Location-specific signatures of Crohn's disease at a multi-omics scale

The emerging role of the gut microbiota and its application in inflammatory bowel disease

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is a complex disorder with an unknown cause. However, the dysbiosis of the gut microbiome has been found to play a role in IBD etiology, including exacerbated immune responses and defective intestinal barrier integrity. The gut microbiome can also be a potential biomarker for several diseases, including IBD. Currently, conventional treatments targeting pro-inflammatory cytokines and pathways in IBD-associated dysbiosis do not yield effective results. Other therapies that directly target the dysbiotic microbiome for effective outcomes are emerging. We review the role of the gut microbiome in health and IBD and its potential as a diagnostic, prognostic, and therapeutic target for IBD. This review also explores emerging therapeutic advancements that target gut microbiome-associated alterations in IBD, such as nanoparticle or encapsulation delivery, fecal microbiota transplantation, nutritional therapies, microbiome/probiotic engineering, phage therapy, mesenchymal stem cells (MSCs), gut proteins, and herbal formulas.

Transcriptomics and epigenetic data integration learning module on Google Cloud

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

HIGHLIGHTS

Untargeted faecal metabolomics for the discovery of biomarkers and treatment targets for inflammatory bowel diseases

The gut microbiome has been recognised as a key component in the pathogenesis of inflammatory bowel diseases (IBD), and the wide range of metabolites produced by gut bacteria are an important mechanism by which the human microbiome interacts with host immunity or host metabolism. High-throughput metabolomic profiling and novel computational approaches now allow for comprehensive assessment of thousands of metabolites in diverse biomaterials, including faecal samples. Several groups of metabolites, including short-chain fatty acids, tryptophan metabolites and bile acids, have been associated with IBD. In this Recent Advances article, we describe the contribution of metabolomics research to the field of IBD, with a focus on faecal metabolomics. We discuss the latest findings on the significance of these metabolites for IBD prognosis and therapeutic interventions and offer insights into the future directions of metabolomics research.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

IMPORTANCE

Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.

Distinct signatures of gut microbiota and metabolites in primary biliary cholangitis with poor biochemical response after ursodeoxycholic acid treatment

BACKGROUND

About 1/3 of primary biliary cholangitis (PBC) patients suffered from poor response worldwide. And these patients present intestinal disturbances. We aimed to identify signatures of microbiota and metabolites in PBC patients with poor response, comparing to patients with response.

METHODS

This study enrolled 25 subjects (14 PBC patients with response and 11 PBC patients with poor response). Metatranscriptomics and metabolomics analysis were carried out on their fecal.

RESULTS

PBC patients with poor response had significant differences in the composition of bacteria, characterized by decreased Gemmiger etc. and increased Ruminococcus etc. The differential microbiota functions characterized by decreased abundance of elongation factor Tu and elongation factor G base on the KO database, as well as decreased abundance of Replicase large subunit etc. based on the SWISS-PROT database. PBC with poor response also had significant differences in 17 kinds of bacterial metabolites, characterized by decreased level of metabolites vital in bile acids metabolism pathway (L-Cysteine etc.) and the all-trans-Retinoic acid, a kind of immune related metabolite. The altered microbiota was associated with the differential expressed metabolites and clinical liver function indicators. 1 bacterial genera, 2 bacterial species and 9 metabolites simultaneously discriminated PBC with poor response from PBC with response with high accuracy.

CONCLUSION

PBC patients with poor response exhibit unique changes in microbiota and metabolite. Gut microbiota and metabolite-based algorithms could be used as additional tools for differential prediction of PBC with poor prognosis.

Spatial features of skip lesions in Crohn's disease

Skip lesions are an enigmatic spatial feature characterizing Crohn's disease (CD). They comprise inflamed and adjacent non-inflamed tissue sections with a clear demarcation. Currently, spatial features of the human gastrointestinal (GI) system lack clarity regarding the organization of microbes, mucus, tissue, and host cells during inflammation. New technologies with multiplexing abilities and innovative approaches provide ways of examining the spatial organization of inflamed and non-inflamed tissues in CD, which may open new avenues for diagnosis, prognosis, and treatment. In this review, we present evidence of the relevance of spatial context in patients with CD and the methods and ideas recently published in studies of spatiality during inflammation. With this review, we aim to provide inspiration for further research to address existing gaps.

Proteomic scrutiny of nasal microbiomes: implications for the clinic

INTRODUCTION

The nasal cavity is the initial site of the human respiratory tract and is one of the habitats where microorganisms colonize. The findings from a growing number of studies have shown that the nasal microbiome is an important factor for human disease and health. 16S rRNA sequencing and metagenomic next-generation sequencing (mNGS) are the most commonly used means of microbiome evaluation. Among them, 16S rRNA sequencing is the primary method used in previous studies of nasal microbiomes. However, neither 16S rRNA sequencing nor mNGS can be used to analyze the genes specifically expressed by nasal microorganisms and their functions. This problem can be addressed by proteomic analysis of the nasal microbiome.

AREAS COVERED

In this review, we summarize current advances in research on the nasal microbiome, introduce the methods for proteomic evaluation of the nasal microbiome, and focus on the important roles of proteomic evaluation of the nasal microbiome in the diagnosis and treatment of related diseases.

EXPERT OPINION

The detection method for microbiome-expressed proteins is known as metaproteomics. Metaproteomic analysis can help us dig deeper into the nasal microbiomes and provide new targets and ideas for clinical diagnosis and treatment of many nasal dysbiosis-related diseases.

Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning

Pediatric Crohn's disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD.

Exploring the intestinal ecosystem: from gut microbiota to associations with subtypes of inflammatory bowel disease

Objective

Significant differences have been discovered between subtypes of Crohn's disease (CD) and ulcerative colitis (UC). The role of gut microbiota in promoting the onset of UC and CD is established, but conclusions regarding subtype-specific analyses remain limited.

Methods

This study aims to explore the influence of gut microbiota on subtypes of UC and CD, offering novel insights into the pathogenesis and treatment of UC and CD.Two-sample Mendelian randomization (MR) analysis was employed to examine the causal relationship between subtypes of UC and CD and gut microbiota composition. Gut microbiota data were sourced from the International Consortium MiBioGen, while UC and CD data were obtained from FINNGEN. Eligible single nucleotide polymorphisms (SNPs) were selected as instrumental variables. Multiple analytical approaches such as inverse variance-weighted (IVW), MR-Egger regression, weighted median, weighted mode, and MR-RAPS were utilized. Sensitivity analyses including MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were conducted for quality control. Subsequently, we employed multivariable IVW, MR-Egger, weighted median, and LASSO regression methods to identify independently significant genera or families and conducted sensitivity analyses.

Results

We have determined that Hungatella, Acidaminococcaceae, and 15 other microbial taxa act as protective factors for various CD and UC subtypes, while Terrisporobacter, Anaerostipes, and 23 other microbial taxa are associated with increased risk for different CD and UC subtypes. Furthermore, through multivariable MR analysis, we have identified significant genera or families with independent effects.

Conclusion

Our study confirms a causal relationship between dysbiosis of gut microbiota and the occurrence of CD and UC subtypes. Furthermore, it validates etiological distinctions among different subtypes of CD and UC. A novel approach to adjunctive therapy involving distinct UC or CD subtypes may involve the use of probiotics and represents a potential avenue for future treatments.

Orally-administered nanomedicine systems targeting colon inflammation for the treatment of inflammatory bowel disease: latest advances

Inflammatory bowel disease (IBD) is a chronic and idiopathic condition that results in inflammation of the gastrointestinal tract, leading to conditions such as ulcerative colitis and Crohn's disease. Commonly used treatments for IBD include anti-inflammatory drugs, immunosuppressants, and antibiotics. Fecal microbiota transplantation is also being explored as a potential treatment method; however, these drugs may lead to systemic side effects. Oral administration is preferred for IBD treatment, but accurately locating the inflamed area in the colon is challenging due to multiple physiological barriers. Nanoparticle drug delivery systems possess unique physicochemical properties that enable precise delivery to the target site for IBD treatment, exploiting the increased permeability and retention effect of inflamed intestines. The first part of this review comprehensively introduces the pathophysiological environment of IBD, covering the gastrointestinal pH, various enzymes in the pathway, transport time, intestinal mucus, intestinal epithelium, intestinal immune cells, and intestinal microbiota. The second part focuses on the latest advances in the mechanism and strategies of targeted delivery using oral nanoparticle drug delivery systems for colitis-related fields. Finally, we present challenges and potential directions for future IBD treatment with the assistance of nanotechnology.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

Comparing the Blood Response to Hyperbaric Oxygen with High-Intensity Interval Training-A Crossover Study in Healthy Volunteers

High-intensity interval training (HIIT) and hyperbaric oxygen therapy (HBOT) induce reactive oxygen species (ROS) formation and have immunomodulatory effects. The lack of readily available biomarkers for assessing the dose-response relationship is a challenge in the clinical use of HBOT, motivating this feasibility study to evaluate the methods and variability. The overall hypothesis was that a short session of hyperbaric oxygen (HBO2) would have measurable effects on immune cells in the same physiological range as shown in HIIT; and that the individual response to these interventions can be monitored in venous blood and/or peripheral blood mononuclear cells (PBMCs). Ten healthy volunteers performed two interventions; a 28 min HIIT session and 28 min HBO2 in a crossover design. We evaluated bulk RNA sequencing data from PBMCs, with a separate analysis of mRNA and microRNA. Blood gases, peripheral venous oxygen saturation (SpvO2), and ROS levels were measured in peripheral venous blood. We observed an overlap in the gene expression changes in 166 genes in response to HIIT and HBO2, mostly involved in hypoxic or inflammatory pathways. Both interventions were followed by downregulation of several NF-κB signaling genes in response to both HBO2 and HIIT, while several interferon α/γ signaling genes were upregulated. Only 12 microRNA were significantly changed in HBO2 and 6 in HIIT, without overlap between interventions. ROS levels were elevated in blood at 30 min and 60 min compared to the baseline during HIIT, but not during/after HBO2. In conclusion, HBOT changed the gene expression in a number of pathways measurable in PBMC. The correlation of these changes with the dose and individual response to treatment warrants further investigation.

Omics and Multi-Omics in IBD: No Integration, No Breakthroughs

The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.

Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex disease with variability in genetic, environmental, and lifestyle factors affecting disease presentation and course. Precision medicine has the potential to play a crucial role in managing IBD by tailoring treatment plans based on the heterogeneity of clinical and temporal variability of patients. Precision medicine is a population-based approach to managing IBD by integrating environmental, genomic, epigenomic, transcriptomic, proteomic, and metabolomic factors. It is a recent and rapidly developing medicine. The widespread adoption of precision medicine worldwide has the potential to result in the early detection of diseases, optimal utilization of healthcare resources, enhanced patient outcomes, and, ultimately, improved quality of life for individuals with IBD. Though precision medicine is promising in terms of better quality of patient care, inadequacies exist in the ongoing research. There is discordance in study conduct, and data collection, utilization, interpretation, and analysis. This review aims to describe the current literature on precision medicine, its multiomics approach, and future directions for its application in IBD.

Are We Ready to Reclassify Crohn's Disease Using Molecular Classification?

Crohn's disease (CD) is a type of inflammatory bowel disease. The number of IBD cases worldwide was estimated to be 4.9 million in 2019. CD exhibits heterogeneity in clinical presentation, anatomical involvement, disease behaviour, clinical course and response to treatment. The classical description of CD involves transmural inflammation with skip lesions anywhere along the entire gastrointestinal tract. The complexity and heterogeneity of Crohn's disease is not currently reflected in the conventional classification system. Though the knowledge of Crohn's pathophysiology remains far from understood, the established complex interplay of the omics-genomics, transcriptomics, proteomics, epigenomics, metagenomics, metabolomics, lipidomics and immunophenomics-provides numerous targets for potential molecular markers of disease. Advancing technology has enabled identification of small molecules within these omics, which can be extrapolated to differentiate types of Crohn's disease. The multi-omic future of Crohn's disease is promising, with potential for advancements in understanding of its pathogenesis and implementation of personalised medicine.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

Structural disruption of Ntox15 nuclease effector domains by immunity proteins protects against type VI secretion system intoxication in Bacteroidales

Bacteroidales use type VI secretion systems (T6SS) to competitively colonize and persist in the colon. We identify a horizontally transferred T6SS with Ntox15 family nuclease effector (Tde1) that mediates interbacterial antagonism among Bacteroidales, including several derived from a single human donor. Expression of cognate (Tdi1) or orphan immunity proteins in acquired interbacterial defense systems protects against Tde1-dependent attack. We find that immunity protein interaction induces a large effector conformational change in Tde nucleases, disrupting the active site and altering the DNA-binding site. Crystallographic snapshots of isolated Tde1, the Tde1/Tdi1 complex, and homologs from Phocaeicola vulgatus (Tde2/Tdi2) illustrate a conserved mechanism of immunity inserting into the central core of Tde, splitting the nuclease fold into two subdomains. The Tde/Tdi interface and immunity mechanism are distinct from all other polymorphic toxin-immunity interactions of known structure. Bacteroidales abundance has been linked to inflammatory bowel disease activity in prior studies, and we demonstrate that Tde and T6SS structural genes are each enriched in fecal metagenomes from ulcerative colitis subjects. Genetically mobile Tde1-encoding T6SS in Bacteroidales mediate competitive growth and may be involved in inflammatory bowel disease. Broad immunity is conferred by Tdi1 homologs through a fold-disrupting mechanism unique among polymorphic effector-immunity pairs of known structure. IMPORTANCE Bacteroidales are related to inflammatory bowel disease severity and progression. We identify type VI secretion system (T6SS) nuclease effectors (Tde) which are enriched in ulcerative colitis and horizontally transferred on mobile genetic elements. Tde-encoding T6SSs mediate interbacterial competition. Orphan and cognate immunity proteins (Tdi) prevent intoxication by multiple Tde through a new mechanism among polymorphic toxin systems. Tdi inserts into the effector central core, splitting Ntox15 into two subdomains and disrupting the active site. This mechanism may allow for evolutionary diversification of the Tde/Tdi interface as observed in colicin nuclease-immunity interactions, promoting broad neutralization of Tde by orphan Tdi. Tde-dependent T6SS interbacterial antagonism may contribute to Bacteroidales diversity in the context of ulcerative colitis.

Arecoline aggravates acute ulcerative colitis in mice by affecting intestinal microbiota and serum metabolites

Arecoline is an alkaloid extracted from betel nut, which has various pharmacological effects. In the present study, we showed that arecoline aggravated experimental acute ulcerative colitis (UC) induced by dextran sodium sulfate (DSS) in mice. We measured body weight and colon length, evaluated disease activity index, colon pathology sections, and levels of colonic inflammatory factors. Arecoline exacerbated the clinical signs of UC and the colonic inflammatory response in mice. The results of 16S rRNA sequencing of fecal samples showed a significant decrease in the percentage of probiotic bacteria Ligilactobacillus, Limosilactobacillus and Lactobacillus and a significant increase in the percentage of conditionally pathogenic bacteria Odoribacter and Bacteroides after arecoline treatment. Serum untargeted metabolomics showed that arecoline intervention reduced the levels of ergothioneine, pentostatin, diadenosine tetraphosphate and other metabolites and modulated nicotinate and nicotinamide metabolism, metabolic pathways, glyoxylate and dicarboxylate metabolism, and other metabolic pathways of intestinal microorganisms. According to the combined microbial and metabolite analysis, arecoline influences metabolite levels by modulating the intestinal microbiota. In summary, it was found that arecoline treatment exacerbated colonic injury and intestinal inflammatory responses in UC mice, disrupted the host's intestinal flora, and affected changes in flora metabolites, thereby exacerbating the development of colonic inflammation. Therefore, the consumption of betel nut can be associated with the risk of aggravating UC.

Faecalibacterium: a bacterial genus with promising human health applications

In humans, many diseases are associated with alterations in gut microbiota, namely increases or decreases in the abundance of specific bacterial groups. One example is the genus Faecalibacterium. Numerous studies have underscored that low levels of Faecalibacterium are correlated with inflammatory conditions, with inflammatory bowel disease (IBD) in the forefront. Its representation is also diminished in the case of several diseases, including colorectal cancer (CRC), dermatitis, and depression. Additionally, the relative presence of this genus is considered to reflect, at least in part, intestinal health status because Faecalibacterium is frequently present at reduced levels in individuals with gastrointestinal diseases or disorders. In this review, we first thoroughly describe updates to the taxonomy of Faecalibacterium, which has transformed a single-species taxon to a multispecies taxon over the last decade. We then explore the links discovered between Faecalibacterium abundance and various diseases since the first IBD-focused studies were published. Next, we examine current available strategies for modulating Faecalibacterium levels in the gut. Finally, we summarize the mechanisms underlying the beneficial effects that have been attributed to this genus. Together, epidemiological and experimental data strongly support the use of Faecalibacterium as a next-generation probiotic (NGP) or live biotherapeutic product (LBP).

Crohn’s disease: Why the ileum?

Crohn’s disease (CD) is an inflammatory bowel disease characterized by immune-mediated flares affecting any region of the intestine alternating with remission periods. In CD, the ileum is frequently affected and about one third of patients presents with a pure ileal type. Moreover, the ileal type of CD presents epidemiological specificities like a younger age at onset and often a strong link with smoking and genetic susceptibility genes. Most of these genes are associated with Paneth cell dysfunction, a cell type found in the intestinal crypts of the ileum. Besides, a Western-type diet is associated in epidemiological studies with CD onset and increasing evidence shows that diet can modulate the composition of bile acids and gut microbiota, which in turn modulates the susceptibility of the ileum to inflammation. Thus, the interplay between environmental factors and the histological and anatomical features of the ileum is thought to explain the specific transcriptome profile observed in CD ileitis. Indeed, both immune response and cellular healing processes harbour differences between ileal and non-ileal CD. Taken together, these findings advocate for a dedicated therapeutic approach to managing ileal CD. Currently, interventional pharmacological studies have failed to clearly demonstrate distinct response profiles according to disease site. However, the high rate of stricturing disease in ileal CD requires the identification of new therapeutic targets to significantly change the natural history of this debilitating disease.

Multi-omics in Crohn's disease: New insights from inside

Crohn's disease (CD) is an inflammatory bowel disease (IBD) with complex clinical manifestations such as chronic diarrhea, weight loss and hematochezia. Despite the increasing incidence worldwide, cure of CD remains extremely difficult. The rapid development of high-throughput sequencing technology with integrated-omics analyses in recent years has provided a new means for exploring the pathogenesis, mining the biomarkers and designing targeted personalized therapeutics of CD. Host genomics and epigenomics unveil heredity-related mechanisms of susceptible individuals, while microbiome and metabolomics map host-microbe interactions in CD patients. Proteomics shows great potential in searching for promising biomarkers. Nonetheless, single omics technology cannot holistically connect the mechanisms with heterogeneity of pathological behavior in CD. The rise of multi-omics analysis integrates genetic/epigenetic profiles with protein/microbial metabolite functionality, providing new hope for comprehensive and in-depth exploration of CD. Herein, we emphasized the different omics features and applications of CD and discussed the current research and limitations of multi-omics in CD. This review will update and deepen our understanding of CD from integration of broad omics spectra and will provide new evidence for targeted individualized therapeutics.

Integrating the serum proteomic and fecal metaproteomic to analyze the impacts of overweight/obesity on IBD: a pilot investigation

BACKGROUND

Inflammatory bowel disease (IBD) encompasses a group of chronic relapsing disorders which include ulcerative colitis (UC) and Crohn's disease (CD). The incidences of IBD and overweight/obesity are increasing in parallel. Here, we investigated alterations in proteomic in serum and metaproteomic in feces of IBD patients with overweight/obesity and aimed to explore the effect of overweight/ obesity on IBD and the underlying mechanism.

METHODS

This prospective observational study (n = 64) comprised 26 health control subjects (HC, 13 with overweight/obesity) and 38 IBD patients (19 with overweight/obesity) at a tertiary hospital. Overweight/obesity was evaluated by body mass index (BMI) and defined as a BMI greater than 24 kg/m². The comprehensive serum proteomic and fecal metaproteomic analyses were conducted by ultra-performance liquid chromatography-Orbitrap Exploris 480 mass spectrometry.

RESULTS

UC and CD presented similar serum molecular profiles but distinct gut microbiota. UC and CD serum exhibited higher levels of cytoskeleton organization- associated and inflammatory response-related proteins than the HC serum. Compared the serum proteome of UC and CD without overweight/obesity, inflammatory response-associated proteins were dramatically decreased in UC and CD with overweight/obesity. Fecal metaproteome identified 66 species in the feces. Among them, Parasutterella excrementihominis was increased in CD compared with that in HC. UC group had a significant enrichment of Moniliophthora roreri, but had dramatically decreased abundances of Alistipes indistinctus, Clostridium methylpentosum, Bacteroides vulgatus, and Schizochytrium aggregatum. In addition, overweight/obesity could improve the microbial diversity of UC. Specifically, the UC patients with overweight/obesity had increased abundance of some probiotics in contrast to those without overweight/obesity, including Parabacteroides distasonis, Alistipes indistincus, and Ruminococcus bromii.

CONCLUSION

This study provided high-quality multi-omics data of IBD serum and fecal samples, which enabled deciphering the molecular bases of clinical phenotypes of IBD, revealing the impacts of microbiota on IBD, and emphasizing the important role of overweight/obesity in IBD.

The diagnostic and prognostic potential of gut bacteria in inflammatory bowel disease

The gut microbiome serves as a signaling hub that integrates environmental inputs with genetic and immune signals to influence the host's metabolism and immunity. Gut bacteria are intricately connected with human health and disease state, with specific bacteria species driving the characteristic dysbiosis found in gastrointestinal conditions such as inflammatory bowel disease (IBD); thus, gut bacteria changes could be harnessed to improve IBD diagnosis, prognosis, and treatment. The advancement in next-generation sequencing techniques such as 16S rRNA and whole-genome shotgun sequencing has allowed the exploration of the complexity of the gut microbial ecosystem with high resolution. Current microbiome data is promising and appears to perform better in some studies than the currently used fecal inflammation biomarker, calprotectin, in predicting IBD from healthy controls and irritable bowel syndrome (IBS). This study reviews current data on the differential potential of gut bacteria within IBD cohorts, and between IBD and other gastrointestinal diseases.