Individual animal sample sequencing metrics
Across the 94 samples, AMR target-enriched metagenomic sequencing yielded an average of 15,926,612 paired-end reads (clusters) per sample (range 3.1M – 25.2M per sample, Supplemental File S1). Filtering to improve overall read quality and to exclude bovine host DNA removed an average of 22.9% of reads per sample (range: 3.0% - 38.5%). Sequencing of the 16S rRNA gene across 120 samples resulted in an average of 147,046 reads per sample (range 101,543 – 208,020 per sample, Supplemental File S1). Following quality filtering, identification of amplicon sequence variants (ASVs) with DADA2, and removal of chloroplast and chimeric sequences, samples averaged 40,855 ASVs (range 20,943 – 65,270 per sample, Supplemental File S1).
Composite pen-floor sample sequencing metrics
Across the 98 pen-floor composite samples, target-enriched AMR metagenomic sequencing resulted in an average of 16,470,078 paired-end reads (clusters) per sample (range 6.2M - 25.5M reads per sample, Supplemental File S2). Filtering to improve overall read quality and to exclude bovine host DNA removed an average of 18.7% of reads per sample (range: 3.3% - 48.0%, Supplemental File S1). 16S rRNA gene sequencing resulted in an average of 197,889 paired -end reads (range 94,433 – 219,918 per sample, Supplemental File S2). After quality filtering, identification of ASVs with DADA2, samples averaged 50,426 ASVs (range 25,435 – 81,128 ASVs per sample).
Resistome composition
Across the 94 fecal samples collected from individual cattle, an average of 421,355 reads per sample were classified as genetic determinants of AMR (Supplemental File S3). The classifications represented 1,152 different published gene sequences that confer resistance to 18 different drug classes through 60 distinct resistance mechanisms. Across all sampling time points, the 9 most abundant drug classes (or multi-compound mechanisms) were tetracyclines (60.8%), drug and biocide resistance (8.2%), aminoglycosides (7.2%), macrolide-lincosamide-streptrogramin (MLS – 5.6%), betalactams (5.5%), sulfonamides (4.2%), phenicols (3.5%), drug and biocide and metal resistance (2.6%), and biocide and metal resistance (1.1%). The remaining 9 classes each comprised less than 1% of all normalized counts. Genes conferring resistance to rifampin were identified in 13/94 samples and resistance to fluoroquinolones was identified in only 1/94 fecal samples. Of the genes that confer tetracycline resistance, 89.1% represented tetracycline resistance ribosomal protection proteins, 8.4% were major facilitator superfamily (MFS) efflux pumps, and 2.5% were tetracycline inactivation enzymes. In the second most abundant group of resistance determinants, multi-compound drug and biocide resistance, 39% of alignments drug and biocide MFS efflux pumps and 30.8% represented drug and biocide RND efflux pumps.
In the 98 composite fecal samples collected from pen-floors, an average of 559,961 reads per sample were classified as genetic determinants of AMR (Supplemental File S3), representing 1,361 genes that confer resistance to 20 different drug classes through 69 distinct resistance mechanisms. Across all sampling time points, the 8 most abundant drug classes or MDR mechanisms were represented by tetracyclines (69.4%), MLS (8.2%), aminoglycosides (6.2 %), betalactams (4.6%), multi-compound drug and biocide resistance (4%), sulfonamides (2.8%), phenicol (2.2%), and drug and biocide and metal resistance mechanisms (1.1%). The remaining 12 classes each consisted of <1% of normalized counts (Figure 1). Gene conferring resistance to rifampin were identified in 15/98 samples, resistance to cationic antimicrobial peptides was identified in 5/98 samples, and fosfomycin, mupirocin, and fluoroquinolone resistance were only identified in a single sample each. Of the genes conferring tetracycline resistance, 89% encoded tetracycline resistance ribosomal protection proteins and 8.7% encoded for MFS efflux pumps with the remaining 2.4% associated with tetracycline inactivation enzymes. In the second most abundant resistance class, MLS, the two most abundant resistance mechanisms were 23S rRNA methyltransferases (49.7%) and MLS resistance MFS efflux pumps (24.3%).
Changes in resistome composition over time
There were significant shifts in richness and Shannon’s diversity indices between sampling time points. At the class level, richness significantly decreased in individual animal samples (W = 1507.5, P = 0.002) and in pen-floor composite samples (W = 1412.5, P <0.001). Shannon’s diversity was also significantly different between sampling points in the individual animal samples (W=1654, P<0.001) and in pen floor composite samples at the class level (W = 1637, P <0.001). Likewise, there was a statistically significant shift in resistome composition over time for fecal samples collected from both individual animals and pen-floor composite samples. The temporal shift in resistome composition was greater among samples collected from individual animals (class level: ANOSIM R = 0.33, P = 0.001; mechanism level: ANOSIM R = 0.34, P = 0.001; Figure 2A) than it was among pen-floor composite samples (class level: ANOSIM R = 0.18, P = 0.001; mechanism level: ANOSIM R = 0.18, P = 0.001; Figure 2B). Temporal differences in the relative abundance of drug classes were more prominent among individual animal samples than pen-floor composites, particularly due to decreases in the second most abundant resistance class, multi-compound drug and biocide resistance. The pen-level resistome was dominated by tetracycline, and MLS resistance at the first sampling time point, and by the second sampling time point tetracycline resistance made up a greater proportion of the resistome in both sample types and significant shifts were limited to the drug classes in lower abundance (Figure 1). Of the 8 drug classes making up greater than 1% of the resistome, 8 were differentially abundant in individual animal samples while only 2 were differentially abundant in pen-floor composite samples (Supplemental File S5). Interestingly, shifts in the composition of individual animal resistomes were primarily the result of significant increases in the abundance of the three most prevalent drug classes (tetracyclines, MLS, and sulfonamides). In contrast, the abundance of these three drug classes did not change over time in pen-floor composite samples (Supplemental File S6). Instead, the 2 drug classes with significant changes in pen-floor composite samples were all associated with less prevalent drug classes that decreased in abundance. Notably classes consisting of multi-drug resistance, drug and biocide resistance, and drug and biocide and metal resistance mechanisms all decreased significantly over time in both individual animal samples and pen-floor composite samples.
AMD exposures in the study population
AMD exposures for the entire study population have previously been described (Benedict et al., 2015; Noyes et al., 2015). The average AMD exposures at the second timepoint in individual cattle selected for this study was 12.7 total ADDs (range 5.9 – 24.6 ADD), which included an average parenteral exposure of 3.1 ADD (range 0 – 7 ADD - Table 1). Parenteral exposures were also dominated by tetracycline drugs (1.6 ADD) and macrolide drugs (1.2 ADD), followed by phenicol (0.2 ADD), and sulfonamide drugs (0.1 ADD) on average. The majority of in-feed AMD exposures were to tetracyclines (average 9.6 ADDs per animal) compared to MLS (0.02 ADDs). Correspondingly, groups of cattle housed in enrolled pens were exposed to on average of 2,113.8 ADDs per pen (range 9.9 – 10,113.3) at the second timepoint, consisting mostly of in-feed AMD exposures. In contrast, pens of cattle were exposed to average of 368.3 ADD by parenteral exposures (range 0 – 1367 ADD) (Table 1). At the pen level, parenteral exposure to tetracycline drugs was most common, with an average of 140.9 ADD per pen (range 0 – 902). At one of the participating feedlots, parenteral exposure to fluoroquinolone drugs was more common in the enrolled pens (n=6), accumulating an average of 1219.5 ADDs at the second time point. This is in comparison to an average of 57.8 fluoroquinolone ADDs in the other 49 pens. Without the influence of these 6 pens, tetracyclines made up the largest percentage of parenteral AMD exposures (65%), followed by fluoroquinolone drugs (30%), and betalactam drugs (2%) with MLS, phenicol, and sulfonamide drugs each making up less than 1% of drug exposures.
Table 1
– Summary statistics for ADD exposure variables, by sample type.
Sample type | Variable | mean | median | min | max | sd |
Individual - 1st time point | Total_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | Total_feed_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | Total_parenteral_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | total_tetracycline_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | total_MLS_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | feed_MLS_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | feed_tetracycline_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | parenteral_tetracycline_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | parenteral_MLS_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | parenteral_phenicol_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | parenteral_sulfonamide_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | parenteral_betalactams_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 1st time point | parenteral_fluoroquinolones_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 2nd time point | Total_ADD | 12.7 | 10.5 | 5.9 | 24.6 | 5.6 |
Individual - 2nd time point | Total_feed_ADD | 9.6 | 7.0 | 3.9 | 18.7 | 5.4 |
Individual - 2nd time point | Total_parenteral_ADD | 3.1 | 3.0 | 0.0 | 7.0 | 1.4 |
Individual - 2nd time point | total_tetracycline_ADD | 11.2 | 9.9 | 5.9 | 18.7 | 4.3 |
Individual - 2nd time point | total_MLS_ADD | 1.2 | 0.1 | 0.0 | 3.1 | 1.5 |
Individual - 2nd time point | feed_MLS_ADD | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 |
Individual - 2nd time point | feed_tetracycline_ADD | 9.6 | 6.9 | 3.9 | 18.7 | 5.4 |
Individual - 2nd time point | parenteral_tetracycline_ADD | 1.6 | 2.0 | 0.0 | 4.0 | 1.4 |
Individual - 2nd time point | parenteral_MLS_ADD | 1.2 | 0.0 | 0.0 | 3.0 | 1.5 |
Individual - 2nd time point | parenteral_phenicol_ADD | 0.2 | 0.0 | 0.0 | 3.0 | 0.7 |
Individual - 2nd time point | parenteral_sulfonamide_ADD | 0.1 | 0.0 | 0.0 | 3.0 | 0.4 |
Individual - 2nd time point | parenteral_betalactams_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Individual - 2nd time point | parenteral_fluoroquinolones_ADD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Pen - 1st time point | Total_ADD | 903.6 | 118.2 | 0.0 | 9469.5 | 2030.6 |
Pen - 1st time point | Total_feed_ADD | 591.2 | 38.1 | 0.0 | 8212.5 | 1710.9 |
Pen - 1st time point | Total_parenteral_ADD | 312.4 | 21.0 | 0.0 | 1326.0 | 419.9 |
Pen - 1st time point | total_tetracycline_ADD | 702.1 | 64.9 | 0.0 | 8230.5 | 1700.4 |
Pen - 1st time point | total_MLS_ADD | 1.2 | 0.0 | 0.0 | 18.0 | 3.7 |
Pen - 1st time point | feed_MLS_ADD | 0.7 | 0.0 | 0.0 | 12.0 | 2.4 |
Pen - 1st time point | feed_tetracycline_ADD | 590.5 | 38.1 | 0.0 | 8212.5 | 1711.1 |
Pen - 1st time point | parenteral_tetracycline_ADD | 111.7 | 0.0 | 0.0 | 1048.0 | 231.0 |
Pen - 1st time point | parenteral_MLS_ADD | 0.5 | 0.0 | 0.0 | 12.0 | 2.1 |
Pen - 1st time point | parenteral_phenicol_ADD | 0.4 | 0.0 | 0.0 | 16.0 | 2.5 |
Pen - 1st time point | parenteral_sulfonamide_ADD | 1.4 | 0.0 | 0.0 | 27.0 | 5.5 |
Pen - 1st time point | parenteral_betalactams_ADD | 0.9 | 0.0 | 0.0 | 24.0 | 4.3 |
Pen - 1st time point | parenteral_fluoroquinolones_ADD | 197.5 | 0.0 | 0.0 | 1218.0 | 396.5 |
Pen - 2nd time point | Total_ADD | 2113.8 | 1054.6 | 9.9 | 10113.3 | 2959.3 |
Pen - 2nd time point | Total_feed_ADD | 1745.5 | 763.3 | 9.9 | 8822.3 | 2560.5 |
Pen - 2nd time point | Total_parenteral_ADD | 368.3 | 143.0 | 0.0 | 1367.0 | 451.7 |
Pen - 2nd time point | total_tetracycline_ADD | 1880.6 | 1012.5 | 9.9 | 8858.3 | 2546.9 |
Pen - 2nd time point | total_MLS_ADD | 8.4 | 6.3 | 0.0 | 33.8 | 8.8 |
Pen - 2nd time point | feed_MLS_ADD | 5.7 | 0.0 | 0.0 | 33.4 | 7.6 |
Pen - 2nd time point | feed_tetracycline_ADD | 1739.8 | 751.9 | 9.9 | 8822.3 | 2562.5 |
Pen - 2nd time point | parenteral_tetracycline_ADD | 140.9 | 18.0 | 0.0 | 902.0 | 231.4 |
Pen - 2nd time point | parenteral_MLS_ADD | 2.7 | 0.0 | 0.0 | 12.0 | 4.1 |
Pen - 2nd time point | parenteral_phenicol_ADD | 2.8 | 0.0 | 0.0 | 27.0 | 6.4 |
Pen - 2nd time point | parenteral_sulfonamide_ADD | 5.0 | 3.0 | 0.0 | 30.0 | 7.6 |
Pen - 2nd time point | parenteral_betalactams_ADD | 7.3 | 3.0 | 0.0 | 46.0 | 10.3 |
Pen - 2nd time point | parenteral_fluoroquinolones_ADD | 209.6 | 9.0 | 0.0 | 1251.0 | 418.5 |
Pen - 3rd time point | Total_ADD | 1340.0 | 1349.6 | 349.5 | 2520.4 | 741.7 |
Pen - 3rd time point | Total_feed_ADD | 1053.0 | 945.1 | 349.5 | 1850.4 | 507.6 |
Pen - 3rd time point | Total_parenteral_ADD | 287.0 | 380.8 | 0.0 | 670.0 | 260.0 |
Pen - 3rd time point | total_tetracycline_ADD | 1228.9 | 1245.2 | 339.7 | 2348.5 | 631.5 |
Pen - 3rd time point | total_MLS_ADD | 27.7 | 20.8 | 0.0 | 112.6 | 32.4 |
Pen - 3rd time point | feed_MLS_ADD | 25.6 | 15.9 | 0.0 | 112.6 | 32.7 |
Pen - 3rd time point | feed_tetracycline_ADD | 1027.5 | 935.1 | 339.7 | 1833.5 | 508.4 |
Pen - 3rd time point | parenteral_tetracycline_ADD | 201.4 | 203.0 | 0.0 | 515.0 | 207.3 |
Pen - 3rd time point | parenteral_MLS_ADD | 2.1 | 0.0 | 0.0 | 9.0 | 3.5 |
Pen - 3rd time point | parenteral_phenicol_ADD | 2.1 | 0.0 | 0.0 | 18.0 | 5.7 |
Pen - 3rd time point | parenteral_sulfonamide_ADD | 3.1 | 1.5 | 0.0 | 14.0 | 4.5 |
Pen - 3rd time point | parenteral_betalactams_ADD | 6.5 | 4.0 | 0.0 | 28.0 | 8.7 |
Pen - 3rd time point | parenteral_fluoroquinolones_ADD | 71.8 | 6.3 | 0.0 | 582.0 | 180.3 |
Potential associations between resistome composition and AMD exposures
Redundancy analysis included investigation of an explanatory variable regarding feedlot, 2 variables regarding timing of sampling, and 14 variables characterizing various aspects of AMD exposures prior to sampling. When including data from all time points for individual animal samples in one model, sampling time point was the only significant variable (P < 0.05), but it was only associated with explaining 2.4% of the constrained variance. For the model investigating data from all time points for pen-floor composite samples, sampling time along with 3 variables describing parenteral exposure to phenicols, macrolides, and sulfonamides were included in the model resulting from step-wise model selection. In all, the sampling time, ADDs for tetracycline exposure, and total ADD exposure were included in the model and were statistically significant (P < 0.05), but only accounted for 0.6%, 0.2%, and 0.1% of the constrained variance, respectively. In both of these models, however, unconstrained variance estimates were much greater than constrained variance estimates, suggesting that these results should be interpreted with caution as only a small amount of the variation in the response (resistome) matrix was represented in the model (Legendre and Gallagher, 2001).
Because of the significant shift observed in resistome composition over time, samples collected at the second time point were analyzed separately with RDA. For samples collected from individual animals, the days-on-feed (DOF) variable was the only statistically significant (P < 0.05) variable included in the final model, describing 0.2% of the constrained variance. For pen-floor composite samples, the final RDA model included only 2 statistically significant variables, parenteral MLS ADD and feedlot ID, explaining only 0.4% and 0.2% of the constrained variance, respectively. Again, the unconstrained variance for this model was much greater than the constrained variance.
Highly important AMR genes
Of the 17 genes identified a priori as being important to medicine when expressed in human pathogens, 11 were identified in at least one sample (Supplemental File S7); bla(IMI), bla(KPC), bla(SHV), bla(CPH), bla(NDM), and mcr genes were not identified in any samples. Alignments to these medically important genes accounted for 0.4% (415K / 93.7M) of all determinants of AMR across all samples. Determinants for betalactamases were the most abundant type of medically important AMR determinant, representing 47% (195K / 415K) of alignments to these genes. Among these, bla(CTX), bla(OXA) and bla(TEM) were the most abundant, representing 30% (126K / 415K), 9% (37K / 415K), and 7% (31K / 415K) of alignments to medically important AMR determinants, respectively. The alignments to bla(CTX) and bla(OXA) genes were fairly evenly distributed across most pen floor composite samples (98/98 and 78/98, respectively), but were more clustered in individual animal samples. This clustering of alignments was even stronger for bla(TEM) among a smaller number samples (7/94 and 13/98 for individual and pen-floor samples, respectively). Interestingly, 90% of bla(OXA) alignments (33K / 37K) were to OXA-347 (MEGARes gene accession MEG_4750, https://megares.meglab.org). There was also an interesting general trend wherein larger numbers of determinants for these 3 gene groups did not cluster in the same samples. That is, samples that had larger number of alignments for one these genes [bla(CTX), bla(OXA), or bla(TEM)] did not have larger numbers of alignments for the other two. Enzymes encoded by these gene determinants are important in members of the ESBL group. bla(OXA) genes have become medically important because they encode for Class D betalactamase enzymes that are active against cephalosporins and carbapenems (Tooke et al., 2019). While these have been commonly identified in Acinetobacter species, bla(OXA) genes can be found in a variety of bacteria. All alignments to the bla(CTX) group were to one of three MEGAREs gene accessions (MEG_2378, MEG_2430, or MEG_2435), which are variants of the CTX-M-9 subgroup. These ESBL belong to Ambler class A beta-lactamases which have become a medical concern in Enterobacterciae isolates (Bonnet, 2004).
The vgbA (streptogramin B esterase), vat (streptogramin A O-acetyltransferase), and vga (multidrug ABC efflux pump) genes confer resistance to quinupristin-dalfopristin (Soltani et al., 2000; Jung et al., 2010). This streptogramin class drug combination is especially important for treatment of infections with resistant Gram-positive bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus spp (VRE). Alignments to this group of genes were the second most abundant among those identified among the set of medically important AMR genes investigated a priori. Collectively, they represented 37% (153K / 415K) of alignments to the subset of medically important genes and were identified in 82% (158/192) of all samples. Alignments to vgbA were the most common among the streptogramin class AMR genes, and the second most abundant among the subset of medically important AMR genes (22% of medically important AMR genes, 92K / 415K). The identification of vgbA, vat and vga were co-located in 24% (23/94) individual animal samples, and 59% (58/98) of pen-floor composite samples.
Other medically important genes were more sparsely identified in the sample set [cfr, bla(SME), bla(CMY), bla(IMP), and bla(GES)]. Reads aligning to cfr were distributed among the sample set, especially those for cfrA, whereas reads aligning to the other ESBL genes listed [bla(SME), bla(CMY), bla(IMP), and bla(GES)] were clustered within a few samples (Supplemental File S7).
Microbiome composition
Across samples from individual animals, taxa from 29 phyla, 63 classes, and 100 orders were represented (Supplemental File S8). Three phyla (Proteobacteria, Firmicutes, and Bacteroidetes) accounted for over 93% of all normalized counts (45.4%, 36.9%, and 10.9%, respectively), and each of these phyla were primarily comprised of a single, dominant taxonomic class (Figure 3). Members of the class Gammaproteobacteria comprised over 99% of all Proteobacteria, while Clostridia made up 75% of all Firmicutes, and Bacteroidia represented 89.8% of all Bacteroidetes. At the level of order, Pseudomonadales (44.7%), Clostridiales (26.9%), Bacteroidales (8.7%), Lactobacillales (6.6%), RF39 (2.7%) and Flavobacteriales (2.0%), and Enterobacteriales (1.1%) were the most abundant and combined to represent nearly 93% of the microbial community. The remaining 93 orders each made up less than 1% of the overall community.
In pen-floor composite samples, taxa from 25 phyla, 73 classes, and 113 orders were represented. Like individual animal samples, Firmicutes (46.5%), Bacteroidetes (21.1%), and Proteobacteria (20.1%) were the three most abundant phyla, albeit in a different order of relative abundance. Additionally, Actinobacteria and Tenericutes were more abundant within pen-floor composite samples and accounted for 5.0% and 4.9% of the microbial community, respectively (Figure 3). As was the case for individual animal samples, the three most abundant phyla were predominantly comprised of a single class. Clostridia comprised 74.7% of all Firmicutes, while Gammaproteobacteria made up 99.4% of all Proteobacteria, and Bacteroidia represented 80.7% of all Bacteroidetes. At the order level, Pseudomonadales (45.3%), Clostridiales (26.7%), Bacteroidales (8.6%), Lactobacillales (6.5%), RF39 (2.6%), Flavobacteriales (2%), and Enterobacteriales (1.1%) were the most abundant and comprised over 93% of the microbial community (Figure 4). The remaining 140 orders each represented less than 1% of the overall community.
Changes in microbiome composition over time
There were no significant changes in richness or Shannon’s diversity indices over time within microbial communities from individual animal samples, but there was a significant decrease in the richness of microbial classes and orders in pen-floor composite samples over time (P < 0.05). At arrival, pen-floor composite microbial communities contained an average of 20.2 classes and 27.3 orders, which decreased over time to 18.6 and 25.3, respectively (Supplemental File S8). As demonstrated in the resistome, ANOSIM confirmed that microbial community composition shifted significantly between the first and second time point. Likewise, individual animals had greater shifts in community composition (phylum: ANOSIM R = 0.19, P < 0.01; class: ANOSIM R= 0.21, P < 0.01; order: ANOSIM R= 0.22, P < 0.01) then pen level communities (phylum: ANOSIM R = 0.08, P = 0.01; class: ANOSIM R= 0.12, P < 0.01; order: ANOSIM R= 0.14, P < 0.01; Figure 4).
Of the 10 phyla in individual animal samples with an average relative abundance over 1%, seven had significant changes in their relative abundance between sampling time points (P-value < 0.05). Bacteroidetes, Proteobacteria, and Spirochaetes significantly increased in relative abundance between first and second time point, while Firmicutes, Cyanobacteria, Actinobacteria, and Verrucomicrobia all decreased (P<0.05) (Supplemental File S9). Of the 11 phyla in pen-floor composites with an average relative abundance over 1%, only six significantly changed in relative abundance from the first to second time point. Tenericutes, Spirochaetes, and Euryarchaeota significantly increased in abundance over time, while Cyanobacteria, Actinobacteria, and Verrucomicrobia all decreased (P < 0.05) (Supplemental File S10).
Potential associations between microbiome composition and AMD exposures
Parallel to the RDA of variance of the resistome composition, RDA of the microbiome composition investigated the effects of 17 explanatory variables, including 14 variables characterizing AMD exposures. Analysis of individual animal samples from both time points identified sampling time, feedlot ID, and in-feed MLS ADD as statistically significant (P<0.05), but only explained 1.2%, 0.6%, and 0.2% of the constrained variance at the phylum level, respectively. For the RDA of pen-floor composite samples from all 3 time points, DOF, total parenteral ADD exposures, and parenteral sulfonamide ADD were statistically significant (P<0.05), but only explained 1%, 0.3%, and 0.3% of the constrained variance, respectively.
When analyzing the samples collected at the second time point separately, 1.3% of the constrained variance of the microbiome of individual animal samples was statistically significantly explained by feedlot ID (P<0.05). For pen-floor composite samples, the variables feedlot ID and total ADD exposure were statistically significant (P<0.05) in the RDA of samples collected at the second time point, explaining 1.2% and 0.5% of the constrained variance at the phylum level, respectively.