Alleviating depressive-like behavior in DSS-induced colitis mice: Exploring naringin and poncirin from Poncirus trifoliata extracts.

Patients with inflammatory bowel diseases (IBDs), including ulcerative colitis (UC) and Crohn's disease (CD), often have concomitant mental disorders such as depression and anxiety. Therefore, a bidirectional approach involving the gut and brain axes is necessary for the prevention and treatment thereof. In this study, we explored the potential of Poncirus trifoliata extract (PT), traditionally known for its neuroprotective effects against gastrointestinal diseases, as a natural treatment agent for IBD in a dextran sulfate sodium (DSS)-induced colitis model. Oral administration of PT ameliorated weight loss and inflammatory responses in mice with DSS-induced colitis. Furthermore, PT treatment effectively restored the colon length and ameliorated enterocyte death by inhibiting DSS-induced reactive oxygen species (ROS)-mediated necroptosis. The main bioactive components of PT, poncirin and naringin, confirmed using ultra-performance liquid chromatography-quadrupole time-of-flight (UPLC-qTOF), can be utilized to regulate necroptosis. The antidepressant-like effects of PT were confirmed using open field test (OFT) and tail suspension test (TST). PT treatment also restored vascular endothelial cell integrity in the hippocampus. In the Cornu Ammonis 1 (CA1) and dentate gyrus (DG) regions of the hippocampus, PT controlled the neuroinflammatory responses of proliferated microglia. In conclusion, PT, which contains high levels of poncirin and naringin, has potential as a bidirectional therapeutic agent that can simultaneously improve IBD-associated intestinal and mental disorders.

Potential Phytotherapy of DSS-Induced Colitis: Ameliorating Reactive Oxygen Species-Mediated Necroptosis and Gut Dysbiosis with a New Crataegus pinnatifida Bunge Variety-Daehong.

Developing new plant varieties plays a crucial role in competitiveness in the agricultural and food industries and enhancing food security. Daehong (DH) is a new variety of Crataegus pinnatifida Bunge (CP); however, its physiological functions and potential as a nutraceutical ingredient remain unknown. Here, the efficacy of DH on inflammatory bowel disease (IBD) was investigated using dextran sulfate sodium (DSS)-induced colitis mice, and its relative pharmacological effects were analyzed against CP. DH improved colitis-induced weight loss, colon shortening, and inflammatory responses and reduced intestinal permeability. The reactive oxygen species (ROS)-mediated necroptotic signal that triggers enterocyte cell death in DSS-induced colitis was effectively controlled by DH, attributed to epicatechin. DSS-induced gut dysbiosis was recovered into a healthy gut microbiome environment by DH, increasing beneficial bacteria, like Akkermansia muciniphila, and changing harmful bacteria, including Bacteroides vulgatus and Peptostreptococcaceae. DH shows potential as a dietary or pharmaceutical ingredient to promote gut health and to prevent and treat IBD.

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-precision data (SLEM) to explore the SNP combination effects on schizophrenia through intermediate molecular and cellular functions. The SLEM technique utilizes two levels of precision data for learning. It constructs initial backbone networks with more precise but small amount of multilevel assay data. Then, it learns strengths of intermediate interactions with the less precise but massive amount of GWAS data. The learned networks facilitate identifying effective SNP interactions from the intractably large space of all possible SNP combinations. We have shown that the extracted SNP combinations show higher accuracy than any single SNPs and preserve the accuracy in an independent dataset. The learned networks also provide interpretations of molecular and cellular interactions of SNP combinations toward schizophrenia etiology.

Shared co-expression networks in frontal cortex of the normal aged brain and schizophrenia.

Previous studies on the brain of people with schizophrenia have identified structural changes and gene expression changes, suggesting that brain aging maybe accelerated in people with schizophrenia. To better characterize gene expression profiles in schizophrenia and in the aged population we constructed co-expression networks using RNA-Seq data from frontal cortex. The first data set analysed was from 62 subjects with schizophrenia and 51 unaffected controls ranging in age from 19 to 63 years. The second separate data set was from normal control individuals ranging in age from 29 to 106 years. In the first data set, we found two co-expression modules significantly associated with schizophrenia. One was a downregulated co-expression module enriched for neuron function related genes and the other was an upregulated immune/inflammation-related module. In the second data set of normal individuals, we found seven co-expression modules significantly correlated with age. A comparison of the co-expression modules from the two data sets revealed a significant consensus in nodes associated with schizophrenia and those associated with normal aging. The results indicate that a co-expression module related to neuronal function is downregulated and an immune/inflammation related co-expression module is upregulated, and associated with cells of the blood vessels, in both schizophrenia and in normal aging. This finding adds further support to the hypothesis that there may be accelerated brain aging in schizophrenia.

Identification of common coexpression modules based on quantitative network comparison.

BACKGROUND: Finding common molecular interactions from different samples is essential work to understanding diseases and other biological processes. Coexpression networks and their modules directly reflect sample-specific interactions among genes. Therefore, identification of common coexpression network or modules may reveal the molecular mechanism of complex disease or the relationship between biological processes. However, there has been no quantitative network comparison method for coexpression networks and we examined previous methods for other networks that cannot be applied to coexpression network. Therefore, we aimed to propose quantitative comparison methods for coexpression networks and to find common biological mechanisms between Huntington's disease and brain aging by the new method. RESULTS: We proposed two similarity measures for quantitative comparison of coexpression networks. Then, we performed experiments using known coexpression networks. We showed the validity of two measures and evaluated threshold values for similar coexpression network pairs from experiments. Using these similarity measures and thresholds, we quantitatively measured the similarity between disease-specific and aging-related coexpression modules and found similar Huntington's disease-aging coexpression module pairs. CONCLUSIONS: We identified similar Huntington's disease-aging coexpression module pairs and found that these modules are related to brain development, cell death, and immune response. It suggests that up-regulated cell signalling related cell death and immune/ inflammation response may be the common molecular mechanisms in the pathophysiology of HD and normal brain aging in the frontal cortex.