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, 8 (1), e53115

Pyrosequencing the Canine Faecal Microbiota: Breadth and Depth of Biodiversity


Pyrosequencing the Canine Faecal Microbiota: Breadth and Depth of Biodiversity

Daniel Hand et al. PLoS One.


Mammalian intestinal microbiota remain poorly understood despite decades of interest and investigation by culture-based and other long-established methodologies. Using high-throughput sequencing technology we now report a detailed analysis of canine faecal microbiota. The study group of animals comprised eleven healthy adult miniature Schnauzer dogs of mixed sex and age, some closely related and all housed in kennel and pen accommodation on the same premises with similar feeding and exercise regimes. DNA was extracted from faecal specimens and subjected to PCR amplification of 16S rDNA, followed by sequencing of the 5' region that included variable regions V1 and V2. Barcoded amplicons were sequenced by Roche-454 FLX high-throughput pyrosequencing. Sequences were assigned to taxa using the Ribosomal Database Project Bayesian classifier and revealed dominance of Fusobacterium and Bacteroidetes phyla. Differences between animals in the proportions of different taxa, among 10,000 reads per animal, were clear and not supportive of the concept of a "core microbiota". Despite this variability in prominent genera, littermates were shown to have a more similar faecal microbial composition than unrelated dogs. Diversity of the microbiota was also assessed by assignment of sequence reads into operational taxonomic units (OTUs) at the level of 97% sequence identity. The OTU data were then subjected to rarefaction analysis and determination of Chao1 richness estimates. The data indicated that faecal microbiota comprised possibly as many as 500 to 1500 OTUs.

Conflict of interest statement

Competing Interests: The authors declare a contribution to funding of this project by WALTHAM Centre for Pet Nutrition, to whom CWP is a consultant and by whom DH was supported in part during the study through partial funding of his studentship. This competing interest does not alter authors‚ adherence to all the PLOS ONE policies on sharing data and materials.


Figure 1
Figure 1. Effect of altering bootstrap score on assignment of sequence reads to genus level.
The graph shows RDP bootstrap score (x axis) and number of reads that would be classified to the genus level (y axis) for each dog.
Figure 2
Figure 2. RDP classification of reads to the genus level.
Sample number is shown on the x axis and percentage reads classified on the y axis. Genera with fewer than 100 reads in all samples were pooled and are shown as ‘rare genera’. MSs1#2 denotes replicated read data on the same DNA sample for dog MSs1. The complete data set is tabulated in table S1.
Figure 3
Figure 3. Rarefaction curves for each study animal.
Number of reads is shown on the x axis and number of OTUs at 97% sequence identity on the y axis. Also shown are the technical replicates of the analysis of DNA from MSs1.
Figure 4
Figure 4. Observed OTUs calculated using the RDP Infernal Aligner and Complete Linkage Clustering tools, and Chao1 richness estimates calculated using Mothur, based on the 10,000 reads analysed from each animal.
Red triangles: observed OTUs; black squares: Chao1 richness estimates; bars represent upper and lower 95% confidence intervals.
Figure 5
Figure 5. Principal component analysis on the log10 (count+1) of each of the most abundant genera (present at >0.1%) identified for each animal.
Each point is labelled with the dog ID followed by the mother ID and is coloured according to the father ID; red (MSC), green (MSD), blue (MSE), black (MSF) and purple (MSG). Dotted circles identify littermates.

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