVES: To investigate whether machine learning, which is widely used in disease prediction and diagnosis based on demographic data and serological markers, can predict herpes occurrence in patients with systemic lupus erythematosus (SLE). METHODS: A total of 286 SLE patients were included in this study, including 200 SLE patients without herpes and 86 SLE patients with herpes. SLE patients were randomly divided into a training group and a test group, and 18 demographic characteristics and serological indicators were compared between the two groups. RESULTS: We selected basophil, monocyte, white blood cell, age, immunoglobulin E, SLE Disease Activity Index, complement 4, neutrophil, and immunoglobulin G as the basic features of modeling. A random forest model had the best performance, but logistic and decision tree analyses had better clinical decision-making benefits. Random forest had a good consistency between feature importance judgment and feature selection. The 10-fold cross-validation showed the optimization of five model parameters. CONCLUSION: The random forest model may be an excellently performing model, which may help clinicians to identify SLE patients whose disease is complicated by herpes early.

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis.

OBJECTIVE: Diagnosis of lupus nephritis (LN) is a complex process, which usually requires renal biopsy. We aim to establish a machine learning pipeline to help diagnosis of LN. METHODS: A cohort of 681 systemic lupus erythematosus (SLE) patients without LN and 786 SLE patients with LN was established, and a total of 95 clinical, laboratory data and 17 meteorological indicators were collected. After tenfold cross-validation, the patients were divided into training set and test set. The features selected by collective feature selection method of mutual information (MI) and multisurf were used to construct the models of logistic regression, decision tree, random forest, naive Bayes, support vector machine (SVM), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN), the models were compared and verified in post-analysis. RESULTS: Collective feature selection method screens out antistreptolysin (ASO), retinol binding protein (RBP), lupus anticoagulant 1 (LA1), LA2, proteinuria and other features, and the hyperparameter optimized XGB (ROC: AUC = 0.995; PRC: AUC = 1.000, APS = 1.000; balance accuracy: 0.990) has the best performance, followed by LGB (ROC: AUC = 0.992; PRC: AUC = 0.997, APS = 0.977; balance accuracy: 0.957). The worst performance is naive Bayes model (ROC: AUC = 0.799; PRC: AUC = 0.822, APS = 0.823; balance accuracy: 0.693). In the composite feature importance bar plots, ASO, RF, Up/Ucr, and other features play important roles in LN. CONCLUSION: We developed and validated a new and simple machine learning pathway for diagnosis of LN, especially the XGB model based on ASO, LA1, LA2, proteinuria, and other features screened out by collective feature selection.

Relationship between SHP2 gene polymorphisms and systemic lupus erythematosus risk.

OBJECTIVE: Systemic lupus erythematosus (SLE) is a complex autoimmune disorder. SHP2, a non-transmembrane member of the protein tyrosine phosphatase (PTP) family, can be involved in multiple signaling pathways in inflammatory response. To date, it remains to be investigated whether polymorphisms in the SHP2 gene are correlated with SLE in the Chinese Han population. METHOD: A study comprising 320 SLE patients and 400 healthy individuals was performed. Three single nucleotide polymorphisms (rs4767860, rs7132778, rs7953150) of the SHP2 gene were genotyped using the Kompetitive Allele-Specific Polymerase Chain Reaction method. RESULTS: Genotypes of rs4767860 (AA, AG + AA) and rs7132778 (AA, AC + AA), and alleles of rs4767860 (A) and rs7132778 (A) were associated with SLE risk. Genotype AA of rs7132778 and allele A of rs7132778 and rs7953150 were associated with oral ulcers in SLE patients. Allele C of rs7132778 and genotype AA and allele A of rs7953150 were associated with pyuria. Patients who carried AA genotype and allele A of rs7953150 are more likely to develop hypocomplementemia. AA and AG genotype frequencies are more raised in patients with SLE with alopecia than in those without alopecia. Patients who carried AA and AG genotypes of rs4767860 had elevated C-reactive protein levels. CONCLUSION: Gene polymorphisms of SHP2 (rs4767860, rs7132778) are relevant to SLE susceptibility.

SHP2: its association and roles in systemic lupus erythematosus.

OBJECTIVE: Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disease. Src homology 2 domain containing protein tyrosine phosphatase (SHP2) is a member of the protein tyrosine phosphatases (PTPs) family. To date, relationship between SHP2 and SLE pathogenesis is not elucidated. METHOD: We measured plasma levels of SHP2 in 328 SLE patients, 78 RA patients, 80 SS patients and 79 healthy controls by ELISA, and discussed association of SHP2 in SLE patients, potential of plasma SHP2 as a SLE biomarker. Moreover, histological and serological changes were evaluated by flow cytometry, HE/Masson examination, immunofluorescence test in pristane-induced lupus mice after SHP2 inhibitor injection to reveal role of SHP2 in lupus development. RESULTS: Results indicated that SHP2 plasma levels were upregulated in SLE patients and correlated with some clinical, laboratory characteristics such as proteinuria, pyuria, and may be a potential biomarker for SLE. After SHP2 inhibitor treatment, hepatosplenomegaly and histological severity of the kidney in lupus mice were improved. SHP2 inhibitor reversed DCs, Th1, and Th17 cells differentiation and downregulated inflammatory cytokines (IL-4, IL-6, IL-10, IL-17A, IFN-γ and TNF-α) and autoantibodies (ANA, anti-dsDNA) production in pristane-lupus mice. CONCLUSION: In summary, SHP2 correlated with SLE pathogenesis and promoted the development of lupus.

Superhydrophobic and easy-to-clean full-packing nanopatterned microlens array with high-quality imaging.

The high-quality imaging and easy cleaning property of microlens array (MLA) are two very important factors for its outdoor work. Herein, a superhydrophobic and easy-to-clean full-packing nanopatterned MLA with high-quality imaging is prepared by thermal reflow together with sputter deposition. Scanning electronic microscopy (SEM) images demonstrate that the sputter deposition method can improve 84% packing density of MLA prepared by thermal reflow to 100% and add nanopattern on the surface of microlens. The prepared full-packing nanopatterned MLA (npMLA) possess clear imaging with a significant increase of signal-to-noise ratio and higher transparency compared with the MLA prepared by thermal reflow. Besides for excellent optical properties, the full-packing surface displays a superhydrophobic property with a contact angle of 151.3°. Further, the full-packing contaminated by chalk dust become easier to be cleaned by nitrogen blowing and deionized water. As a result, the prepared full-packing is considered to be potential for various applications in the outdoor.