The role of gut microbiome in inflammatory bowel disease diagnosis and prognosis

Atorvastatin improved ulcerative colitis in association with gut microbiota-derived tryptophan metabolism

AIMS

Atorvastatin is a commonly used cholesterol-lowering drug that possesses non-canonical anti-inflammatory properties. However, the precise mechanism underlying its anti-inflammatory effects remains unclear.

MATERIALS AND METHODS

The acute phase of ulcerative colitis (UC) was induced using a 5 % dextran sulfate sodium (DSS) solution for 7 consecutive days and administrated with atorvastatin (10 mg/kg) from day 3 to day 7. mRNA-seq, histological pathology, and inflammatory response were determined. Intestinal microbiota alteration, tryptophan, and its metabolites were analyzed through 16S rRNA sequencing and untargeted metabolomics.

KEY FINDINGS

Atorvastatin relieved the DSS-induced UC in mice, as evidenced by colon length, body weight, disease activity index score and pathological staining. Atorvastatin treatment reduced the level of pro-inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α). Atorvastatin also relieved the intestinal microbiota disorder caused by UC and decreased the proliferation of pernicious microbiota such as Akkermansia and Bacteroides. Atorvastatin dramatically altered tryptophan metabolism and increased the fecal contents of tryptophan, indolelactic acid (ILA), and indole-3-acetic acid (IAA). Furthermore, atorvastatin enhanced the expression level of aryl hydrocarbon receptor (AhR) and interleukin-22 (IL-22) and further promoted the expression level of intestinal tight junction proteins, such as ZO-1 and occludin, in colitis mice.

SIGNIFICANCE

These findings indicated that atorvastatin could alleviate UC by regulating intestinal flora disorders, promoting microbial tryptophan metabolism, and repairing the intestinal barrier.

Octreotide attenuates intestinal barrier damage by maintaining basal autophagy in Caco2 cells

The intestinal mucosal barrier is of great importance for maintaining the stability of the internal environment, which is closely related to the occurrence and development of intestinal inflammation. Octreotide (OCT) has potential applicable clinical value for treating intestinal injury according to previous studies, but the underlying molecular mechanisms have remained elusive. This article is based on a cell model of inflammation induced by lipopolysaccharide (LPS), aiming to explore the effects of OCT in protecting intestinal mucosal barrier function. A Cell Counting Kit‑8 assay was used to determine cell viability and evaluate the effectiveness of OCT. Gene silencing technology was used to reveal the mediated effect of somatostatin receptor 2 (SSTR2). The changes in intestinal permeability were detected through trans‑epithelial electrical resistance and fluorescein isothiocyanate‑dextran 4 experiments, and the alterations in tight junction proteins were detected using immunoblotting and reverse transcription fluorescence‑quantitative PCR technology. Autophagosomes were observed by electron microscopy and the dynamic changes of the autophagy process were characterized by light chain (LC)3‑II/LC3‑I conversion and autophagic flow. The results indicated that SSTR2‑dependent OCT can prevent the decrease in cell activity. After LPS treatment, the permeability of monolayer cells decreased and intercellular tight junctions were disrupted, resulting in a decrease in tight junction protein zona occludens 1 in cells. The level of autophagy‑related protein LC3 was altered to varying degrees at different times. These abnormal changes gradually returned to normal levels after the combined application of LPS and SSTR2‑dependent OCT, confirming the role of OCT in protecting intestinal barrier function. These experimental results suggest that OCT maintains basal autophagy and cell activity mediated by SSTR2 in intestinal epithelial cells, thereby preventing the intestinal barrier dysfunction in inflammation injury.

An integrative multi-omic analysis defines gut microbiota, mycobiota, and metabolic fingerprints in ulcerative colitis patients

Background

Ulcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches.

Methods

The 16S rRNA- and ITS2-based metataxonomics and gas chromatography-mass spectrometry/solid phase microextraction (GC-MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis.

Results

In the UC cohort, we reported the increase of Streptococcus, Bifidobacterium, Enterobacteriaceae, TM7-3, Granulicatella, Peptostreptococcus, Lactobacillus, Veillonella, Enterococcus, Peptoniphilus, Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia; Ruminococcaceae; Ruminococcus; Gemmiger; Methanobrevibacter; Oscillospira; Coprococus; Christensenellaceae; Clavispora; Vishniacozyma; Quambalaria; hexadecane; cyclopentadecane; 5-hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3-methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota. Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions.

Conclusion

In this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC.

Alterations in Gut Microbiota as Early Biomarkers for Predicting Inflammatory Bowel Disease Onset and Progression: A Systematic Review

Inflammatory bowel disease (IBD) is a chronic ailment impacting the digestive system, triggered by an unusual reaction of the immune system. It includes two types of diseases: ulcerative colitis and Crohn's disease. Nonetheless, the diagnosis and evaluation of disease progression in IBD are difficult due to the absence of distinct indicators. While conventional biomarkers from blood plasma and feces, such as C-reactive protein, fecal calprotectin, and S100A12, can be employed to gauge inflammation, they are not exclusive to IBD. There is a broad consensus that intestinal microorganisms significantly contribute to the onset of intestinal imbalance, a condition intimately linked with the cause and development of IBD. Numerous studies have indicated that the makeup of intestinal microorganisms varies between individuals with IBD and those who are healthy, particularly concerning the diversity of microbes and the proportional prevalence of certain bacteria. A total of 1475 records underwent examination. Following the eligibility assessment, 17 reports were considered. The final review encompassed 12 studies, as five articles were excluded due to insufficient details regarding cases, controls, and comparability. This article suggests that gut microbiota has potential biomarkers for the noninvasive evaluation of IBD activity. Recognizing the microbiome linked with disease activity paves the way for the development of a group of microbiota-derived indicators to evaluate the initiation and advancement of IBD. This article discusses whether changes in gut microbial composition can serve as early indicators of IBD onset and progression.

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.

A Machine Learning-Based Diagnostic Model for Crohn's Disease and Ulcerative Colitis Utilizing Fecal Microbiome Analysis

Recent research has demonstrated the potential of fecal microbiome analysis using machine learning (ML) in the diagnosis of inflammatory bowel disease (IBD), mainly Crohn's disease (CD) and ulcerative colitis (UC). This study employed the sparse partial least squares discriminant analysis (sPLS-DA) ML technique to develop a robust prediction model for distinguishing among CD, UC, and healthy controls (HCs) based on fecal microbiome data. Using data from multicenter cohorts, we conducted 16S rRNA gene sequencing of fecal samples from patients with CD (n = 671) and UC (n = 114) while forming an HC cohort of 1462 individuals from the Kangbuk Samsung Hospital Healthcare Screening Center. A streamlined pipeline based on HmmUFOTU was used. After a series of filtering steps, 1517 phylotypes and 1846 samples were retained for subsequent analysis. After 100 rounds of downsampling with age, sex, and sample size matching, and division into training and test sets, we constructed two binary prediction models to distinguish between IBD and HC and CD and UC using the training set. The binary prediction models exhibited high accuracy and area under the curve (for differentiating IBD from HC (mean accuracy, 0.950; AUC, 0.992) and CD from UC (mean accuracy, 0.945; AUC, 0.988)), respectively, in the test set. This study underscores the diagnostic potential of an ML model based on sPLS-DA, utilizing fecal microbiome analysis, highlighting its ability to differentiate between IBD and HC and distinguish CD from UC.