Integrated analysis of microbiome and metabolome reveals disease-specific profiles in inflammatory bowel diseases and intestinal Behcet's disease

국문 초록 (Abstract)

배경 및 목적: 장내 마이크로바이옴과 대사체 변화는 염증성 장질환인 궤양성 대장염과 크론병과 연관된 것으로 알려져 있다. 그러나 염증성 장질환과 임상적으로 공통점이 많은 베체트 장염에서는 이러한 마이크로바이옴이나 대사체 변화에 대한 연구가 부족하며, 병인론적으로 베체트 장염과 염증성 장질환의 유사성이 제기되고 있으나 아직 베체트 장염의 병인에 대해서는 많은 연구가 필요하다. 본 연구는 베체트 장염 환자의 장 내 마이크로바이옴 특징 및 기능적 변화, 대사체의 변화를 염증성 장질환 및 대조군과 비교하여 확인하고, 베체트 장염과 궤양성 대장염, 크론병을 구분하는 데 도움이 되는 미생물 및 대사체 마커를 발견하기 위해 시행되었다.
연구 방법: 진단 대장내시경 검사를 받는 성인 염증성 장질환 및 베체트 장염 환자와 건강한 자원자를 대상으로 회맹부에서 획득한 대장 조직 샘플을 이용하여 16S 리보솜 RNA (rRNA) 서열 분석을 시행하였으며, 미생물 군집의 다양성, 분류학적 조성 및 기능적 특징을 대조군, 궤양성 대장염, 크론병 및 베체트 장염 그룹간 비교하였다. 또 대조군과 염증성 장질환 환자의 대변 샘플을 16S rRNA 서열 분석을 위해 수집하고 분석하였다. 대조군, 궤양성 대장염, 크론병 및 베체트 장염 환자로부터 혈액을 채취하고 가스 및 액체 크로마토그래피 질량분석을 이용하여 혈장 대사체 분석을 시행하였다.
결과: 총 100명의 환자 (35명의 궤양성 대장염, 30명의 크론병, 35명의 베체트 장염) 및 41명의 건강한 자원자가 연구에 참여하였다. 73개의 조직 샘플 (12명의 대조군, 24명 궤양성 대장염, 14명 크론병, 23명 베체트장염)과 19개의 대변 샘플 (5명 대조군, 9명 궤양성 대장염, 5명 크론병)에 대한 16S rRNA 서열 분석을 시행하였고, 100개의 혈액 샘플 (25명 대조군, 24명 궤양성 대장염, 26명 크론병, 25명 베체트 장염)에 대한 대사체 분석을 시행하였다. 대장 조직의 미생물 다양성은 크론병에서만 의미 있게 감소하였으며, 베체트 장염에서는 유의한 감소를 보이지 않았다. 베체트 장염의 미생물 분류상 특징은 궤양성 대장염이나 크론병보다 대조군과 더 유사한 양상을 나타내며, 염증성 장질환과는 뚜렷이 구분되는 특징을 보였다. 그러나 염증성 장질환과 베체트 장염에서 공통적인 변화도 확인되었으며, 단쇄지방산을 생성하는 유익균 중 다섯 가지 (Fusicatenibacter saccharivorans, Coprococcus comes, Blautia obeum, Dorea formicigenerans, and Roseburai ceciola)의 감소였다. 베체트 장염에서는 추가적으로 Subdoligranulum variable과 Blautia wexlerae의 감소가 확인되었는데 이는 궤양성 대장염 또는 크론병과 공통된 특징이었다. 베체트 장염에 특이적인 변화로 Bacteroides 속, 특히 Bacteroides fragilis 종의 감소가 확인되었다. 베체트 장염의 대사체 특징은 크론병과 가장 유사하였고, 대조군이나 궤양성 대장염과는 구분되었다. 그러나 궤양성 대장염, 크론병, 베체트 장염 각각 특징적인 대사체 결과를 나타냈다. 전반적으로 베체트 장염은 궤양성 대장염이나 크론병처럼 큰 미생물 변화를 나타내지 않으면서도, 에너지 대사, 아미노산, 탄수화물 및 지질 대사, 보조 인자 및 비타민 대사, 뉴클레오타이드 대사, 유전 정보 처리 등의 대사 경로가 증가되어 있고 그에 해당하는 대사체 변화를 보였으며, 이러한 변화가 미생물 변화와 잘 연관지어 나타남을 확인할 수 있었다.
결론: 염증성 장질환과 베체트 장염에서의 미생물 및 대사체 통합 분석에서 베체트 장염은 대조군 및 염증성 장질환과 공유되는 특징과 함께 구분되는 질환 특이적 특징을 나타내어, 이 질환들간의 유사성 및 차이점을 유추해 볼 수 있었고 이러한 미생물 및 대사체 특징은 추후 이들 질환의 진단 및 감별 진단의 마커로서 사용될 가능성이 있다.

다국어 초록 (Multilingual Abstract)

Background and aims: Gut microbial and metabolite alterations have been linked to the pathogenesis of inflammatory bowel diseases (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD). However, investigations into microbial and metabolic characteristics in intestinal Behçet’s disease (BD), a condition sharing many clinical similarites with UC and CD, are largely lacking. The current study aimed to evaluate alterations in the gut microbiome and plasma metabolites in patients with intestinal BD, as well as UC and CD, compared with those in healthy controls. We also sought to discover microbial and metabolomic biomarkers that can aid in differentiating UC, CD, and intestinal BD.
Methods: Patients with IBD and intestinal BD undergoing diagnostic endoscopies, as well as healthy volunteers with endoscopy but no signs of inflammation, were enrolled. We conducted 16S ribosomal RNA (rRNA) sequencing on colon tissue samples obtained during colonoscopy and compared the diversity of microbial communities, taxonomic composition, and functional profiling between the control group and the UC, CD, and intestinal BD groups. Additionally, we collected and analyzed stool samples from the control group and IBD patients for 16S rRNA sequencing. Blood samples were drawn from the control group, UC, CD, and intestinal BD patients, and plasma metabolomic analysis was performed using gas chromatography time-of-flight mass spectrometry (GC–TOF–MS) and ultra-performance liquid chromatography–quadrupole/time-of-flight mass spectrometry (UPLC–Q–TOF–MS) analysis.
Results: A total of 100 patients (35 UC, 30 CD, and 35 BD) and 41 healthy volunteers were enrolled in the study. We conducted 16S rRNA sequencing on 73 tissue samples (12 control, 24 UC, 14 CD, and 23 BD) and 19 stool samples (5 control, 9 UC, and 5 CD). Metabolite analysis was performed on 100 blood samples (25 control, 24 UC, 26 CD, and 25 BD). The microbial diversity of colon tissue was reduced only in CD, with no significant decrease observed in BD. The microbial taxonomic profile of intestinal BD displayed a pattern more similar to healthy controls than UC or CD, and it exhibited distinctive features setting it apart from both UC and CD. However, there were common changes across all three conditions (UC, CD, and BD), which is a decrease in five beneficial bacteria responsible for producing short-chain fatty acids: Fusicatenibacter saccharivorans, Coprococcus comes, Blautia obeum, Dorea formicigenerans, and Roseburai ceciola. Additional changes in intestinal BD included a decreased abundance of Subdoligranulum variable and Blautia wexlerae, which were shared features with either UC or CD. As a specific alteration unique to BD, a decrease in the genus Bacteroides, particularly the species Bacteroides fragilis, was identified. The metabolomic profile of intestinal BD was most similar to CD and distinct from both controls and UC. However, UC, CD, and BD each exhibited distinct metabolomic profiles. Overall, BD exhibited pronounced functional changes and metabolite alterations, including changes in energy metabolism, amino acid, carbohydrate, and lipid metabolism, cofactor and vitamin metabolism, nucleotide metabolism, and genetic information processing, while not showing as substantial microbial taxonomic changes as UC or CD. The microbial functions analyzed by phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) showed a good alignment with the enriched pathways identified by qualitative enrichment analysis of plasma metabolite.
Conclusion: In this integrative analysis of microbiome and metabolome in IBD and intestinal BD, we observed that intestinal BD exhibited profiles that were both shared with and distinct from those of the control group, UC, and CD.

목차 (Table of Contents)

• ABSTRACT ⅴ
• I. INTRODUCTION 1
• II. MATERIALS AND METHODS 4
• 1. Study subjects 4
• 2. Clinical data collection 4
• 3. Collecting tissue, blood, and stool samples 5
• 4. Microbiome analysis 5
• A. DNA extraction 5
• B. PCR amplification and 16S rRNA amplicon sequencing 6
• C. Microbiome data analysis 6
• 5. Metabolomic analysis 7
• A. Sample preparation for metabolomic analysis 7
• B. GC-TOF-MS analysis 8
• C. UPLC-Q-TOF-MS analysis 8
• D. Data analysis 9
• 6. Statistical analysis 11
• III. RESULTS 11
• 1. Study population 11
• 2. Changes in microbiome in IBD and intestinal BD 18
• A. Changes in tissue and fecal microbiota composition in IBD and intestinal BD 18
• B. Taxonomic biomarker evaluation 22
• C. Predictive functional profiling of microbiome 28
• 3. Metabolomic analysis 30
• A. GC-TOF-MS analysis 30
• (1) Changes in metabolite between groups 30
• (2) Metabolomic biomarker discovery 36
• (3) Functional aspects of metabolomic biomarkers 38
• B. UPLC-Q-TOF-MS analysis 45
• 4. Integration of microbial and metabolomic biomarkers 47
• IV. DISCUSSION 49
• V. CONCLUSION 55
• REFERENCES 56
• ABSTRACT(IN KOREAN) 63

참고문헌 (Reference)

1. Bacteroides thetaiotaomicron, Luis AS, Martens EC, Porter NT, Trends Microbiol26:966-7, , 2018

2. 27 Multivariate analyses in microbial ecology, Ramette A., FEMS Microbiol Ecol62:142-60, , 2007

3. Current understanding of the human microbiome, Jansson JK, Lynch SV, Gilbert JA, Caporaso JG, Blaser MJ, Knight R., 24:392-400, , 2018

4. The Gut Microbiota in Inflammatory Bowel Disease, Fu L, Zhang Z, Qiu P, Liu Y, Ishimoto T, Zhang J, Front Cell Infect Microbiol12:733992, , 2022

5. 3 Behçet's Disease: An Overview of Etiopathogenesis, Leccese P, Alpsoy E., 10:1067, , 2019

6. Microbial genes and pathways ininflammatory bowel disease, Garner A, Vlamakis H, Schirmer M, Xavier RJ, 17:497-511, , 2019

7. The treatment-naive microbiome in new-onset Crohn's disease, Vázquez-Baeza Y, Kugathasan S, Ren B, et al, Gevers D, Denson LA, Van Treuren W, 15:382-92, , 2014

8. Metabolomics reveals metabolic biomarkers of Crohn's disease, Jansson J, Dicksved J, Willing B, Lucio M, Fekete A, Halfvarson J et al, 4:e6386, , 2009

9. Metabolism of inorganic N compounds by ammonia-oxidizing bacteria, Arp DJ, Stein LY, Crit Rev Biochem Mol Biol38:471-95, , 2003

10. Blood metabolome predicts gut microbiome alpha-diversity in humans, Rappaport N, Wilmanski T, Magis AT, Lovejoy J et al, Earls JC, Manor O, 37:1217-28, , 2019

11. Dynamics of the human gut microbiome in inflammatory bowel disease, Halfvarson J, Walters WA, Lamendella R, Brislawn CJ, Bramer LM, et al, Vázquez-Baeza Y, 2:17004, , 2017

12. A microbial symbiosis factor prevents intestinal inflammatory disease, Mazmanian SK, Kasper DL, Round JL, 453:620-5, , 2008

13. Behcet's disease: epidemiology, clinical manifestations, and diagnosis, Akhlaghi M et al, Shams H, Shahram F, Chams-Davatchi C, Nadji A, Davatchi F, 13:57-65, , 2017

14. Faecal and Serum Metabolomics in Paediatric Inflammatory Bowel Disease, Velagapudi V., Kolho KL, Pessia A, Jaakkola T, de Vos WM, 11:321-34, , 2017

15. 60 The role of pattern recognition receptors in intestinal inflammation, Fukata M, Arditi M., Mucosal Immunol6:451-63, , 2013

16. Metabolomic Analysis in Inflammatory Bowel Disease: A Systematic Review, Gallagher K, Griffin JL, Holmes E, Williams HRT, Catesson A, 15:813-26, , 2021

17. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases, Lloyd-Price J, Schirmer M, Poon TW et al, Arze C, Ananthakrishnan AN, Avila-Pacheco J, 569:655-62, , 2019

18. Gut microbiome structure and metabolic activity in inflammatory bowel disease, Avila-Pacheco J, Franzosa EA, Reinker S et al, Sirota-Madi A, Haiser HJ, Fornelos N, 4:293-305, , 2019

19. Techniques used to characterize the gut microbiota: a guide for the clinician, Fraher MH, Quigley EM, O'Toole PW, 9:312-22, , 2012

20. A global test for groups of genes: testing association with a clinical outcome, van Houwelingen HC, de Kort F, Goeman JJ, van de Geer SA, 20:93-9, , 2004

21. Fusicatenibacter saccharivorans gen. nov., sp. nov., isolated from human faeces, Nomoto K., Takada T, Tsuji H, Kurakawa T, 63:3691-6, , 2013

22. Gut microbiota in the early stage of Crohn's disease has unique characteristics, Xu J et al, Zhang W, Wang D, Lu X, Ma X, Yang L, 14:46, , 2022

23. Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders, Metwaly A, Haller D., Reitmeier S, 19:383-97, , 2022

24. EzEditor: a versatile sequence alignment editor for both rRNA-and protein-coding genes, Cho YJ, Ha SM et al, Park SC, Lee K, Kim BS, Jeon YS, 64:689-91, , 2014

25. Integrated microbiota and metabolite profiles link Crohn's disease to sulfur metabolism, Lagkouvardos I, Corraliza AM, et al, Raj ACD, Waldschmitt N, Dunkel A, Metwaly A, 11:4322, , 2020

26. A Single Species of Clostridium Subcluster XIVa Decreased in Ulcerative Colitis Patients, Mizuno S, Mikami Y, Matsuoka K et al, Sujino T, Saigusa K, Takeshita K, Inflamm Bowel Dis22:2802-10, , 2016

27. The role of gut microbiota in gout: Is gut microbiota a potential target for gout treatment, Chen X, Zhang P, Wang Z et al, Chen M, Tong S, Cheng Q, Front Cell Infect Microbiol12:1051682, , 2022

28. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences, Langille MG, Reyes JA et al, McDonald D, Knights D, Caporaso JG, Zaneveld J, 31:814-21, , 2013

29. Comparative In silico Analysis of Butyrate Production Pathways in Gut Commensals and Pathogens, Kaur H, Mande SS, Anand S, Front Microbiol7:1945, , 2016

30. An immunomodulatory molecule of symbiotic bacteria directs maturation of the host immune system, Kasper DL, Tzianabos AO, Liu CH, Mazmanian SK, 122:107-18, , 2005

31. Increased abundance of proteobacteria in aggressive Crohn's disease seven years after diagnosis, Vester-Andersen MK, Mortensen CO, Träger C,, Skovsen K, et al, Mirsepasi-Lauridsen HC, Prosberg MV, 9:13473, , 2019

32. Impact of enrofloxacin on the human intestinal microbiota revealed by comparative molecular analysis, Kim JN, Chun J, Cerniglia CE, Yoon SH, Kim BS, 18:310-20, , 2012

33. Metabolomics as a Promising Resource Identifying Potential Biomarkers for Inflammatory Bowel Disease, Bauset C, Gisbert-Ferrándiz L, Cosín-Roger J., 10, , 2021

34. The anti-obesity effect of Ephedra sinica through modulation of gut microbiota in obese Korean women, Kim BS, Song MY, Kim H., 152:532-9, , 2014

35. Analysis of bacterial community profiles of endodontically infected primary teeth using pyrosequencing, Moon CY, Lee HS, Yun KH, Nam OH, Choi SC, Lee JH, 27:56-65, , 2017

36. Study of microbiome changes in patients with ulcerative colitis in the Central European part of Russia, Syromyatnikov MY, Solodskikh SA, Gryaznova MV, Panevina AV, Dvoretskaya YD, Sviridova TN et al, Heliyon7:e06432, , 2021

37. Ecology of bacteria in the human gastrointestinal tract--identification of keystone and foundation taxa, de Muinck EJ, Trosvik P, Microbiome3:44, , 2015

38. False discovery rate control is a recommended alternative toBonferroni-type adjustments in health studies, Glickman ME, Rao SR, Schultz MR., 67:850-7., , 2014

39. Differences in Gut Microbiota in Patients With vs Without Inflammatory Bowel Diseases: A Systematic Review, Pittayanon R, Leontiadis GI, Yuan Y, Lau JT, Tse F, Surette M et al, 158:930-46. e1, , 2020

40. Bacteroides fragilis polysaccharide A induces IL-10 secreting B and T cells that prevent viral encephalitis, Ramakrishna C, Cantin EM, Li L, Kujawski M, Mazmanian SK, Chu H, 10:2153, , 2019

41. Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome, Collij V, Mujagic Z, et al, Vich Vila A, Imhann F, Jankipersadsing SA, Gurry T, Sci Transl Med10, , 2018

42. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies, Ha SM, Yoon SH, Kwon S, Seo H et al, Kim Y, Lim J, 67:1613-7, , 2017

43. From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis, Pavoine S, Chessel D., Dufour AB, 228:523-37, , 2004

44. Alteration of the Fecal but Not Salivary Microbiome in Patients with Behçet's Disease According to Disease Activity Shift, Lee ES, Park S, Kim JC, Park MJ, 9, , 2021

45. Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease, Imhann F, Vich Vila A, Visschedijk MC, et al, Fu J, Bonder MJ, Gevers D, Gut67:108-19, , 2018

46. Detection of HSV-1 DNA in patients with Behçet's syndrome and in patients with recurrent oral ulcers by the polymerase chain reaction, Studd M, Lehner T., McCance DJ, 34:39-43, , 1991

47. 3rd European Evidence-based Consensus on the Diagnosis and Management of Crohn's Disease 2016: Part 1: Diagnosis and Medical Management, Gomollon F, Tilg H, Annese V, Van Assche G, Lindsay JO et al, Dignass A, 11:3-25, , 2017

48. Alterations in Lipid, Amino Acid, and Energy Metabolism Distinguish Crohn's Disease from Ulcerative Colitis and Control Subjects by Serum Metabolomic Profiling, Williams CS et al, Scoville EA, Horst SN, Brown CT, Motley AK, Allaman MM, 14:17, , 2018

49. Relationship Between Microbiota of the Colonic Mucosa vs Feces and Symptoms, Colonic Transit, and Methane Production in Female Patients With Chronic Constipation, Wolf PG et al, Parthasarathy G, O'Connor HM, Chia N, Chen J, Chen X, 150:367-79. e1, , 2016