Background: Intestinal dysbiosis is associated with inflammatory bowel disease (IBD). Ulcerative colitis (UC) and Crohn’s disease (CD), two subtypes of IBD, are characterized by unique microbial signatures, respectively. However, it is unclear whether UC or CD has a specific causal relationship with gut microbiota.Objective: To investigate the potential causal associations between gut microbial genera and IBD, UC, or CD, two-sample Mendelian randomization (MR) analyses were conducted.Materials and Methods: We obtained genome-wide association study (GWAS) summary statistics of gut microbiota and IBD, UC, or CD from published GWASs. Two-sample MR analyses were performed to identify potential causal gut microbial genera for IBD, UC, and CD using the inverse-variance weighted (IVW) method. Sensitivity analyses were also conducted to validate the robustness of the primary results of the MR analyses. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation.Results: Combining the results from the primary and sensitivity analyses, six bacterial genera were associated with the risk of IBD, UC, or CD in the IVW method. Briefly, Eubacterium ventriosum group was associated with a lower risk of IBD (P=0.011) and UC (P=1.00×10^-4), whereas Coprococcus 2 was associated with a higher risk of IBD (P=0.022) and UC (P=0.007). In addition, we found a positive association between Oxalobacter with IBD (P=0.001) and CD (P=0.002), and Ruminococcaceae UCG014 with IBD (P=0.005) and CD (P=0.007). We also noticed a negative association between Enterorhabdus (P=0.044) and IBD, and between Lachnospiraceae UCG001 (P=0.023) and CD. We did not find causal effects of IBD, UC, or CD on these bacterial genera in the reverse MR analysis.Conclusion: This study expanded gut microbial genera that were causally associated with the risk of IBD, and also revealed specificity-gut microbial genera for UC or CD.

Background: Growing evidences indicate that the alterations in gut microbiota are associated with the efficacy of glucocorticoids (GCs) in the treatment of systemic lupus erythematosus (SLE). However, there is no evidence to prove whether gut microbiota directly mediates the effects of GCs. Methods: Using the MRL/lpr mice, this study firstly addressed the effects of three doses of prednisone on gut microbiota. Then, this study used fecal microbiota transplantation (FMT) to transfer the gut microbiota of prednisone-treated MRL/lpr mice into the blank MRL/lpr mice to reveal whether the gut microbiota regulated by prednisone had similar therapeutic efficiency and side effects as prednisone. Results: The effects of prednisone on gut microbiota were dose-dependent in the treatment of MRL/lpr mice. After transplantation into MRL/lpr mice, prednisone-regulated gut microbiota could alleviate lupus, which might be due to decreasing Ruminococcus and Alistipes and retaining the abundance of Lactobacillus. However, prednisone-regulated gut microbiota did not exhibit side effects as prednisone. The reason might be that the pathogens upregulated by prednisone could not survive in the MRL/lpr mice as exogenous microbiota, such as Parasutterella, Parabacteroides, and Escherichia-Shigella. Conclusions: These data demonstrated that the transplantation of gut microbiota may be an effective method to obtain the therapeutic effects of GCs and avoid the side effects of GCs.

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.

The receptor-interacting protein kinase 4 (RIPK4), a member of the RIPK family, was originally described as an interaction partner of protein kinase C (PKC) β and PKCδ. RIPK4 is identified as a key regulator of keratinocyte differentiation, cutaneous inflammation, and cutaneous wound repair. The mechanism by which RIPK4 integrates upstream signals to initiate specific responses remains elusive. Previous studies have indicated that RIPK4 can regulate several signaling pathways, including the NF-κB, Wnt/β-catenin, and RAF/MEK/ERK pathways. Furthermore, RIPK4-related biological signaling pathways interact with each other to form a complex network. Mounting evidence suggests that RIPK4 is aberrantly expressed in various kinds of cancers. In several types of squamous cell carcinoma (SCC), the mutations that drive aggressive SCC have been found in RIPK4. In addition, the function of RIPK4 in carcinogenesis is probably tissue-specific, since RIPK4 can play a dual role as both a tumor promoter and a tumor suppressor in different tumor types. Therefore, RIPK4 may represent as an independent prognostic factor and a promising novel therapeutic target, which can be used to identify the risks of patients and guide personalized treatments. In future, RIPK4-interacting pathways and precise molecular targets need to be investigated in order to further elucidate the mechanisms underlying epidermal differentiation and carcinogenesis.

Background: Humidity was an unfavorable factor for patients with rheumatoid arthritis (RA). RA disease activity was severe in high humidity conditions. However, there is no evidence to demonstrate the effects of humidity on arthritis in the animal experiments and explore its relevant mechanism.Methods: Using the DBA/1 mice, this study addressed the effects of a high humidity (80 ± 5%) on arthritis in collagen-induced arthritis (CIA) mice. Then, this study used the gas chromatography-mass spectrometer (GC-MS) to explore alterations in serum metabolome caused by the high humidity. Furthermore, xylitol and L-pyroglutamic acid, which were both significantly upregulated by the high humidity, were selected to further study their effects on arthritis in the CIA mice.Results: The high humidity (80 ± 5%) could aggravate arthritis variables including increasing arthritis score and swelling, serum autoantibodies (anti-COII and anti-CCP), and proinflammatory cytokines (IL-6, IL-17A, and G-CSF). In addition, the high humidity could cause significant alterations in serum metabolome in the CIA mice. Xylitol and L-pyroglutamic acid were the representative serum metabolites that were significantly upregulated by the high humidity. Further experiments demonstrated that the supplementation of 0.4 mg/mL xylitol in drinking water after inducing the CIA model and 2.0 mg/mL in drinking water before inducing the CIA model could both aggravate arthritis in the CIA mice.Conclusions: These data demonstrated that high humidity was not beneficial for arthritis development and its mechanism might be associated with xylitol and L-pyroglutamic acid.