A metagenomic glimpse into the gut of wild and domestic animals: Quantification of antimicrobial resistance and more

Abstract

Antimicrobial resistance (AMR) in bacteria is a complex subject, why one need to look at this phenomenon from a wider and holistic perspective. The extensive use of the same antimicrobial classes in human and veterinary medicine as well as horticulture is one of the main drivers for the AMR selection. Here, we applied shotgun metagenomics to investigate the AMR epidemiology in several animal species including farm animals, which are often exposed to antimicrobial treatment opposed to an unique set of wild animals that seems not to be subjected to antimicrobial pressure. The comparison of the domestic and wild animals allowed to investigate the possible anthropogenic impact on AMR spread. Inclusion of animals with different feeding behaviors (carnivores, omnivores) enabled to further assess which AMR genes that thrives within the food chain. We tested fecal samples not only of intensively produced chickens, turkeys, and pigs, but also of wild animals such as wild boars, red foxes, and rodents. A multi-directional approach mapping obtained sequences to several databases provided insight into the occurrence of the different AMR genes. The method applied enabled also analysis of other factors that may influence AMR of intestinal microbiome such as diet. Our findings confirmed higher levels of AMR in farm animals than in wildlife. The results also revealed the potential of wildlife in the AMR dissemination. Particularly in red foxes, we found evidence of several AMR genes conferring resistance to critically important antimicrobials like quinolones and cephalosporins. In contrast, the lowest abundance of AMR was observed in rodents originating from natural environment with presumed limited exposure to antimicrobials. Shotgun metagenomics enabled us to demonstrate that discrepancies between AMR profiles found in the intestinal microbiome of various animals probably resulted from the different antimicrobial exposure, habitats, and behavior of the tested animal species.

Fig 1. Total level of antimicrobial resistance genes by drug class and animal source.

Stacked column chart with relative abundances (FPKM) of AMR genes aggregated to corresponding drug classes (y-axis) by sample (x-axis). The height of each bar chart relates to the relative AMR gene abundances in a sample.

Fig 2. Resistome and bacteriome diversity and richness.

Shannon, Simpson diversity indexes and Chao1- richness calculated from the read counts.

Fig 3. The most abundant antimicrobial resistance genes by animal source.

AMR genes abundances heat map based on log transformed relative abundances—FPKM values. Colors scale from red (high abundance) to blue (low abundance) represent log transformed relative abundance. Dark blue (0 on a scale) means no resistance detected.

Fig 4. Plasmids possibly involved in resistance transfer.

Heat map based on plasmids relative abundances with Z-score scaling. Samples with high relative abundances get positive values (red color) and those with relatively low get negative values (blue colors). Complete-linkage clustering of Euclidian distances was applied for clustering the samples.

Fig 5. Bacterial composition at phylum level by animal source.

Stacked column chart with relative abundances of the most abundant bacterial phyla based on relative abundances.

Fig 6. Bacterial composition at genera level by animal source.

Heat map presents the 50 most abundant bacterial genera based on relative abundances values with Z-score scaling. Samples with high relative abundances get positive values (red color) and those with relatively low get negative values (blue colors). Complete-linkage clustering of Euclidian distances was applied for clustering the samples.

Fig 7. Pathogens occurrence by source animal.

Heat map of selected bacterial pathogens based on relative abundances values with Z-score scaling. Samples with high relative abundances get positive values (red color) and those with relatively low get negative values (blue colors). Complete-linkage clustering of Euclidian distances was applied for clustering the samples.

Fig 8. Diet of tested animals.

Heat map based on relative abundances (in RPM: reads per million) of selected plants, vertebrates and invertebrates with Z-score scaling. Samples with high relative abundances get positive values (red color) and those with relatively low get negative values (blue colors). Grey color corresponds to host DNA. Complete-linkage clustering of Euclidian distances was applied for clustering the samples.