Characterization of the upper respiratory tract microbiome of patients with acute respiratory infections by 16S rRNA sequencing

Introduction. Acute respiratory infections (ARI) are one of the main causes of morbidity and mortality from infectious diseases in the world. Respiratory infections can be caused by pathogens of various etiologies: viruses, bacteria, mycoplasmas, etc. Rapid and accurate identification of pathogens, such as bacteria, in biological samples is an important task, for which 16S rRNA gene sequencing using new generation platforms is used.

The objective was a comparative analysis of the qualitative characteristics of the oropharyngeal microbiome of healthy volunteers and patients with ARI of unknown etiology based on the 16S rRNA gene sequencing.

Methods and materials. Using V3–V4 region of 16S rRNA Illumina MiSeq sequences from oropharyngeal swabs, we analyzed the microbiome of hospitalized patients with ARI symptoms and healthy patients.

Results. In this study, we conducted V3–V4 region of 16S rRNA sequencing analyses of the oropharyngeal samples from 116 hospitalized patients with ARI symptoms and 81 healthy patients. Patients with ARI exhibited higher abundance of opportunistic pathogens, particularly Staphylococcus, Ralstonia, Aeribacillus, Acinetobacter baumannii, Methylobacterium-Methylorubrum, Rhodococcus equi. In the control samples, normal commensal respiratory tract microbiota, such as Neisseria, Prevotella, Fusobacterium, Veilonella was dominated.

Conclusions. The microbiota samples of hospitalized patients with ARI showed a predominance of opportunistic and potentially pathogenic microbiota, while normal representatives of the respiratory tract microbiota predominate in healthy volunteers. For a more detailed analysis, data on the species composition of the microbiota is required, which can be obtained by sequencing the complete sequence of the 16S rRNA gene.

1. Kurskaya O. G., Prokopyeva E. A., Sobolev I. A. et al. Changes in the Etiology of Acute Respiratory Infections among Children in Novosibirsk, Russia, between 2019 and 2022: The Impact of the SARS-CoV-2 Virus // Viruses. 2023;15(4):934. https://doi.org/10.3390/v15040934.

2. Burtseva E. I., Kolobukhina L. V., Voronina O. L. et al. Features of the Circulation of ARVI Pathogens During of Emergence and Widespread of SARS-CoV-2 in the 2018–2021 // Epidemiology and Vaccinal Prevention. 2022;21(4):16‒26. (In Russ.). https://doi.org/10.31631/2073-3046-2022-21-4-16-26.

3. Sominina A. A., Danilenko D. M., Stolyarov K. A. et al. Interference of SARS-CoV-2 with other Respiratory Viral Infections agents during Pandemic // Epidemiology and Vaccinal Prevention. 2021;20(4):28‒39. (In Russ.). https://doi.org/10.31631/2073-3046-2021-20-4-28-39.

4. Huang X. B., Yuan L., Ye C. X. et al. Epidemiological characteristics of respiratory viruses in patients with acute respiratory infections during 2009-2018 in Southern China // Int J Infect Dis. 2020;98:21‒32. https://doi.org/10.1016/j.ijid.2020.06.051.

5. Sanz I., Perez D., Rojo S. et al. Coinfections of influenza and other respiratory viruses are associated to children // An Pediatr (Engl Ed). 2022;96(4):334‒341. https://doi.org/10.1016/j.anpede.2021.03.002. PMID: 35609953.

6. Milucky J. L., Compaore T., Obulbiga F. et al. Estimating the catchment population and incidence of severe acute respiratory infections in a district hospital in Boussé, Burkina Faso // J Glob Health. 2020;10(1):010422. https://doi.org/10.7189/jogh.10.010422.

7. Simner P.J., Miller S., Carroll K. C. Understanding the Promises and Hurdles of Metagenomic Next-Generation Sequencing as a Diagnostic Tool for Infectious Diseases // Clin Infect Dis. 2018;10;66(5):778‒788. https://doi.org/10.1093/cid/cix881.

8. Datta S., Budhauliya R., Das B. et al. Next-generation sequencing in clinical virology: Discovery of new viruses // World J Virol. 2015;4(3):265‒76. https://doi.org/10.5501/wjv.v4.i3.265.

9. Clarridge J. E. 3rd. Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases // Clin Microbiol Rev. 2004;17(4):840‒62. https://doi.org/10.1128/CMR.17.4.840-862.2004.

10. Mokhov A. S., Azarov D. V., Kolodzhieva V. V. et al. Features of the microbiome structure of the upper respiratory tract of outpatient and patients with a new coronavirus infection // Preventive and Clinical Medicine. 2022;3(84)51–56. (In Russ.). https://doi.org/10.47843/2074-9120_2022_3_51.

11. Low L., Fuentes-Utrilla P., Hodson J. et al. Evaluation of full-length nanopore 16S sequencing for detection of pathogens in microbial keratitis // PeerJ. 2021;9:e10778. https://doi.org/10.7717/peerj.10778.

12. 16S Metagenomic Sequencing Library Preparation. URL: https://support.illumina.com/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf (accessed 01.10.2024).

13. Bolyen E., Rideout J. R., Dillon M. R. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 // Nat Biotechnol. 2019;37(8):852‒857. https://doi.org/10.1038/s41587-019-0209-9.

14. Shannon C. E. A mathematical theory of communication // The Bell System Technical Journal. 1948;27(3):379‒423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.

15. Chao A. Nonparametric Estimation of the Number of Classes in a Population // Scandinavian Journal of Statistics. 1984;11(4):265–70.

16. Segata N., Izard J., Waldron L. et al. Metagenomic biomarker discovery and explanation // Genome Biol. 2011;12(6):R60. https://doi.org/10.1186/gb-2011-12-6-r60.

17. Anderson M. J. Permutational Multivariate Analysis of Variance (PERMANOVA) // Wiley StatsRef: Statistics Reference Online. 2017;1–15. https://doi.org/10.1002/9781118445112.stat07841.

18. Odintsova V. E., Klimenko N., Tyakht A. V. Note on the Difference between the Principal Balance Analysis with NearestBalance and Constrained Methods // mSystems. 2023;8(2):e0016423. https://doi.org/10.1128/msystems.00164-23.

19. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2021. URL: https://www.R-project.org/ (accessed 01.10.2024).

20. Lopatin A. S., Azizov I. S., Kozlov R. S. Microbiome of the nasal cavity and the paranasal sinuses in health and disease (literature review). Part I // Russian Rhinology. 2021;29(1):23‒30. (In Russ.). https://doi.org/10.17116/rosrino20212901123.

21. De Boeck I., Wittouck S., Wuyts S. et al. Comparing the Healthy Nose and Nasopharynx Microbiota Reveals Continuity As Well As Niche-Specificity // Front Microbiol. 2017;8:2372. https://doi.org/10.3389/fmicb.2017.02372.

22. Natalini J. G., Singh S., Segal L. N. The dynamic lung microbiome in health and disease // Nat Rev Microbiol. 2023;21:222–235. https://doi.org/10.1038/s41579-022-00821-x.

23. Li R., Li J., Zhou X. Lung microbiome: new insights into the pathogenesis of respiratory diseases // Signal Transduct Target Ther. 2024;9(1):19. https://doi.org/10.1038/s41392-023-01722-y.

24. Türkmen Recen Ö., Gazi H., Bayturan Şen S. et al. SARS-CoV-2 Pandemisinin İlerleyen Dönemlerinde Akut Solunum Yolu Enfeksiyonu ile Başvuran Çocuklarda Viral Patojenlerin Mevsimsel Eğilimleri ve Etkileşimleri [Seasonal Trends and Interactions of Viral Pathogens in Children Presenting with Acute Respiratory Tract Infections in the Advancing Periods of SARSCoV-2 Pandemic]// Mikrobiyol Bul. 2023;57(4):580‒596. Turkish. https://doi.org/10.5578/mb.20239947.

25. Kostinov M. P., Meshcheriakova A. K., Foshina E. P. et al. Clinical course of acute respiratory infection and the state of microbiocenosis of upper respiratory tract in pregnant women // Zh Mikrobiol Epidemiol Immunobiol. 2012; (5):12‒6. (In Russ.).

26. Campos M., Cickovski T., Fernandez M. et al. Lower respiratory tract microbiome composition and community interactions in smokers // Access Microbiol. 2023;5(3):acmi000497.v3. https://doi.org/10.1099/acmi.0.000497.v3.

27. Yoon J. G., Lim S., Hyun H. J. et al. Respiratory microbiome and clinical course of carbapenem-resistant Acinetobacter baumannii pneumonia in critically Ill patients // Medicine (Baltimore). 2024;103(31):e38988. https://doi.org/10.1097/MD.0000000000038988.

28. Xiao T., Guo Q., Zhou Y. et al. Comparative Respiratory Tract Microbiome Between Carbapenem-Resistant Acinetobacter baumannii Colonization and Ventilator Associated Pneumonia // Front Microbiol. 2022;13:782210. https://doi.org/10.3389/fmicb.2022.782210.

29. Xie L., Chen L., Li X. et al. Analysis of Lung Microbiome in COVID-19 Patients during Time of Hospitalization // Pathogens. 2023;12(7):944. https://doi.org/10.3390/pathogens12070944.

30. Xia X., Chen J., Cheng Y. et al. Comparative analysis of the lung microbiota in patients with respiratory infections, tuberculosis, and lung cancer: A preliminary study // Front Cell Infect Microbiol. 2022;12:1024867. https://doi.org/10.3389/fcimb.2022.1024867.

31. Lin W. V., Kruse R. L., Yang K. et al. Diagnosis and management of pulmonary infection due to Rhodococcus equi // Clin Microbiol Infect. 2019;25(3):310‒315. https://doi.org/10.1016/j.cmi.2018.04.033.