Characterization of Specific Signatures of the Oral Cavity, Sputum, and Ileum Microbiota in Patients With Crohn's Disease

Kai Xia, Renyuan Gao, Xiaocai Wu, Jing Sun, Jian Wan, Tianqi Wu, Jakub Fichna, Lu Yin, Chunqiu Chen, Kai Xia, Renyuan Gao, Xiaocai Wu, Jing Sun, Jian Wan, Tianqi Wu, Jakub Fichna, Lu Yin, Chunqiu Chen

Abstract

Background: Crohn's disease (CD) is a chronic nonspecific inflammatory bowel disease (IBD) with an increasing incidence worldwide. The etiology of CD is still obscure, but microbial dysbiosis has been recognized as an essential factor contributing to CD. However, few studies have revealed the microbiome's signatures and reciprocal correlations between multiple sites in patients with CD over different disease stages. This study investigated the specific microbial architectures of the oral cavity, sputum, and ileum in patients with CD in the active and remission stages.

Methods: Microbial samples from the oral cavity, sputum, and ileum were collected from patients with CD in the active and remission stages and healthy controls. The microbial composition was assessed by 16S ribosomal RNA (rRNA) gene sequencing. In addition, bioinformatics methods were used to demonstrate the microbial signatures, functional changes, and correlations between microbiota and clinical data in CD.

Results: Compared with healthy controls, a distinct microbiota dysbiosis in the oral cavity, sputum, and ileum of patients with CD was identified, characterized by alterations in microbiota biodiversity and composition. The oral cavity and sputum microbiota showed significantly lower microbial diversity in patients with CD than in healthy controls. In terms of microbiota composition, the microbiota changes in the oral cavity of patients with CD were similar to those in the sputum, while they were different from those in the ileum. In the oral cavity and sputum of patients with CD, a lower relative abundance of Firmicutes and Actinobacteria was observed compared to healthy controls, which was most prominent in the active stage. In contrast, an increased relative abundance of Fusobacteria, Porphyromonas, and Haemophilus was observed in patients with CD. The predicted metabolic pathways involved in the oral cavity, sputum, and ileum were similar, predominantly involving metabolism, environmental information processing, and genetic information processing.

Conclusion: The results revealed the alterations of microbiota architecture in the oral cavity, sputum, and ileum of patients with CD, which varied across disease stages. Studying microbiota dysbiosis may bring new insights into the etiology of CD and lead to novel treatments.

Trial registration: ClinicalTrials.gov NCT04965584.

Keywords: 16S rRNA gene sequence; Crohn’s disease; ileum; microbiota; oral cavity; sputum.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Xia, Gao, Wu, Sun, Wan, Wu, Fichna, Yin and Chen.

Figures

Figure 1
Figure 1
Human body model diagram and experimental flow diagram. (A) Microbial samples were collected from the tongue base, sputum and ileal stoma. (B) Microbial samples from the oral cavity, sputum, and ileum were collected from patients with CD in the active and remission stages and healthy controls. The microbial composition was assessed by 16S rRNA gene sequencing. Bioinformatics methods were used to demonstrate the alpha and beta diversity of microbiota, the structure of microbiota, correlations between microbiota and clinical factors, and functional characterization of microbiota.
Figure 2
Figure 2
Alpha and beta diversity of microbiota in the oral cavity and sputum of patients with CD and healthy controls. (A–D) Chao diversity index of phylum and genus levels in the samples of oral cavity and sputum. (E–H) Shannon diversity index of phylum and genus levels in the samples of oral cavity and sputum. (I–N) Principle coordinate analysis (PCoA) on the phylum and genus levels using the unweighted UniFrac distance and Adonis test. Red color represents samples from active stage, blue indicates remission stage, while green color indicates healthy controls. *, **, and *** correspond to p-values <0.05, 0.01, and 0.001, respectively.
Figure 3
Figure 3
Structure analysis of the oral microbiota in patients with CD. (A, B) Venn diagram indicating the overlap of OTUs in the categories on the phylum and genus levels, respectively. (C, D) Barplot of the relative abundances of different taxa on the phylum and genus levels, respectively. (E, F) Heatmap revealing the association between oral microbiota and clinical factors on the phylum and genus levels, respectively. *, **, and *** correspond to p-values <0.05, 0.01, and 0.001, respectively.
Figure 4
Figure 4
Inter-group difference analysis of microbiota in the oral cavity of patients with CD and healthy controls. (A–C) Wilcoxon rank-sum test bar plot of different taxa on the phylum level. (D–F) Wilcoxon rank-sum test bar plot of different taxa on the genus level. *, **, and *** correspond to p-values <0.05, 0.01, and 0.001, respectively.
Figure 5
Figure 5
Structure analysis of the sputum microbiota in patients with CD. (A, B) Venn diagram indicating the overlap of OTUs in the categories on the phylum and genus levels, respectively. (C, D) Barplot of the relative abundances of different taxa on the phylum and genus levels, respectively. (E, F) Heatmap revealing the association between sputum microbiota and clinical factors on the phylum and genus levels, respectively. *, **, and *** correspond to p-values <0.05, 0.01, and 0.001, respectively.
Figure 6
Figure 6
Inter-group difference analysis of sputum microbiota of patients with CD and healthy controls. (A–C) Wilcoxon rank-sum test bar plot of different taxa on the phylum level. (D–F) Wilcoxon rank-sum test bar plot of different taxa on the genus level. *, **, and *** correspond to p-values <0.05, 0.01, and 0.001, respectively.
Figure 7
Figure 7
Structure analysis of the ileal microbiota in patients with CD. (A, B) Barplot of the relative abundances of different taxa on the phylum and genus levels, respectively. (C, D) Wilcoxon rank-sum test bar plot of different taxa on the phylum and genus levels, respectively. (E, F) Heatmap revealing the association between ileal microbiota and clinical factors on the phylum and genus levels, respectively. *, **, and *** correspond to p-values <0.05, 0.01, and 0.001, respectively.
Figure 8
Figure 8
Relative abundances of metagenomes of different KEGG pathways level in microbiota of patients with CD. (A) Relative abundances of metagenomes of different KEGG pathways level in oral microbiota of patients with CD. (B) Relative abundances of metagenomes of different KEGG pathways level in sputum microbiota of patients with CD. (C) Relative abundances of metagenomes of different KEGG pathways level in ileal microbiota of patients with CD. Red color represents samples from active stage, blue indicates remission stage, while green color indicates healthy controls, respectively.

References

    1. Anand S., Mande S. S. (2018). Diet, Microbiota and Gut-Lung Connection. Front. Microbiol. 9. doi: 10.3389/fmicb.2018.02147
    1. Barcik W., Boutin R. C. T., Sokolowska M., Finlay B. B. (2020). The Role of Lung and Gut Microbiota in the Pathology of Asthma. Immunity. 52 (2), 241–255. doi: 10.1016/j.immuni.2020.01.007
    1. Bernstein C. N., Forbes J. D. (2017). Gut Microbiome in Inflammatory Bowel Disease and Other Chronic Immune-Mediated Inflammatory Diseases. Inflammation Intest Dis. 2 (2), 116–123. doi: 10.1159/000481401
    1. Budden K. F., Gellatly S. L., Wood D. L., Cooper M. A., Morrison M., Hugenholtz P., et al. . (2017). Emerging Pathogenic Links Between Microbiota and the Gut-Lung Axis. Nat. Rev. Microbiol. 15 (1), 55–63. doi: 10.1038/nrmicro.2016.142
    1. Budden K. F., Shukla S. D., Rehman S. F., Bowerman K. L., Keely S., Hugenholtz P., et al. . (2019). Functional Effects of the Microbiota in Chronic Respiratory Disease. Lancet Respir. Med. 7 (10), 907–920. doi: 10.1016/S2213-2600(18)30510-1
    1. Cozzi D., Moroni C., Addeo G., Danti G., Lanzetta M. M., Cavigli E., et al. . (2018). Radiological Patterns of Lung Involvement in Inflammatory Bowel Disease. Gastroenterol. Res. Pract. 2018, 1–10. doi: 10.1155/2018/5697846
    1. Danve A. (2019). Thoracic Manifestations of Ankylosing Spondylitis, Inflammatory Bowel Disease, and Relapsing Polychondritis. Clin. Chest Med. 40 (3), 599–608. doi: 10.1016/j.ccm.2019.05.006
    1. Dewhirst F. E., Chen T., Izard J., Paster B. J., Tanner A. C., Yu W. H., et al. . (2010). The Human Oral Microbiome. J. Bacteriol. 192 (19), 5002–5017. doi: 10.1128/JB.00542-10
    1. Ding N. S., McDonald J. A. K., Perdones–Montero A., Rees D. N., Adegbola S. O., Misra R., et al. (2020). Metabonomics and the Gut Microbiome Associated With Primary Response to Anti-TNF Therapy in Crohn's Disease. J. Crohns. Colitis. 14 (8), 1090–1102. doi: 10.1093/ecco-jcc/jjaa039
    1. Dong L. N., Wang M., Guo J., Wang J. P. (2019). Role of Intestinal Microbiota and Metabolites in Inflammatory Bowel Disease. Chin. Med. J. (Engl) 132 (13), 1610–1614. doi: 10.1097/CM9.0000000000000290
    1. Dovrolis N., Michalopoulos G., Theodoropoulos G. E., Arvanitidis K., Kolios G., Sechi L. A., et al. . (2020). The Interplay Between Mucosal Microbiota Composition and Host Gene-Expression is Linked With Infliximab Response in Inflammatory Bowel Diseases. Microorganisms 8 (3), 438. doi: 10.3390/microorganisms8030438
    1. El Aidy S., van den Bogert B., Kleerebezem M. (2015). The Small Intestine Microbiota, Nutritional Modulation and Relevance for Health. Curr. Opin. Biotechnol. 32, 14–20. doi: 10.1016/j.copbio.2014.09.005
    1. Fesler Z., Mitova E., Brubaker P. L. (2020). GLP-2, EGF, and the Intestinal Epithelial IGF-1 Receptor Interactions in the Regulation of Crypt Cell Proliferation. Endocrinology 161 (4), 1–14. doi: 10.1210/endocr/bqaa040
    1. Gao R., Zhu Y., Kong C., Xia K., Li H., Zhu Y., et al. . (2021). Alterations, Interactions, and Diagnostic Potential of Gut Bacteria and Viruses in Colorectal Cancer. Front. Cell Infect. Microbiol. 11. doi: 10.3389/fcimb.2021.657867
    1. Hattori S., Nakamura M., Yamamura T., Maeda K., Sawada T., Mizutani Y., et al. . (2020). The Microbiome Can Predict Mucosal Healing in Small Intestine in Patients With Crohn’s Disease. J. Gastroenterol. 55 (12), 1138–1149. doi: 10.1007/s00535-020-01728-1
    1. Ismail Y., Mahendran V., Octavia S., Day A. S., Riordan S. M., Grimm M. C., et al. . (2012). Investigation of the Enteric Pathogenic Potential of Oral Campylobacter Concisus Strains Isolated From Patients With Inflammatory Bowel Disease. PloS One 7 (5), e38217. doi: 10.1371/journal.pone.0038217
    1. Ji X., Wang L., Lu D. (2014). Pulmonary Manifestations of Inflammatory Bowel Disease. World J. Gastroenterol. 20 (37), 13501–13511. doi: 10.1016/S1579-2129(06)60404-7
    1. Katsanos K. H., Torres J., Roda G., Brygo A., Delaporte E., Colombel J. F. (2015). Review Article: non-Malignant Oral Manifestations in Inflammatory Bowel Diseases. Aliment Pharmacol. Ther. 42 (1), 40–60. doi: 10.1111/apt.13217
    1. Kirk K. F., Méric G., Nielsen H. L., Pascoe B., Sheppard S. K., Thorlacius-Ussing O., et al. . (2018). Molecular Epidemiology and Comparative Genomics of Campylobacter Concisus Strains From Saliva, Faeces and Gut Mucosal Biopsies in Inflammatory Bowel Disease. Sci. Rep. 8 (1), 1902. doi: 10.1038/s41598-018-20135-4
    1. Kondrup J., Rasmussen H. H., Hamberg O., Stanga Z. (2003). Nutritional Risk Screening (NRS 2002): A New Method Based on an Analysis of Controlled Clinical Trials. Clin. Nutr. 22 (3), 321–336. doi: 10.1016/s0261-5614(02)00214-5
    1. Kong C., Yan X., Liu Y., Huang L., Zhu Y., He J., et al. . (2021). Ketogenic Diet Alleviates Colitis by Reduction of Colonic Group 3 Innate Lymphoid Cells Through Altering Gut Microbiome. Signal Transduct Target Ther. 6 (1), 154. doi: 10.1038/s41392-021-00549-9
    1. Liu W., Zhang R., Shu R., Yu J., Li H., Long H., et al. . (2020). Study of the Relationship Between Microbiome and Colorectal Cancer Susceptibility Using 16srrna Sequencing. BioMed. Res. Int. 2020, 1–17. doi: 10.1155/2020/7828392
    1. Lloyd-Price J., Arze C., Ananthakrishnan A. N., Schirmer M., Avila-Pacheco J., Poon T. W., et al. . (2019). Multi-Omics of the Gut Microbial Ecosystem in Inflammatory Bowel Diseases. Nature. 569 (7758), 655–662. doi: 10.1038/s41586-019-1237-9
    1. Looft T., Allen H. K. (2012). Collateral Effects of Antibiotics on Mammalian Gut Microbiomes. Gut Microbes 3 (5), 463–467. doi: 10.4161/gmic.21288
    1. Man S. M. (2011). The Clinical Importance of Emerging Campylobacter Species. Nat. Rev. Gastroenterol. Hepatol. 8 (12), 669–685. doi: 10.1038/nrgastro.2011.191
    1. Mansi A., Cucchiara S., Greco L., Sarnelli P., Pisanti C., Franco M. T., et al. . (2000). Bronchial Hyperresponsiveness in Children and Adolescents With Crohn's Disease. Am. J. Respir. Crit. Care Med. 161 (3 Pt 1), 1051–1054. doi: 10.1164/ajrccm.161.3.9906013
    1. Mateer S. W., Maltby S., Marks E., Foster P. S., Horvat J. C., Hansbro P. M., et al. . (2015). Potential Mechanisms Regulating Pulmonary Pathology in Inflammatory Bowel Disease. J. Leukoc. Biol. 98 (5), 727–737. doi: 10.1189/jlb.3RU1114-563R
    1. Nielsen H. L., Nielsen H., Ejlertsen T., Engberg J., Günzel D., Zeitz M., et al. . (2011). Oral and Fecal Campylobacter Concisus Strains Perturb Barrier Function by Apoptosis Induction in HT-29/B6 Intestinal Epithelial Cells. PloS One 6 (8), e23858. doi: 10.1371/journal.pone.0023858
    1. Olaisen M., Flatberg A., Granlund A. V. B., Martinsen T. C., Sandvik A. K., Fossmark R. (2021). Bacterial Mucosa-Associated Microbiome in Inflamed and Proximal Noninflamed Ileum of Patients With Crohn's Disease. Inflammation Bowel Dis. 27 (1), 12–24. doi: 10.1093/ibd/izaa107
    1. Piersigilli F., Van Grambezen B., Hocq C., Danhaive O. (2020). Nutrients and Microbiota in Lung Diseases of Prematurity: The Placenta-Gut-Lung Triangle. Nutrients. 12 (2), 469. doi: 10.3390/nu12020469
    1. Qi Y., Zang S., Wei J., Yu H., Yang Z., Wu H., et al. . (2021). High-Throughput Sequencing Provides Insights Into Oral Microbiota Dysbiosis in Association With Inflammatory Bowel Disease. Genomics. 113 (1 Pt 2), 664–676. doi: 10.1016/j.ygeno.2020.09.063
    1. Ramakrishna C., Kujawski M., Chu H., Li L., Mazmanian S. K., Cantin E. M. (2019). Bacteroides Fragilis Polysaccharide A Induces IL-10 Secreting B and T Cells That Prevent Viral Encephalitis. Nat. Commun. 10 (1), 2153. doi: 10.1038/s41467-019-09884-6
    1. Rautava J., Pinnell L. J., Vong L., Akseer N., Assa A., Sherman P. M. (2015). Oral Microbiome Composition Changes in Mouse Models of Colitis. J. Gastroenterol. Hepatol. 30 (3), 521–527. doi: 10.1111/jgh.12713
    1. Read E., Curtis M. A., Neves J. F. (2021). The Role of Oral Bacteria in Inflammatory Bowel Disease. Nat. Rev. Gastroenterol. Hepatol. 18 (10), 731–742. doi: 10.1038/s41575-021-00488-4
    1. Ruigrok R. A. A. A., Collij V., Sureda P., Klaassen M. A. Y., Bolte L. A., Jansen B. H., et al. . (2021). The Composition and Metabolic Potential of the Human Small Intestinal Microbiota Within the Context of Inflammatory Bowel Disease. J. Crohns Colitis. 15 (8), 1326–1338. doi: 10.1093/ecco-jcc/jjab020
    1. Said H. S., Suda W., Nakagome S., Chinen H., Oshima K., Kim S., et al. . (2014). Dysbiosis of Salivary Microbiota in Inflammatory Bowel Disease and Its Association With Oral Immunological Biomarkers. DNA Res. 21 (1), 15–25. doi: 10.1093/dnares/dst037
    1. Sokol H., Brot L., Stefanescu C., Auzolle C., Barnich N., Buisson A., et al. . (2020). Prominence of Ileal Mucosa-Associated Microbiota to Predict Postoperative Endoscopic Recurrence in Crohn's Disease. Gut. 69 (3), 462–472. doi: 10.1136/gutjnl-2019-318719
    1. Sokol H., Leducq V., Aschard H., Pham H. P., Jegou S., Landman C., et al. . (2017). Fungal Microbiota Dysbiosis in IBD. Gut. 66 (6), 1039–1048. doi: 10.1136/gutjnl-2015-310746
    1. Thorburn A. N., Mckenzie C. I., Shen S., Stanley D., Macia L., Mason L. J., et al. . (2015). Evidence That Asthma is a Developmental Origin Disease Influenced by Maternal Diet and Bacterial Metabolites. Nat. Commun. 6, 7320. doi: 10.1038/ncomms8320
    1. Trompette A., Gollwitzer E. S., Yadava K., Sichelstiel A. K., Sprenger N., Ngom-Bru C., et al. . (2014). Gut Microbiota Metabolism of Dietary Fiber Influences Allergic Airway Disease and Hematopoiesis. Nat. Med. 20 (2), 159–166. doi: 10.1038/nm.3444
    1. Ubags N. D. J., Marsland B. J. (2017). Mechanistic Insight Into the Function of the Microbiome in Lung Diseases. Eur. Respir. J. 50 (3), 1602467. doi: 10.1183/13993003.02467-2016
    1. Weiser R., Oakley J., Ronchetti K., Tame J. D., Hoehn S., Jurkowski T. P., et al. . (2022). The Lung Microbiota in Children With Cystic Fibrosis Captured by Induced Sputum Sampling. J. Cyst Fibros. 7, 54. doi: 10.1016/j.jcf.2022.01.006
    1. Xun Z., Zhang Q., Xu T., Chen N., Chen F. (2018). Dysbiosis and Ecotypes of the Salivary Microbiome Associated With Inflammatory Bowel Diseases and the Assistance in Diagnosis of Diseases Using Oral Bacterial Profiles. Front. Microbiol. 9. doi: 10.3389/fmicb.2018.01136
    1. Yang D., Xing Y., Song X., Qian Y. (2020). The Impact of Lung Microbiota Dysbiosis on Inflammation. Immunology. 159 (2), 156–166. doi: 10.1111/imm.13139
    1. Yoshida E. M. (1999). The Crohn's Disease Activity Index, its Derivatives and the Inflammatory Bowel Disease Questionnaire: A Review of Instruments to Assess Crohn's Disease. Can. J. Gastroenterol. 13 (1), 65–73. doi: 10.1155/1999/506915
    1. Zhang T., Kayani M. U. R., Hong L., Zhang C., Zhong J., Wang Z., et al. . (2020). Dynamics of the Salivary Microbiome During Different Phases of Crohn's Disease. Front. Cell Infect. Microbiol. 10. doi: 10.3389/fcimb.2020.544704
    1. Zmora N., Zilberman-Schapira G., Suez J., Mor U., Dori-Bachash M., Bashiardes S., et al. . (2018). Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics is Associated With Unique Host and Microbiome Features. Cell 174 (6), 1388–1405. doi: 10.1016/j.cell.2018.08.041

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