Lupus nephritis (LN) is the most common and significant complication of systemic lupus erythematosus (SLE) due to its poor prognosis and mortality rates in SLE patients. There is a critical need for new drugs as the pathogenesis of LN remains to be elucidated and immunosuppressive therapy comes with many deficiencies. In this study, 23 hub genes (IFI6, PLSCR1, XAF1, IFI16, IFI44, MX1, IFI44L, IFIT3, IFIT2, IFI27, DDX58, EIF2AK2, IFITM1, RTP4, IFITM3, TRIM22, PARP12, IFIH1, OAS1, HERC6, RSAD2, DDX60, and MX2) were identified through bioinformatics and network analysis and are closely related to interferon production and function. Interestingly, immune cell infiltration analysis and correlation analysis demonstrate a positive correlation between the expression of 23 hub genes and monocyte infiltration in glomeruli and M2 macrophage infiltration in the tubulointerstitium of LN patients. Additionally, the CTD database, DsigDB database, and DREIMT database were used to explore the bridging role of genes in chemicals and LN as well as the potential influence of these chemicals on immune cells. After comparison and discussion, six small molecules (Acetohexamide, Suloctidil, Terfenadine, Prochlorperazine, Mefloquine, and Triprolidine) were selected for their potential ability in treating lupus nephritis.

BackgroundDysbiosis of the gut microbiota is closely related to chronic systemic inflammation and autoimmunity, playing an essential role in the pathogenesis of primary Sjögren’s syndrome (pSS). Abnormalities in the proportions of blood T lymphocyte subtype, that is Th17/Treg, were detected in pSS patients. We aimed to determine the associations between gut microbiota and Th17/Treg in pSS.Method98 pSS patients and 105 healthy controls (NC) were enrolled between Dec 1, 2018, and Aug 31, 2019. The baseline information and clinical parameters on pSS patients and healthy controls were collected. 16S rRNA sequencing was performed to characterize the gut microbiome and identify gut microbes that are differentially abundant between patients and healthy controls. Lastly, associations between relative abundances of specific bacterial taxa in the gut and clinical outcome parameters were evaluated.ResultsPatients with pSS show decreased gut microbial diversity and richness, decreased abundance of butyrate producing bacteria, such as Roseburia and Coprococcus, and increased abundance of other taxa, such as Eubacterium rectale and Roseburia inulinivorans. These bacteria are enriched with functions related to glycolytic and lipogenic, energy, substance, galactose, pentose metabolism pathways and glucuronate interconversions, decreased with functions related to peptidoglycan biosynthesis, pyrimidine metabolism pathways. An integrative analysis identified pSS-related specific bacterial taxa in the gut, for which the abundance of Eubacterium rectale is negatively correlated with Th17/Treg. Furthermore, the pathways of biosynthesis of secondary metabolites, biosynthesis of amino acids, peptidoglycan biosynthesis and pyrimidine, galactose, pentose, microbial metabolism in diverse environments, glyoxylate and dicarboxylate metabolism are associated with Treg or Th17/Treg.ConclusionsPrimary Sjögren’s syndrome could lead to decreased gut microbial diversity and richness of intestinal flora in patients. The proportions of Th17 and Treg cells induced by microbiota were predictive pSS manifestations and accounted for the pSS severity.

BackgroundIgA nephropathy (IgAN) is the most frequent glomerulonephritis in inflammatory bowel disease (IBD). However, the inter-relational mechanisms between them are still unclear. This study aimed to explore the shared gene effects and potential immune mechanisms in IgAN and IBD.MethodsThe microarray data of IgAN and IBD in the Gene Expression Omnibus (GEO) database were downloaded. The differential expression analysis was used to identify the shared differentially expressed genes (SDEGs). Besides, the shared transcription factors (TFs) and microRNAs (miRNAs) in IgAN and IBD were screened using humanTFDB, HMDD, ENCODE, JASPAR, and ChEA databases. Moreover, weighted gene co-expression network analysis (WGCNA) was used to identify the shared immune-related genes (SIRGs) related to IgAN and IBD, and R software package org.hs.eg.db (Version3.1.0) were used to identify common immune pathways in IgAN and IBD.ResultsIn this study, 64 SDEGs and 28 SIRGs were identified, and the area under the receiver operating characteristic curve (ROC) of 64 SDEGs was calculated and two genes (MVP, PDXK) with high area under the curve (AUC) in both IgAN and IBD were screened out as potential diagnostic biomarkers. We then screened 3 shared TFs (SRY, MEF2D and SREBF1) and 3 miRNAs (hsa-miR-146, hsa-miR-21 and hsa-miR-320), and further found that the immune pathways of 64SDEGs, 28SIRGs and 3miRNAs were mainly including B cell receptor signaling pathway, FcγR-mediated phagocytosis, IL-17 signaling pathway, toll-like receptor signaling pathway, TNF signaling pathway, TRP channels, T cell receptor signaling pathway, Th17 cell differentiation, and cytokine-cytokine receptor interaction.ConclusionOur work revealed the differentiation of Th17 cells may mediate the abnormal humoral immunity in IgAN and IBD patients and identified novel gene candidates that could be used as biomarkers or potential therapeutic targets.

ObjectiveTo explore the common differential flora of IgAN, Kawasaki disease and IgA vasculitis by screening and analyzing the differential intestinal flora between the three disease groups of IgAN, Kawasaki disease and IgA vasculitis and their healthy controls.MethodsPapers on 16srRNA sequencing-related intestinal flora of IgAN, Kawasaki disease and IgA vasculitis were searched in databases, the literature was systematically collated and analysed, the original data was download from the relevant databases, and then the operational taxonomic unit and species classification analysis were performed. Besides, Alpha diversity analysis and Beta diversity analysis were performed to screen for IgAN, Kawasaki disease and I1gA vasculitis groups and finally compare the common intestinal differential flora among the three groups.ResultsAmong the common differential flora screened, Lachnospiracea_incertae_sedis was lower in both the IgAN and Kawasaki disease groups than in the respective healthy controls; Coprococcus was low in the IgAN group but high in the IgA vasculitis group. Fusicatenibacter was lower in both the Kawasaki disease and IgA vasculitis groups than in their respective healthy controls, and Intestinibacter was low in the Kawasaki disease group, but its expression was high in the IgA vasculitis group.ConclusionThe dysbiosis of the intestinal flora in the three groups of patients with IgAN, Kawasaki disease and IgA vasculitis, its effect on the immunity of the organism and its role in the development of each disease group remain unclear, and the presence of their common differential flora may further provide new ideas for the association of the pathogenesis of the three diseases.

<abstract> <p>Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.</p> </abstract>