Current and emerging biomarkers for ulcerative colitis

Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with an idiopathic origin, characterized by persistent mucosal inflammation. Anoikis is a programmed cell death mechanism activated during carcinogenesis to eliminate undetected isolated cells from the extracellular matrix. Although existing evidence indicates that anoikis contributes to the modulation of immune response, the involvement of anoikis-related genes (ARGs) in UC pathogenesis and their interaction with infiltrating immune cells has not been thoroughly explored. The GSE75214, GSE92415, and GSE16879 datasets were acquired and integrated from the GEO database. Additionally, 58 ARGs were identified through the GSEA database. Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). Receiver operating characteristic (ROC) analysis was utilized to evaluate the diagnostic accuracy of each gene. Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. Besides, unsupervised cluster analysis was conducted to categorize the UC samples into distinct subgroups, followed by comparing subtype differences. Finally, the upstream regulatory network was constructed and visualized. A comprehensive analysis of the involvement of ARGs in UC was performed, revealing their expression profile, correlation with infiltrating immune cells, and enrichment analyses. We identified five key anoikis-DEGs (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. Moreover, CEACAM6, CFB, CX3CL1, and HLA-DMA exhibited positive associations with infiltrating immune cells in UC, whereas PDK4 displayed a negative correlation with all immune cells. Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. Our analysis identified five key anoikis-DEGs as characteristic biomarkers of UC. These genes were strongly associated with the infiltration of both innate and adaptive immune cells, as well as immune pathways. This study highlights the role of anoikis genes in UC pathophysiology and offers valuable insights for further elucidating UC pathogenesis and individualized therapy.

Patient and physician factors contributing to delays in inflammatory bowel diseases: Enhancing timely diagnosis

In this article, we comment on the article by Blüthner et al. The article provides a comprehensive analysis of the factors contributing to the late detection of Crohn's disease and ulcerative colitis within a German cohort. It highlights the consequences on patient outcomes, particularly disease progression and the increased risk of developing complications. The study identifies specific predictors associated with both patient-related and physician-related delays, offering a detailed exploration of the initial approach. Additionally, the article delves into the distinct patterns observed in the German population, stressing the unique aspects of diagnostic delays that may differ from those reported in other regions. This detailed examination offers valuable insights into the specific challenges faced within the German healthcare system and underscores the necessity of targeted interventions to facilitate early diagnosis. The importance of improved screening tools, patient education, and better healthcare infrastructure is emphasized as crucial steps toward improving patient care in inflammatory bowel disease.

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data-Independent Acquisition Mass Spectrometry (DIA-MS) Discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data-independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA-MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation based on 8- or 11-point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

Revealing therapeutic targets and drugs from Chinese medicine for ulcerative colitis using bioinformatics

Pathogenesis and therapeutic drugs for ulcerative colitis (UC) have plagued researchers worldwide. In this study, therapeutic targets, and drugs from Chinese medicines for UC were screened using bioinformatics. We downloaded five datasets from the GEO database and three machine learning algorithms were used for screening diagnostic biomarkers of UC. Combined with the differential genes for UC, gene sets related to bile acid metabolism, short-chain fatty acids, apoptosis, pyroptosis, G-protein-coupled receptors, mitochondria, and autophagy were collected to screen the core targets, and analyze the association of therapeutic genes (diagnostic biomarkers and core targets) with immune cells. In addition, screening ingredients of Chinese medicines based on UC therapeutic targets was performed. Molecular docking, molecular dynamics simulation, and literature validation were also performed. The screening yielded 37 key therapeutic targets, including 5 diagnostic biomarkers (CCL11, CXCL1, PDZK1IP1, TIMP1, and UGT2A3) and 32 core targets based on hot gene sets. Immune cell infiltration was strongly associated with therapeutic targets in UC, especially neutrophils, macrophages, mast cells, and dendritic cells. Furthermore, a total of 33 compounds with high safety had been recognized as having potential to mitigate UC by reverse prediction from Chinese medicines, and molecular docking, molecular dynamics simulation, and literature reports preliminarily validated the screening results. Although further experimental validation is needed, this work provides some potential therapeutic targets and drugs from Chinese medicines against UC.

Protective role of TRPM7 knockdown in ulcerative colitis via blocking NLRP3 inflammasome-mediated pyroptosis

Transient receptor potential melastatin 7 (TRPM7) has been emerged as a potent drug target for immunomodulation with ion conductance and kinase activities. The research is projected to characterize the influences of TRPM7 on the course of ulcerative colitis (UC) and dissect the latent response mechanisms. The in vivo murine model and in vitro cell model of UC were both stimulated by DSS. RT-qPCR and western blotting tested the abundance of TRPM7. Colonic damage was estimated by Hematoxylin-eosin staining, calculation of colon length, measurement of DAI and MPO assay kit. CCK-8 method and TUNEL staining severally ascertained cell activity and apoptosis. ELISA method assayed the inflammatory levels and relevant assay kits determined oxidative stress levels. FITC-dextran flux, immunohistochemistry, TEER as well as western blotting evaluated intestinal barrier function. Immunofluorescence staining and western blotting appraised NLR family pyrin domain containing 3 (NLRP3)-dependent pyroptosis. Depleted TRPM7 retarded inflammation, oxidative damage as well as intestinal barrier damage both in vitro and in vivo. TRPM7 reduction repressed the pyroptosis mediated by NLRP3 inflammasome. NLRP3 agonist nigericin partly abolished the protection elicited by TRPM7 silencing against inflammation, oxidative damage as well as intestinal barrier damage in vitro. Collectively, TRPM7 deletion might possess the therapeutic potential in UC, the working mechanism of which might involve the inactivation of NLRP3-dependent pyroptosis.

Microcapsules based on biological macromolecules for intestinal health: A review

Intestinal dysfunction is becoming increasingly associated with neurological and endocrine issues, raising concerns about its impact on world health. With the introduction of several breakthrough technologies for detecting and treating intestinal illnesses, significant progress has been made in the previous few years. On the other hand, traditional intrusive diagnostic techniques are expensive and time-consuming. Furthermore, the efficacy of conventional drugs (not capsules) is reduced since they are more likely to degrade before reaching their target. In this context, microcapsules based on different types of biological macromolecules have been used to encapsulate active drugs and sensors to track intestinal ailments and address these issues. Several biomacromolecules/biomaterials (natural protein, alginate, chitosan, cellulose and RNA etc.) are widely used for make microcapsules for intestinal diseases, and can significantly improve the therapeutic effect and reduce adverse reactions. This article systematically summarizes microencapsulated based on biomacromolecules material for intestinal health control and efficacy enhancement. It also discusses the application and mechanism research of microencapsulated biomacromolecules drugs in reducing intestinal inflammation, in addition to covering the preparation techniques of microencapsulated drug delivery systems used for intestinal health. Microcapsule delivery systems' limits and potential applications for intestinal disease diagnosis, treatment, and surveillance were highlighted.

The Contribution of Genetic and Epigenetic Factors: An Emerging Concept in the Assessment and Prognosis of Inflammatory Bowel Diseases

Inflammatory bowel disease (IBD) represents heterogeneous and relapsing intestinal conditions with a severe impact on the quality of life of individuals and a continuously increasing prevalence. In recent years, the development of sequencing technology has provided new means of exploring the complex pathogenesis of IBD. An ideal solution is represented by the approach of precision medicine that investigates multiple cellular and molecular interactions, which are tools that perform a holistic, systematic, and impartial analysis of the genomic, transcriptomic, proteomic, metabolomic, and microbiomics sets. Hence, it has led to the orientation of current research towards the identification of new biomarkers that could be successfully used in the management of IBD patients. Multi-omics explores the dimension of variation in the characteristics of these diseases, offering the advantage of understanding the cellular and molecular mechanisms that affect intestinal homeostasis for a much better prediction of disease development and choice of treatment. This review focuses on the progress made in the field of prognostic and predictive biomarkers, highlighting the limitations, challenges, and also the opportunities associated with the application of genomics and epigenomics technologies in clinical practice.

Precision Medicine in Inflammatory Bowel Disease: A Spotlight on Emerging Molecular Biomarkers

Inflammatory bowel diseases (IBD) remain challenging in terms of understanding their causes and in terms of diagnosing, treating, and monitoring patients. Modern diagnosis combines biomarkers, imaging, and endoscopic methods. Common biomarkers like CRP and fecal calprotectin, while invaluable tools, have limitations and are not entirely specific to IBD. The limitations of existing markers and the invasiveness of endoscopic procedures highlight the need to discover and implement new markers. With an ideal biomarker, we could predict the risk of disease development, as well as the possibility of response to a particular therapy, which would be significant in elucidating the pathogenesis of the disease. Recent research in the fields of machine learning, proteomics, epigenetics, and gut microbiota provides further insight into the pathogenesis of the disease and is also revealing new biomarkers. New markers, such as BAFF, PGE-MUM, oncostatin M, microRNA panels, αvβ6 antibody, and S100A12 from stool, are increasingly being identified, with αvβ6 antibody and oncostatin M being potentially close to being presented into clinical practice. However, the specificity of certain markers still remains problematic. Furthermore, the use of expensive and less accessible technology for detecting new markers, such as microRNAs, represents a limitation for widespread use in clinical practice. Nevertheless, the need for non-invasive, comprehensive markers is becoming increasingly important regarding the complexity of treatment and overall management of IBD.

Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection

This study, utilizing high-throughput technologies and Machine Learning (ML), has identified gene biomarkers and molecular signatures in Inflammatory Bowel Disease (IBD). We could identify significant upregulated or downregulated genes in IBD patients by comparing gene expression levels in colonic specimens from 172 IBD patients and 22 healthy individuals using the GSE75214 microarray dataset. Our ML techniques and feature selection methods revealed six Differentially Expressed Gene (DEG) biomarkers (VWF, IL1RL1, DENND2B, MMP14, NAAA, and PANK1) with strong diagnostic potential for IBD. The Random Forest (RF) model demonstrated exceptional performance, with accuracy, F1-score, and AUC values exceeding 0.98. Our findings were rigorously validated with independent datasets (GSE36807 and GSE10616), further bolstering their credibility and showing favorable performance metrics (accuracy: 0.841, F1-score: 0.734, AUC: 0.887). Our functional annotation and pathway enrichment analysis provided insights into crucial pathways associated with these dysregulated genes. DENND2B and PANK1 were identified as novel IBD biomarkers, advancing our understanding of the disease. The validation in independent cohorts enhances the reliability of these findings and underscores their potential for early detection and personalized treatment of IBD. Further exploration of these genes is necessary to fully comprehend their roles in IBD pathogenesis and develop improved diagnostic tools and therapies. This study significantly contributes to IBD research with valuable insights, potentially greatly enhancing patient care.

Challenges in IBD Research 2024: Precision Medicine

Precision medicine is part of 5 focus areas of the Challenges in IBD Research 2024 research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. Building on Challenges in IBD Research 2019, the current Challenges aims to provide a comprehensive overview of current gaps in inflammatory bowel diseases (IBDs) research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient-centric research prioritization. In particular, the precision medicine section is focused on the main research gaps in elucidating how to bring the best care to the individual patient in IBD. Research gaps were identified in biomarker discovery and validation for predicting disease progression and choosing the most appropriate treatment for each patient. Other gaps were identified in making the best use of existing patient biosamples and clinical data, developing new technologies to analyze large datasets, and overcoming regulatory and payer hurdles to enable clinical use of biomarkers. To address these gaps, the Workgroup suggests focusing on thoroughly validating existing candidate biomarkers, using best-in-class data generation and analysis tools, and establishing cross-disciplinary teams to tackle regulatory hurdles as early as possible. Altogether, the precision medicine group recognizes the importance of bringing basic scientific biomarker discovery and translating it into the clinic to help improve the lives of IBD patients.

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.