Metagenomics-Based Approach to Source-Attribution of Antimicrobial Resistance Determinants – Identification of Reservoir Resistome Signatures

Abstract

Metagenomics can unveil the genetic content of the total microbiota in different environments, such as food products and the guts of humans and livestock. It is therefore considered of great potential to investigate the transmission of foodborne hazards as part of source-attribution studies. Source-attribution of antimicrobial resistance (AMR) has traditionally relied on pathogen isolation, while metagenomics allows investigating the full span of AMR determinants. In this study, we hypothesized that the relative abundance of fecal resistome components can be associated with specific reservoirs, and that resistomes can be used for AMR source-attribution. We used shotgun-sequences from fecal samples of pigs, broilers, turkeys- and veal calves collected across Europe, and fecal samples from humans occupationally exposed to livestock in one country (pig slaughterhouse workers, pig and broiler farmers). We applied both hierarchical and flat forms of the supervised classification ensemble algorithm Random Forests to classify resistomes into corresponding reservoir classes. We identified country-specific and -independent AMR determinants, and assessed the impact of country-specific determinants when attributing AMR resistance in humans. Additionally, we performed a similarity percentage analysis with the full spectrum of AMR determinants to identify resistome signatures for the different reservoirs. We showed that the number of AMR determinants necessary to attribute a resistome into the correct reservoir increases with a larger reservoir heterogeneity, and that the impact of country-specific resistome signatures on prediction varies between countries. We predicted a higher occupational exposure to AMR determinants among workers exposed to pigs than among those exposed to broilers. Additionally, results suggested that AMR exposure on pig farms was higher than in pig slaughterhouses. Human resistomes were more similar to pig and veal calves’ resistomes than to those of broilers and turkeys, and the majority of these resistome dissimilarities can be explained by a small set of AMR determinants. We identified resistome signatures for each individual reservoir, which include AMR determinants significantly associated with on-farm antimicrobial use. We attributed human resistomes to different livestock reservoirs using Random Forests, which allowed identifying pigs as a potential source of AMR in humans. This study thus demonstrates that it is possible to apply metagenomics in AMR source-attribution.

Risk Factors for Antimicrobial Resistance in Turkey Farms: A Cross-Sectional Study in Three European Countries

“…Metagenomic sequencing and processing was performed as described previously, with modifications [25,38]. The reads are available in the European Nucleotide Archive, under project accession number PRJEB39685.…”

Intestinal microbiota research from a global perspective

“…This observation is in line with a previous study assessing >2,000 full bacterial genomes, which found that horizontal AMR and non-AMR gene transfer is mostly shaped by ecology [ 164 ]. A recent study assessing AMR transfer between farmers and their animals showed that microbial strains and AMR genes are shared more easily between farmers and pigs than farmers and other domestic animals [ 165 ]. Since the gastrointestinal tract of pigs closely resembles the gut ecosystem of humans, it is plausible that the horizontal gene transfer and AMR exchange are most likely between strains sharing the same ecology.…”

FI: The Fecobiome Initiative

“…Considering the growing number of shotgun metagenomic fecal data in cattle during the past 3 years (Noelle Noyes et al , 2016a, b; Doster et al , 2018 ; Zaheer et al, 2018 , 2019; Liu et al , 2019 ; Rovira et al , 2019 ; Salaheen et al, 2019 , 2020; Weinroth et al , 2019 ; Gaeta et al , 2020 ; Haley et al , 2020 ; Lim et al , 2020 ; Duarte et al , 2021 ), launching the FI is timely, as its website ( ) can serve as a central repository to organize metadata and facilitate meta-analyses to fully exploit the potential of such data.…”

“…In cattle, genomic approaches (both culture and nonculture-based) have mostly focused on the rumen (Pitta et al , 2016 ; Seshadri et al , 2018 ; Stewart et al , 2018 ; Bickhart et al , 2019 ). Despite the clear role of the large intestine in many aspects of health, nutrition, and the spread of zoonotic pathogens and AMR, the knowledge of its microbiota is mainly limited to: descriptive 16S/18S rRNA targeted sequencing (Holman and Gzyl, 2019 ), single bacterial species (i.e., Escherichia and Salmonella ) and their antagonists that pose a threat to the cattle host or human consumers (Wang et al , 2017 ; Holschbach and Peek, 2018 ), and the occurrence and distribution of AMR genes in the feces (Noelle Noyes et al , 2016a , b ; Doster et al , 2018 ; Zaheer et al, 2018 , 2019 ; Keijser et al , 2019 ; Liu et al , 2019 ; Rovira et al , 2019 ; Weinroth et al , 2019 ; Gaeta et al , 2020 ; Haley et al , 2020 ; Lim et al , 2020 ; Salaheen et al , 2020 ; Duarte et al , 2021 ) (see also ). …”

“…the occurrence and distribution of AMR genes in the feces (Noelle Noyes et al , 2016a , b ; Doster et al , 2018 ; Zaheer et al, 2018 , 2019 ; Keijser et al , 2019 ; Liu et al , 2019 ; Rovira et al , 2019 ; Weinroth et al , 2019 ; Gaeta et al , 2020 ; Haley et al , 2020 ; Lim et al , 2020 ; Salaheen et al , 2020 ; Duarte et al , 2021 ) (see also ).…”