{"title":"Pathogenic Mechanism of the Lactylation-Related Gene DCBLD1 in Ulcerative Colitis: A Multi-Omics and Machine Learning Analysis.","authors":"Changan Chen, Yuping Yang, Tingmei Yang, Caiyuan Yu, Guixia Zhang, Lijiao Cui, Yu Zhou, Zhenkai Li, Zihang Hu, Yijie Weng","doi":"10.2174/0113862073403664250911055311","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The incidence of ulcerative colitis (UC) has been steadily increasing in recent years. Current treatments are only effective for some patients, highlighting the need to find novel therapeutic targets. Lactylation, a post-translational modification, remains poorly understood in UC. This study examines the role of the lactylation-related gene DCBLD1 in the pathogenesis of UC through multi-omics analysis.</p><p><strong>Methods: </strong>Summary-data-based Mendelian Randomization (SMR) analysis identified DCBLD1 as a lactylation-related gene associated with UC risk. Single-cell RNA sequencing (scRNA-seq) examined DCBLD1 expression in UC and healthy intestinal tissues, coupled with cellular communication, metabolic pathway, KEGG enrichment, and GO annotations. Diagnostic models were built based on differential expression between DCBLD1+ and DCBLD1- epithelial cells. In addition, RNA sequencing (RNA-seq) was used for analysis. Ultimately, qPCR was performed to validate DCBLD1 expression.</p><p><strong>Results: </strong>SMR demonstrated that DCBLD1 positively correlated with UC risk. scRNA-seq revealed that DCBLD1+ epithelial cells exhibited enhanced cellular communication and metabolic activity. Seventeen hub genes were screened for machine learning, yielding AUC values of 0.69 (CATboost), 0.63 (XGBoost), and 0.55 (NGboost) in the test set. RNA-seq confirmed the association of DCBLD1 with immune responses. qPCR confirmed elevated DCBLD1 expression in UC tissues versus controls.</p><p><strong>Discussion: </strong>Intestinal epithelial cells expressing DCBLD1 may promote inflammation in UC by lactylation, regulating immunometabolism, and participating in immunological responses, all of which require further investigation in the future.</p><p><strong>Conclusion: </strong>DCBLD1 may promote UC progression through lactylation, immune-metabolic regulation, and involvement in immune responses, serving as a potential therapeutic target.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Combinatorial chemistry & high throughput screening","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0113862073403664250911055311","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The incidence of ulcerative colitis (UC) has been steadily increasing in recent years. Current treatments are only effective for some patients, highlighting the need to find novel therapeutic targets. Lactylation, a post-translational modification, remains poorly understood in UC. This study examines the role of the lactylation-related gene DCBLD1 in the pathogenesis of UC through multi-omics analysis.
Methods: Summary-data-based Mendelian Randomization (SMR) analysis identified DCBLD1 as a lactylation-related gene associated with UC risk. Single-cell RNA sequencing (scRNA-seq) examined DCBLD1 expression in UC and healthy intestinal tissues, coupled with cellular communication, metabolic pathway, KEGG enrichment, and GO annotations. Diagnostic models were built based on differential expression between DCBLD1+ and DCBLD1- epithelial cells. In addition, RNA sequencing (RNA-seq) was used for analysis. Ultimately, qPCR was performed to validate DCBLD1 expression.
Results: SMR demonstrated that DCBLD1 positively correlated with UC risk. scRNA-seq revealed that DCBLD1+ epithelial cells exhibited enhanced cellular communication and metabolic activity. Seventeen hub genes were screened for machine learning, yielding AUC values of 0.69 (CATboost), 0.63 (XGBoost), and 0.55 (NGboost) in the test set. RNA-seq confirmed the association of DCBLD1 with immune responses. qPCR confirmed elevated DCBLD1 expression in UC tissues versus controls.
Discussion: Intestinal epithelial cells expressing DCBLD1 may promote inflammation in UC by lactylation, regulating immunometabolism, and participating in immunological responses, all of which require further investigation in the future.
Conclusion: DCBLD1 may promote UC progression through lactylation, immune-metabolic regulation, and involvement in immune responses, serving as a potential therapeutic target.
期刊介绍:
Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal:
Target identification and validation
Assay design, development, miniaturization and comparison
High throughput/high content/in silico screening and associated technologies
Label-free detection technologies and applications
Stem cell technologies
Biomarkers
ADMET/PK/PD methodologies and screening
Probe discovery and development, hit to lead optimization
Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries)
Chemical library design and chemical diversity
Chemo/bio-informatics, data mining
Compound management
Pharmacognosy
Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products)
Natural Product Analytical Studies
Bipharmaceutical studies of Natural products
Drug repurposing
Data management and statistical analysis
Laboratory automation, robotics, microfluidics, signal detection technologies
Current & Future Institutional Research Profile
Technology transfer, legal and licensing issues
Patents.