Research Summary - 2
Microbial profiling of Mycoplasma bovis, Pasteurella multocida, and Mannheimia haemolytica associated with polymicrobial infections in feedlot cattle mortalities
| Date/Time: |
8/28/2026
17:45
|
| Author: |
Harley Henderson |
| Clinic: |
Texas A&M University |
| City, State, ZIP: |
Canyon, TX 79015 |
H. Henderson, BS
1
;
D.W. Nielsen, PhD
3
;
C. Booker, DVM, MS
4
;
S. Hannon, DVM, MS, PhD
4
;
B. Schultz, DVM
4
;
J. Richeson, MS, PhD
5
;
M. Scott, DVM, PhD
2
;
R. Valeris-Chacin, DVM, MS, PhD
1
;
1Department of Veterinary Pathobiology, Texas A&M University, Canyon, TX, 79015
2Department of Large Animal Clinical Sciences, Texas A&M University, Canyon, TX, 79015
3USDA NADC, Aimes, IA, 50010
4Telus Agriculture Feedlot Health, Okotoks, Alberta, Canada
5Department of Agricultural Sciences, West Texas A&M University, TX, 79015
Introduction:
Complex polymicrobial diseases such as Bovine Respiratory Disease, arthritis, and liver abscesses have a large economic impact on North American cattle feedlots. To have a better understanding of these diseases, it is important to view the animal as a whole. This project was designed to determine how three major respiratory pathogens, Mycoplasma bovis, Pasteurella multocida, and Mannheimia haemolytica might be involved with multiple disease states within individual animals. Whole Genome Sequencing (WGS) was utilized to gain insights into the phylogenetics, as well as the distribution of virulence and antimicrobial resistance genes in these pathogens isolated from different body locations.
Materials and methods:
Following a cross-sectional study design, samples were taken from the following six locations: heart, liver, lung, nasopharynx, stifle joint, and gut-associated lymphoid tissue (GALT) from thirty-nine feedlot cattle mortalities in the Texas panhandle region. Twenty animals were sampled from a research feedlot, and nineteen were sampled from four commercial feedlots. Mycoplasma was isolated on PPLO Amp plates, and a single colony was subcultured in PPLO Amp broth. PacBio HMW NanoBind kits were used to extract DNA from isolates. Pasteurella multocida and M. haemolytica were isolated on Blood agar plates, and DNA extractions were performed using the Zymo Quick-DNA HMW MagBead Kit. The native barcoding kit 24 v14 was used for library prep, and whole genome sequencing was performed using GridION flow cells (version R10.4.1), with 24 isolates per flow cell. For bioinformatics, Flye was utilized to produce assemblies and Medaka was utilized for polishing. The presence of single nucleotide polymorphisms (SNPs) in the core genome was inferred using Snippy, and subsequently used to create a phylogenetic tree in IQ-TREE. The Virulence Factor Database was used with ABRicate to search the assemblies for virulence genes; CARD was used to search for antimicrobial resistance genes. Scoary2 was employed to evaluate the association of virulence genes with metadata points.
Results:
This study found that M. bovis isolates (n=68) in multi-site samples did not cluster strongly by sample type, but by feedlot type. Genes coding for variable surface proteins were prevalent in our samples. We found the antimicrobial resistant gene, tet44, in two animals. M. bovis isolates from commercial feedlots were more genetically diverse compared to isolates from the research feedlot. Mannheimia haemolytica isolates (n=25) also clustered by feedlot type with increased diversity in commercial feedlots. A total of 14 AMR genes were found within these isolates, particular genes of interest found were erm(42), floR, OXA-2, ROB-1,and tet(h). Within the P. multocida isolates (n= 15), we observed a similar trend of clustering by feedlot and increased diversity in commercial feedlots. A total of 13 AMR genes were found in nine of the fifteen isolates, including erm(42), floR, OXA-2, and tet(h).
Significance:
This study has shown the importance of viewing M. bovis, P. multocida, and M. haemolytica with a broader scope to determine how these pathogens interact and are involved with different disease states. Phylogenetics, virulence genes, and antimicrobial resistance data have provided us insight into multiple pathogens throughout multiple sample sites.
