Quantitative and standardized research on syndrome differentiation has always been at the forefront of modernizing Traditional Chinese Medicine (TCM) theory. However, the majority of existing databases primarily concentrate on the network pharmacology of herbal prescriptions, and there are limited databases specifically dedicated to TCM syndrome differentiation. In response to this gap, we have developed the Traditional Chinese Medical Syndrome Standardization Database (TCMSSD, http://tcmssd.ratcm.cn). TCMSSD is a comprehensive database that gathers data from various sources, including TCM literature such as TCM Syndrome Studies (Zhong Yi Zheng Hou Xue) and TCM Internal Medicine (Zhong Yi Nei Ke Xue) and various public databases such as TCMID and ETCM. In our study, we employ a deep learning approach to construct the knowledge graph and utilize the BM25 algorithm for syndrome prediction. The TCMSSD integrates the essence of TCM with the modern medical system, providing a comprehensive collection of information related to TCM. It includes 624 syndromes, 133,518 prescriptions, 8,073 diseases (including 1,843 TCM-specific diseases), 8,259 Chinese herbal medicines, 43,413 ingredients, 17,602 targets, and 8,182 drugs. By analyzing input data and comparing it with the patterns and characteristics recorded in the database, the syndrome prediction tool generates predictions based on established correlations and patterns. The TCMSSD fills the gap in existing databases by providing a comprehensive resource for quantitative and standardized research on TCM syndrome differentiation and laid the foundation for research on the biological basis of syndromes.

Purpose. Despite the discovery of many important molecules in diabetic nephropathy, there has been very limited progress in the management of diabetic kidney diseases and the design of new drugs. To fill this gap, the present study explored the expression of SIRT2 in high-glucose murine kidney foot cells and its impact on cell biological functions. Methods. Expression levels of SIRT2 in the MPC-5 of murine kidney foot cells after high and normal glucose treatment or in cells targeted with siRNA were detected using qRT-PCR. Cellular proliferation and programmed cell death were analyzed via the CCK8 assay and flow cell technique, separately. Levels of autophagy markers were measured by western blotting, and chloroquine treatment was applied to the cells to observe the effect of SIRT2 on cell proliferation and apoptosis after treatment. Results. The expression level of SIRT2 was remarkably upregulated in the high-GLU group in contrast to the low-GLU group. The cell proliferation and autophagy levels were significantly reduced, and apoptosis was remarkably reinforced in the high-GLU group in contrast to the normal GLU group. However, knocking down the expression level of SIRT2 caused an increase in cell proliferation and cell autophagy levels and significantly weakened apoptosis. Chloroquine influenced cell proliferation and apoptosis in cells targeted with SIRT2 siRNA. Conclusion. SIRT2 expression was upregulated in hyperglycaemic murine kidney foot cells, and knocking down the expression level of SIRT2 affected the biological function of the cells. We found that SIRT2 may modulate cell proliferation and apoptosis by regulating cell autophagy.

Background. Systemic lupus erythematosus (SLE) is an autoimmune disease with strong heterogeneity, leading to variable clinical symptoms, which makes diagnosis and activity evaluation difficult. Methods. The original dataset of GSE88884 was analyzed to screen differentially expressed genes (DEGs) of SLE and the correlation between DEGs and clinical parameters (SLEDAI, anti-dsDNA, C3, and C4). The result was validated by microarray GSE121239 and SLE patients with RT-qPCR. Next, receiver operator characteristic (ROC) analysis, correlation analysis, and ordinal logistic regression were applied, respectively, to evaluate the capability of diagnosis and prediction of the candidate biomarker. Subsequently, the biological functions of the candidate biomarker were investigated through KEGG and GO enrichment, protein–protein interaction network, and the correlation matrix. Results. A total of 283 DEGs were screened, and seven of them were overlapped with SLE-related genes. DDX60 was identified as the candidate biomarker. Analyses of GSE88884, GSE121239, and SLE patients with RT-qPCR indicated that DDX60 expression level is significantly higher in patients with high disease activity. ROC analysis and the area under the ROC curve ( ) suggested that DDX60 has good diagnostic performance. DDX60 expression level was positively correlated with SLEDAI scores ( ). For every 1-unit increase in DDX60 expression value, the odds of a higher stage of activity of SLE disease are multiplied by 1.47. The function of DDX60 mainly focuses on IFN-I-induced antiviral activities, RIG-I signaling, and innate immune. Moreover, DDX60 plays a synergistic role with DDX58, IFIH1, OASL, IFIT1, and other related genes in the SLE pathogenesis. Conclusions. DDX60 is differently expressed in SLE, and it is significantly related to both serological indicators and the disease activity of SLE. We suggested that DDX60 might be a potential biomarker for SLE diagnosis and management.

Background: Accumulating evidence suggests that an imbalance of gut microbiota is commonly observed in patients with rheumatoid arthritis (RA). However, it remains unclear whether gut microbiota dysbiosis is a cause or consequence of RA, and the mechanisms by which gut dysbiosis contributes to RA have not been fully understood. This study aimed to investigate the causal relationship between gut microbiota and metabolites with RA.Methods: A two-sample Mendelian randomization analysis was performed to estimate the causality of gut microbiota and metabolites on RA. A genome-wide association study (GWAS) of 211 gut microbiota and 217 metabolites was used as the exposure, whereas RA was treated as the outcome. Inverse variance weighted (IVW) was regarded as the primary approach for calculating causal estimates. MR Egger method, Weighted median method, Simple mode method, and weighted mode method were used for sensitive analysis. Metabolic pathway analysis was performed via the web-based Metaconflict 5.0. Additionally, an animal study was undertaken to evaluate the results inferred by Mendelian randomization.Result: This study indicated that six gut microbiota taxa (RuminococcaceaeUCG013, Erysipelotrichia, Erysipelotrichaceae, Erysipelotrichales, Clostridia, and Veillonellaceae) were estimated to exert a positive impact on RA. Conversely, seven gut microbiota taxa (Oxalobacter, Cyanobacteria, RuminococcaceaeUCG002, LachnospiraceaeUCG010, Christensenellaceae, Oxalobacteraceae, Anaerostipes) were estimated to exert a negative impact on RA. Three metabolites, namely indole-3-propionate (IPA), glycine and sphingomyelin (SM 16:1), were found to be linked to lower RA risk, while five metabolites (argininosuccinate, CE 20_4, TAG 58_8, PC 40_6, and LPC 20_4) were linked to higher RA risk. Additionally, four metabolic pathways were identified by metabolic pathway analysis. The collagen-induced arthritis (CIA) rats exhibited a higher relative abundance of Class_Clostridia and a lower abundance of Genus_Lachnospiraceae (p < 0.05) than the healthy controls.Conclusion: This study identified causal associations between specific gut microbiota, metabolites, and RA. These findings support the significant role of gut microbiota and metabolites in RA pathogenesis.

Background: Primary Sjögren’s syndrome (pSS) is an autoimmune disease with lymphocytic infiltration of the salivary and lachrymal glands, whose present disease-specific objective indicators are few and have shortcomings that should be addressed. An integrated analysis of sequencing data from different cohorts has the potential to unveil novel biomarkers in pSS. Methods: We identified 3 GEO datasets, including gene expression data from minor salivary gland (MSG) biopsy samples of 49 patients with pSS and 31 non-pSS and whole blood cells of 30 pSS patients and 30 healthy controls (HCs). Differentially expressed genes (DEGs) involved in pSS were identified from these datasets. Function Enrichment Analyses of common upregulated DEGs and PPI (protein-protein interaction) networks were performed. Furthermore, we have carried out further analysis of these DEGs to explore their potential clinical significance and diagnostic efficacy as a biomarker for pSS. Sterile Alpha Motif Domain Containing 9 Like (SAMD9L), one of the DEGs, has been identified as a promising candidate biomarker that correlates with the severity of pSS. This has been validated by analyzing local clinical samples from 30 pSS and non-pSS patients’ MSG biopsies, as well as serum samples of 18 pSS and HC individuals. Finally, we performed correlation analysis to understand the relationship between SAMD9L and infiltrated immune cells. Results: We identified 10 common highly expressed DEGs in pSS of different tissues. These genes were mainly involved in virus infection-related pathways and inferno-related pathways. GEO data and our clinical data showed that SAMD9L increases with disease severity. Public and local cohorts showed that SAMD9L has high diagnostic performance (AUC=0.845– 0.867) as a biomarker, and its AUC was comparable to the Focus score when combined with RF or SSA. Conclusion: Up-regulated SAMD9L may serve as a promising novel pSS diagnostic biomarker and have potential value for evaluating the severity of pSS.