The impacts of viral infection and subsequent antimicrobials on the microbiome-resistome of growing pigs

Abstract

Background Antimicrobials are used in food-producing animals for purposes of preventing, controlling, and/or treating infections. In swine, a major driver of antimicrobial use is porcine reproductive and respiratory syndrome (PRRS), which is caused by a virus that predisposes infected animals to secondary bacterial infections. Numerous antimicrobial protocols are used to treat PRRS, but we have little insight into how these treatment schemes impact antimicrobial resistance (AMR) dynamics within the fecal microbiome of commercial swine. The aim of this study was to determine whether different PRRS-relevant antimicrobial treatment protocols were associated with differences in the fecal microbiome and resistome of growing pigs. To accomplish this, we used a metagenomics approach to characterize and compare the longitudinal wean-to-market resistome and microbiome of pigs challenged with PRRS virus and then exposed to different antimicrobial treatments, and a group of control pigs not challenged with PRRS virus and having minimal antimicrobial exposure. Genomic DNA was extracted from pen-level composite fecal samples from each treatment group and subjected to metagenomic sequencing and microbiome-resistome bioinformatic and statistical analysis. Microbiome-resistome profiles were compared over time and between treatment groups. Results Fecal microbiome and resistome compositions both changed significantly over time, with a dramatic and stereotypic shift between weaning and 9 days post-weaning (dpw). Antimicrobial resistance gene (ARG) richness and diversity were significantly higher at earlier time points, while microbiome richness and diversity were significantly lower. The post-weaning shift was characterized by transition from a Bacteroides-dominated enterotype to Lactobacillus- and Streptococcus-dominated enterotypes. Both the microbiome and resistome stabilized by 44 dpw, at which point the trajectory of microbiome-resistome maturation began to diverge slightly between the treatment groups, potentially due to physical clustering of the pigs. Challenge with PRRS virus seemed to correspond to the re-appearance of many very rare and low-abundance ARGs within the feces of challenged pigs. Despite very different antimicrobial exposures after challenge with PRRS virus, resistome composition remained largely similar between the treatment groups. Differences in ARG abundance between the groups were mostly driven by temporal changes in abundance that occurred prior to antimicrobial exposures, with the exception of ermG, which increased in the feces of treated pigs, and was significantly more abundant in the feces of these pigs compared to the pigs that did not receive post-PRRS antimicrobials. Conclusions The fecal microbiome-resistome of growing pigs exhibited a stereotypic trajectory driven largely by weaning and physiologic aging of the pigs. Events such as viral illness, antimicrobial exposures, and physical grouping of the pigs exerted significant yet relatively minor influence over this trajectory. Therefore, the AMR profile of market-age pigs is the culmination of the life history of the individual pigs and the populations to which they belong. Disease status alone may be a significant driver of AMR in market-age pigs, and understanding the interaction between disease processes and antimicrobial exposures on the swine microbiome-resistome is crucial to developing effective, robust, and reproducible interventions to control AMR.

Age influences the temporal dynamics of microbiome and antimicrobial resistance genes among fecal bacteria in a cohort of production pigs

“…An age-related decline in phenotypic as well as genotypic (ARGs) quantity in fecal bacteria from pigs raised with or without antibiotics has been documented [ 11 , 16 , 67 ]. A recent study in wean-to-market pigs also showed that despite significant differences in the intensity of antibiotic exposures, resistome composition remained largely similar between those pigs that received minimal to moderate versus intensive antibiotic exposures [ 68 ]. Thus, overall reduction in the number of ARGs could well be due to age-related shifts in underlying microbial taxa.…”

“…Factors other than antimicrobial exposure have been shown to be associated with AMR gene levels in the feces of pigs [ 82 ]. Additionally, events such as viral illness and physical grouping of pigs have been shown to influence the dynamics of the microbiome and resistome in pigs [ 68 ]. These findings, along with our own results, suggest that factors driving AMR require investigation beyond merely focusing on antimicrobial use [ 83 ].…”

The gut microbiome and resistome of conventionally- vs. pasture-raised pigs

“…Here, compared to the pasture-raised pigs, the abundance of ARGs within nearly all antimicrobial classes was significantly higher in the conventionally-raised pigs. These ARGs included many that are typically abundant in commercial pigs such as aph(3')-IIIa, cfxA2, erm(B), erm(F), lnu(C), tet (44), tet(Q), tet(W), and tet(X) [2,[47][48][49]. Some of these ARGs were also assembled and binned into specific MAGs thus providing taxonomic information.…”

Massive expansion of the pig gut virome based on global metagenomic mining

“…Viral sequences were identified based on VirSorter2's SOP with (Guo et al, 2021b) and P>=0.95 using DeepVriFinder. We collected metagenomics data from various sources, including SRP188615/PRJNA526405 (Gaio et al, 2022;Gaio et al, 2021), CNP0000824 (Chen et al, 2021b), PRJEB11755 (Xiao et al, 2016), PRJNA788462 (Gaire et al, 2022), PRJNA775062 (Tao et al, 2022), PRJEB22062 (Luiken et al, 2020), PRJCA009609 (Wu et al, 2022a), PRJEB44118 (Zhang et al, 2022), and 70 samples collected in our lab. In total, 4,650 samples were used in our study to extract the viral contigs.…”

“…Several databases, such as GOV2 (Gregory et al, 2019), IMG/VR4 (Camargo et al, 2023a), GVD (Gregory et al, 2020), MGV (Nayfach et al, 2021b), and GPD (Camarillo-Guerrero et al, 2021), have been established to facilitate the analysis of virome from various environments using metagenomic data. Since the establishment of these databases, the number of publicly available pig gut microbiota datasets has rapidly increased (Chen et al, 2021b;Gaio et al, 2022;Gaio et al, 2021;Gaire et al, 2022;Luiken et al, 2020;Tao et al, 2022;Wu et al, 2022a;Xiao et al, 2016;Zhang et al, 2022).…”