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MULTI-COHORT CROSS-DISEASE METAGENOMIC ANALYSIS OF IMMUNE-MEDIATED INFLAMMATORY DISEASES IDENTIFIES SHARED OR UNIQUE GUT MICROBIAL SIGNATURES AND THEIR POTENTIAL CLINICAL IMPLICATIONS IN INFLAMMATORY BOWEL DISEASE

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MULTI-COHORT CROSS-DISEASE METAGENOMIC ANALYSIS OF IMMUNE-MEDIATED INFLAMMATORY DISEASES IDENTIFIES SHARED OR UNIQUE GUT MICROBIAL SIGNATURES AND THEIR POTENTIAL CLINICAL IMPLICATIONS IN INFLAMMATORY BOWEL DISEASE

Yeonjae Jung 1, Hyun-Seok Oh 1, Mauricio Chalita 1, Jeongsup Moon 1, Junwon Yang 1, Kihyun Lee 1, Sein Park 1, Yeong Ouk Kim 1, Jongsik Chun 1

Affiliations

1 CJ Bioscience, Inc., Seoul, Korea, Republic of

Summary

AI Generated

Multi-cohort gut metagenome analysis of 2,252 samples across six immune-mediated inflammatory diseases identified both shared and disease-specific microbial signatures, with trained models achieving an average AUC of 0.818 in external IBD validation.

Cross-cohort validation showed significantly higher prediction performance within the same disease (W=23278, p<0.001) than across diseases, though inter-disease prediction remained comparable, suggesting both shared and disease-specific gut microbiome contributions to IMIDs.
Several bacterial features were commonly enriched across at least five IMIDs, including Enterocloster bolteae and pyridine nucleotide-disulfide oxidoreductase (K21739) in cases, and Faecalibacterium sp. and Oscillibacter sp. in controls.
IBD-specific control-enriched signatures included Gemmiger formicilis and Alistipes shahii, which were not consistently enriched in non-gastrointestinal IMIDs such as MS, AS, COPD, and PsO.
Dysosmobacter welbionis showed negative correlation with Harvey Bradshaw Index in two CD sub-cohorts (Spearman's rho=-0.5 and -0.26, p=0.009 and 0.039), suggesting potential clinical relevance for disease activity monitoring.
The study used uniform taxonomic and functional profiling across 28 discovery case-control sub-cohorts (10 CD, 9 UC, 4 AS, 2 COPD, 2 MS, 1 PsO) and validated findings in 253 external IBD samples from seven sub-cohorts.

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This summary was generated by an AI large language model based on the content transcript. It is for informational purposes only and should not be considered a substitute for clinical judgment. Always rely on your professional expertise and the full clinical context when making clinical decisions.

Abstract

Introduction

Immune-mediated inflammatory diseases (IMID) involve immune dysregulation, potentially damaging various organs [1]. Gut microbiome regulates immune homeostasis, and abnormality of it is associated with various inflammatory diseases [2,3]. However, previously reported biomarkers have considerable limitations. Findings are often specific or inconsistent across studies, and while some studies assess common microbial patterns across diverse diseases, they often overlook disease pathobiology. To address this, we conducted a consensus-based approach to identify and dissect robust biomarkers common or unique to different IMIDs.

Aims & Methods

We collected and uniformly profiled public human gut metagenome data of IMIDs, using universal bacterial core gene-based taxonomic profiling [4] and pangenome-based functional profiling. A total of 2,252 samples from 18 studies of 28 case-control sub-cohorts of IMIDs (10 for Crohn’s disease;CD, 9 for ulcerative colitis;UC, 4 for ankylosing spondylitis;AS, 2 for chronic obstructive pulmonary disease;COPD, 2 for multiple sclerosis;MS, and 1 for psoriasis;PsO) were included as discovery set, and 253 samples from 3 studies for 7 case-control sub-cohort sets of CD or UC were included as external validation set. Cross-cohort and one-study-leave out validations were performed for the discovery set, and the external validation set was evaluated using the trained model from the IMID discovery set. For the individual marker analysis, significance scores were combined for each criteria, including disease diagnosis, and inflammatory bowel disease (IBD) and nonGI-IMIDs (MS, AS, COPD, and PsO). Additionally, multi cohort-based networks of IBD studies were assessed. Furthermore, to explore potential interpretation of the signatures in other clinical settings, we examined the IBD studies with disease activity as well as FMT study.

Results

Cross-cohort validation of IMID case-control sub-cohorts of the discovery set at species-level showed that intra-cohort performance had significantly higher area under curve (AUC) compared to inter-cohort (W=3701, p-value<0.001), and among the inter-cohort, intra-disease had significantly higher AUC than inter-disease (W=23278, p-value<0.001). When the external IBD sub-cohorts were tested, the trained model of the IMID set showed the average AUC 0.818 (SD=0.089). In individual signatures, there were several features commonly found to be highly scored in at least 5 IMIDs, including Enterocloster bolteae and probable pyridine nucleotide-disulfide oxidoreductase (K21739) in the case-side and Faecalibacterium sp. (CLG_10009380_s) and Oscillibacter sp. (MSSCM00948369_s) in the control-side. There were also disease or disease category-specific patterns such as Gemmiger formicilis and Alistipes shahii consistently found to be enriched in control-side in IBD but not in nonGI-IMIDs. Furthermore, we checked the signatures obtained from our analysis in the other clinical settings such as IBD samples with disease activity scores as well as FMT study. For example, Dysosmobacter welbionis was negatively correlated with Harvey Bradshaw Index in 2 CD sub-cohorts (Spearman’s rho=-0.5 and -0.26, p-value=0.009 and 0.039, respectively).

Conclusion

Our study analyzed multi-cohort gut metagenome data to identify consistent signatures across various IMIDs. Cross-cohort validation showed better intra-disease prediction compared to inter-disease, yet the performance of predictions between diseases was still comparable. Several microbial signatures were either shared or unique across IMIDs, with some showing potential clinical relevance in IBD.

References

1. McInnes IB, Gravallese EM. Immune-mediated inflammatory disease therapeutics: past, present and future. Nat Rev Immunol. 2021 Oct;21(10):680-686. doi: 10.1038/s41577-021-00603-1. Epub 2021 Sep 13. PMID: 34518662; PMCID: PMC8436867.
2. Forbes JD, Van Domselaar G, Bernstein CN. The Gut Microbiota in Immune-Mediated Inflammatory Diseases. Front Microbiol. 2016 Jul 11;7:1081. doi: 10.3389/fmicb.2016.01081. PMID: 27462309; PMCID: PMC4939298.
3. Forbes JD, Chen CY, Knox NC, Marrie RA, El-Gabalawy H, de Kievit T, Alfa M, Bernstein CN, Van Domselaar G. A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Microbiome. 2018 Dec 13;6(1):221. doi: 10.1186/s40168-018-0603-4. PMID: 30545401; PMCID: PMC6292067.
4. Chalita M, Ha SM, Kim YO, Oh HS, Yoon SH, Chun J. Improved Metagenomic Taxonomic Profiling Using a Curated Core Gene-Based Bacterial Database Reveals Unrecognized Species in the Genus Streptococcus. Pathogens. 2020 Mar 10;9(3):204. doi: 10.3390/pathogens9030204. PMID: 32164338; PMCID: PMC7157611.

Disclosure

Y Jung, HS Oh, M Chalita, J Moon, J Yang, K Lee, S Park, YO Kim and J Chun are employees of CJ Bioscience, Inc.

Event

UEG Week Vienna 2024

Topics

Digestive Oncology Gut Microbiota IBD

Submission format

Abstract

Session

Microbiota, chronic inflammation and cancer

Citation

United European Gastroenterology Journal 2024; 12 (Supplement 8)

Published

2024

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