#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Sputum microbiota and inflammation at stable state and during exacerbations in a cohort of chronic obstructive pulmonary disease (COPD) patients


Autoři: Solveig Tangedal aff001;  Rune Nielsen aff001;  Marianne Aanerud aff002;  Louise J. Persson aff002;  Harald G. Wiker aff001;  Per S. Bakke aff001;  Pieter S. Hiemstra aff003;  Tomas M. Eagan aff001
Působiště autorů: Dept. of Clinical Science, Faculty of Medicine, University of Bergen, Bergen, Norway aff001;  Dept. of Thoracic Medicine, Haukeland University Hospital, Bergen, Norway aff002;  Dept of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands aff003;  Dept. of Microbiology, Haukeland University Hospital, Bergen, Norway aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222449

Souhrn

Background

Exacerbations of chronic obstructive pulmonary disease (COPD) are debilitating events and spur disease progression. Infectious causes are frequent; however, it is unknown to what extent exacerbations are caused by larger shifts in the airways’ microbiota. The aim of the current study was to analyse the changes in microbial composition between stable state and during exacerbations, and the corresponding immune response.

Methods

The study sample included 36 COPD patients examined at stable state and exacerbation from the Bergen COPD Cohort and Exacerbations studies, and one patient who delivered sputum on 13 different occasions during the three-year study period. A physician examined the patients at all time points, and sputum induction was performed by stringent protocol. Only induced sputum samples were used in the current study, not spontaneously expectorated sputum. Sputum inflammatory markers (IL-6, IL-8, IL-18, IP-10, MIG, TNF-α) and antimicrobial peptides (AMPs, i.e. LL-37/hCAP-18, SLPI) were measured in supernatants, whereas target gene sequencing (16S rRNA) was performed on corresponding cell pellets. The microbiome bioinformatics platform QIIME2TM and the statistics environment R were applied for bioinformatics analyses.

Results

Levels of IP-10, MIG, TNF-α and AMPs were significantly different between the two disease states. Of 36 sample pairs, 24 had significant differences in the 12 most abundant genera between disease states. The diversity was significantly different in several individuals, but not when data was analysed on a group level. The one patient case study showed longitudinal dynamics in microbiota unrelated to disease state.

Conclusion

Changes in the sputum microbiota with changing COPD disease states are common, and are accompanied by changes in inflammatory markers. However, the changes are highly individual and heterogeneous events.

Klíčová slova:

Biology and life sciences – Anatomy – Body fluids – Mucus – Sputum – Physiology – Microbiology – Medical microbiology – Microbiome – Microbial genomics – Genetics – Genomics – Taxonomy – Medicine and health sciences – Pulmonology – Chronic obstructive pulmonary disease – Inflammatory diseases – Immunology – Immune response – Inflammation – Diagnostic medicine – Signs and symptoms – Pathology and laboratory medicine – Computer and information sciences – Data management – Research and analysis methods – Database and informatics methods – Bioinformatics – Sequence analysis – Sequence databases – Biological databases


Zdroje

1. Hilty M, Burke C, Pedro H, Cardenas P, Bush A, Bossley C, et al. Disordered microbial communities in asthmatic airways. PLoS One. 2010;5(1):e8578. doi: 10.1371/journal.pone.0008578 20052417; PubMed Central PMCID: PMC2798952.

2. Sze MA, Dimitriu PA, Suzuki M, McDonough JE, Campbell JD, Brothers JF, et al. Host Response to the Lung Microbiome in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2015;192(4):438–45. doi: 10.1164/rccm.201502-0223OC 25945594; PubMed Central PMCID: PMC4595667.

3. Gronseth R, Drengenes C, Wiker HG, Tangedal S, Xue Y, Husebo GR, et al. Protected sampling is preferable in bronchoscopic studies of the airway microbiome. ERJ Open Res. 2017;3(3). doi: 10.1183/23120541.00019–2017 28875147; PubMed Central PMCID: PMC5576223 openres.ersjournals.com.

4. O'Donnell R, Breen D, Wilson S, Djukanovic R. Inflammatory cells in the airways in COPD. Thorax. 2006;61(5):448–54. doi: 10.1136/thx.2004.024463 16648353; PubMed Central PMCID: PMC2111192.

5. Barnes PJ. Inflammatory mechanisms in patients with chronic obstructive pulmonary disease. J Allergy Clin Immunol. 2016;138(1):16–27. doi: 10.1016/j.jaci.2016.05.011 27373322.

6. Eagan TM, Ueland T, Wagner PD, Hardie JA, Mollnes TE, Damas JK, et al. Systemic inflammatory markers in COPD: results from the Bergen COPD Cohort Study. Eur Respir J. 2010;35(3):540–8. doi: 10.1183/09031936.00088209 19643942.

7. Agusti AG, Noguera A, Sauleda J, Sala E, Pons J, Busquets X. Systemic effects of chronic obstructive pulmonary disease. Eur Respir J. 2003;21(2):347–60. doi: 10.1183/09031936.03.00405703 12608452.

8. Caramori G, Ruggeri P, Di Stefano A, Mumby S, Girbino G, Adcock IM, et al. Autoimmunity and COPD: Clinical Implications. Chest. 2018;153(6):1424–31. doi: 10.1016/j.chest.2017.10.033 29126842.

9. Hurst JR, Vestbo J, Anzueto A, Locantore N, Mullerova H, Tal-Singer R, et al. Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med. 2010;363(12):1128–38. doi: 10.1056/NEJMoa0909883 20843247.

10. Sapey E, Stockley RA. COPD exacerbations. 2: aetiology. Thorax. 2006;61(3):250–8. doi: 10.1136/thx.2005.041822 16517585; PubMed Central PMCID: PMC2080749.

11. Wilkinson TM, Hurst JR, Perera WR, Wilks M, Donaldson GC, Wedzicha JA. Effect of interactions between lower airway bacterial and rhinoviral infection in exacerbations of COPD. Chest. 2006;129(2):317–24. doi: 10.1378/chest.129.2.317 16478847.

12. Sethi S, Evans N, Grant BJ, Murphy TF. New strains of bacteria and exacerbations of chronic obstructive pulmonary disease. N Engl J Med. 2002;347(7):465–71. doi: 10.1056/NEJMoa012561 12181400.

13. Wang Z, Bafadhel M, Haldar K, Spivak A, Mayhew D, Miller BE, et al. Lung microbiome dynamics in COPD exacerbations. Eur Respir J. 2016;47(4):1082–92. doi: 10.1183/13993003.01406-2015 26917613.

14. Tangedal S, Aanerud M, Persson LJ, Brokstad KA, Bakke PS, Eagan TM. Comparison of inflammatory markers in induced and spontaneous sputum in a cohort of COPD patients. Respir Res. 2014;15(1):138. doi: 10.1186/s12931-014-0138-6 25398249; PubMed Central PMCID: PMC4237726.

15. Global Strategy for the Diagnosis, Management and Prevention of Chronic Obstructive Pulmonary Disease 2019 report, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2013. Available from: https://goldcopd.org/wp-content/uploads/2018/11/GOLD-2019-v1.7-FINAL-14Nov2018-WMS.pdf. Date last updated: January 2019. Date last accessed: February 18 2019.

16. Husebo GR, Bakke PS, Aanerud M, Hardie JA, Ueland T, Gronseth R, et al. Predictors of exacerbations in chronic obstructive pulmonary disease—results from the Bergen COPD cohort study. PLoS One. 2014;9(10):e109721. doi: 10.1371/journal.pone.0109721 25279458; PubMed Central PMCID: PMC4184893.

17. Tangedal S, Aanerud M, Gronseth R, Drengenes C, Wiker HG, Bakke PS, et al. Comparing microbiota profiles in induced and spontaneous sputum samples in COPD patients. Respir Res. 2017;18(1):164. doi: 10.1186/s12931-017-0645-3 28851370; PubMed Central PMCID: PMC5576328.

18. Persson LJ, Aanerud M, Hardie JA, Miodini Nilsen R, Bakke PS, Eagan TM, et al. Antimicrobial peptide levels are linked to airway inflammation, bacterial colonisation and exacerbations in chronic obstructive pulmonary disease. Eur Respir J. 2017;49(3). doi: 10.1183/13993003.01328–2016 28298400.

19. Tjabringa GS, Vos JB, Olthuis D, Ninaber DK, Rabe KF, Schalkwijk J, et al. Host defense effector molecules in mucosal secretions. FEMS Immunol Med Microbiol. 2005;45(2):151–8. doi: 10.1016/j.femsim.2005.03.004 16051067.

20. Bolyen E RJ, Dillon MR, Bokulich NA, Abnet C, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope E, Da Silva R, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley G, Janssen S, Jarmusch AK, Jiang L, Kaehler B, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MG, Lee J, Ley R, Liu Y, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton J, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson II MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CH, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. PeerJ Preprints. 2018;6:e27295v2. doi: 10.7287.

21. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. doi: 10.1038/nmeth.3869 27214047; PubMed Central PMCID: PMC4927377.

22. Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. doi: 10.7717/peerj.2584 27781170; PubMed Central PMCID: PMC5075697.

23. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6(1):226. doi: 10.1186/s40168-018-0605-2 30558668; PubMed Central PMCID: PMC6298009.

24. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590–6. doi: 10.1093/nar/gks1219 23193283; PubMed Central PMCID: PMC3531112.

25. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26(7):1641–50. doi: 10.1093/molbev/msp077 19377059; PubMed Central PMCID: PMC2693737.

26. Yue JC, Clayton MK. A similarity measure based on species proportions. Commun Stat Theory Methods. 2005;34(11):2123–31. doi: 10.1080/sta-200066418

27. Gloor GB, Reid G. Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can J Microbiol. 2016;62(8):692–703. doi: 10.1139/cjm-2015-0821 27314511.

28. Jari Oksanen FGB, Michael Friendly, Roeland Kindt, Pierre Legendre, Dan McGlinn, Peter R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos, M. Henry H. Stevens, Eduard Szoecs, Helene Wagner. vegan: Community Ecology Package. R package version 2.5–2. 2018.

29. Seemungal TA, Donaldson GC, Bhowmik A, Jeffries DJ, Wedzicha JA. Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2000;161(5):1608–13. doi: 10.1164/ajrccm.161.5.9908022 10806163.

30. Fenwick PS, Macedo P, Kilty IC, Barnes PJ, Donnelly LE. Effect of JAK Inhibitors on Release of CXCL9, CXCL10 and CXCL11 from Human Airway Epithelial Cells. PLoS One. 2015;10(6):e0128757. doi: 10.1371/journal.pone.0128757 26090665; PubMed Central PMCID: PMC4474874.

31. Liu M, Guo S, Hibbert JM, Jain V, Singh N, Wilson NO, et al. CXCL10/IP-10 in infectious diseases pathogenesis and potential therapeutic implications. Cytokine Growth Factor Rev. 2011;22(3):121–30. doi: 10.1016/j.cytogfr.2011.06.001 21802343; PubMed Central PMCID: PMC3203691.

32. Egesten A, Eliasson M, Johansson HM, Olin AI, Morgelin M, Mueller A, et al. The CXC chemokine MIG/CXCL9 is important in innate immunity against Streptococcus pyogenes. J Infect Dis. 2007;195(5):684–93. doi: 10.1086/510857 17262710.

33. Waters JP, Pober JS, Bradley JR. Tumour necrosis factor in infectious disease. J Pathol. 2013;230(2):132–47. doi: 10.1002/path.4187 23460469.

34. Mayhew D, Devos N, Lambert C, Brown JR, Clarke SC, Kim VL, et al. Longitudinal profiling of the lung microbiome in the AERIS study demonstrates repeatability of bacterial and eosinophilic COPD exacerbations. Thorax. 2018;73(5):422–30. doi: 10.1136/thoraxjnl-2017-210408 29386298; PubMed Central PMCID: PMC5909767.

35. Huang YJ, Sethi S, Murphy T, Nariya S, Boushey HA, Lynch SV. Airway microbiome dynamics in exacerbations of chronic obstructive pulmonary disease. J Clin Microbiol. 2014;52(8):2813–23. doi: 10.1128/JCM.00035-14 24850358; PubMed Central PMCID: PMC4136157.

36. Su YC, Jalalvand F, Thegerstrom J, Riesbeck K. The Interplay Between Immune Response and Bacterial Infection in COPD: Focus Upon Non-typeable Haemophilus influenzae. Front Immunol. 2018;9:2530. doi: 10.3389/fimmu.2018.02530 30455693; PubMed Central PMCID: PMC6230626.

37. Tufvesson E, Bjermer L, Ekberg M. Patients with chronic obstructive pulmonary disease and chronically colonized with Haemophilus influenzae during stable disease phase have increased airway inflammation. Int J Chron Obstruct Pulmon Dis. 2015;10:881–9. doi: 10.2147/COPD.S78748 26005341; PubMed Central PMCID: PMC4427610.


Článek vyšel v časopise

PLOS One


2019 Číslo 9
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Svět praktické medicíny 1/2024 (znalostní test z časopisu)
nový kurz

Koncepce osteologické péče pro gynekology a praktické lékaře
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Význam metforminu pro „udržitelnou“ terapii diabetu
Autoři: prof. MUDr. Milan Kvapil, CSc., MBA

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#