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Intractable functional constipation (IFC) is the most severe form of constipation, but its etiology has long been unknown. We hypothesized that IFC is caused by refractory infection by a pathogenic bacterium. Here, we isolated from patients with IFC a Shigella species--peristaltic contraction-inhibiting bacterium (PIB)--that significantly inhibited peristaltic contraction of the colon by production of docosapentenoic acid (DPA). PIB colonized mice for at least 6 months. Oral administration of PIB was sufficient to induce constipation, which was reversed by PIB-specific phages. A mutated PIB with reduced DPA was incapable of inhibiting colonic function and inducing constipation, suggesting that DPA produced by PIB was the key mediator of the genesis of constipation. PIBs were detected in stools of 56% (38 of 68) of the IFC patients, but not in those of nonIFC or healthy individuals (0 of 180). DPA levels in stools were elevated in 44.12% (30 of 68) of the IFC patients but none of the healthy volunteers (0 of 97). Our results suggest that Shigella sp. PIB may be the critical causative pathogen for IFC, and detection of fecal PIB plus DPA may be a reliable method for IFC diagnosis and classification., Introduction Chronic constipation (CC) is a common and multifactorial disorder characterized by reduced bowel movement, hard stools, and excessive fecal straining (1-3). CC may be primary (idiopathic) or associated with [...]

Background: The incidence of gallbladder diseases is as high as 20%, but whether gallbladder diseases contribute to hepatic disorders remains unknown., Methods: Here, we established an animal model of gallbladder dysfunction and assessed the role of a diseased gallbladder in cholestasis-induced hepatic fibrosis (CIHF)., Results: Mice with smooth muscle-specific deletion of Mypt1, the gene encoding the main regulatory subunit of myosin light chain phosphatase (myosin phosphatase target subunit 1 [MYPT1]), had apparent dysfunction of gallbladder motility. This dysfunction was evidenced by abnormal contractile responses, namely, inhibited cholecystokinin 8-mediated contraction and nitric oxide-resistant relaxation. As a consequence, the gallbladder displayed impaired bile filling and biliary tract dilation comparable to the alterations in CIHF. Interestingly, the mutant animals also displayed CIHF features, including necrotic loci by the age of 1 month and subsequently exhibited progressive fibrosis and hyperplastic/dilated bile ducts. This pathological progression was similar to the phenotypes of the animal model with bile duct ligation and patients with CIHF. The characteristic biomarker of CIHF, serum alkaline phosphatase activity, was also elevated in the mice. Moreover, we observed that the myosin phosphatase target subunit 1 protein level was able to be regulated by several reagents, including lipopolysaccharide, exemplifying the risk factors for gallbladder dysfunction and hence CIHF., Conclusions: We propose that gallbladder dysfunction caused by myosin phosphatase target subunit 1 ablation is sufficient to induce CIHF in mice, resulting in impairment of the bile transport system., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases.)

Over the years, extensive research has highlighted the functional roles of small nucleolar RNAs in various biological processes associated with the development of complex human diseases. Therefore, understanding the existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis and treatment. However, classical biological experiments for identifying snoRNA-disease associations are expensive and time-consuming. Therefore, there is an urgent need for cost-effective computational techniques that can enhance the efficiency and accuracy of prediction. While several computational models have already been proposed, many suffer from limitations and suboptimal performance. In this study, we introduced a novel Graph Neural Network-based (GNN) classification model, called SAGESDA, which is implemented through the GraphSAGE architecture with attention for the prediction of snoRNA-disease associations. The classifier leverages local neighbouring nodes in a heterogeneous network to generate new node embeddings through message passing. The mini-batch gradient descent technique was applied to divide the graph into smaller sub-graphs, which enhances the model's accuracy, speed and scalability. With these advancements, SAGESDA attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 using the standard dot product classifier, surpassing previous related studies. This notable performance demonstrates that SAGESDA is a promising model for predicting unknown snoRNA-disease associations with high accuracy. The SAGESDA implementation details can be obtained from https://github.com/momanyibiffon/SAGESDA.git., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)