The potential of metabolic and lipid profiling in inflammatory bowel diseases: A pilot study

Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations

Fecal metabolites effectively discriminate inflammatory bowel disease (IBD) and show differential associations with diet. Metabolomics and AI-based models, including explainable AI (XAI), play crucial roles in understanding IBD. Using datasets from the UK Biobank and the Human Microbiome Project Phase II IBD Multi'omics Database (HMP2 IBDMDB), this study uses multiple machine learning (ML) classifiers and Shapley additive explanations (SHAP)-based XAI to prioritize plasma and fecal metabolites and analyze their diet correlations. Key findings include the identification of discriminative metabolites like glycoprotein acetyl and albumin in plasma, as well as nicotinic acid metabolites andurobilin in feces. Fecal metabolites provided a more robust disease predictor model (AUC [95%]: 0.93 [0.87-0.99]) compared to plasma metabolites (AUC [95%]: 0.74 [0.69-0.79]), with stronger and more group-differential diet-metabolite associations in feces. The study validates known metabolite associations and highlights the impact of IBD on the interplay between gut microbial metabolites and diet.

An Engineered Butyrate-Derived Polymer Nanoplatform as a Mucosa-Healing Enhancer Potentiates the Therapeutic Effect of Magnolol in Inflammatory Bowel Disease

Colonic epithelial damage and dysregulated immune response are crucial factors in the progression and exacerbation of inflammatory bowel disease (IBD). Nanoenabled targeted drug delivery to the inflamed intestinal mucosa has shown promise in inducing and maintaining colitis remission, while minimizing side effects. Inspired by the excellent antioxidative and anti-inflammatory efficacy of naturally derived magnolol (Mag) and gut homeostasis regulation of microbiota-derived butyrate, we developed a pH/redox dual-responsive butyrate-rich polymer nanoparticle (PSBA) as an oral Mag delivery system for combinational therapy of IBD. PSBA showed a high butyrate content of 22% and effectively encapsulated Mag. The Mag-loaded nanoparticles (PSBA@Mag) demonstrated colonic pH and reduction-responsive drug release, ensuring efficient retention and adhesion in the colon of colitis mice. PSBA@Mag not only normalized the level of reactive oxygen species and inflammatory effectors in inflamed colonic mucosa but also restored the epithelial barrier function in both ulcerative colitis and Crohn's disease mouse models. Importantly, PSBA promoted the migration and healing ability of intestinal epithelial cells in vitro and in vivo, sensitizing the therapeutic efficacy of Mag in animal models. Moreover, transcriptomics and metabolism analyses revealed that PSBA@Mag mitigated inflammation by suppressing the production of pro-inflammatory cytokines and chemokines and restoring the lipid metabolism. Additionally, this nanomedicine modulated the gut microbiota by inhibiting pathogenic Proteus and Escherichia-Shigella and promoting the proliferation of beneficial probiotics, including Lachnoclostridium, Lachnospiraceae_NK4A136_group and norank_f_Ruminococcaceae. Overall, our findings highlight the potential of butyrate-functionalized polymethacrylates as versatile and effective nanoplatforms for colonic drug delivery and mucosa repair in combating IBD and other gastrointestinal disorders.

Cassane diterpenoid ameliorates dextran sulfate sodium-induced experimental colitis by regulating gut microbiota and suppressing tryptophan metabolism

Ulcerative colitis (UC) is one form of inflammatory bowel disease (IBD), characterized by chronic relapsing intestinal inflammation. As increasing morbidity of UC and deficiency of conventional therapies, there is an urgent need for attractive treatment. Cassane diterpenoids, the characteristic chemical constituents of Caesalpinia genus plants, have been studied extensively owing to various and prominent biological activities. This study attempted to investigate the bioactivity of caesaldekarin e (CA), a cassane diterpenoid isolated from C. bonduc in our previous work, on dextran sulfate sodium (DSS)-induced experimental colitis and clarify the function mechanism. The results indicated that CA ameliorated mice colitis by relieving disease symptoms, suppressing inflammatory infiltration and maintaining intestinal barrier integrity. Furthermore, 16S rRNA gene sequencing analysis indicated that CA could improve the gut microbiota imbalance disrupted by DSS and especially restored abundance of Lactobacillus. In addition, untargeted metabolomics analysis suggested that CA regulated metabolism and particularly the tryptophan metabolism by inhibiting the upregulation of indoleamine 2,3-dioxygenase 1 (IDO-1). It also been proved in IFN-γ induced RAW264.7 cells. Overall, this study suggests that CA exhibits anti-UC effect through restoring gut microbiota and regulating tryptophan metabolism and has the potential to be a treatment option for UC.

Exploring the Phosphatidylcholine in Inflammatory Bowel Disease: Potential Mechanisms and Therapeutic Interventions

BACKGROUND

Inflammatory bowel disease (IBD) is a significant health problem with an increasing financial burden worldwide. Although various treatment strategies have been used, the results were not satisfactory. More and more researches have proved that the application of phosphatidylcholine (PC) may become an alternative therapy for IBD.

OBJECTIVE

This review aims to provide an overview of the possible mechanisms of PC and promote the potential application of PC for IBD therapy further.

METHODS

A comprehensive literature search was performed in PubMed with the following keywords: 'phosphatidylcholine', 'inflammatory bowel disease', 'Crohn's disease', 'inflammation', 'ulcerative colitis', 'therapy', 'nanomedicines', 'PKCζ', 'lysophosphatidylcholine', 'microbiota' and 'drug carrier'. The logical operators "AND" and "OR" were applied to combine different sets of the search results.

RESULTS

Studies suggested that PC displays a significant effect in the treatment of IBD by modulating gut barrier function, remodeling gut microbiota structure, regulating polarization of macrophages, and reducing the inflammatory response. PC has also been exploited as a drug carrier for anticancer or anti-inflammation agents in multiple forms, which implies that PC has immense potential for IBD therapy.

CONCLUSION

PC has shown promising potential as a new therapeutic agent or a drug carrier, with a novel, stable, prolonged mechanism of action in treating IBD. However, more high-quality basic and clinical studies are needed to confirm this.

The effect of tuina on ulcerative colitis model mice analyzed by gut microbiota and proteomics

Tuina can effectively alleviate ulcerative colitis-related symptoms, but the mechanism of action is unknown. The purpose of this research is to explore potential pathways for the treatment of tuina through gut microbiota and proteomics techniques. Thirty-two male BALB/c mice were divided into four groups, the control, model, mesalazine, and tuina groups. The ulcerative colitis model was established by freely drinking a 3% dextran sulphate sodium solution for 7 days. The mesalazine group and the tuina group, respectively, received 7 days of mesalazine and tuina treatment. Subsequently, their body weights, feces properties, colon length, histomorphological changes, gut microbiota, and colon proteomics were determined. Body weights, disease activity index score, colon histological scores, and microbiota diversity were restored in the tuina group. At the phylum level, Firmicutes was increased and Bacteroidota decreased. At the family level, Lachnospiraceae increased and Prevotellaceae decreased. At the genus level, the Lachnospiraceae_NK4A136_group was increased. Proteomics detected 370 differentially expressed proteins regulated by tuina, enriched to a total of 304 pathways, including biotin metabolism, Notch signaling pathway, linoleic acid metabolism, and autophagy. Tuina can effectively improve the symptoms of weight loss, fecal properties, and colon inflammation in ulcerative colitis mice and restore the gut microbiota diversity, adjusting the relative abundance of microbiota. The therapeutic effects of tuina may be achieved by modulating the signaling pathways of biotin metabolism, Notch signaling pathway, linoleic acid metabolism, and autophagy.

Inflammatory Bowel Disease and COVID-19: How Microbiomics and Metabolomics Depict Two Sides of the Same Coin

The integrity of the gastrointestinal tract structure and function is seriously compromised by two pathological conditions sharing, at least in part, several pathogenetic mechanisms: inflammatory bowel diseases (IBD) and coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. IBD and COVID-19 are marked by gut inflammation, intestinal barrier breakdown, resulting in mucosal hyperpermeability, gut bacterial overgrowth, and dysbiosis together with perturbations in microbial and human metabolic pathways originating changes in the blood and fecal metabolome. This review compared the most relevant metabolic and microbial alterations reported from the literature in patients with IBD with those in patients with COVID-19. In both diseases, gut dysbiosis is marked by the prevalence of pro-inflammatory bacterial species and the shortfall of anti-inflammatory species; most studies reported the decrease in Firmicutes, with a specific decrease in obligately anaerobic producers short-chain fatty acids (SCFAs), such as Faecalibacterium prausnitzii. In addition, Escherichia coli overgrowth has been observed in IBD and COVID-19, while Akkermansia muciniphila is depleted in IBD and overexpressed in COVID-19. In patients with COVID-19, gut dysbiosis continues after the clearance of the viral RNA from the upper respiratory tract and the resolution of clinical symptoms. Finally, we presented and discussed the impact of gut dysbiosis, inflammation, oxidative stress, and increased energy demand on metabolic pathways involving key metabolites, such as tryptophan, phenylalanine, histidine, glutamine, succinate, citrate, and lipids.

Analysis of Intestinal Microflora and Metabolites From Mice With DSS-Induced IBD Treated With Schistosoma Soluble Egg Antigen

Objective: This study aimed to analyze the changes in intestinal flora and metabolites in the intestinal contents of mice with inflammatory bowel disease (IBD) to preliminarily clarify the mechanism of action of Schistosoma soluble egg antigen (SEA) on IBD, thus, laying a research foundation for the subsequent treatment of IBD. Methods: A total of 40 Institute of Cancer Research (ICR) mice were divided into four groups: control, SEA 50 μg, dextran sulfate sodium salt (DSS), and SEA 50 μg + DSS. The overall state of the animals was observed continuously during modeling. The colonic length was measured after 10 days of modeling. The degree of colonic inflammation was observed by hematoxylin and eosin staining. 16srRNA and liquid chromatography-mass spectrometry sequencing techniques were used to determine the abundance of bacteria and metabolites in the intestinal contents of mice in the DSS and SEA 50 μg + DSS groups, and the differences were further analyzed. Results: After SEA intervention, the disease activity index score of mice with IBD decreased and the colon shortening was reduced. Microscopically, the lymphocyte aggregation, glandular atrophy, goblet cell disappearance, and colonic inflammation were less in the SEA 50 μg + DSS group than in the DSS group (p < 0.0001). After SEA intervention, the abundance of beneficial bacteria prevotellaceae_UCG-001 was upregulated, while the abundance of the harmful bacteria Helicobacter, Lachnoclostridium, and Enterococcus was downregulated in the intestinal tract of mice with IBD. The intestinal metabolite analysis showed that SEA intervention decreased the intestinal contents of glycerophospholipids (lysophosphatidylcholine, lysophosphatidylethanolamine, phatidylcholine, and phatidylethanolamine) and carboxylic acids (L-alloisoleucine and L-glutamate), whereas increased bile acids and their derivatives (3B,7A,12a-trihydroxy-5A-cholanoic acid and 3A,4B, 12a-trihydroxy-5b-cholanoic acid). Combined microbiota-metabolite analysis revealed a correlation between these differential microbiota and differential metabolites. At the same time, the changes in the contents of metabolites and differential metabolites in the two groups also correlated with the abundance of the gut microbiome. Conclusions: The study showed that SEA reduced DSS-induced inflammation in IBD and improved the symptoms of IBD in mice through the combined regulation of intestinal flora and intestinal metabolism. It suggested a potential possibility for the use of SEA in treating and regulating intestinal flora and metabolism in patients with IBD.

Metabolomics Insights into Inflammatory Bowel Disease: A Comprehensive Review

Inflammatory bowel disease (IBD) is a chronic, complex relapsing disorder characterised by immune dysregulation, gut microbiota alteration, and disturbed intestinal permeability. The diagnosis and the management of IBD are challenging due to the recurrent nature and complex evolution of the disease. Furthermore, the molecular mechanism underlying the aetiology and pathogenesis of IBD is still poorly understood. There is an unmet need for novel, reliable, and noninvasive tools for diagnosing and monitoring IBD. In addition, metabolomic profiles may provide a priori determination of optimal therapeutics and reveal novel targets for therapies. This review tries to gather scientific evidence to summarise the emerging contribution of metabolomics to elucidate the mechanisms underlying IBD and changes associated with disease phenotype and therapies, as well as to identify biomarkers with metabolic imbalance in those patients. Metabolite changes during health and disease could provide insights into the disease pathogenesis and the discovery of novel indicators for the diagnosis and prognosis assessment of IBD. Metabolomic studies in IBD have shown changes in tricarboxylic acid cycle intermediates, amino-acid and fatty-acid metabolism, and oxidative pathways. Metabolomics has made progress towards identifying metabolic alterations that may provide clinically useful biomarkers and a deeper understanding of the disease. However, at present, there is insufficient evidence evaluating the predictive accuracy of these molecular signatures and their diagnostic ability, which is necessary before metabolomic data can be translated into clinical practice.

Systematic Review of Recent Lipidomics Approaches Toward Inflammatory Bowel Disease

Researchers have endeavored to identify the etiology of inflammatory bowel diseases, including Crohn’s disease and ulcerative colitis. Though the pathogenesis of inflammatory bowel diseases remains unknown, dysregulation of the immune system in the host gastrointestinal tract is believed to be the major causative factor. Omics is a powerful methodological tool that can reveal biochemical information stored in clinical samples. Lipidomics is a subset of omics that explores the lipid classes associated with inflammation. One objective of the present systematic review was to facilitate the identification of biochemical targets for use in future lipidomic studies on inflammatory bowel diseases. The use of high-resolution mass spectrometry to observe alterations in global lipidomics might help elucidate the immunoregulatory mechanisms involved in inflammatory bowel diseases and discover novel biomarkers for them. Assessment of the characteristics of previous clinical trials on inflammatory bowel diseases could help researchers design and establish patient selection and analytical method criteria for future studies on these conditions. In this study, we curated literature exclusively from four databases and extracted lipidomics-related data from literature, considering criteria. This paper suggests that the lipidomics approach toward research in inflammatory bowel diseases can clarify their pathogenesis and identify clinically valuable biomarkers to predict and monitor their progression.

MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.

The microbiome has shown to functionally interact with its host or environment at a metabolic level, however the exact nature of these interactions is not well understood. In addition, metabolic dysregulation caused by the microbiome is believed to contribute to the development of diseases such as inflammatory bowel disease, diabetes mellitus, and obesity. In this manuscript, we introduce a computational framework to integrate microbiome and metabolome data to uncover microbe-metabolite interactions in a data-driven manner. Our model uses neural networks to predict metabolite abundances from microbe abundances. The trained models are then used to derive microbe-metabolite feature scores, which are used for clustering microbes and metabolites into functional modules. These module-based interactions are useful in generating biological insights and facilitating hypothesis generation for the investigation of their roles in various metabolic diseases. The software of our model is made freely available to interested researchers.