Behcet's Disease (BD) is a multisystem vasculitis characterized by disease alternated with recurrent episodes and remissions, involving genital, oral, ocular uvea, cutaneous, and articular manifestations. The nuclear factor (NF)-κB signaling pathway paly an important role in the BD progression. It encompasses diverse gene, protein, and cellular regulatory mechanisms operating across various levels, alongside microbiological and experimental studies involving animals and cells. At the protein research findings, activation of the NF-κB pathway in BD patients is marked by elevated plasma levels of soluble CD40 ligand, which stimulates neutrophils to release reactive oxygen species and extracellular traps, thereby promoting inflammation. At the cellular research findings, macrophages in BD patients polarize towards classically activated macrophages phenotype through the NF-κB pathway, exacerbating the inflammatory response. The activation of NF-κB is associated with increased expression of anti-apoptotic proteins in T cells, leading to prolonged inflammation. Microbiological investigations reveal that the decreased gut microbiota diversity in BD patients compromises intestinal barrier integrity. NF-κB pathway involvement in regulating neutrophil and type 1 helper T cell (Th) 1/Th17 cell function worsens inflammation. Genetically, BD patients exhibit polymorphisms in immune regulatory genes, which contribute to inflammation through the NF-κB pathway. Mutations in NF-κB-associated genes elevate the risk of BD, while mutations in the endogenous inhibitor A20 lead to abnormal NF-κB activity, sustaining inflammation. Animal experiments and in vitro experiments corroborate the efficacy of NF-κB inhibitors in attenuating inflammation. Targeting upstream inflammatory factors within the NF-κB pathway yields positive outcomes in BD patients. In summary, the NF-κB signaling pathway plays a pivotal role in the development of BD. Developing NF-κB inhibitors may open new avenues for treating BD. Further research is necessary to comprehensively elucidate the precise mechanisms by which NF-κB operates in the pathogenesis of BD, as well as its potential clinical applications in therapy.
End-stage renal disease is a late complication of chronic kidney disease (CKD) and one of the leading causes of high mortality worldwide. Over the years, the impacts of gut microbiota and their associated uremic toxins on kidney diseases through the intricate “gut-kidney axis” have been extensively studied. However, translation of microbiome-related omics results into specific mechanisms is still a significant challenge. In this paper, we review the interaction between gut microbiome and blood purification, as well as the current microbiota-based therapies in CKD. Additionally, the current sequencing technologies and progresses in the gut microbiome research are also discussed.
Microorganism distributes in the organs of human body which connect with external environment, especially those organs in the gastrointestinal tracts, and it also plays a fundamental role in the physiopathology of the host's body. Because the microorganism is very small and has a great variety, it is difficult to reveal the significance of microorganism in the human physiopathology comprehensively and deeply. With the development of molecular biology, genomics, bioinformatics and other disciplines, the microbiome research will be more possible and easier. There are two key contents of microecology. One of these is to identify and quantify the diversity of microorganism, and the other is to reveal activity and the physiopathological function of microorganism in the host. Microbiome research methods, therefore, can be summarized as the traditional detection methods, construction of gene library, the genetic fingerprint analysis and molecular hybridization techniques and so on.
Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. MethodsA total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. ResultsThere were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.
[Abstract]The pathogenesis of aortic disease is not fully understood. Gut dysbiosis may play a role in the occurrence and development of aortic diseases. Several studies showed that the diversity of microbiota in abdominal aortic aneurysms significantly decreases and is correlated with the diameter of the aneurysm. Characteristic microbial communities associated with abdominal aortic aneurysm, such as Roseburia, Bifidobacterium, Ruminococcus, Akkermansia have been found in human and animal studies. The gut microbiota of patients with aortic dissection varies greatly. Characteristic microbial communities like Lachnospiraceae and Ruminococcus present a potential impact on the pathogenesis of aortic dissection. Bifidobacterium may be associated with Takayasu arteritis and thoracic aortic aneurysm. The gut microbiota affects the physiological functions of the host by synthesizing bioactive metabolites, which causes aortic diseases, mainly involving metabolites such as trimethylamine N-oxide (TMAO), lipopolysaccharides (LPS), tryptophan, and short chain fatty acids. More and more evidence supports the causal relationship between gut microbiota dysbiosis and aortic disease. Clarifying abnormal changes in gut microbiota may provide clues for finding potential therapeutic targets.
Lung microbiome is defined as the specific microbiota of lung. Lung microbiome can make the lung in a state of chronic inflammation through direct destruction, activation of inflammatory cells and release of inflammatory factors, and then progress to lung cancer. There are significant differences in lung microbiome between lung cancer patients and healthy people. Some specific microbial flora can be used as a diagnostic marker of lung cancer. Specific microbial communities are related to the efficacy of immunotherapy, and microbial composition may be used as a marker of immune-related adverse events. There are both challenges and opportunities for research on the relationship between lung microbiome and lung cancer. This review will focus on the significance and value of lung microbiome in the occurrence, diagnosis and immunotherapy of lung cancer, in order to provide a reference for basic and clinical researchers in related fields.
There is increasing evidence that microorganisms play a complex and important role in human health and disease, and that the in vivo microbiome can directly or indirectly affect the host’s immune system, endocrine system, and nervous system. Therefore, a relatively stable equilibrium between the host and the microbiome is crucial in human health. However, in the special pathophysiological state of the perioperative period, preoperative anxiety and sleep deprivation, anesthesia intervention and surgical injury, postoperative medication and complications may all have different effects on the microbial composition of various organs in the body, resulting in pathogenic microorganisms, and the balance between beneficial microorganisms is altered. This may affect patient the outcomes and prognosis in a direct or indirect manner. This paper will provide a systematic review of key studies to understand the impact of perioperative stress on the commensal microbiome, provide a fresh perspective on optimizing perioperative management strategies, and discuss possible potential interventions to restore microbiome-mediated steady state.
Childhood obesity is a global public health problem that seriously affects the normal growth and development of children. In recent years, a large number of studies have pointed out that the intestinal microbiome is closely related to childhood obesity, and the treatment strategies targeting the intestinal microbiome have a certain improvement effect on childhood obesity. This article elaborates on the establishment and development of intestinal microbiome, intestinal microbiome characteristics, the mechanisms of intestinal microbiome involvement in the occurrence and development of childhood obesity, and potential intervention strategies, so as to provide more ideas for basic and clinical research on childhood obesity.
ObjectiveTo summarize the changes of gut microbiota after cholecystectomy, the mechanisms of changes, and the relation with colorectal cancer, nonalcoholic fatty liver disease and post-cholecystectomy syndrome after cholecystectomy, in order to provide new ideas for the perioperative management of patients undergoing cholecystectomy. MethodThe studies related to gut microbiota after cholecystectomy at home and abroad were searched and analyzed for review. ResultsThe cholecystectomy disrupted the liver–bile acid–gut flora axis of the patients, and the composition and diversity of the gut microbiota of the patients were altered, and the alteration might lead to the occurrence of colorectal cancer, nonalcoholic fatty liver disease, and post-cholecystectomy syndrome, but the exact mechanism remained unclear. ConclusionsThe balance of intestinal microecology is disrupted after cholecystectomy, and the relation between cholecystectomy and gut microbiota may provide new ideas for the perioperative management of cholecystectomy patients and the prevention and treatment of diseases or symptoms after cholecystectomy, but the effect of cholecystectomy on gut microbiota and the relation with diseases or symptoms still need to be further studied.
Objective To explore the relationship between the gut microbiome (GM) and psoriasis using a two-sample two-way Mendelian randomization (MR) approach. Methods The forward analysis uses the gut microbiota as the exposure factor, and its genetic data are derived from the genome-wide association study dataset published by the MiBioGen consortium. Psoriasis was used as the outcome variable, and its genetic data were obtained from the UK Biobank. The reverse MR analysis, on the other hand, took psoriasis as the exposure and the specific gut microbiota taxonomic units identified in the forward analysis as the outcome variable. MR analysis was conducted using maximum likelihood, MR Egger regression, weighted median, inverse variance weighting (IVW), and weighted models to study the causal relationship between the gut microbiota and psoriasis. Then, sensitivity analyses including horizontal pleiotropy test, Cochran’s Q test, and leave-one-out analysis were used to evaluate the reliability of the results. Results A total of 51 single nucleotide polymorphisms from 5 fungi were included in the forward study. The forward IVW analysis results showed that, the class Mollicutes [odds ratio (OR)=1.003, 95% confidence interval (CI) (1.001, 1.006), P=0.004], genus Lachnospiraceae FCS020 group [OR=1.003, 95%CI (1.000, 1.006), P=0.041], and phylum Tenericutes [OR=1.003, 95%CI (1.001, 1.006), P=0.004] were causally associated with an increased risk of psoriasis. The family Victivallaceae [OR=0.998, 95%CI (0.997, 1.000), P=0.005] and order Pasteurellales [OR=0.998, 95%CI (0.996, 1.000), P=0.047] were also linked to a decreased risk of psoriasis. The results of the sensitivity analysis were robust. There was no evidence of a reverse causal relationship from psoriasis to the identified bacterial taxa found in the results of reverse MR analysis results. Conclusions The abundance of three species, class Mollicutes, genus Lachnospiraceae and phylum Tenericutes, may increase the risk of psoriasis. The abundance of two species, family Victivallaceae and order Pasteurellales may reduce the risk of psoriasis. These results provide new directions for the prevention and treatment of psoriasis in the future, but further research is needed to explore how the aforementioned microbiome affects the progression of psoriasis.