Gene-centric metagenomics analysis of feline intestinal microbiome using 454 junior pyrosequencing

Abstract

The feline gastrointestinal microbiota have direct influence on feline health and also human health as a reservoir for potential zoonotic pathogens and antibiotic resistant bacterial strains. In order to describe the feline gastrointestinal microbial diversity, fecal samples from cats have been characterized using both culture-dependent and culture-independent methods. However, data correlating total microbial composition and their functions are lacking. Present descriptive study evaluated both phylogenetic and metabolic diversity of the feline intestinal microbiota using GS Junior titanium shotgun pyrosequencing. A total of 152,494 pyrosequencing reads (5405 assembled contigs) were generated and classified into both phylogenetic and metabolic profiles of the feline intestinal microbiota. The Bacteroides/Chlorobi group was the most predominant bacterial phylum comprising ~68% of total classified diversity, followed by Firmicutes (~13%) and Proteobacteria (~6%) respectively. Archaea, fungi and viruses made up the minor communities in the overall microbial diversity. Interestingly, this study also identified a range of potential enteric zoonotic pathogens (0.02-1.25%) and genes involved in antimicrobial resistance (0.02-0.7%) in feline fecal materials. Based on clustering among nine gastrointestinal metagenomes from five different monogastric hosts (dog, human, mice, cat and chicken), the cat metagenome clustered closely together with chicken in both phylogenetic and metabolic level (>80%). Future studies are required to provide deeper understandings on both intrinsic and extrinsic effects such as impact of age, genetics and dietary interventions on the composition of the feline gastrointestinal microbiome.

Fig. 1

Phylogenetic clustering among feline, canine, human, mouse and chicken gastrointestinal metagenomes. A double hierarchical dendogram was performed using weight-pair group clustering method based on non-scaling Manhattan distance. It shows phylogenetic prevalence of microorganisms among nine metagenomes from five different hosts including feline (CFM), canine (K9C and K9BP), human (F1S and HSM), murine (LMC and OMC) and chicken (CCA and CCB). The linkages of the dendogram are not showing phylogenetic relationship of the bacterial classes, but it based on relative abundance of taxonomic profiles. The heat map color represents the relative percentage of the microbial descriptions within each sample, with the legend indicated at the upper left corner. Branch length indicate Manhattan distances of the samples along the x axis (scale at the upper right corner) and of the microbial classes along the y axis (scale at the lower left corner).

Fig. 2

Metabolic clustering among feline, canine, human, mouse and chicken gastrointestinal metagenomes. A double hierarchical dendogram was performed using weight-pair group clustering method based on non-scaling Manhattan distance. It shows distribution of functional categories among nine metagenomes from five different hosts including feline (CFM), canine (K9C and K9BP), human (F1S and HSM), murine (LMC and OMC) and chicken (CCA and CCB). The linkages of the dendogram are based on relative abundance of metabolic profiles. The heat map color represent the relative percentage of functional categories within each sample, with the legend indicated at the upper left corner. Branch length indicate Manhattan distances of the samples along the x axis (scale at the upper right corner) and of the functional categories along the y axis (scale at the lower left corner).

Fig. 3

Non-metric multidimensional analysis based on relative abundance of (A) taxonomic profiles and (B) metabolic profiles among feline, canine, human, mouse and chicken gastrointestinal metagenomes. Samples of the same host species are indicated by the same symbol. Superimposed circles represent clusters of samples at different similarity values of 20%, 40%, 60% and 80% (Bray–Curtis similarity).