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Home / Equine Research Bank / Equine Vet J / Equine sinusitis aetiology is linked to sinus microbiome by ...
Equine veterinary journal2022; 55(5); 798-807; doi: 10.1111/evj.13884

Equine sinusitis aetiology is linked to sinus microbiome by amplicon sequencing.

Abstract: Information regarding the microbiome in sinusitis using genetic sequencing is lacking and more-in-depth understanding of the microbiome could improve antimicrobial selection and treatment outcomes for cases of primary sinusitis. Objective: To describe sinus microbiota in samples from horses with sinusitis and compare microbiota and the presence of antimicrobial resistance genes between primary, dental-related and other secondary causes of sinusitis. Methods: Retrospective case series. Methods: Records of equine sinusitis from 2017 to 2021 were reviewed and historical microbial amplicon sequence data were obtained from clinical diagnostic testing of sinus secretions. Following bioinformatic processing of bacterial and fungal sequence data, the sinus microbiota and importance of sinusitis aetiology among other factors were investigated from the perspectives of alpha diversity (e.g., number of operational taxonomic units [OTUs], Hill1 Diversity), beta diversity, and differentially abundant taxa. Quantitative PCR allowed for comparisons of estimated bacterial abundance and detection rate of common antibiotic resistance-associated genes. In a smaller subset, longitudinal analysis was performed to evaluate similarity in samples over time. Results: Of 81 samples analysed from 70 horses, the bacterial microbiome was characterised in 66, and fungal in five. Only sinusitis aetiology was shown to significantly influence microbiome diversity and composition (p < 0.05). Dental-related sinusitis (n = 44) was associated with a significantly higher proportion of obligate anaerobic bacteria, whereas primary sinusitis (n = 12) and other (n = 10) groups were associated with fewer bacteria and higher proportions of facultative anaerobic and aerobic genera. Antimicrobial resistance genes and fungal components were exclusively identified in dental-related sinusitis. Conclusions: Retrospective nature, incomplete prior antimicrobial administration data. Conclusions: Molecular characterisation in sinusitis identifies microbial species which may be difficult to isolate via culture, and microbiome profiling can differentiate sinusitis aetiology, which may inform further treatment, including antimicrobial therapy. Unassigned: Es fehlen Informationen über das Mikrobiom bei Sinusitis, die durch genetische Sequenzierung gewonnen wurden, und ein tieferes Verständnis des Mikrobioms könnte die Auswahl antimikrobieller Mittel und die Behandlungsergebnisse bei primärer Sinusitis verbessern. Unassigned: Beschreibung der Sinus-Mikrobiota in Proben von Pferden mit Sinusitis und Vergleich der Mikrobiota und des Vorhandenseins antimikrobieller Resistenzgene zwischen primären, zahnbedingten und anderen sekundären Ursachen der Sinusitis. Methods: Retrospektive Betrachtung. Methods: Aufzeichnungen über Sinusitis bei Pferden aus den Jahren 2017-2021 wurden überprüft und historische mikrobielle Amplikon-Sequenzdaten aus klinischen diagnostischen Tests von Sinus-Sekreten gewonnen. Nach der bioinformatischen Verarbeitung von Bakterien- und Pilzsequenzdaten wurden die Mikrobiota der Nasennebenhöhlen und die Bedeutung der Ätiologie der Sinusitis unter anderem unter dem Gesichtspunkt der Alpha-Diversität (z. B. Anzahl der operativen taxonomischen Einheiten [OTUs], Hill1-Diversität), der Beta-Diversität und der unterschiedlich häufigen Taxa untersucht. Die quantitative PCR ermöglichte einen Vergleich der geschätzten Bakterienhäufigkeit und der Nachweisrate von Genen, die mit der Antibiotikaresistenz in Verbindung stehen. Bei einer kleineren Untergruppe wurde eine Längsschnittanalyse durchgeführt, um die Ähnlichkeit der Proben im Laufe der Zeit zu bewerten. Unassigned: Von 81 analysierten Proben von 70 Pferden wurde das bakterielle Mikrobiom von 66 und das Pilzmikrobiom von fünf Pferden charakterisiert. Nur die Ätiologie der Sinusitis zeigte einen signifikanten Einfluss auf die Diversität und Zusammensetzung des Mikrobioms (p < 0,05). Zahnbedingte Sinusitis (n = 44) war mit einem signifikant höheren Anteil an obligat anaeroben Bakterien verbunden, während primäre Sinusitis (n = 12) und andere (n = 10) Gruppen mit weniger Bakterien und einem höheren Anteil an fakultativ anaeroben und aeroben Gattungen verbunden waren. Antimikrobielle Resistenzgene und Pilzkomponenten wurden ausschließlich bei zahnbedingter Sinusitis identifiziert. Serielle Proben ähnelten den Ausgangsproben tendenziell mehr als Proben von anderen Pferden. Unassigned: Retrospektiver Charakter, unvollständige Daten über die vorherige Verabreichung antimikrobieller Mittel. Unassigned: Durch die molekulare Charakterisierung der Sinusitis werden Mikrobenarten identifiziert, die sich möglicherweise nur schwer über eine Kultur isolieren lassen, und durch die Erstellung von Mikrobiomprofilen kann die Ätiologie der Sinusitis differenziert werden, was wiederum Aufschluss über die weitere Behandlung, einschließlich einer antimikrobiellen Therapie, geben kann.
© 2022 The Authors. Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
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Publication Date: 2022-11-07 PubMed ID: 36199163DOI: 10.1111/evj.13884Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research examines the root causes of sinusitis in horses by exploring sinus microbiota from sample data. The study seeks to improve antimicrobial treatment options by understanding the link between sinusitis etiology and the presence of certain microbes and their resistance to drugs.

Objective and Methods

  • The study aims to better understand the microbiome that develops in horses with sinusitis. This involves comparing microbiota and the occurrence of antimicrobial resistance genes in cases of primary sinusitis, dental-related sinusitis, and other secondary causes of sinusitis.
  • The researchers carried out a retrospective case series to meet this objective. Records of equine sinusitis from 2017 to 2021 were reviewed and historical microbial amplicon sequence data from sinus secretions were obtained for analysis.
  • The analysis involved bioinformatic processing of bacterial and fungal sequence data. Information on sinus microbiota and the relevance of sinusitis etiology was evaluated according to several criteria. These included alpha diversity or number of operational taxonomic units (OTUs), Hill1 Diversity, beta diversity, and differentially abundant taxa.
  • A quantitative PCR was also performed to make comparisons in estimated bacterial abundance and the detection rate of common antibiotic resistance-associated genes.
  • In a smaller subset, a longitudinal analysis was done to evaluate the similarity in samples over time.

Results

  • The analysis included 81 samples collected from 70 horses. Of these, a bacterial microbiome was characterized in 66 samples and fungal elements were discovered in five.
  • The study found that sinusitis aetiology significantly influences the diversity and composition of the microbiome.
  • Dental-related sinusitis, primary sinusitis, and other types of sinusitis were associated with different proportions of bacteria and types of bacterial organisms.
  • Antimicrobial resistance genes and fungal components were exclusively identified in the cases of dental-related sinusitis.
  • The study also noted that issues with retrospective nature and incomplete prior antimicrobial administration data.

Conclusions

  • The molecular characterisation in sinusitis was able to identify microbial species that would have been difficult to isolate via culture.
  • Microbiome profiling has the potential to differentiate sinusitis aetiology, which can, in turn, inform further treatment, including antimicrobial therapy.

Cite This Article

APA
Lowman ME, Tipton CD, Labordère AL, Brown JA. (2022). Equine sinusitis aetiology is linked to sinus microbiome by amplicon sequencing. Equine Vet J, 55(5), 798-807. https://doi.org/10.1111/evj.13884

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 55
Issue: 5
Pages: 798-807

Researcher Affiliations

Lowman, Megan E
  • Marion duPont Scott Equine Medical Center, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Leesburg, VA, USA.
Tipton, Craig D
  • RTL Genomics, MicroGenDX, Lubbock, TX, USA.
Labordère, Alexandra L
  • Marion duPont Scott Equine Medical Center, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Leesburg, VA, USA.
Brown, James A
  • Marion duPont Scott Equine Medical Center, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Leesburg, VA, USA.

MeSH Terms

  • Horses / genetics
  • Animals
  • Retrospective Studies
  • Sinusitis / veterinary
  • Paranasal Sinuses / microbiology
  • Bacteria
  • Microbiota / genetics
  • Anti-Infective Agents
  • RNA, Ribosomal, 16S / genetics
  • Horse Diseases / microbiology

Grant Funding

  • Virginia Polytechnic Institute and State University

References

This article includes 35 references
  1. Dixon PM, Parkin TD, Collins N, Hawkes C, Townsend NB, Fisher G. Historical and clinical features of 200 cases of equine sinus disease. Vet Rec 2011;169:439.
    doi: 10.1136/vr.d4844google scholar: lookup
  2. Tremaine WH, Dixon PM. A long-term study of 277 cases of equine sinonasal disease. Part 2: treatments and results of treatments. Equine Vet J 2001;33:283-9.
    doi: 10.2746/042516401776249787google scholar: lookup
  3. Dixon PM, Kennedy R, Poll K, Barakzai S, Reardon RJM. A long-term study of sinoscopic treatment of equine paranasal sinus disease: 155 cases (2012-2019). Equine Vet J 2021;53:979-89.
    doi: 10.1111/evj.13393google scholar: lookup
  4. Beste KJ, Lawhon SD, Chamoun-Emanuelli AM, Duff AH, Coleman MC, Griffin CE. Culture-independent and dependent evaluation of the equine paranasal sinus microbiota in health and disease. Equine Vet J 2020;52:455-63.
    doi: 10.1111/evj.13168google scholar: lookup
  5. Gergeleit H, Verspohl J, Rohde J, Rohn K, Ohnesorge B, Bienert-Zeit A. A prospective study on the microbiological examination of secretions from the paranasal sinuses in horses in health and disease. Acta Vet Scand 2018;60:1-9.
    doi: 10.1186/s13028-018-0394-4google scholar: lookup
  6. Boase S, Foreman A, Cleland E, Tan L, Melton-Kreft R, Pant H. The microbiome of chronic rhinosinusitis: culture, molecular diagnostics and biofilm detection. BMC Infect Dis 2013;13:210.
    doi: 10.1186/1471-2334-13-210google scholar: lookup
  7. Hauser LJ, Feazel LM, Ir D, Fang R, Wagner BD, Robertson CE. Sinus culture poorly predicts resident microbiota. Int Forum Allergy Rhinol 2015;5:3-9.
    doi: 10.1002/alr.21428google scholar: lookup
  8. Copeland E, Leonard K, Carney R, Kong J, Forer M, Naidoo Y. Chronic rhinosinusitis: potential role of microbial dysbiosis and recommendations for sampling sites. Front Cell Infect Microbiol 2018;8:57.
    doi: 10.3389/fcimb.2018.00057google scholar: lookup
  9. Zhao YC, Bassiouni A, Tanjararak K, Vreugde S, Wormald P-J, Psaltis AJ. Role of fungi in chronic rhinosinusitis through its sequencing. Laryngoscope 2018;128:16-22.
    doi: 10.1002/lary.26702google scholar: lookup
  10. Quinn G, Kidd J, Lane J. Modified frontonasal sinus flap surgery in standing horses: surgical findings and outcomes of 60 cases. Equine Vet J 2005;37:138-42.
    doi: 10.2746/0425164054223750google scholar: lookup
  11. Tipton CD, Wolcott RD, Sanford NE, Miller C, Pathak G, Silzer TK. Patient genetics is linked to chronic wound microbiome composition and healing. PLoS Pathog 2020;16:e1008511.
    doi: 10.1371/journal.ppat.1008511google scholar: lookup
  12. Goswami K, Tipton C, Clarkson S, Chang G, Tan TL, Fram B. Fracture-associated microbiome and persistent nonunion: next-generation sequencing reveals new findings. J Orthop Trauma 2022;36:S40-6.
    doi: 10.1097/bot.0000000000002305google scholar: lookup
  13. Edgar RC. Search and clustering orders of magnitude faster than blast. Bioinformatics 2010;26:2460-1.
    doi: 10.1093/bioinformatics/btq461google scholar: lookup
  14. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. Uchime improves sensitivity and speed of chimera detection. Bioinformatics 2011;27:2194-200.
    doi: 10.1093/bioinformatics/btr381google scholar: lookup
  15. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate illumina paired-end read merger. Bioinformatics 2014;30:614-20.
    doi: 10.1093/bioinformatics/btt593google scholar: lookup
  16. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 2013;10:996-8.
    doi: 10.1038/nmeth.2604google scholar: lookup
  17. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 2004;32:1792-7.
    doi: 10.1093/nar/gkh340google scholar: lookup
  18. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 2009;26:1641-50.
    doi: 10.1093/molbev/msp077google scholar: lookup
  19. Beule L, Karlovsky P. Improved normalization of species count data in ecology by scaling with ranked subsampling (srs): application to microbial communities. PeerJ 2020;8:e9593.
    doi: 10.7717/peerj.9593google scholar: lookup
  20. Oksanen J, Simpson GL, Blanchet FG. Vegan: community ecology package. R Package 2011 1.17-18 edn.
  21. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 2015;26:27663.
    doi: 10.3402/mehd.v26.27663google scholar: lookup
  22. Gilbert JA, Field D, Swift P, Thomas S, Cummings D, Temperton B. The taxonomic and functional diversity of microbes at a temperate coastal site: a ‘multi-omic'study of seasonal and diel temporal variation. PloS one 2010;5:e15545.
    doi: 10.1371/journal.pone.0015545google scholar: lookup
  23. Borkent D, Reardon RJM, McLachlan G, Glendinning L, Dixon PM. A microbiome analysis of equine peripheral dental caries using next generation sequencing. Equine Vet J 2020;52:67-75.
    doi: 10.1111/evj.13126google scholar: lookup
  24. Kennedy R, Lappin DF, Dixon PM, Buijs MJ, Zaura E, Crielaard W. The microbiome associated with equine periodontitis and oral health. Vet Res 2016;47:49.
    doi: 10.1186/s13567-016-0333-1google scholar: lookup
  25. More SN, Hernandez O, Castleman WL. Mycotic rhinitis and sinusitis in Florida horses. Vet Pathol 2019;56:586-98.
    doi: 10.1177/0300985818817046google scholar: lookup
  26. Fiske-Jackson A, Pollock P, Witte T, Woolford L, Perkins JD. Fungal sinusitis resulting in suspected trigeminal neuropathy as a cause of headshaking in five horses. Equine Vet Educ 2012;24:126-33.
    doi: 10.1111/j.2042-3292.2011.00249.xgoogle scholar: lookup
  27. Pujol R, Tessier C, Manneveau G, De Fourmestraux C. Suspected primary mycotic rhinitis and paranasal sinusitis in seven horses (2013-2019). Equine Vet Educ 2021;33:e259-66.
    doi: 10.1111/eve.13275google scholar: lookup
  28. Dixon PM, Tremaine WH, Pickles K, Kuhns L, Hawe C, McCann J. Equine dental disease part 4: a long-term study of 400 cases: apical infections of cheek teeth. Equine Vet J 2000;32:182-94.
    doi: 10.2746/042516400776563581google scholar: lookup
  29. Zhu YG, Johnson TA, Su JQ, Tiedje JM. Diverse and abundant antibiotic resistance genes in chinese swine farms. Proc Natl Acad Sci USA 2013;110:3435-40.
    doi: 10.1073/pnas.1222743110google scholar: lookup
  30. Li B, Yang Y, Ma L, Ju F, Guo F, Tiedje JM. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME J 2015;9:2490-502.
    doi: 10.1038/ismej.2015.59google scholar: lookup
  31. Paramasivan S, Bassiouni A, Shiffer A, Dillon MR, Cope EK, Cooksley C. The international sinonasal microbiome study: a multicentre, multinational characterization of sinonasal bacterial ecology. Allergy 2020;75:2037-49.
    doi: 10.1111/all.14276google scholar: lookup
  32. Lauber CL, Zhou N, Gordon JI, Knight R, Fierer N. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol Lett 2010;307:80-6.
    doi: 10.1111/j.1574-6968.2010.01965.xgoogle scholar: lookup
  33. Choo JM, Leong LE, Rogers GB. Sample storage conditions significantly influence faecal microbiome profiles. Sci Rep 2015;5:16350.
    doi: 10.1038/srep16350google scholar: lookup
  34. Townsend KS, Johnson PJ, LaCarrubba AM, Martin LM, Ericsson AC. Exodontia associated bacteremia in horses characterized by next generation sequencing. Sci Rep 2021;11:6314.
    doi: 10.1038/s41598-021-85484-zgoogle scholar: lookup
  35. Rontal M, Bernstein JM, Rontal E, Anon J. Bacteriologic findings from the nose, ethmoid, and bloodstream during endoscopic surgery for chronic rhinosinusitis: implications for antibiotic therapy. Am J Rhinol 1999;13:91-6.
    doi: 10.2500/105065899782106788google scholar: lookup

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