Bacteroides vulgatus alleviates dextran sodium sulfate-induced colitis and depression-like behaviour by facilitating gut-brain axis balance

Wu, Xing; Xu, Jiahao; Li, Jingbo; Deng, Minzi; Shen, Zhaohua; Nie, Kai; Luo, Weiwei; Zhang, Chao; Ma, Kejia; Chen, Xuejie; Wang, Xiaoyan

doi:10.3389/fmicb.2023.1287271

Cited by 2 publications

(1 citation statement)

References 49 publications

(66 reference statements)

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“…Wu et al. 's 87 results showed that the faecal foundation of Bacteroides vulgaris ( B. vulgaris ) was lower in patients with IBD than in those with IBDND. Bai et al confirmed that as a common complication of IBD, Clostridium difficile infection (CDI) has been shown to not only exacerbate the symptoms of IBD, but also lead to unexpected outcomes, including death.…”

Section: Resultsmentioning

confidence: 99%

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

Chen,

Zhu,

Yuan

2024

J Cellular Molecular Medi

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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.

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Section: Resultsmentioning

confidence: 99%

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

Chen,

Zhu,

Yuan

2024

J Cellular Molecular Medi

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Serum Metabolites Relate to Mucosal and Transmural Inflammation in Paediatric Crohn Disease

Suarez,

Guruprasad,

Tata

et al. 2024

Journal of Crohn's and Colitis

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Background and aims We aimed to identify serum metabolites associated with mucosal and transmural inflammation in pediatric Crohn disease (pCD). Methods Fifty-six pCD patients were included through a pre-planned sub-study of the multicenter, prospective, ImageKids cohort, designed to develop the Pediatric Inflammatory Crohn's MRE Index (PICMI). Children were included throughout their disease course when undergoing ileocolonoscopy and magnetic resonance enterography (MRE) and followed for 18 months when MRE was repeated. Serum metabolites were identified using liquid chromatography/mass spectroscopy. Outcomes included: PICMI, the simple endoscopic score (SES), faecal calprotectin (FCP), and C-reactive protein (CRP), to assess transmural, mucosal, and systemic inflammation, respectively. Random forest models were built by outcome. Maximum relevance minimum redundancy (mRMR) feature selection with a j-fold cross validation scheme identified the best subset of features and hyperparameter settings. Results Tryptophan and glutarylcarnitine were the top common mRMR metabolites linked to pCD inflammation. Random forest models established that amino acids and amines were among the most influential metabolites for predicting transmural and mucosal inflammation. Predictive models performed well, each with an area under the curve (AUC) > 70%. In addition, serum metabolites linked with pCD inflammation mainly related to perturbations in citrate cycle (TCA cycle), aminoacyl-tRNA biosynthesis, tryptophan metabolism, butanoate metabolism, and tyrosine metabolism. Conclusions We extend on recent studies, observing differences in serum metabolite between healthy controls and Crohn disease patients, and suggest various associations of serum metabolites with transmural and mucosal inflammation. These metabolites could improve the understanding of pCD pathogenesis and assess disease severity.

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Bacteroides vulgatus alleviates dextran sodium sulfate-induced colitis and depression-like behaviour by facilitating gut-brain axis balance

Cited by 2 publications

References 49 publications

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

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

Serum Metabolites Relate to Mucosal and Transmural Inflammation in Paediatric Crohn Disease

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