Science of the Total Environment 857 (2023) 159609 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv Impacts of dietary copper on the swine gut microbiome and antibiotic resistome Julius Emil Brinck a,g, Simon Bo Lassen a,b, Asal Forouzandeh a,c, Ting Pan d, Yan-Zi Wang d, Alessandra Monteiro e, ⁎ Laia Blavi c, David Solà-Oriol c, Hans H. Stein f, Jian-Qiang Su d, Kristian K. Brandt a,b, a Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark b Sino-Danish Center for Education and Research (SDC), University of Chinese Academy of Sciences, 380 Huaibeizhuang, Beijing, China c Animal Nutrition and Welfare Service (SNiBA), Department of Animal and Food Sciences, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain d Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China e Animine, Annecy 74960, France f Department of Animal Sciences, University of Illinois, Urbana 61801, USA g National Food Institute, Technical University of Denmark, Kgs. Lyngby, Denmark H I G H L I G H T S G R A P H I C A L A B S T R A C T • Impacts of two Cu-based feed additives on the swine gut microbiome were studied. • Cu treatments increased bioavailable Cu in feces by at least 3 orders of magnitude. • Antibiotic resistance genes were highly abundant and diverse in fecal samples. • Microbiomes differed between pig growth stages, but only minor impacts of Cu. • High levels of dietary Cu (250 ppm) did not co-select antibiotic resistance genes. A R T I C L E I N F O A B S T R A C T Editor: Fang Wang Restrictions on antibiotic growth promoters have prompted livestock producers to use alternative growth promoters, and dietary copper (Cu) supplementation is currently being widely used in pig production. However, elevated doses of Keywords: dietary Cu constitute a risk for co-selection of antibiotic resistance and the risk may depend on the type of Cu-based Antimicrobial resistance feed additives being used. We here report the first controlled experiment investigating the impact of two contrasting Antibiotic resistance genes Cu-based feed additives on the overall swine gut microbiome and antibiotic resistome. DNA was extracted from Bacterial community composition Co-selection fecal samples (n = 96) collected at four time points during 116 days from 120 pigs allotted to three dietary treatments: HT-qPCR control, divalent copper sulfate (CuSO4; 250 μg Cu g−1 feed), and monovalent copper oxide (Cu2O; 250 μg Cu g−1 Pig microbiome feed). Bacterial community composition, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs) were assessed, and bioavailable Cu ([Cu]bio) was determined using whole-cell bacterial bioreporters. Cu supplementa- tion to feed increased total Cu concentrations ([Cu]total) and [Cu]bio in feces 8–10 fold and at least 670–1000 fold, re- spectively, but with no significant differences between the two Cu sources. The swine gut microbiome harbored highly abundant and diverse ARGs and MGEs irrespective of the treatments throughout the experiment. Microbiomes differed significantly between pig growth stages and tended to converge over time, but only minor changes in the bacterial community composition and resistome could be linked to Cu supplementation. A significant correlation between bac- terial community composition (i.e., bacterial taxa present) and ARG prevalence patterns were observed by Procrustes ⁎ Corresponding author at: Section for Microbial Ecology and Biotechnology, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg, Denmark. E-mail address: kkb@plen.ku.dk (K.K. Brandt). http://dx.doi.org/10.1016/j.scitotenv.2022.159609 Received 23 July 2022; Received in revised form 17 October 2022; Accepted 17 October 2022 Available online 21 October 2022 0048-9697/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 analysis. Overall, results of the experiment did not provide evidence for Cu-induced co-selection of ARGs or MGEs even at a Cu concentration level exceeding the maximal permitted level for pig diets in the EU (25 to 150 μg Cu g−1 feed depending on pig age). 1. Introduction 2. Materials and methods Antibiotic resistance is recognized as one of the major threats to human 2.1. Description of Cu supplementation swine feeding trial health (WHO, 2014). Swine farms contribute to antibiotic resistance devel- opment and dissemination, and swine microbiomes comprise important Pig fecal samples were derived from a feeding trial carried out at the reservoirs of antibiotic resistance genes (ARGs) due to the use of antimicro- University of Illinois in Urbana-Champaign, Illinois, USA. The feeding bials in swine production (Argudín et al., 2017). Due to the emergence of trial protocol was reviewed and approved by the University of Illinois' Insti- antibiotic resistance in pathogenic bacteria in animal production, the EU tutional Animal Care and Use Committee. Offsprings of L 359 boars mated prohibited the use of antibiotic growth promoters (AGPs) in 2006 (EU, to Camborough females (60 barrows, and 60 gilts; Pig Improvement Com- 2005). In the United States and China, AGPs administered in animal pro- pany, Hendersonville, TN) were used in the feeding trial, which has previ- duction became restricted in 2017 and 2020, respectively (U.S. Food and ously been described in detail (Blavi et al., 2021; Forouzandeh et al., 2022). Drug Administration, 2018; Hu and Cowling, 2020). Due to the reduced Importantly, no antibiotics were administered during the study. In brief, a use of AGPs, the livestock industry currently relies on alternative growth total of 120 pigs with an average initial body weight of 11.5 ± 0.98 kg promoters such as in-feed supplementation with elevated concentrations were used in a 116 d trial. Pigs were randomly allotted to a randomized of copper (Cu) or zinc (Zn) (Heo et al., 2013; Liu et al., 2018; Zhao et al., complete block design with two blocks of 60 pigs, three treatments, five 2021). pigs per pen, and four replicate pens per treatment in each block. Treat- However, dietary supplementation with Cu and Zn in swine production ments were arranged as follows: control diet with 20 μg CuSO4 g−1 feed may also increase the risk of dissemination of antibiotic resistance via co- (control group), which is the minimal recommended dose of Cu for grow- selection and mobilization of ARGs and their subsequent transfer to ing/finishing pigs, control diet supplemented with 250 μg CuSO4 g−1 humans (Ashbolt et al., 2013; Yazdankhah et al., 2014; Zhao et al., 2018; feed (CuSO4 group), or control diet supplemented with 250 μg Cu2O g−1 Muurinen et al., 2021). Co-selection may occur if ARGs and metal resis- feed (Cu2O group). A total of eight fecal samples per treatment group tance genes (MRGs) are genetically linked (co-resistance), if the same resis- were collected directly from the rectum of the pigs at four different time tance mechanism confers resistance to both metals and antibiotics (cross- points (26, 61, 96, and 116 d after the start of the experiment). Fecal sam- resistance), or if a common regulator controls the expression of resistance ples were subsequently placed into liquid nitrogen and stored at -80 °C until systems to both metals and antibiotics (co-regulation; Baker-Austin et al., samples were shipped on dry ice to the University of Copenhagen. 2006; Poole, 2017). Indeed, elevated doses of dietary Cu and Zn used for swine growth promotion have been shown to select for Cu or Zn resistance 2.2. Fecal DNA extraction and to co-select for resistance to certain antibiotics in specific groups of swine gut bacteria harboring pathogenic strains (Hasman et al., 2006; DNA was extracted from thawed fecal samples (0.25 g, n = 96) using Medardus et al., 2014; Yazdankhah et al., 2014). Very recently, orally admin- the DNeasy PowerLyzer PowerSoil DNA Isolation Kit (Qiagen) according istered veterinary medicinal products containing Zn have been prohibited in to the instruction manual. The quality and quantity of the extracted DNA the EU (EU, 2017). This is likely to prompt European pig farmers to use the were assessed using nanodrop and Qubit. DNA concentrations varied be- maximal allowed levels of Cu corresponding to 150 ppm Cu from lactation tween 80 and 220 ng μl−1 for the fecal samples. The A260/A280 ratios until 4 weeks after weaning, 100 ppm between the 5th and 8th weeks after were >1.8 for all DNA extracts. Extracted DNA was freeze-dried and used weaning, and 25 ppm after the 8th week (regulation EU 2018/1039). for 16S rRNA gene amplicon sequencing (Section 2.3) and high- The animal growth-promoting effects of Cu and the underlying mecha- throughput qPCR (Section 2.4). nisms have recently been reviewed (Espinosa and Stein, 2021). Copper ions exist in two states: oxidized cupric (Cu2+) and reduced cuprous (Cu+; 2.3. 16S rRNA gene amplicon sequencing and data processing Linder and Hazegh-Azam, 1996). Divalent copper (II) sulfate (CuSO4) is a common growth promoter in pig diets (Park and Kim, 2016), but monova- The V4 region of the bacterial 16S rRNA gene was amplified by PCR lent copper oxide (Cu2O) also improve the growth performance of poultry using the modified forward primer pair 515F (GTGYCAGCMGCCGCGGTA (Hamdi et al., 2018; Forouzandeh et al., 2021) and swine (Blavi et al., A) and 806R (GGACTACNVGGGTWTCTAAT) (Walters et al., 2016). A 2021; Forouzandeh et al., 2022). Importantly, the two latter studies showed small-fragment library was constructed, followed by paired-end sequencing that CuSO4 and Cu2O exerted differential effects on several animal perfor- using the Novaseq PE250 at Novogene Bioinformatics Technology Co. Ltd. mance indicators, but it is presently not known if the ability of Cu to co- (UK). The 16S rRNA gene amplicon sequences were processed using the select ARGs depends on the metal sources being used. To shed light on DADA2 pipeline (Callahan et al., 2016) in R version 3.6.3. Default settings this issue, we analyzed fecal samples collected from a previously reported ex- were used for filtering and trimming. Identical sequencing reads were com- periment comparing the effects of CuSO4 and Cu2O on swine growth promo- bined using the dereplication function. Paired-end reads were merged, chi- tion during a 116-day feeding trial (Blavi et al., 2021; Forouzandeh et al., meras were removed, an amplicon sequence variant (ASV) table was 2022). Our primary objective was to determine the effects of the two sources constructed, and taxonomy was assigned using the RDP trainset 16/release of dietary Cu on the swine gut microbiome and antibiotic resistome as 11.5 (Callahan et al., 2017). A phylogenetic tree was constructed using the assessed by 16S rRNA gene amplicon sequencing and high-throughput “DECIPHER” (Wright, 2015) and the “phangorn” R packages. DADA2 data qPCR (HT-qPCR), respectively. In order to assess the selective pressures im- outputs were combined into a phyloseq object using the “phyloseq” pack- posed by the two forms of dietary Cu, we further measured Cu bioavailability age for further analysis. using a whole-cell bacterial bioreporter assay. In addition to being the first study comparing effects of two contrasting sources of Cu in high-Cu diets 2.4. High-throughput qPCR and data processing on antibiotic resistance in pigs, our study is also the first to specifically exam- ine impacts of high-Cu diets on the swine gut antibiotic resistome using a bac- HT-qPCR reactions were performed by the Wafergen SmartChip Real- terial community-wide, cultivation-independent approach. time PCR system, using a total of 384 validated primer sets targeting 319 2 J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 ARGs and 57 MGEs, as previously described (Stedtfeld et al., 2018). Out of and time points with the function betadisper. Procrustes test for correlation the 96 fecal samples analyzed, a total of 20 samples could not be included in analysis between bacterial communities and ARG/MGE composition based the HT-qPCR analysis due to a technical error during HT-qPCR analysis and on NMDS ordinations was completed with the protest function. Mantel's test insufficient amounts of DNA to repeat the analysis. As a consequence, be- using Spearman's rank correlation was used to further analyze the links be- tween five and eight replicates per treatment group were available at tween bacterial community structure and ARG/MGE profiles using the each time point. mantel function (Legendre and Legendre, 1998). Heatmap was produced All qPCR reactions were performed in technical duplicates and for each with the “pheatmap” R package. Significant differences in the relative run, a non-template control was included. A threshold cycle (CT) of 31 was abundance of ARG or MGE classes (grouped according to classes of antibi- used as the detection limit for the individual PCR reactions. CT values otics that they confer resistance to or the type of MGE that they encoded) higher than 31 were set to 0 and only genes detected in both duplicates were assessed by one-way Analysis of Variance (ANOVA) combined with were regarded as positive. Relative gene copy numbers were calculated Dunnett's test for pairwise comparisons (Dunnett, 1955). The ANOVA test with the formula: relative gene copy numbers = 10(31-Ct)/(10/3) as described was performed using the anova function and the glht function of in previously (Looft et al., 2012), where Ct refers to HT-qPCR results and 31 “multicomp” package in R. To investigate significant differences in relative refers to the detection limit. HT-qPCR data were normalized among sam- abundance of individual genes across treatments groups, Kruskal-Wallis ples by dividing the relative gene copy numbers by the corresponding rank sum test was conducted using the kruskal.test function of the “stats” 16S rRNA gene copy numbers. Cell-specific ARG copy numbers were calcu- R package (Kruskal and Wallis, 1952). Significant Spearman's rank correla- lated assuming an average of four 16S rRNA genes per bacterium tions between the relative abundance (genes/16S rRNA gene) of individual (Klappenbach et al., 2001). ARGs/MGEs and the relative abundance (%) of bacterial taxa at the Genus level were determined using the associate function of the Microbiome pack- 2.5. Total and bioavailable Cu in fecal samples age. P-values were adjusted using the Benjamin-Hochberg procedure, and only correlations with a p-value below 0.01 and a correlation coefficient Total Cu ([Cu]total) in fecal samples was analyzed using inductively above 0.5 or below −0.5 were considered statistically robust. Network coupled plasma-optical emission spectrometry as described previously analysis was carried out using Cytoscape version 3.8.2 with CoNet plugin (Blavi et al., 2021). as previously described (Hu et al., 2017). ANOVA was used to examine dif- Bioavailable Cu ([Cu]bio) was determined in fecal samples (n = 18) col- ferences in fecal Cu content ([Cu]total) and the fraction of [Cu]bio among lected at the end of the experiment (d 116) using a whole-cell bacterial samples from the different treatments. bioreporter assay based on two isogenic, bioluminescent strains of Pseudo- monas fluorescens (Tom-Petersen et al., 2001; Nybroe et al., 2008). In 3. Results brief, 0.2 g feces samples were mixed with 1 ml Milli-Q water in a 5 ml Fal- con tube and incubated for 2 h on a horizontal shaker (200 rpm, 22 °C). 3.1. Total and bioavailable Cu in feces Subsequently, the samples were centrifuged (10.000 g, 22 °C, 10 min) and supernatants were collected and stored at −20 °C for later bioreporter anal- The fecal Cu content ([Cu]total) in samples obtained from both Cu treat- ysis. [Cu]bio was operationally defined as Cu species that were able to in- ment groups was 8–10 times higher than in the corresponding control sam- duce expression of Cu-regulated luxAB genes in P. fluorescens DF57-Cu15 ples (P < 0.001), confirming the differences in Cu concentration in dietary bioreporter cell suspensions within a 1.5 h incubation period (Brandt treatments (Table 1). In addition, [Cu]total levels were slightly higher for the et al., 2008). P. fluorescens DF57-40E7 bioreporter cells with constitutive CuSO4 group compared with the Cu2O group (P < 0.05; Table 1), but we expression of luxAB genes were used as a reference bioreporter strain also notice considerable replicate-to-replicate variability for these data indi- allowing for corrections of sample matrix effects (e.g. masking of emitted cating some heterogeneity. The level of bioavailable Cu ([Cu]bio) was at light) as described in detail previously (Brandt et al., 2008). least 670–1000 fold higher in fecal samples obtained from the two Cu treat- ments compared with the corresponding control (P < 0.05), but [Cu]bio 2.6. Statistical analyses levels were not significantly different between the two Cu treatments (ANOVA and Tukey's test, P = 0.453; Table 1). In control samples, [Cu] −1 All statistical analysis and data exploration were completed in R version bio was below the detection limit of the assay (<0.0007 μg g ). The rela- 3.6.3. Data from pigs of the same treatment and sampling time point were tive Cu bioavailability (i.e. [Cu]bio/[Cu]total) was not different between grouped to investigate potential Cu effects on the bacterial community the two Cu treatments (ANOVA and Tukey's test, P = 0.114). structure and ARG/MGE composition. Bacterial community composition was examined with “phyloseq” and “ggplot2” R packages (Callahan et al., 3.2. Impacts of dietary Cu and pig growth stage on bacterial community 2016; Wickham, 2016). Alpha diversity of samples was displayed using composition the plot_richness function with Chao1 and Shannon measures, and statistical differences between treatment groups and time points were assessed by After quality filtering, a total of 10,899,893 paired-end 250-bp se- Wilcoxon rank-sum test. Beta diversity of samples was displayed using quences were acquired from all fecal samples (n = 96), ranging between non-metric multidimensional scalings (NMDS) based on Bray-Curtis dissim- 47,132 to 137,805 sequences per sample. Sequences were clustered into ilarity metrics using the function plot_ordination. Indicator genera 9682 ASVs assigned to 48 different phyla and 704 genera. Phylogenetic (Streptococcus, Lactobacillus, and Bifidobacterium) affected by dietary Cu treatments were selected based on previous studies (Højberg et al., 2005; Table 1 Muurinen et al., 2021; Zhang et al., 2019), and the relative abundance Bioavailable Cu ([Cu]bio) determined with Pseudomonas fluorescens bioreporter as- was assessed between treatment groups. Differential abundance of ASVs say in fecal samples (n = 18) from pigs allocated to different dietary Cu treatments: was evaluated among the different treatment groups using analysis of com- control (no Cu supplementation), CuSO4 (copper sulfate, 250 μg Cu g−1 feed), or positions of microbiomes with bias correction (ANCOM-BC) as described Cu2O (monovalent copper oxide, 250 μg Cu g−1 feed). The total fecal Cu content by Lin and Peddada (2020). Permutation multivariate analysis of variance ([Cu]total) determined using inductively coupled plasma-optical emission spectrom- etry is shown. Means ± standard deviations are shown. (PERMANOVA) was used to investigate differences in bacterial community composition or ARG/MGE compositions among treatment groups and time Treatment [Cu]bio (μg g−1) [Cu]total (μg g−1) [Cu]bio/[Cu]total (%) –4a points with the function adonis (Anderson, 2001). ARG/MGE profiles were Control <7 × 10 295 ± 74 <2.49 × 10−4 analyzed using “vegan” R package (Oksanen et al., 2019). Principal coordi- CuSO4 0.47 ± 0.25 2900 ± 438 0.016 ± 0.007 Cu2O 0.70 ± 0.50 2356 ± 357 0.029 ± 0.019 nates analysis (PCoA) was performed using Bray-Curtis dissimilarity met- rics to display dissimilarities of ARG/MGE compositions across treatments a [Cu]bio of control samples was below the detection limit (<7 × 10−4 μg g−1). 3 J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 Fig. 1. Boxplots showing alpha diversity (Chao1 and Shannon Index) of the bacterial community across all fecal samples (n = 96). Communities grouped by treatment (dietary Cu): Control, CuSO4 (copper sulfate, 250 μg g−1), and Cu2O (monovalent copper oxide, 250 μg g−1) during gut microbiome maturation (days post-weaning): A. 26, B. 61, C. 96, and D. 116. E. Communities grouped by degree of gut microbiome maturation (days post-weaning): 26, 61, 96, and 116. Boxes represent the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively), and the horizontal line inside the boxes defines the median. Whiskers show the lowest and highest values within 1.5 times the IQR from the first and third quartiles. Every sample is represented by a colored dot. Dots beyond the end of the whiskers are outliers. Solid lines and asterisks indicate a significant difference between groups. The level of significance is indicated as follows: Wilcoxon rank- sum test; *** P < 0.001, * P < 0.05. 4 J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 analysis of 16S rRNA gene amplicon sequences showed that the swine gut Correspondingly, the relative abundances of Streptococcus, Lactobacillus, and microbiome was dominated by Firmicutes and Bacteroidetes phyla among Bifidobacterium ASVs were consistently reduced in response to both Cu treat- all treatments and time points (Fig. S1). At a higher phylogenetic resolu- ments compared with the control (ANCOM-BC, P < 0.05). In contrast, the rel- tion, Prevotella spp. was consistently one of the most abundant genera rep- ative abundances of different ASVs including Alloprevotella, Blautia, and resenting up to 30 % of all classifiable bacteria (Fig. S2). Eubacterium hallii were increased in response to the Cu treatments at certain Species richness estimator (Chao1) or Shannon diversity indices indi- time points. cated that alpha diversity of the gut bacterial community was not affected by the two Cu treatments at any time point. Shannon diversity index re- 3.3. Impacts of dietary Cu and pig growth stage on ARGs and MGEs vealed significant differences between the two Cu treatments 96 days after the start of the experiment and between the CuSO4 group and the con- In total, 193 unique ARGs and 39 MGEs were detected by HT-qPCR chip trol group 116 days after the start of the experiment, but the differences analysis. The detected ARGs conferred resistance to all major classes of anti- were not consistent. By contrast, the richness and diversity of the gut bacte- biotics targeted, of which most conferred resistance to aminoglycosides (42), rial communities increased as pigs became older (Wilcoxon rank-sum test, P multidrug determinants (33), Macrolide-Lincosamide-Streptogramins B < 0.001) (Fig. 1). (MLSBs) (30), and β-lactams (27) (Fig. S7A). The most common resistance A gut microbiome maturation effect was also indicated by an altered mechanism was antibiotic deactivation, contributing to nearly half of the de- configuration of bacterial communities as pigs became older (Fig. 2). tected ARGs (Fig. S7B). Among detected MGEs, most encoded insertional se- Hence, NMDS ordinations indicated that microbiomes clustered according quences (18), plasmids (10), and transposases (8) (Fig. S7C). to the time of sampling and that community composition tended to con- No differences among treatment groups for ARG and MGE profiles were verge (less variability between samples) after 96 d consistent with a mature observed when microbiomes from all time points were compared (Fig. 4). gut microbiome developing over time. Specifically, approx. 31 % of the var- Likewise, the diversity of detected ARGs and MGEs were not different be- iance was explained by the age of the pigs (PERMANOVA, R2 = 0.307, P < tween the two Cu treatment groups, but increased between the first time 0.001) (Fig. 2). point (d 26) and later time points (d 61, 96, and 116) (P < 0.001) Cu treatments did not affect bacterial community composition at the phy- (Fig. 5A, B). lum level. Hence, NMDS ordinations displayed no clustering of replicate sam- Overall, the gut antibiotic resistome appeared unaffected by dietary Cu ples from each Cu treatment, demonstrating no marked overall effects of Cu treatments (Fig. 5). The relative abundance of ARGs (normalized to the cor- supplementation on gut bacterial community composition at any time point responding 16S rRNA gene copy number) ranged from 0.31 to 1.46 with an of the experiment (PERMANOVA, R2 = 0.019, P = 0.48) (Fig. 2; Fig. S3). In- average of 0.71, whereas the relative abundance of MGEs fluctuated from dicator genera (Streptococcus, Lactobacillus, and Bifidobacterium) were selected 0.21 to 2.70 with an average of 0.97. The most abundant ARGs conferred based on previous studies (see Section 2.6). The relative abundance of the in- resistance to aminoglycosides (primarily aph3-III, aphA3, sat4, ant6-Ia, dicator genera was reduced at most time points in both Cu treatment groups and ant6-Ib), MLSBs (primarily lnuC, ermB, ermF, and ermQ), and tetracy- when compared with the control (ANOVA, P < 0.05) (Fig. 3). This pattern clines (primarily tet44 and tet32). The relative abundance of ARGs was was confirmed using the ANCOM-BC approach, which showed differentially greater in samples collected at the first time point (d 26) compared with abundant ASVs among different treatment groups at each time point the other time points (P < 0.001). The relative abundance of MGEs was (Figs. S4–S6). In general, both Cu treatments tended to decrease the relative highly dominated by transposase encoding genes (primarily Tp614 and abundances of a wide range of ASVs during the experiment, whereas only IS613), constituting 99.7 % of all MGEs detected. To further explore the minor changes were observed when comparing the two Cu treatments. less abundant MGE groups, transposases were removed from the dataset. Fig. 2. Differences in bacterial community composition as revealed by non-metric multidimensional scaling (NMDS) ordination using Bray-Curtis dissimilarity index across all fecal samples (n = 96) based on the relative abundance of amplicon sequence variants (ASVs). Communities grouped by treatment (dietary Cu): Control (circles), CuSO4 (copper sulfate, 250 μg g−1) (triangles), and Cu2O (monovalent copper oxide, 250 μg g−1) (squares), and by the degree of gut microbiome maturation (days post-weaning): 26 (red dots), 61 (blue dots), 96 (green dots), and 116 (purple dots). 5 J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 (PERMANOVA, R2 = 0.323, P < 0.001). By contrast, the dietary Cu treat- ments could not be distinguished using PCoA, indicating that they shared similar ARG profiles (Fig. 6B). Similar patterns were observed for MGE pro- files, which changed composition over time (PERMANOVA, R2 = 0.252, P < 0.01) without being affected by dietary Cu treatments (Fig. S9). However, when comparing the relative abundance of each individual gene, a few genes were impacted (P < 0.05) by Cu treatments at the different time points (Fig. S10-S13). For instance, the genes arr3, ermX, aac(6)-ir, pAKD1-incP1, and lnuB were reduced by the Cu treatments in samples col- lected 26 d after the start of the experiment compared with the control (Kruskal-Wallis, P < 0.05). In general, different genes were impacted at dif- ferent time points, and only a few genes appeared to be consistently im- pacted throughout the trial. These genes included tetM, lnuB, and lnuA, which all were reduced by dietary Cu during multiple time points. More- over, a gene associated with enterococci (i.e., a group of bacteria containing Enterococcus and Streptococcus) was reduced by the Cu treatments through- out multiple time points, which corresponds to the data obtained for the Streptococcus genus by the 16S rRNA gene amplicon sequencing. We further performed network analysis to visualize co-occurrence pat- terns between individual ARGs and MGEs (Fig. S14). Networks of the differ- ent treatment groups were quite similar with only few strong (r > 0.8) and statistically significant (P < 0.05) correlations being observed. However, the networks of the control group appeared to be slightly simpler (i.e., less edges) compared to both Cu treatments. The transposase encoding gene, tnpA-5, was correlated with the beta-lactamase marker, ampC. In ad- dition, the IS element, IS630, correlated with the tetracycline resistance gene, tetD. These correlations between antibiotic resistance determinants indicate the potential for horizontal transfer of ARGs in the swine gut. 3.4. Linkage between bacterial community composition and ARGs/MGEs Procrustes analysis was used to examine the correlation between ARG and MGE profiles generated from HT-qPCR data and bacterial community composition as revealed by 16S rRNA gene amplicon sequencing analysis. ARG profiles were correlated with bacterial community composition (Pro- crustes sum of squares M2 = 0.82, R2 = 0.42, P = 0.0001, 9999 free per- mutations) (Fig. S15A). MGE profiles were also correlated with bacterial community composition (Procrustes sum of squares M2 = 0.95, R2 = 0.24, P = 0.03, 9999 free permutations), but not as clear as the ARG pro- files (Fig. S15B). A Mantel's test using Spearman's rank correlation was also performed and confirmed a correlation between the ARG profile and Fig. 3. Boxplots showing the relative abundance of indicator genera affected by the bacterial community composition based on Bray-Curtis metrics (R2 = dietary Cu treatments (Control; CuSO4, 250 μg g−1; Cu2O, 250 μg g−1) during gut 0.14, P = 0.01, 999 free permutations). For the MGE profile, Mantel's test microbiome maturation (26, 61, 96, and 116 d post-weaning). A. Streptococcus, B. did not confirm a correlation with bacterial community structure (R2 = Lactobacillus, or C. Bifidobacterium. Boxes represent the interquartile range 0.01, P = 0.42, 999 free permutations). between the first and third quartiles (25th and 75th percentiles, respectively), and Spearman's rank correlation analysis revealed positive and negative cor- the horizontal line inside the boxes defines the median. Whiskers show the lowest relations (P < 0.05) among bacterial genera and ARGs/MGEs in the swine and highest values within 1.5 times the IQR from the first and third quartiles. Outliers are represented by black dots. One-way ANOVA was used to evaluate gut (Fig. S16–17). A positive correlation (P < 0.05) in occurrence of differences in relative abundance between samples of different treatment groups Bifidobacterium and the tetracycline resistance gene, tetM, was observed. at each time point (p-values are shown). This corresponds well with the other data, indicating that Cu treatments re- duce the abundance of Bifidobacterium, and thereby, also the tetM gene. In addition, Streptococcus and Lactobacillus were positively correlated (P < The relative abundance of genes encoding insertional sequences (primarily 0.05) with the lincomycin resistance gene, lnuA. In addition, Escherichia IS91, IS26, and IS1247), plasmids (primarily trb-C), and integrases (primar- and Shigella genera were highly correlated (P < 0.05) with numerous mul- ily intl2) varied between approx. 6 × 10−4 and 9 × 10−3 throughout the tidrug resistance genes (acrB, acrF tolC, IS26, mdtA, mdtE, mdtH, sugE). trial, but no Cu treatment effects were observed (Fig. S8). When normalized against the number of bacterial cells, the relative ARG copy number ranged 4. Discussion between 1.24 and 5.84, and averaged 2.84 ARGs per cell. For MGEs (pri- marily transposases), the relative copy number ranged between 0.84 and 4.1. Dietary Cu supplements had a marginal impact on the gut microbiome 10.8 with an average of almost 4 MGEs per cell. The composition of the antibiotic resistome changed (P < 0.05) during To the best of our knowledge, we here report the first controlled longi- gut microbiome maturation (Fig. 6). Hence, irrespective of dietary Cu treat- tudinal study examining the impacts of two contrasting Cu sources on the ment, PCoA ordination demonstrated that resistomes from different time swine gut bacterial community and resistome. The microbiomes were dom- points clustered together (PCoA ordination), indicating that the ARG com- inated by ASVs belonging to the phyla Firmicutes and Bacteroidetes and the position changed during gut microbiome maturation (Fig. 6A). The age of genus Prevotella spp., in accordance with previous studies on the swine pigs explained approximately 32 % of the variance in the ARG composition gut (Kim et al., 2011; Lamendella et al., 2011; Niu et al., 2015). The 6 J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 Tp614 5 IS613 lnuC tet.44. 4 aph3.III tet32 ermB 3 ermQ erm.F. ant.6..Ib 2 ant.6..Ia aphA3 sat4 1 aac.6...im lnuA tetM 0 aacC4 tetB.P. lnuB qepA_1_2 ere.A. mefA trb.C czcA aadA17 aadA_99 tnpA.6 tetA.P. aac.3..Via erm.36. blaROB TN5 aadA5 NDM.new copA dfrA22 beta_ccra aadB pAKD1.IncP.1. vanA aac.6...ir cphA ermX terW mexB pikR2 vanC2_vanC3 blaCTX.M.1_3_15 IncN_korA vanHB mobA vanRB tnpA.7 ISAba3.Acineto nonmobile.blaBEL aac.6...iw dfrA15 vatB CfiA sul1.NEW marR ISEcp1 tnpA.5 ampC vat.A. mcr.1 fabK blaTEM mepA ermS IS256 aacA.aphD aac.6...Ie.APH.2....Ia.2 vanTG IS6100 spcN aph6ic aac.3..iid_iii_iif_iia_iie pcoA IS200.2 IS200.1 apmA ceoA ermO erm.35. mphA IS630 oprD vanTC vat.E. pbrT ermE aph4ib aadA7 bla.ACT aph.3....ia mdtG tetD IS1247 tetG QnrB4 blaOXY.1 APH.6..Ia IS26 IS91 mdtH mdtA acrB tolC acrF sugE mdtE_yhiU acrA intl2 pBS228.IncP.1. tetR Control_26_1 Control_26_2 Control_26_3 Control_26_4 Control_26_5 Control_61_1 Control_61_2 Control_61_3 Control_61_4 Control_61_5 Control_61_6 Control_61_7 Control_61_8 Control_96_1 Control_96_2 Control_96_3 Control_96_4 Control_96_5 Control_96_6 Control_116_1 Control_116_2 Control_116_3 Control_116_4 Control_116_5 Control_116_6 CuSO4_26_1 CuSO4_26_2 CuSO4_26_3 CuSO4_26_4 CuSO4_26_5 CuSO4_61_1 CuSO4_61_2 CuSO4_61_3 CuSO4_61_4 CuSO4_61_5 CuSO4_61_6 CuSO4_61_7 CuSO4_61_8 CuSO4_96_1 CuSO4_96_2 CuSO4_96_3 CuSO4_96_4 CuSO4_96_5 CuSO4_96_6 CuSO4_116_1 CuSO4_116_2 CuSO4_116_3 CuSO4_116_4 CuSO4_116_5 CuSO4_116_6 Cu2O_26_1 Cu2O_26_2 Cu2O_26_3 Cu2O_26_4 Cu2O_26_5 Cu2O_26_6 Cu2O_61_1 Cu2O_61_2 Cu2O_61_3 Cu2O_61_4 Cu2O_61_5 Cu2O_61_6 Cu2O_61_7 Cu2O_61_8 Cu2O_96_1 Cu2O_96_2 Cu2O_96_3 Cu2O_96_4 Cu2O_96_5 Cu2O_96_6 Cu2O_116_1 Cu2O_116_2 Cu2O_116_3 Cu2O_116_4 Cu2O_116_5 Cu2O_116_6 Fig. 4. Heatmap of most abundant genes (n = 116) related to antibiotic resistance and mobile genetic elements. The colour gradient represents the log-transformed relative gene abundance (normalized to the corresponding 16S rRNA gene copy numbers). Rows represent the results of each primer set (assay) shown on the y-axis. Columns represent fecal samples (n = 76) grouped by treatment (dietary Cu): Control, CuSO4 (copper sulfate, 250 μg g−1), and Cu2O (monovalent copper oxide, 250 μg g−1). The labels on the x-axis (i.e. Control_26_1) indicate the treatment, days post-weaning (26, 61, 96, or 116), and replicate number of the samples. Rows were clustered based on Euclidean distances. 7 J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 Fig. 5. Diversity and relative abundance of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in pig fecal samples (n = 76) derived from three dietary Cu treatments (Control; CuSO4, 250 μg g−1; Cu2O, 250 μg g−1). Fecal samples were taken 26, 61, 96, and 116 days post-weaning. A. Average number of unique ARGs; B. Average number of unique MGEs C. Average relative abundance of ARGs; D. Average relative abundance of MGEs. The relative abundance data refer to the sum of relative abundance for all targeted ARGs or MGEs, respectively. MLSB, Macrolide-Lincosamide-Streptogramin B resistance. Error bars show the standard deviation. bacterial community composition and antibiotic resistome (e.g., the rela- both Cu treatments consistent with findings from a recent in-feed metal tive abundance of ARGs and MGEs) were different among pig growth stages (Zn + Cu) supplement trial in pigs (Muurinen et al., 2021). Correspond- and microbiomes tended to converge over time, indicating gut microbiome ingly, the relative abundance of a few ARGs (tetM, lnuB, and lnuA) was con- maturation as reported previously (Kim et al., 2011; Lu et al., 2014; Niu sistently reduced in response to both Cu treatments throughout the et al., 2015). ARG profiles correlated with changes in bacterial community experiment, and these genes all correlated with the relative abundance of composition during gut microbiome maturation, indicating that bacterial the three genera (Streptococcus, Lactobacillus, and Bifidobacterium) that community composition was a determining factor for the composition of were affected by the dietary Cu treatments. the swine gut resistome. Previous swine gut microbiome studies have also Although the two Cu supplements exerted similar and rather subtle im- demonstrated correlations between phylogeny and ARGs (Looft et al., pacts on the fecal microbiomes, some differences between the two Cu treat- 2012; Munk et al., 2018), whereas other studies have indicated a partial un- ments were demonstrated in previous studies (Blavi et al., 2021; coupling between phylogenetic composition and antibiotic resistance pro- Forouzandeh et al., 2022). Hence, Cu2O was a more efficient pig growth files under selective pressures from antibiotics (Johnson et al., 2016) or promoter than CuSO4, but CuSO4 resulted in greater Cu concentrations in non-antibiotic pig growth promoters (Muurinen et al., 2021). the liver and spleen confirming different bioavailability of the two Cu Both Cu supplements consistently reduced the relative abundance of gut sources when fed to pigs. Likewise, Cu2O was more effective than CuSO4 commensal bacteria belonging to the genera Lactobacillus and Streptococcus, at reducing the relative abundance of Streptococcaceae and Lactobacillaceae which has also been reported in previous studies with CuSO4 (Højberg when fed to broiler chickens, (Forouzandeh et al., 2021). Strongly contrast- et al., 2005; Zhang et al., 2019). In addition, the relative abundance of ing effects of mono- and divalent Cu forms in bacteria are well-known from ASVs belonging to the genus Bifidobacterium also decreased in response to in vitro studies, and monovalent Cu inhibit growth and deplete populations 8 J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 Fig. 6. Differences in gut bacterial resistome composition as revealed by principal coordinates analysis (PCoA) of the relative abundance (normalized to the corresponding 16S rRNA gene copy numbers) of antibiotic resistance genes (ARGs) using Bray-Curtis dissimilarity index. A. Resistomes grouped by degree of gut microbiome maturation (days post-weaning): 26 (black circles), 61 (red triangles), 96 (green plus signs), and 116 (blue crosses). B. Resistomes grouped by treatment (dietary Cu): Control (green crosses); CuSO4, 250 μg g−1 (red triangles); Cu2O, 250 μg g−1 (black circles). of E. coli and Staphylococcus aureus much more effectively than divalent Cu antibiotic resistance in pig farming, of which pathogens belonging to En- (Saphier et al., 2018; Popov et al., 2020). However, the differential effects terobacteriaceae tended to have more DNA-fragments with co-occurring of mono- and divalent Cu sources was expected to be much weaker in the ARGs, MRGs, and MGEs (Li et al., 2022). These results are consistent with pig gut due to the convergence of chemical Cu speciation during gut pas- results from the present experiment indicating that the relative abundance sage. This hypothesis is in accordance with results from the present study of Escherichia/Shigella was highly correlated with several multidrug resis- demonstrating that bioavailability of fecal Cu to bacteria was not different tance genes, of which acrB/F and mdtA/E/H can confer cross-resistance to- between the two sources of Cu as assessed by our P. fluorescens bioreporter wards antibiotics and metals including Cu. Transcription of these genes assay. encoding multidrug efflux pumps can be activated by the BaeRS two- component system, which is induced by Cu and Zn stress (Nishino et al., 4.2. Does Cu-based feed supplements constitute a risk for co-selection of ARGs in 2007; Nishino et al., 2005). livestock? Despite a clear co-selection potential in swine gut microbiomes (as judged from the literature), we did not observe any evidence that high die- Results of the present experiment demonstrate that swine gut tary levels of Cu did actually co-select ARGs. We propose two potential ex- microbiomes, including ARGs and MGEs, were only modestly affected by planations for this result. The first explanation has been termed ‘ecological dietary Cu treatments and that the observed changes in ARG profiles was fallacy’ and relates to the used total microbiome approach based on analysis linked to phylogeny rather than co-selection of antibiotic resistance. This of metagenomic DNA (Agga et al., 2015). The strength of the used fecal main finding is remarkable, as the two dietary Cu sources resulted in at microbiome approach is that it allows experimenters to study the total bac- least 670- or 1000-fold increased levels of bioavailable Cu in feces. Bioavail- terial community with very high community coverage, but this at the same able Cu to bacteria (i.e., P. fluorescens bioreporter) thus increased by at least time offers some limitations, as it is not possible to link the presence of spe- two orders of magnitude more than total Cu did as a result of dietary Cu cific ARGs and MGEs to specific taxa with high confidence. Hence, it is in- supplementation demonstrating a high bioavailability of Cu added to feed deed possible that Cu could have co-selected for Cu and antibiotic as CuSO4 or Cu2O. Such levels of Cu are highly likely to exert significant se- resistance in specific groups of gut bacteria in our study. On the other lection pressure for Cu resistance in gut bacteria in accordance with previ- hand, it is also possible that selection for Cu resistance may lead to en- ous research (Hasman et al., 2006; Medardus et al., 2014; Yazdankhah hanced antibiotic susceptibility in other groups of gut bacteria by a phe- et al., 2014). The current results thus indicate that gut bacterial communi- nomenon termed ‘collateral sensitivity’ (Pál et al., 2015) and such effects ties may develop Cu resistance without affecting the overall bacterial com- may also be overlooked by the used total microbiome approach. It is also munity composition in a similar fashion as previously reported for bacterial a possibility that Cu-induced changes in the antibiotic resistome may sim- communities in soil (Brandt et al., 2010). ply be a result of a Cu-induced change in the bacterial community compo- The observed inability of Cu-based growth promoters to co-select ARGs sition as different taxa tend to harbor different ARGs. (our study) is consistent with results from a related experiment investigat- The second explanation for a lack of Cu-induced co-selection of antibi- ing effects of non-antibiotic growth promoters including a feed supplement otic resistance is that the studied pig microbiomes carried high background treatment with both ZnO (2–3 g kg−1) and CuSO4 (125 mg kg−1) levels of ARGs and MGEs combined with high persistence of ARGs (Muurinen et al., 2021). However, results of these studies employing simi- (Kazimierczak et al., 2009; Looft et al., 2012; Pollock et al., 2020; lar experimental designs (controlled field experiments during one pig Muurinen et al., 2021). Hence, the previous use of antibiotics may have growth cycle) and methodologies (HT-qPCR and 16S rRNA gene amplicon supported the establishment of a high background level of antibiotic resis- sequencing) contrast with other reports indicating risk for Cu- and/or Zn- tance in the swine gut microbiomes giving rise to legacy effects from previ- induced co-selection of antibiotic resistance in the pig gut and other envi- ous generations of pigs. High background levels of antibiotic resistance in ronments as reviewed previously (Yazdankhah et al., 2014; Wales and swine may also explain why only limited or transient effects of antibiotic Davies, 2015; Poole, 2017). Several studies have demonstrated co- treatment on the gut microbiome have been observed in some previous ex- selection of resistance to Cu and antibiotics in specific groups of pig gut bac- periments (Holman and Chénier, 2014; Pollock et al., 2020). Antibiotic re- teria. It is also well-known that some plasmids contain genes conferring re- sistance is typically associated with a fitness cost and in the absence of sistance to both Cu and antibiotics and such plasmids are enriched in selection pressure, this should in principle benefit the antibiotic- bacteria of fecal origin relative to other environments (Pal et al., 2015; susceptible bacteria enabling them to outcompete the resistant bacteria Fang et al., 2016). Moreover, a recent high-throughput metagenomic over time (Andersson and Hughes, 2010). However, antibiotic resistance study in pigs indicated that metals contributed to the maintenance of at the community level can be highly persistent for a number of reasons 9 J.E. Brinck et al. Science of the Total Environment 857 (2023) 159609 such as fitness-compensatory evolution, cost-free adaptive mutations, and Appendix A. Supplementary data co-selection of ARGs (Andersson and Hughes, 2010). Even if antibiotic re- sistance comes with a fitness cost, antibiotic-susceptible bacteria may not Supplementary data to this article can be found online at https://doi. always be able to replace resistant populations belonging to the same spe- org/10.1016/j.scitotenv.2022.159609. cies, if the latter has been already firmly established in the gut due to prior- ity effects resulting in colonization resistance (Segura Munoz et al., 2022). References 5. Conclusions and perspectives Agga, G.E., Scott, H.M., Vinasco, J., Nagaraja, T.G., Amachawadi, R.G., Bai, J., Norby, B., Renter, D.G., Dritz, S.S., Nelssen, J.L., Tokach, M.D., 2015. Effects of chlortetracycline The swine gut microbiome is of significant concern for public health as and copper supplementation on the prevalence, distribution, and quantity of antimicro- the transmission of antibiotic resistant bacteria (including pathogens) to bial resistance genes in the fecal metagenome of weaned pigs. Prev. Vet. 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Forouzandeh, A., Blavi, L., Abdelli, N., Melo-Duran, D., Vidal, A., Rodríguez, M., Monteiro, A., Pérez, J., Darwich, L., Solà-Oriol, D., 2021. Effects of dicopper oxide and copper sulfate Declaration of competing interest on growth performance and gut microbiota in broilers. Poult. Sci. 100, 101224. https://doi.org/10.1016/j.psj.2021.101224. Alessandra Monteiro is an employee at Animine. The European Code of Forouzandeh, A., Blavi, L., Pérez, J.F., D'Angelo, M., González-Solé, F., Monteiro, A., Stein, H.H., Solà, D., 2022. How copper can impact pig growth: comparing the effect of copper Conduct for Research Integrity is followed by Animine (Drenth, 2010). Laia sulfate and monovalent copper oxide on oxidative status, inflammation, gene abundance, Blavi is now an AB Neo employee. There are no conflicts of interest among and microbial modulation as potential mechanisms of action. J. Anim. Sci. https://doi. the other authors. org/10.1093/jas/skac224. 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