Clinical Value of Multiomics-Based Biomarker Signatures in Inflammatory Bowel Diseases: Challenges and Opportunities

Integrating multi-omics data to reveal the host-microbiota interactome in inflammatory bowel disease

Numerous studies have accelerated the knowledge expansion on the role of gut microbiota in inflammatory bowel disease (IBD). However, the precise mechanisms behind host-microbe cross-talk remain largely undefined, due to the complexity of the human intestinal ecosystem and multiple external factors. In this review, we introduce the interactome concept to systematically summarize how intestinal dysbiosis is involved in IBD pathogenesis in terms of microbial composition, functionality, genomic structure, transcriptional activity, and downstream proteins and metabolites. Meanwhile, this review also aims to present an updated overview of the relevant mechanisms, high-throughput multi-omics methodologies, different types of multi-omics cohort resources, and computational methods used to understand host-microbiota interactions in the context of IBD. Finally, we discuss the challenges pertaining to the integration of multi-omics data in order to reveal host-microbiota cross-talk and offer insights into relevant future research directions.

BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a chronic illness with a multifactorial etiology and heterogeneous symptomatology, posing major challenges for diagnosis and treatment. Here, we present BioMapAI, a supervised deep neural network trained on a four-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data, and detailed clinical symptoms. By simultaneously modeling these diverse data types to predict clinical severity, BioMapAI identifies disease- and symptom-specific biomarkers and robustly classifies ME/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct the first connectivity map spanning the microbiome, immune system, and plasma metabolome in health and ME/CFS, adjusted for age, gender, and additional clinical factors. This map uncovers disrupted associations between microbial metabolism (e.g., short-chain fatty acids, branched-chain amino acids, tryptophan, benzoate), plasma lipids and bile acids, and heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT) secreting IFNγ and GzA. Overall, BioMapAI provides unprecedented systems-level insights into ME/CFS, refining existing hypotheses and hypothesizing new pathways associated to the disease's heterogeneous symptoms.

Navigating the complexities of drug development for inflammatory bowel disease

Inflammatory bowel disease (IBD) - consisting of ulcerative colitis and Crohn's disease - is a complex, heterogeneous, immune-mediated inflammatory condition with a multifactorial aetiopathogenesis. Despite therapeutic advances in this arena, a ceiling effect has been reached with both single-agent monoclonal antibodies and advanced small molecules. Therefore, there is a need to identify novel targets, and the development of companion biomarkers to select responders is vital. In this Perspective, we examine how advances in machine learning and tissue engineering could be used at the preclinical stage where attrition rates are high. For novel agents reaching clinical trials, we explore factors decelerating progression, particularly the decline in IBD trial recruitment, and assess how innovative approaches such as reconfiguring trial designs, harmonizing end points and incorporating digital technologies into clinical trials can address this. Harnessing opportunities at each stage of the drug development process may allow for incremental gains towards more effective therapies.

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.

Recent Updates on the Therapeutics Benefits, Clinical Trials, and Novel Delivery Systems of Chlorogenic Acid for the Management of Diseases with a Special Emphasis on Ulcerative Colitis

Ulcerative colitis (UC) is a multifactorial disorder of the large intestine, especially the colon, and has become a challenge globally. Allopathic medicines are primarily available for the treatment and prevention of UC. However, their uses are limited due to several side effects. Hence, an alternative therapy is of utmost importance in this regard. Herbal medicines are considered safe and effective for managing human health problems. Chlorogenic acid (CGA), the herbal-derived bioactive, has been reported for pharmacological effects like antiinflammatory, immunomodulatory, antimicrobial, hepatoprotective, antioxidant, anticancer, etc. This review aims to understand the antiinflammatory and chemopreventive potential of CGA against UC. Apart from its excellent therapeutic potential, it has been associated with low absorption and poor oral bioavailability. In this context, colon-specific novel drug delivery systems (NDDS)are pioneering to overcome these problems. The pertinent literature was compiled from a thorough search on various databases such as ScienceDirect, PubMed, Google Scholar, etc., utilizing numerous keywords, including ulcerative colitis, herbal drugs, CGA, pharmacological activities, mechanism of actions, nanoformulations, clinical updates, and many others. Relevant publications accessed till now were chosen, whereas non-relevant papers, unpublished data, and non-original articles were excluded. The present review comprises recent studies on pharmacological activities and novel drug delivery systems of CGA for managing UC. In addition, the clinical trials of CGA against UC have been discussed.

Clinical Validity of Anti-Proteinase 3 Antibodies in Patients with Inflammatory Bowel Disease: A Short Meta-Analysis

Anti-neutrophil cytoplasmic antibodies (ANCA) directed to proteinase 3 (PR3) represent highly established markers for patients with ANCA-associated vasculitis (AAV). PR3-ANCA have also demonstrated utility in the management of inflammatory bowel disease (IBD). More specifically, PR3-ANCA discriminate individuals with ulcerative colitis (UC) from Crohn's disease (CD) patients and are associated with disease severity, activity, and treatment non-response. Here, we aim to summarize the current data on the diagnostic utility of PR3-ANCA in IBD. A structured, systematic literature review, including three electronic databases, was conducted on June 6th, 2023, to identify studies assessing the diagnostic accuracy of the QUANTA Flash® PR3 assay in UC vs. CD patients. Electronic searches were supplemented by hand searching. A hierarchical, bivariate, mixed-effect meta-analysis was conducted using the metandi function, as per the Cochrane collaboration recommendations. Study quality was assessed using the QUADAS-2 tool, which considers the risk of bias and applicability. Six out of a hundred and eleven citations met the inclusion criteria and reported QUANTA Flash® PR3 diagnostic accuracy in UC vs. CD (UC, n = 667, CD, n = 682 patients). The sensitivity/specificity point estimate for UC was 34.9%/95.9%. This resulted in a Diagnostic Odds Ratio (DOR) of 12.6. The risk of bias was low in the index test and reference standard domains. Four of the six studies (67%) showed an unclear risk of bias in patient selection and in flow and timing domains. All studies had low concerns about applicability in all the domains. PR3-ANCA measured with the QUANTA Flash® PR3 assay represent novel diagnostic markers in IBD and enables discrimination between UC and CD.