The Common and Unique Pattern of Microbiome Profiles among Saliva, Tissue, and Stool Samples in Patients with Crohn’s Disease

Shin, Seung Yong; Kim, Sounkou; Choi, Jin-Tak; Kang, Sang Bum; Kim, Tae Oh; Seo, Geom Seog; Myung, Jae; Chun, Jaeyoung; Jung, Yunho; Im, Jong Pil; Bang, Ki Bae; Choi, Chang Hwan; Park, Soo-Kyung; Park, Dong Il

doi:10.3390/microorganisms10071467

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…Details of the sample preparation and sequencing have been published in [ 22 ]. In summary, the samples underwent centrifugation at 15,000 rpm for 20 min at 4 °C to separate the cellular pellet from the cell-free supernatant.…”

Section: Methodsmentioning

confidence: 99%

Potential Oral Microbial Markers for Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis Using Machine Learning Models

Kang

Kim

et al. 2023

Microorganisms

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Although gut microbiome dysbiosis has been associated with inflammatory bowel disease (IBD), the relationship between the oral microbiota and IBD remains poorly understood. This study aimed to identify unique microbiome patterns in saliva from IBD patients and explore potential oral microbial markers for differentiating Crohn’s disease (CD) and ulcerative colitis (UC). A prospective cohort study recruited IBD patients (UC: n = 175, CD: n = 127) and healthy controls (HC: n = 100) to analyze their oral microbiota using 16S rRNA gene sequencing. Machine learning models (sparse partial least squares discriminant analysis (sPLS-DA)) were trained with the sequencing data to classify CD and UC. Taxonomic classification resulted in 4041 phylotypes using Kraken2 and the SILVA reference database. After quality filtering, 398 samples (UC: n = 175, CD: n = 124, HC: n = 99) and 2711 phylotypes were included. Alpha diversity analysis revealed significantly reduced richness in the microbiome of IBD patients compared to healthy controls. The sPLS-DA model achieved high accuracy (mean accuracy: 0.908, and AUC: 0.966) in distinguishing IBD vs. HC, as well as good accuracy (0.846) and AUC (0.923) in differentiating CD vs. UC. These findings highlight distinct oral microbiome patterns in IBD and provide insights into potential diagnostic markers.

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Section: Methodsmentioning

confidence: 99%

Potential Oral Microbial Markers for Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis Using Machine Learning Models

Kang

Kim

et al. 2023

Microorganisms

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“…Information regarding sample preparation and sequencing can be found in a previous report [ 23 ]. Briefly, the samples were centrifuged at 15,000 rpm for 20 min at 4 °C to separate the cellular pellet from the cell-free supernatant.…”

Section: Methodsmentioning

confidence: 99%

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

Kim,

Na,

Kim

et al. 2023

Microorganisms

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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.

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“…Several studies have pointed out that inflammatory bowel disease (IBD) pathogenesis is a combination of an inherited susceptibility, environmental factors, and altered mucosal immune responses, yet the underlying mechanisms remain elusive [ 1 , 2 ]. In the last decade, an increased number of single nucleotide polymorphisms (SNPs), epigenetic traits, and environmental factors such as dietary habits and microbial colonization have been suggested to play a role in the development of IBD [ 3 , 4 , 5 ]. Microbes may contribute to inflammatory bowel disease (IBD) in various ways.…”

Section: Introductionmentioning

confidence: 99%

“…Microbes may contribute to inflammatory bowel disease (IBD) in various ways. In fact, the composition of gut microbiota appears to be a significant predisposing factor, as dysbiosis has been frequently observed in IBD patients, especially in Crohn’s disease (CD) [ 3 , 4 , 5 , 6 ]. Pathobionts, which are potentially pathological microorganisms that are normally non-harming symbionts, have also been suggested to play a role as triggers for the development and exacerbation of the disease.…”

Section: Introductionmentioning

confidence: 99%

Viable Mycobacterium avium subsp. paratuberculosis Colonizes Peripheral Blood of Inflammatory Bowel Disease Patients

et al. 2023

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Pathobionts, particularly Mycobacterium avium subsp. paratuberculosis (MAP) and Escherichia coli isolates with adherence/invasive ability (AIEC) have been associated with inflammatory bowel disease (IBD), particularly Crohn’s disease (CD). This study aimed to evaluate the frequency of viable MAP and AIEC in a cohort of IBD patients. As such, MAP and E. coli cultures were established from faecal and blood samples (with a total n = 62 for each) of patients with CD (n = 18), ulcerative colitis (UC, n = 15), or liver cirrhosis (n = 7), as well as from healthy controls (HC, n = 22). Presumptive positive cultures were tested by polymerase chain reaction (PCR), for a positive confirmation of MAP or E. coli identity. E. coli-confirmed isolates were then tested for AIEC identity using adherence and invasion assays in the epithelial cell line of Caco-2 and survival and replication assays in the macrophage cell line of J774. MAP sub-culture and genome sequencing were also performed. MAP was more frequently cultured from the blood and faecal samples of patients with CD and cirrhosis. E. coli presumptive colonies were isolated from the faecal samples of most individuals, in contrast to what was registered for the blood samples. Additionally, from the confirmed E. coli isolates, only three had an AIEC-like phenotype (i.e., one CD patient and two UC patients). This study confirmed the association between MAP and CD; however, it did not find a strong association between the presence of AIEC and CD. It may be hypothesized that the presence of viable MAP in the bloodstream of CD patients contributes to disease reactivation.

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The Common and Unique Pattern of Microbiome Profiles among Saliva, Tissue, and Stool Samples in Patients with Crohn’s Disease

Cited by 5 publications

References 41 publications

Potential Oral Microbial Markers for Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis Using Machine Learning Models

Potential Oral Microbial Markers for Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis Using Machine Learning Models

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

Viable Mycobacterium avium subsp. paratuberculosis Colonizes Peripheral Blood of Inflammatory Bowel Disease Patients

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