Identification of key genes and biological processes contributing to colitis associated dysplasia in ulcerative colitis

Revealing Novel Genes Related to Parkinson's Disease Pathogenesis and Establishing an associated Model

Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein-protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3, HIST2H2BE, and HDAC1, all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3, HIST2H2BE, and HDAC1, were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3, HIST2H2BE, and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD.

Colonic Dysregulation of Major Metabolic Pathways in Experimental Ulcerative Colitis

Inflammatory bowel disease (IBD) is multifactorial chronic inflammatory disease in the gastrointestinal tract, affecting patients' quality of life profoundly. The incidence of IBD has been on the rise globally for the last two decades. Because the molecular mechanisms underlying the disease remain not well understood, therapeutic development is significantly impeded. Metabolism is a crucial cellular process to generate the energy needed for an inflammatory response and tissue repair. Comprehensive understanding of the metabolic pathways in IBD would help to unravel the disease pathogenesis/progression and facilitate therapeutic discoveries. Here, we investigated four metabolic pathways altered in experimental colitis. C57BL/6J mice were treated with dextran sulfate sodium (DSS) in drinking water for 7 days to induce experimental ulcerative colitis (UC). We conducted proteomics analysis for the colon samples using LC/MS, to profile key metabolic intermediates. Our findings revealed significant alterations in four major metabolic pathways: antioxidative defense, β-oxidation, glycolysis, and TCA cycle pathways. The energy metabolism by β-oxidation, glycolysis, and TCA cycle pathways were downregulated under UC, together with reduced antioxidative defense pathways. These results reveal metabolic re-programming in intestinal cells under UC, showing dysregulation in all four major metabolic pathways. Our study underscores the importance of metabolic drivers in the pathogenesis of IBD and suggests that the modification of metabolism may serve as a novel diagnostic/therapeutic approach for IBD.

Single-cell dissection, hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis

Ulcerative colitis (UC) develops as a result of complex interactions between various cell types in the mucosal microenvironment. In this study, we aim to elucidate the pathogenesis of ulcerative colitis at the single-cell level and unveil its clinical significance. Using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis, we identify a subpopulation of plasma cells (PCs) with significantly increased infiltration in UC colonic mucosa, characterized by pronounced oxidative stress. Combining 10 machine learning approaches, we find that the PC oxidative stress genes accurately distinguish diseased mucosa from normal mucosa (independent external testing AUC=0.991, sensitivity=0.986, specificity=0.909). Using MCPcounter and non-negative matrix factorization, we identify the association between PC oxidative stress genes and immune cell infiltration as well as patient heterogeneity. Spatial transcriptome data is used to verify the infiltration of oxidatively stressed PCs in colitis. Finally, we develop a gene-immune convolutional neural network deep learning model to diagnose UC mucosa in different cohorts (independent external testing AUC=0.984, sensitivity=95.9%, specificity=100%). Our work sheds light on the key pathogenic cell subpopulations in UC and is essential for the development of future clinical disease diagnostic tools.

TOR1AIP1-Associated Nuclear Envelopathies

Human TOR1AIP1 encodes LAP1, a nuclear envelope protein expressed in most human tissues, which has been linked to various biological processes and human diseases. The clinical spectrum of diseases related to mutations in TOR1AIP1 is broad, including muscular dystrophy, congenital myasthenic syndrome, cardiomyopathy, and multisystemic disease with or without progeroid features. Although rare, these recessively inherited disorders often lead to early death or considerable functional impairment. Developing a better understanding of the roles of LAP1 and mutant TOR1AIP1-associated phenotypes is paramount to allow therapeutic development. To facilitate further studies, this review provides an overview of the known interactions of LAP1 and summarizes the evidence for the function of this protein in human health. We then review the mutations in the TOR1AIP1 gene and the clinical and pathological characteristics of subjects with these mutations. Lastly, we discuss challenges to be addressed in the future.

Protective effect and mechanism insight of purified Antarctic kill phospholipids against mice ulcerative colitis combined with bioinformatics

Antarctic krill oil is functional oil and has a complex phospholipids composition that poses difficulties in elucidating its effect mechanism on ulcerative colitis (UC). The mechanism of UC action was studied by bioinformatics, and the therapeutic effect of Antarctic krill phospholipids (APL) on dextran sulfate sodium (DSS)-induced colitis mice was verified. GO functional enrichment analysis uncovered an enrichment of these genes in the regulation of cell-cell adhesion, membrane region, signaling receptor activator activity, and cytokine activity. Meanwhile, the KEGG results revealed the genes were enriched in the TNF signaling pathway, pathogenic Escherichia coli infection, inflammatory bowel disease and tight junction. Animal experiments showed that APL treatment alleviated the UC symptoms and reduced inflammatory damage. Meanwhile, the expressions of the tight junction (TJ) proteins, ZO-1 and occludin, were restored, and the levels of IL-6 and TNF-α were reduced. Moreover, Firmicutes/Bacteroidetes ratio in the intestinal microbiota was regulated, and the contents of short-chain fatty acids metabolites were raised. These findings would provide an insight for the beneficial effects of APL and dietary therapy strategies for UC.

Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P  <  0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.

Active Ingredients and Potential Mechanisms of the Gan Jiang-Huang Qin-Huang Lian-Ren Shen Decoction against Ulcerative Colitis: A Network Pharmacology and Molecular Docking-Based Study

BACKGROUND

Ulcerative colitis (UC), a chronic and nonspecific inflammatory bowel disease, seriously affects the quality of patients' life. Han Re Bing Yong Fa (treating diseases with both cool- and warm-natured herbs) is a classical therapeutic principle of traditional Chinese medicine (TCM), which is often used to treat chronic diseases, including UC. The Gan Jiang-Huang Qin-Huang Lian-Ren Shen decoction (GJHQHLRSD), a representative of Han Re Bing Yong Fa, is effective in alleviating inflammatory symptoms in UC. However, the pharmacological mechanism underlying its anti-inflammatory effect remains unclear.

METHODS

A network pharmacology strategy, including the construction and analysis of the drug-disease network, was used to explore the complex mechanism of GJHQHLRSD treatment of UC. In addition, molecular docking technology was used to preliminarily examine the binding ability of the potential active components and core therapeutic targets of GJHQHLRSD.

RESULTS

The network pharmacology results revealed 140 targets of GJHQHLRSD which are involved in UC. The PPI network analysis identified seven target genes: BCL2L1, NR3C1, ALOX5, S1PR5, NR1I2, CYP2D6, and LPAR6. The molecular docking results revealed that the following displayed strongest combined effects: EGFR with kaempferol, ERK1 with worenine, STAT3 with Palmidin A, BCL2L1 with diop and VEGFA with ginsenoside Rg3. The KEGG and gene ontology enrichment analyses results indicated that GJHQHLRSD functions by regulating the EGFR signaling pathway in UC treatment. Other effective biological processes involved in UC treatment included cancer-related as well as inflammation and viral infection signaling pathways, such as the "MicroRNAs in cancer," "TNF signaling pathway," and "JAK-STAT signaling pathway."

CONCLUSIONS

This study reflects the multicomponent, multitarget, and multipathway characteristics of the action mechanism of GJHQHLRSD in treating UC. Furthermore, it helps better understand the TCM therapeutic principle of Han Re Bing Yong Fa and explore novel candidate drug targets for UC treatment.