Inflammatory bowel disease and Clostridium difficile infection: clinical presentation, diagnosis, and management

Role of gut microbiota in Crohn's disease pathogenesis: Insights from fecal microbiota transplantation in mouse model

BACKGROUND

Inflammatory bowel disease, particularly Crohn's disease (CD), has been associated with alterations in mesenteric adipose tissue (MAT) and the phenomenon termed "creeping fat". Histopathological evaluations showed that MAT and intestinal tissues were significantly altered in patients with CD, with these tissues characterized by inflammation and fibrosis.

AIM

To evaluate the complex interplay among MAT, creeping fat, inflammation, and gut microbiota in CD.

METHODS

Intestinal tissue and MAT were collected from 12 patients with CD. Histological manifestations and protein expression levels were analyzed to determine lesion characteristics. Fecal samples were collected from five recently treated CD patients and five control subjects and transplanted into mice. The intestinal and mesenteric lesions in these mice, as well as their systemic inflammatory status, were assessed and compared in mice transplanted with fecal samples from CD patients and control subjects.

RESULTS

Pathological examination of MAT showed significant differences between CD-affected and unaffected colons, including significant differences in gut microbiota structure. Fetal microbiota transplantation (FMT) from clinically healthy donors into mice with 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced CD ameliorated CD symptoms, whereas FMT from CD patients into these mice exacerbated CD symptoms. Notably, FMT influenced intestinal permeability, barrier function, and levels of proinflammatory factors and adipokines. Furthermore, FMT from CD patients intensified fibrotic changes in the colon tissues of mice with TNBS-induced CD.

CONCLUSION

Gut microbiota play a critical role in the histopathology of CD. Targeting MAT and creeping fat may therefore have potential in the treatment of patients with CD.

Predicting potential microbe-disease associations based on dual branch graph convolutional network

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.

The Networked Interaction between Probiotics and Intestine in Health and Disease: A Promising Success Story

Probiotics are known to promote human health either precautionary in healthy individuals or therapeutically in patients suffering from certain ailments. Although this knowledge was empirical in past tomes, modern science has already verified it and expanded it to new limits. These microorganisms can be found in nature in various foods such as dairy products or in supplements formulated for clinical or preventive use. The current review examines the different mechanisms of action of the probiotic strains and how they interact with the organism of the host. Emphasis is put on the clinical therapeutic use of these beneficial microorganisms in various clinical conditions of the human gastrointestinal tract. Diseases of the gastrointestinal tract and particularly any malfunction and inflammation of the intestines seriously compromise the health of the whole organism. The interaction between the probiotic strains and the host's microbiota can alleviate the clinical signs and symptoms while in some cases, in due course, it can intervene in the underlying pathology. Various safety issues of the use of probiotics are also discussed.