Abstract: Transport-associated pneumonia contributes substantially to morbidity, impaired welfare, and economic loss in high-performance horses and may be associated with alterations in the respiratory microbiota. Objective: Examine the effect of long-distance transport on the respiratory microbiota and correlate changes in microbiota diversity and composition with systemic and airway inflammation. Methods: Seventeen client-owned performance horses transported from New England to Florida under optimized trailering conditions, and 12 non-traveling horses. Methods: Physical examination, blood testing, nasopharyngeal wash, endoscopy, tracheal aspirates, and thoracic ultrasonography were performed 48 h before, and 24 and 72 h after transport (T1-T3). Upper and lower respiratory microbiota were characterized using high-throughput 16S rRNA sequencing and correlated with clinical variables using constrained ordination. Transport effects were evaluated using repeated measures analysis of variance (ANOVA). Results: Cortisol concentrations decreased post-transport (P = .02), whereas ultrasound scores (P = .01) and serum amyloid A concentrations (P = .03) increased from T1 to T3. Timepoint explained a small but significant portion of microbiota variability (P < .001). Upper and lower airway microbiota differed, with the lower airway showing more β diversity (lower stability; P < .001). Together, timepoint and ultrasound scores explained 19% and 10%, respectively, of nasal and tracheal bacterial microbiota variability. Operational taxonomic units with the highest fit to timepoint and ultrasound were enriched for plant-associated bacterial taxa, mainly Hyphomicrobiales. Conclusions: Even under standardized, optimized transport conditions, respiratory microbiota alterations occurred in healthy, athletic horses, correlating with ultrasonographic evidence of pulmonary inflammation. Inclusion of the fungal mycobiome may further improve our understanding of transport-associated respiratory disease.
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
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.
Overview
This study investigates how long-distance transport affects the respiratory microbiota and inflammation in high-performance horses, potentially contributing to transport-associated pneumonia.
It examines changes in respiratory bacterial communities and inflammation markers before and after transport under optimized conditions.
Background and Importance
Transport-associated pneumonia is a significant health problem in performance horses, causing illness, welfare issues, and economic losses.
Previous research suggests that alterations in the respiratory microbiota may play a role in the development of pneumonia during transport.
This study aims to clarify how long-distance transport alters the respiratory microbiota and whether these changes are linked to inflammation and clinical signs of respiratory disease.
Study Design and Methods
Subjects: Seventeen client-owned performance horses were transported from New England to Florida, and 12 horses served as non-transport control subjects.
Transport conditions: Horses underwent transport under optimized trailer conditions designed to reduce stress and disease risk.
Sample collection: Measurements were taken at three time points:
48 hours before transport (T1)
24 hours after transport (T2)
72 hours after transport (T3)
Clinical assessments included:
Physical examination
Blood testing (including cortisol and serum amyloid A levels)
Nasopharyngeal wash (upper airway sampling)
Endoscopy
Tracheal aspirates (lower airway sampling)
Thoracic ultrasonography to detect lung inflammation
Microbiota analysis:
16S rRNA gene sequencing characterized bacterial communities in both upper and lower respiratory tracts.
Statistical analyses, including constrained ordination and repeated measures ANOVA, evaluated the relationship between microbiota changes and clinical variables over time.
Key Findings
Inflammation markers and clinical changes:
Cortisol levels decreased after transport, indicating a possible reduction in acute stress response.
Ultrasound scores, which reflect pulmonary inflammation, increased after transport, suggesting lung involvement.
Serum amyloid A, an acute phase protein indicating systemic inflammation, also increased post-transport.
Microbiota dynamics:
Time after transport explained a small but statistically significant amount of variation in respiratory microbiota composition, meaning transport influenced bacterial communities.
Upper (nasal) and lower (tracheal) airway microbiota were distinctly different, with the lower airway microbiota exhibiting higher beta diversity, indicating less stability in this region.
The combination of time and ultrasound scores explained 19% of nasal and 10% of tracheal bacterial variability, linking microbiota changes to inflammation severity.
Specific bacterial taxa associated with plant material (mainly Hyphomicrobiales) increased following transport, which could relate to environmental exposure during transit or altered airway conditions.
Conclusions and Implications
Even under carefully controlled and optimized transport conditions, healthy athletic horses experienced changes in their respiratory microbiota after long-distance transport.
These microbiota shifts correlated with ultrasonographic findings of pulmonary inflammation, implicating microbiota alterations as a component of transport-associated respiratory disease.
The study suggests that monitoring both clinical signs and microbiota changes could improve detection and prevention of transport-associated pneumonia.
Future research including analysis of fungal communities (mycobiome) is recommended to build a more comprehensive understanding of respiratory microbiota and the pathogenesis of transport-associated respiratory diseases in horses.
Cite This Article
APA
Mahalingam-Dhingra A, Bedenice D, da Silva DRR, Mazan M, Hall T, Tenney W, Widmer G.
(2026).
Clinical and microbiota alterations in performance horses undergoing long-distance transport.
J Vet Intern Med, 40(4), aalag137.
https://doi.org/10.1093/jvimsj/aalag137
Department of Large Animal Clinical Sciences, Cummings School of Veterinary Medicine at Tufts University, North Grafton, MA 01536, United States.
Bedenice, Daniela
Department of Large Animal Clinical Sciences, Cummings School of Veterinary Medicine at Tufts University, North Grafton, MA 01536, United States.
da Silva, Debora Regina Romualdo
Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine at Tufts University, North Grafton, MA 01536, United States.
Mazan, Melissa
Department of Large Animal Clinical Sciences, Cummings School of Veterinary Medicine at Tufts University, North Grafton, MA 01536, United States.
Hall, Tiffany
Hagyard Medical Institute, Lexington, KY 40511, United States.
Tenney, Wade
Department of Large Animal Clinical Sciences, Cummings School of Veterinary Medicine at Tufts University, North Grafton, MA 01536, United States.
Widmer, Giovanni
Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine at Tufts University, North Grafton, MA 01536, United States.
MeSH Terms
Animals
Horses / microbiology
Microbiota
Transportation
Horse Diseases / microbiology
Male
RNA, Ribosomal, 16S / genetics
Female
Hydrocortisone / blood
Serum Amyloid A Protein / analysis
Grant Funding
Dorothy Russell Havemeyer Foundation
Companion Animal Health Fund
R21AI173461 / National Institute of Allergy and Infectious Diseases
Huang YJ, Kim E, Cox MJ. A persistent and diverse airway microbiota present during chronic obstructive pulmonary disease exacerbations.. OMICS 2010;14:9-59.
Zeineldin M, Elolimy AA, Barakat R. Meta-analysis of bovine respiratory microbiota: link between respiratory microbiota and bovine respiratory health.. FEMS Microbiol Ecol 2020;96:96.
Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research--an update.. Bioinformatics 2012;28:2537-2539.
ter Braak CJF, Šmilauer P. CANOCO reference manual and CanoDraw for Windows user's guide: software for canonical community ordination (version 4.5).. .
Baker GC, Smith JJ, Cowan DA. Review and re-analysis of domain-specific 16S primers.. J Microbiol Methods 2003;55:541-555.
diCenzo GC, Yang Y, Young JPW, Kuzmanović N. Refining the taxonomy of the order Hyphomicrobiales (Rhizobiales) based on whole genome comparisons of over 130 type strains.. Int J Syst Evol Microbiol 2024;74.
Lozupone C, Hamady M, Knight R. UniFrac--an online tool for comparing microbial community diversity in a phylogenetic context.. BMC Bioinformatics 2006;7:371.
Pastores SM, Annane D, Rochwerg B, Corticosteroid Guideline Task Force of SCCM and ESICM. Guidelines for the diagnosis and Management of Critical Illness-Related Corticosteroid Insufficiency (CIRCI) in critically ill patients (part II): Society of Critical Care Medicine (SCCM) and European Society of Intensive Care Medicine (ESICM) 2017.. Crit Care Med 2018;46:146-148.
Koskinen H, Raekallio M, Rahkonen E, Rajamäki M, Mykkänen A. Comparison of bronchoalveolar lavage and tracheal aspirate results in 64 non-thoroughbred horses presenting with respiratory signs or exercise intolerance.. Equine Vet J 2014;46:23-23.
Zhao L, Luo JL, Ali MK, Spiekerkoetter E, Nicolls MR. The human respiratory microbiome: current understandings and future directions.. Am J Respir Cell Mol Biol 2023;68:245-255.
Howe S, Kegley B, Powell J, Chen S, Zhao J. Effect of bovine respiratory disease on the respiratory microbiome: a meta-analysis.. Front Cell Infect Microbiol 2023;13:1223090.
Pirolo M, Espinosa-Gongora C, Bogaert D, Guardabassi L. The porcine respiratory microbiome: recent insights and future challenges.. Anim Microbiome 2021;3:9.
Dickson RP, Erb-Downward JR, Freeman CM. Spatial variation in the healthy human lung microbiome and the adapted island model of lung biogeography.. Ann Am Thorac Soc 2015;12:821-830.
Gerber V, Lindberg A, Berney C, Robinson NE. Airway mucus in recurrent airway obstruction—short-term response to environmental challenge.. J Vet Intern Med 2004;18:92-97.
Mahalingam-Dhingra A, Bedenice D, South A. An in-hospital clinical trial assessing nebulized lidocaine compared to saline for treatment of equine asthma.. J Vet Intern Med 2025;40.
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform.. Appl Environ Microbiol 2013;79:5112-5120.
Schloss PD, Westcott SL, Ryabin T. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.. Appl Environ Microbiol 2009;75:7537-7541.
Quast C, Pruesse E, Yilmaz P. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools.. Nucleic Acids Res 2013;41:D590-D596.
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.. Appl Environ Microbiol 2007;73:5261-5267.