Marina L Leis1, Matheus O Costa2,3. 1. Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada. 2. Faculty of Veterinary Medicine, Department of Farm Animal Health, Utrecht University, Utrecht, Netherlands. 3. Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada.
Abstract
OBJECTIVE: To characterize the bacterial community residing on the conjunctiva of clinically healthy dogs. METHODS: Bacterial DNA from conjunctival swabs of 10 dogs with normal ocular examinations (both OD and OS, n = 20) was extracted, and 16S rRNA amplicons were sequenced using Illumina MiSeq 600. Resulting data were subjected to quality control steps, and analyzed for bacterial community richness and diversity, within- and between-group dissimilarity, and relative taxonomic composition. RESULTS: High-quality reads (2.22 million bp) resulted in a mean of 159 068 sequences per sample. Bacterial community evenness and diversity was high when compared to other species, and did not significantly differ when samples were grouped by dogs or eyes. As expected, within-dog samples were more similar than between-dog samples. Taxonomic classification revealed that >95% of the community consisted of Firmicutes (34.9 ± 8.8%), Actinobacteria (26.3 ± 7.1%), Proteobacteria (26.2 ± 6.6%), and Bacteroidetes (9.4 ± 2.4%). Key members of the dog ocular surface microbiome, found in all dogs and corresponding to >25% of all identified OTUs (operational taxonomic units), were part of the Bifidobacteriaceae, Lachnospiraceae, Moraxellaceae, Corynebacteriaceae families. Genera previously thought to account for the majority of the core ocular surface microbiome in the dog (Staphylococcus sp., Streptococcus sp., and Bacillus sp.) were associated with only 2.63% of overall reads. CONCLUSIONS: This study shows the feasibility of conjunctival swabs and high-throughput sequencing to profile the bacterial community structure of the canine ocular surface. A core ocular surface microbiome was identified for this canine population.
OBJECTIVE: To characterize the bacterial community residing on the conjunctiva of clinically healthy dogs. METHODS: Bacterial DNA from conjunctival swabs of 10 dogs with normal ocular examinations (both OD and OS, n = 20) was extracted, and 16S rRNA amplicons were sequenced using Illumina MiSeq 600. Resulting data were subjected to quality control steps, and analyzed for bacterial community richness and diversity, within- and between-group dissimilarity, and relative taxonomic composition. RESULTS: High-quality reads (2.22 million bp) resulted in a mean of 159 068 sequences per sample. Bacterial community evenness and diversity was high when compared to other species, and did not significantly differ when samples were grouped by dogs or eyes. As expected, within-dog samples were more similar than between-dog samples. Taxonomic classification revealed that >95% of the community consisted of Firmicutes (34.9 ± 8.8%), Actinobacteria (26.3 ± 7.1%), Proteobacteria (26.2 ± 6.6%), and Bacteroidetes (9.4 ± 2.4%). Key members of the dog ocular surface microbiome, found in all dogs and corresponding to >25% of all identified OTUs (operational taxonomic units), were part of the Bifidobacteriaceae, Lachnospiraceae, Moraxellaceae, Corynebacteriaceae families. Genera previously thought to account for the majority of the core ocular surface microbiome in the dog (Staphylococcus sp., Streptococcus sp., and Bacillus sp.) were associated with only 2.63% of overall reads. CONCLUSIONS: This study shows the feasibility of conjunctival swabs and high-throughput sequencing to profile the bacterial community structure of the canine ocular surface. A core ocular surface microbiome was identified for this canine population.
Authors: Joshua E Darden; Erin M Scott; Carolyn Arnold; Elizabeth M Scallan; Bradley T Simon; Jan S Suchodolski Journal: PLoS One Date: 2019-10-11 Impact factor: 3.240
Authors: Katrina E V Jones; Michala de Linde Henriksen; Søren Saxmose Nielsen; Joshua B Daniels; Michael R Lappin Journal: Vet Ophthalmol Date: 2022-02-28 Impact factor: 1.444
Authors: Callie M Rogers; Erin M Scott; Benjamin Sarawichitr; Carolyn Arnold; Jan S Suchodolski Journal: PLoS One Date: 2020-06-09 Impact factor: 3.240