Characterization of Antibiotic Resistance Genes in the Species of the Rumen Microbiota

Home , Acetitomaculum, Acetitomaculum ruminis, Butyrivibrio, Clostridium intestinale, Lachnospiraceae, Methanomicrobium

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https://doi.org/10.1038/s41467-019-13118-0 OPEN Characterization of antibiotic resistance genes in the species of the rumen microbiota

Yasmin Neves Vieira Sabino1, Mateus Ferreira Santana1, Linda Boniface Oyama2, Fernanda Godoy Santos2, Ana Júlia Silva Moreira1, Sharon Ann Huws2* & Hilário Cuquetto Mantovani 1*

Infections caused by multidrug resistant bacteria represent a therapeutic challenge both in clinical settings and in livestock production, but the prevalence of antibiotic resistance genes

1234567890():,; among the species of bacteria that colonize the gastrointestinal tract of ruminants is not well characterized. Here, we investigate the resistome of 435 ruminal microbial genomes in silico and confirm representative phenotypes in vitro. We find a high abundance of genes encoding tetracycline resistance and evidence that the tet(W) gene is under positive selective pressure. Our findings reveal that tet(W) is located in a novel integrative and conjugative element in several ruminal bacterial genomes. Analyses of rumen microbial metatranscriptomes confirm the expression of the most abundant antibiotic resistance genes. Our data provide insight into antibiotic resistange gene profiles of the main species of ruminal bacteria and reveal the potential role of mobile genetic elements in shaping the resistome of the rumen microbiome, with implications for human and animal health.

1 Departamento de Microbiologia, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. 2 Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, UK. *email: [email protected]; [email protected]

NATURE COMMUNICATIONS | (2019) 10:5252 | https://doi.org/10.1038/s41467-019-13118-0 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13118-0

ithout immediate action to tackle the current and ResFinder13, Resfams14, and ARG-ANNOT15 varied according to escalating threat of antimicrobial resistance (AMR), it differences in specificity, sensitivity, and search method used by W fi is estimated that resistant infections may kill one each computational tool. ResFinder identi ed a total of 141 person every 3 s by the year 2050, raising the death toll worldwide acquired ARGs distributed in 72 rumen microbial genomes and to 10 million annually1. This antibiotic-resistance crisis can be across 11 antibiotic classes (Table 1). ARG-ANNOT detected 754 linked to several issues, including the overuse of these compounds genes from 10 distinct antibiotic classes in 93 genomes of rumen for medical and agricultural purposes, inappropriate antibiotic bacteria, while Resfams predicted 3148 sequences related to the prescribing, and limited discovery/development of novel and resistance of 9 classes of antibiotics in 430 rumen microbial effective antibiotics2. The widespread use of antibiotics in the genomes (Table 1; Supplementary Fig. 1). The most abundant agricultural sector plays an underrepresented role in the AMR ARGs detected by all three bioinformatic tools were related with context. Large amounts of antimicrobials are frequently used in resistance to beta-lactams (726 genes), glycopeptides (510), tet- livestock to prevent diseases and promote animal growth3. More racycline (307), and aminoglycosides (193). than 2 million kilograms of medically important antibiotics were Resfams detected the majority of the aminoglycosides and sold in the USA for use in cattle in 2017, which corresponded to glycopeptides-resistance genes, while ARG-ANNOT identified 42% of the total used in food-producing animals, and ruminants the most beta-lactam-resistance genes in the rumen bacterial have been recognized as a potential reservoir of antibiotic- genomes. Fluoroquinolone, fosfomycin, nitroimidazole, and resistance genes (ARGs)4,5. trimethoprim were the antibiotic classes with the lowest number The rumen ecosystem is colonized by a complex and genetically of ARGs identified using ResFinder, Resfams, and ARG-ANNOT. diverse microbiota, in which dense populations of microbes often These putative ARGs were more prevalent among members of the exist in close proximity within biofilms6. Therefore, mechanisms phyla Firmicutes, Proteobacteria, Bacteroidetes, and Actinobac- that lead to horizontal acquisition of novel genes could provide teria, but their abundances varied according to the bioinformatic competitive advantage for resource competition and facilitate the tool used for ARG detection (Table 1; Supplementary Fig. 2). exchange of genetic material between members of the rumen microbiota and also with allochthonous species that occupy the same site in the ruminant gastrointestinal tract (GIT)7–10. In addi- Phylogenetic distribution of ARGs. Although ARGs were widely tion, feeding antimicrobials through ruminant diets can select for distributed across the genomes of ruminal bacteria (Fig. 1), resistant organisms, potentially modifying the autochthonous resistance to specific antibiotic classes were more prevalent in ruminal microbiota11.Thesefindings corroborate with recent ana- some bacterial taxa, especially at the family and genus level. lyses of the ovine rumen resistome, which identified resistances to 30 known antibiotics, including high abundance of genes for dap- Table 1 In silico detection of ARGs in ruminal microbial 5 tomycin and colistin resistance, two clinically relevant antibiotics . genomes Ruminants represent a major source of animal protein for human consumption worldwide (through milk and meat pro- General features ResFinder ARG- Resfams duction), and cattle are raised in close proximity with humans, ANNOT such as in rural areas, particularly in lower-middle-income ARGs detected 141 754 3148 nations, where a great proportion of the livestock farms are small, Genomes with ARGs 72 93 430 family-owned properties. Nonetheless, little is known about the ARGs by antibiotic class/ occurrence and distribution of ARGs among the bacterial species function of the core rumen microbiome. Recently, hundreds of reference Aminoglycosides 10 26 157 genomes of cultured ruminal bacteria and archaea have been Beta-lactams 11 446 269 made available through the Hungate Project (http://www. Colistin 0 0 0 hungate1000.org.nz/), representing ~75% of the genus-level bac- Fosfomycin 2 2 0 terial and archaeal taxa present in the rumen12. We therefore Fluoroquinolones 4 4 2 hypothesized that genome mining of the Hungate genomic Fusidic acid 0 0 0 resources could reveal the distribution and genetic context of Glycopeptides 22 76 412 MLS 22 44 44 ARGs within major ruminal species represented in the core Nitromidazole 3 0 0 rumen microbiome. Oxazolidinone 0 0 0 As such we analyzed 435 genomes of ruminal bacteria and Phenicols 3 10 19 archaea to search for ARGs using different computational Rifampicin 0 0 0 approaches. Identified ARGs were also evaluated for selective Sulfonamides 2 2 0 pressure, genetic organization, association with mobile genetic Tetracyclines 61 130 116 elements, as well as their expression in different rumen meta- Trimethoprim 1 14 0 transcriptome data sets. The resistant phenotype was confirmed for ABC efllux ––1749 fl –– some cultured representative ruminal bacteria from the sequenced RND e lux 165 Othersa ––215 Hungate1000 collection using in vitro testing and a novel inte- ARG’s by taxonomic category grative and conjugative element (ICE) associated with tetracycline = fi Actinobacteria (n 32) 7 11 164 resistance was identi ed in the genomes of rumen bacteria. Our Bacteroidetes (n = 50) 30 67 199 findings shed light on the distribution and frequency of ARGs Fibrobacteres (n = 2) 0 0 4 among the main species of bacteria colonizing the rumen, thereby Firmicutes (n = 313) 89 233 2502 improving our understanding of the mechanisms underlying Fusobacteria (n = 1) 0 0 2 antibiotic resistance within this microbial ecosystem. Proteobacteria (n = 22) 15 443 244 Spirochetes (n = 5) 0 0 27 Euryarchaeota (n = 10) 0 0 6

Results MLS Macrolides, Lincosamides, Streptogramins Detection of ARGs in ruminal microbial genomes. The number aARGs that could not be ranked in any of the above antibiotic classes/functions; (−) these and classes of ARGs detected in rumen microbial genomes using ARGs are not included in the ResFinder and ARG-ANNOT data sets

2 NATURE COMMUNICATIONS | (2019) 10:5252 | https://doi.org/10.1038/s41467-019-13118-0 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13118-0 ARTICLE

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Fig. 1 Antibiotic-resistance distribution in ruminal microbes. The 16S rRNA gene sequences used to construct the tree were obtained from the Hungate1000 collection and aligned using RDP Aligner. The phylogenetic tree was generated with the Maximum-Likelihood method using FastTree (1000 replicates), and visualized and annotated using iTOL. Black circles outside the tree represent the genomes, in which ARGs were identiﬁed from at least two bioinformatics tools applied in this study

Members of the Proteobacteria phylum showed a high proportion rRNA gene phylogeny (Fig. 2). Majority of the genomes (69.2%) of genomes harboring resistance genes, with ARGs being detected showed resistance to one antibiotic class, with a dominance of in all genomes of the Enterobacteriaceae family. In the Bacter- tetracycline-resistance genes, which were distributed in bacterial oidetes and Actinobacteria phyla, ARGs were concentrated in the genomes within the phyla Actinobacteria, Firmicutes, Proteobac- genus Bacteroides and Bifidobacterium, respectively. In the Fir- teria, and Bacteroidetes. Phylogenetic analysis of the most micutes phylum (which corresponded to the largest number of abundant tetracycline-resistance genes (tet(W), tet(Q), and tet genomes analyzed in this study), resistance genes were detected (O)) revealed that tet(W) was highly conserved across different across members of the Lachnospiraceae (Lachnosclostridium, taxa of ruminal bacteria, with a minimum sequence identity of Clostridium, and Blautia) and Veillonellaceae (Selenomonas, 94.9 and 99% coverage in at least 28 ruminal bacterial genomes Megamonas, and Megasphaera) families, as well as in all species of (Supplementary Figs. 3–5). the genus Enterococcus and among strains of Staphylococcus Moreover, as shown in Fig. 2, metronidazole and fosfomycin epidermidis. No antibiotic-resistance genes were detected in resistance were only identified in the genus Prevotella and in two genomes belonging to the Spirochetes, Fibrobacteres, Fusobacteria, strains of Staphylococcus epidermidis, respectively. Staphylococcus and Euryarchaeota phyla using Resfinder and ARG-ANNOT, but epidermidis together with Citrobacter sp. NLAE-zl-C269 were the it should be noted that the number of genomes representing these only species harboring resistance genes to quinolones. Escherichia taxa were much lower compared with the other phyla analyzed in coli PA-3 was the single ruminal genome showing the highest this study. Although Resfams detected some potential ARGs in number of ARGs (n = 5), including tetracycline resistance, these microbial taxa, the majority were ABC efflux pumps. The beta-lactam resistance, aminoglycoside resistance, trimethoprim ABC efflux pumps represent one of the largest protein families in resistance, and sulfonamide resistance. Bacteroides ovatus NLAE- microorganisms, contributing not only to reduce the intracellular zl-C500 was the only species in Bacteroidetes-possessing genes for concentrations of toxic compounds but also to the influx of sulfonamide resistance. Beta-lactam-resistance genes were con- substrates and other nutrients, and are not necessarily related to centrated in the family Enterobacteriaceae, in the genus antibiotic resistance16. Bacteroides, and in two clades harboring the genera Selenomonas, The distribution of the ARGs in ruminal microbial genomes Staphylococcus, Succiniclasticum, and Bacillus. Proteus mirabilis was represented by the antibiotic class and according to the 16S NLAE-zl-G534 and Proteus mirabilis NLAE-zl-G285 shared the

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Fig. 2 Distribution of ARGs in species of ruminal bacteria. The distribution of the resistance genes detected by at least two bioinformatic tools used in this study is presented in front of the tree as ﬁlled colored boxes. The 16S rRNA gene sequences used to construct the tree were obtained from the Hungate1000 collection and aligned using RDP Aligner. The tree was generated with the Maximum-Likelihood method using FastTree (1000 replicates) and visualized and annotated using iTOL. Only bootstrap values >0.7 are shown

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a Blautia schinkii DSM 10518

5000 10,000 15,000 20,000 25,000

Soj SpoJ VirD4 TetW VirD1 RepA Dnal VirB6 TraE ATPase TnpW Sigma 70 protein Mobilization Recombinase Recombinase Phage proteins Endonuclease Methyltransferase Methyltransferase

Transcriptional regulator Acessory genes Regulation

Mobilization Tetracycline resistance

Recombination Unknown function

b 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10,000 10,500 11,000 11,500 12,000 12,500 13,000 13,500 14,000 14,500 15,000 15,500 16,000 16,500 17,000 17,500 18,000 18,500 19,000 19,500 20,000 20,500 21,000

Blautia schinkii DSM10518.fasta –2000 –1500 –1000 –500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10,000 10,500 11,000 11,500 12,000 12,500 13,000 13,500 14,000 14,500 15,000 15,500 16,000

Butyrivibrio hungatei DSM14810.fasta –1000 –500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10,000 10,500 11,000 11,500 12,000 12,500 13,000 13,500 14,000 14,500 15,000 15,500 16,000

Lachnoclostridium aminophilum DSM10710.fasta –3000 –2500 –2000 –1500 –1000 –500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10,000 10,500 11,000 11,500 12,000 12,500 13,000 13,500 14,000 14,500 15,000 15,500 16,000 16,500 17,000 17,500 18,000

Lachnoclostridium citroniae NLAE-zl-G70.fasta –4500 –4000 –3500 –3000 –2500 –2000 –1500 –1000 –500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10,000 10,500

Lachnoclostridium lavalense NLAE-zl-G277.fasta –3500 –3000 –2500 –2000 –1500 –1000 –500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10,000 10,500 11,000 11,500 12,000 12,500 13,000 13,500 14,000 14,500 15,000 15,500 16,000

Pseudobutyrivibrio sp. NOR37.fasta –2500 –2000 –1500 –1000 –500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000 9500 10,000 10,500 11,000 11,500 12,000 12,500 13,000 13,500 14,000 14,500 15,000 15,500 16,000

Pseudobutyrivibrio sp. OR37.fasta

Fig. 3 ICE_RbtetW_07 structure and conservation. a ICE_RbtetW_07 structure in Blautia schinkii DSM 10518. The genes of the ICE are represented by arrows classified and colored in modules according to their function. The essential modules of the ICE machinery are represented in orange, blue, and pink, for mobilization, recombination, and regulation genes, respectively. The ICE is located in scaffold 17 (nucleotide positions from 88079 to 111915) of the Blautia schinkii DSM 10518 genome. b Alignment of the ICE_RbtetW_07 from ruminal bacterial genomes Blautia schinkii DSM 10518, Butyrivibrio hungatei DSM 14810, Lachnoclostridium aminophilum DSM 10710, Lachnoclostridium citroniae NLAE-zl-G70, Lachnoclostridium lavalense NLAE-zl-G277, Pseudobutyrivibrio sp. NOR37, and Pseudobutyrivibrio sp. OR37. Lines connecting blocks with identical colors represent the aligned regions, and show synteny or gene rearrangements. The tet(W) gene is represented in blue in the center of the figure same pattern of resistance to tetracycline and phenicol, while involved in bacterial conjugation, such as relaxases, plasmid Staphylococus epidermidis AG42 and Staphylococus epidermidis mobilization proteins (e.g., MobC), and conjugation proteins NLAE-zl-G239 harbored highly homologous resistance genes for (e.g., TraE, TraG/TraD). In addition, genes enconding phage beta-lactams, quinolones, and fosfomycin. proteins (e.g., PhiRv2) were detected in the flanking regions of the tet(W) genes in ten ruminal microbial genomes (Supplementary Data 1). Nonetheless, the scaffolds carrying the tet(W) genes ARGs genetic context. The genetic locus of the tet(W) genes as lacked complete mobile elements showing the structural organi- well as their flanking regions were analyzed to investigate their zation commonly found in plasmids, phages, and transposons. mobility potential. For this, the 28 genomes harboring tet(W) A more detailed analysis of the sequences flanking the tet(W) genes detected by ResFinder were evaluated in sequences of up to gene revealed that at least seven genomes contained the complete 4000 bp within the scaffold where tet(W) was located. Genes machinery required for a functional ICE, despite the fact that no encoding a putative methyltransferase protein were detected such genetic elements were found when the bacterial genomes adjacent to or located immediately upstream or downstrem of the were screened for using ICEberg17. The genetic organization of tet(W) gene in 18 genomes. From these, the gene encoding the the ICEs identified in the genomes of ruminal bacteria, as protein under the accession number D1PK82 [https://www. exemplified for Blautia schinkii DSM 10518 (Fig. 3a), contained uniprot.org/uniprot/D1PK82] was the most prevalent, and putative genes encoding for conjugation proteins (e.g., TraE), showed 98.9–100% sequence identity across the genomes of secretion proteins (e.g., VirD4), proteins controlling the cellular ruminal bacteria. In seven of these genomes, a gene encoding the cycle (e.g., RepA, DnaI, σ70), and recombinases, in addition to Maff-2 protein, which is known to be involved in bacterial other accessory proteins commonly found associated with these resistance to tetracycline, was also found flanking the tet(W) gene. mobile elements (e.g., ATPases, methyltransferases, transposons, Genes encoding transposon proteins (e.g., TnpW, TnpV, Tnp, and phage-related proteins). The hypothesis that a novel ICE TnpX, and ISPsy9) were identified flanking the tet(W) gene in 10 (named here as ICE_RbtetW_07) could be mediating tetracycline ruminal microbial genomes, and 22 out of the 28 genomes har- resistance in the genomes of rumen bacteria was reinforced by the boring tet(W) genes also had genes encoding proteins potentially presence of conserved elements (reversed organization in three

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LNB a 5

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200 0 0 50 100 150 200 250 300 350 400 450 c Fig. 4 PCR detection of the ICE_RbtetW_07 in Blautia schinkii DSM 10518. 5 The expected amplicon size of the region from the virD4 gene to the tet(W) gene was 4794 pb. L: 1 kb HyperLadder, N: negative sample, and B: Blautia 4 schinkii DSM 10518 3 genomes) in the ICE carrying the tet(W) genes on ruminal bacterial chromosomes (Fig. 3b). Moreover, phylogenetic analyses 2 indicated that the ICE_RbtetW_07 had low sequence homology to other integrative conjugative elements previously reported 1 (Supplementary Fig. 6). 0 Detection of the ICE_RbtetW_07 in B. schinkii DSM 10518.To 0 50 100 150 200 250 300 350 400 450 500 550 600 650 confirmthepresenceoftheICEcarryingthetet(W) genes in rumen Site bacteria, Blautia schinkii DSM 10518 was grown in the batch cul- Fig. 5 Selection pressure analysis in tetracycline-resistance genes. The ture, and a fragment of 4794 bp covering the region from the virD4 d /d ratio was calculated for the tet(W) (a), tet(Q) (b), and tet(O) genes fi N S gene to the tet(W) gene was PCR ampli ed from the genomic DNA (c) using JCoDA. The lines in each panel represent dN/dS scores by site. (Fig. 4). The amplicon was subjected to shotgun sequencing per- The phylogenetic tree generated by JCoDA for dN/dS analysis was formed on the MiSeq with 300 cycles using the next-Flex Illumina reconstructed using the aligned protein sequences of the ARGs and workflow, and the assembled reads confirmed the genetic organi- Maximum-Likelihood approach with 100 replicates. Source data are zation of the ICE (type IV secretion system gene adjacent to the provided as a Source Data file tetracycline-resistance gene) predicted in the Blautia schinkii DSM – – – 10518 genome (accession number PRJNA223460, [https://www. 141 265, 310 341, and 600 639, with an average dN/dS ratio per ncbi.nlm.nih.gov/bioproject/223460]) with 100% of nucleotide site of 1.9, 2.6, and 3.1, respectively. For the tet(Q) and tet(O) identity and 99.7% of coverage (Supplementary Data 2). genes, our analysis indicated 25 and 17 amino acid sites with positive selection pressure, respectively (Fig. 5b, c). Nonetheless, tet Selection pressure analysis of resistance genes. Because the the overall dN/dS ratio calculated for these genes was <1, and the tetracycline-resistance genes tet(W), tet(Q), and tet(O) were likelihood ratio test data (LRT) for the substitution models did highly abundant and broadly distributed in the genomes of not present statistically significant difference at 95% confidence ruminal bacteria, we investigated if these genes are evolvable and level, indicating absence of positive selection for tet(Q) and tet if genetic variants are being selected for in the genomes of (O) genes. To confirm that the tet(W) gene is under positive ruminal bacteria. For this purpose, the number of non- selection, neutrality tests were applied in these ARG sequences. ’ = − = synonymous per synonymous substitutions (dN/dS ratio) was The results of Tajima s D (D 1.65)andFuandLitest(D* calculated to determine the codon substitution rate, which could −2.22. p <0.05; F* = −2.39, p < 0.05) corroborate with our indicate the evolution of the gene in ruminal microbial genomes. previous findings. Ouranalysisconfirmedthepresenceofpositiveselectionpres- sure in the tet(W) gene, with a dN/dS ratio > 1 for 36 out of the 639 amino acid residues in the protein (Fig. 5a). Sites showing Confirmation of resistance phenotypes. To validate the resis- positive selection were mainly clustered in three distinct regions tance phenotype that was computationally predicted in the gen- of the TetW protein, located between amino acids positions omes of ruminal bacteria, 26 pure cultures matching the genomes

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Bacteria BLA AMG TET VAN CHL MAC CLD Acetitomaculum ruminis DSM 5522 Bifidobacterium adolescentis DSM 20087 Bifidobacterium boum DSM 20432 Bifidobacterium merycicum DSM 6492 Bifidobacterium pseudolongum DSM 20092 Bifidobacterium thermophilum DSM 20212 Blautia schinkii DSM 10518 Butyrivibrio fibrisolvens DSM 3071 Butyrivibrio proteoclasticum B316 Butyrivibrio sp. M55 Lachnoclostridium aerotolerans DSM 5434 Clostridium intestinale DSM 6191 ResFinder Clostridium lundense DSM 17049 ARG-ANNOT Lachnoclostridium aminophilum DSM 10710 In vitro Lachnoclostridium polysaccharolyticum DSM 1081 Resfams Lachnospira multiparus D15d Lactobacillus ruminis DSM 20403 Megasphaera elsdenii T81 Mitsuokella jalalundinii DSM 13811 Olsenella umbonata DSM 22619 Prauserella rugosa DSM 43194 Proteiniclasticum ruminis DSM 24773 Ruminococcus flavefaciens FD-1 Selenomonas ruminantium DSM 2872 Sharpea azabuenensis DSM 18934 Sharpea azabuenensis DSM 20406

Fig. 6 Concordance between antibiotic resistance detected in silico and in vitro. Twenty six bacteria were analyzed both in silico and in vitro in this study. Colors represent the approach used to predict the ARG or to detect the resistance phenotype of each bacteria. BLA (beta-lactam); AMG (aminoglycoside); TET (tetracycline); VAN (vancomycin); CHL (Chloramphenicol); MAC (macrolide); CLD (clindamycin) analyzed in silico were selected for further in vitro characteriza- lactam, macrolide, tetracycline, and vancomycin) detected in tion. Our in silico analysis predicted 57 resistance determinants in rumen bacterial genomes to 15 rumen metranscriptome data sets the ruminal bacteria that were selected for in vitro testing. of dairy and beef cattle and sheep. At least one resistance gene Minimum inhibitory concentration (MIC values) were deter- from each antibiotic class was expressed in one or more meta- mined using the Epsilometer test (E-test) method18, and 18 transcriptomes (Fig. 7). In general, tetracycline-resistance genes resistance phenotypes were confirmed in vitro (Supplementary presented the highest level of expression, mainly the tet(O), tet Table 1). From this, eight ARGs (~44.0%) were detected/con- (Q), tet(W), and tet(37) gene. Moreover, individual genes varied firmed both in silico and in vitro by all approaches used in this in their expression levels between different data sets, being more study (Supplementary Fig. 7). ARG-ANNOT predicted 14 ARGs prevalent in sheep and beef cattle. Some of the ARGs identified in in the 26 cultures of ruminal bacteria tested in vitro, and 10 the ruminal microbial genomes, such as strA, cepA, ermB, tet(B), resistance phenotypes (71.4%) were confirmed in the E-test and vanC, among others, were not expressed in any of the assays. ResFinder and Resfams predicted 14 and 53 ARGs in the metatranscriptome data sets investigated in this study (Fig. 7). bacterial cultures, and resistance to 9 (64.3%) and 17 (32.1%) of these antibiotics were confirmed phenotypically (Supplementary Fig. 7). Therefore, resistance phenotypes predicted by ResFinder Discussion and ARG-ANNOT agreed, for the most part, with the in vitro The hypothesis that livestock animals represent a reservoir of results, while Resfams predicted several resistance mechanisms ARGs is not new, and previous studies have demonstrated that that could not be confirmed by other computational or in vitro bacteria isolated from fecal samples of monogastrics and rumi- methodologies used in this study. nants harbor genetic elements that can confer resistance to In addition, tetracycline resistance was detected simultaneously clinically used antibiotics19. However, less attention has been by at least three approaches in 69% of the genomes and bacterial given to the occurrence of ARGs in the rumen ecosystem and cultures, providing further evidence that tetracycline resistance is their potential to be transferred to commensal and pathogenic widespread in the rumen ecosystem. Moreover, some resistance bacteria. Microorganisms colonizing the rumen also disseminate phenotypes were confirmed by our in vitro assays even when to the environment through animal saliva during rumination and the presence of resistance determinants were predicted by only one due to the flow of rumen microbial biomass to the omasum, bioinformatic tool. This was observed for vancomycin resistance in abomasum and to the distal parts of the GIT, being released Clostridium and Lachnoclostridium strains and in Lactobacillus through fecal discharge into the soil20. ruminis DSM 20403, for beta-lactams in Mitsuokella jalalundinii In this work, we investigated the occurrence and distribution of DSM 13811 and Selenomonas ruminantium DSM 2872, for ARGs in hundreds of ruminal microbial genomes made available aminoglycosides in Bifidobacterium pseudolongum DSM 20092 through the Hungate1000 project12. Our results indicate that and for macrolides in Prauserella rugosa DSM 43194 (Fig. 6). ARGs are widely distributed among ruminal bacteria, with genes conferring resistance to tetracycline being frequently detected in the genomes of several species. The regions flanking the Expression of ARGs in metatranscriptomes. To confirm if the tetracycline-resistance genes showed a conserved pattern in an ARGs detected in ruminal microbial genomes are expressed in the apparently novel integrative and conjugative element (ICE), rumen ecosystem, we aligned the genes conferring resistance to suggesting dissemination of antibiotic resistance through hor- the five most abundant antibiotic classes (aminoglycoside, beta- izontal gene transfer in the rumen microbiome. In addition, we

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excisionases, and recombinases, while genes encoding proteins involved in DNA transcription are found in the regulation module. Moreover, several accessory genes, including methyltransferases, transposons, phages, and plasmid related proteins, in addition to 27 SRR8050517 SRR8050516 SRR8050514 SRR3257011 SRR3256999 SRR5805554 SRR5805529 SRR594654 SRR3169847 SRR3169851 SRR6201277 SRR6201276 SRR873462 SRR873454 SRR1138697 the ARGs, can be present in an ICE structure . At least one aadE essential gene from each module and several accessory genes were detected in the ﬂanking region of the tet(W) gene in seven ruminal blaACl-1 rpkm

mef(A) 0 microbial genomes analyzed in this study, suggesting that ICEs could be mediating the horizontal transfer of tetracycline-resistance tet(32) 0.607 1.214 genes between ruminal bacteria. Some essential genes of integrative tet(40) 1.82 and conjugative elements were also found in other genomes of tet(J) 2.427 rumen bacteria harboring the tet(W) gene, but the genetic structure tet(O) 3.034 of these elements appeared incomplete. The structure of ICE_Rb- tet(Q) 3.641 tetW_07 was conserved in the genomes of ruminal bacteria, and the 4.248 fi tet(W) 4.854 presence of the tet(W) gene on the ICE was con rmed in the genome of Blautia schinkii DSM 10518 by PCR amplification and tet(37) 5.461 6.068 shotgun sequencing. ICEs are known to be frequently transferred vanR-C between distant bacterial taxa28, and our analysis suggest that these vanR-D genetic elements might have great mobility in the rumen ecosystem. If this is indeed the case, there is a potential threat of these genetic Beef cattle Dairy cattle Sheep elements to be transferred to bacterial pathogens in the GIT of Fig. 7 Expression levels (RPKM) of ARGs in ruminal metatranscriptomes. ruminants and into the environment. Beef and dairy cattle and sheep data sets were analyzed, and only ARGs Some groups of microorganisms, such as the Enterobacter- that were expressed in at least one metatranscriptome are represented. iaceae, are frequently associated with the horizontal transfer of Source data are provided as a Source Data file ARGs29,30. The enrichment of ARGs appears to be a common feature in the mobile resistome of Proteobacteria from both the animal and the human gut microbiome31. This may be asso- show evidence of positive selective pressure on the tet(W) gene, ciated with the frequent use of antibiotics targeting Enter- which may contribute to the selection of genetic variants of this obacteriaceae to control infections in the respiratory, urinary, gene, conferring ribosome protection against tetracycline anti- and GIT of humans and livestock, thus selecting for resistant biotics in ruminal bacteria. strains5. Our in silico results demonstrated that members of the Tetracyclines are broad-spectrum antibiotics that inhibit pro- Proteobacteria phylum that inhabit the rumen can harbor tein synthesis in susceptible bacteria21 and have been used in multiple ARGs, as exemplified by the genome of E. coli PA-3, a human and veterinary medicine to fight bacterial infections and ruminal isolate that contains five genes conferring resistance to to promote the growth of food-producing animals, improving four distinct antibiotics. Although generic Escherichia coli feed efficiency and animal performance22. The growth-promoting populations are often not so abundant in the rumen (<106 cells/ activity of tetracyclines in livestock was first reported in 1949 ml), cattle are considered a natural reservoir for pathogenic when feeds supplemented with dried Streptomyces aureofaciens strains of E. coli32,33. In addition, management and nutritional mash were administrated to poultry23. However, the long-term practices, such as feeding the animals a high-grain diet, can use of subtherapeutic doses of tetracyclines has been associated cause significant increases in ruminal and fecal populations of E. with increased levels of bacterial resistance in the GIT of food- coli and influence the abundance and diversity of ARGs in the producing animals24, which led countries such as the USA and ruminal resistome33,34. members of the European Union to ban the use of tetracyclines as Furthermore, a complete operon for vancomycin resistance growth promoters since 197525. The prevalence of tetracycline- (vanC) was found in the genomes of Enterococcus casseliflavus resistance genes among ruminal bacteria has a potential negative and in Enterococcus gallinarum SKF1, which makes these species impact in livestock health given that tetracyclines account for up intrinsically resistant to this antibiotic35. Vancomycin is one of to 24% of the antibiotics used to treat respiratory, uterine, or the last-option antibiotic used to treat infections caused by Gram- locomotion diseases in Europe26. positive bacteria36, but little is known about vancomycin resis- In our study, tet(W), tet(Q), and tet(O) were the main tance in ruminal bacteria. Our analyses predicted resistance to tetracycline-resistance genes identified by ResFinder, but tet(W) vancomycin in Clostridium, Lachnoclostridium, and Blautia, and was the most abundant gene, being distributed in 28 genomes of in vitro experiments confirmed these phenotypes in Blautia ruminal bacteria with high similarity of nucleotide sequences. schinkii DSM 10518, Lachnoclostridium aerotolerans DSM 5434, Since ResFinder is an online platform that uses whole-genome Clostridium intestinale DSM 6191, and Clostridium lundense sequencing data to identify acquired AMR genes in bacteria13, the DSM 17049. However, our transcriptomic analysis indicated tet(W) gene was selected for further analysis with an aim to that the expression of vancomycin-resistance genes in the investigate if it could be horizontally transferred among ruminal rumen microbiome is low. In addition, vancomycin has limited bacteria. use for therapeutic purposes in animals, and vancomycin analogs Ouranalysessuggestthatthetet(W) gene of several species of have been restricted as growth promoters in livestock36. ruminal bacteria are located on an ICE with a unique modular Overall, our in vitro assays confirmed 31.6% of the resistance pattern, not previously reported for tetracycline resistance phenotypes predicted by computational approaches. Nonetheless, (ICE_RbtetW_07). The organization of the ICE structure is char- 64.3% and 71.4% of the resistance phenotypes based on the ARGs acterized by a core region containing essential modules for con- predicted by ResFinder and ARG-ANNOT, respectively, were jugation, recombination, and regulation27. The conjugation confirmed in vitro. These results show concordance between machinery often contains relaxases and secretion systems to med- these different approaches, despite the fact that some predicted iate the transfer of DNA between donor and recipient cells. The ARGs might not be functional in vitro. In addition, our tran- recombination module usually contains genes encoding integrases, scriptomic analyses showed higher expression of tetracycline-

8 NATURE COMMUNICATIONS | (2019) 10:5252 | https://doi.org/10.1038/s41467-019-13118-0 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13118-0 ARTICLE resistance genes and low expression of other classes or ARGs Phylogenetic analysis and distribution of ARGs. To evaluate if the ARGs (such as beta-lactams and aminoglycosides) in the rumen data grouped according to the evolutionary history of the microbial species, phyloge- sets, which also agreed with the resistance phenotype observed netic trees were reconstructed using the 16S rRNA gene sequences from the genomes analyzed in this study. The 16S rRNA gene sequences were obtained from the in vitro for the pure cultures of ruminal bacteria. Therefore, the Hungate1000 collection12. Next, all the sequences were organized in a single txt file results presented here, based on genomic, transcriptomic and and aligned using the RDP Release 11.5 aligner in the Ribosomal Database Project phenotypic data, increase the confidence that ARGs are dis- website (RDP, https://rdp.cme.msu.edu/)39. FastTree v2.1 (http://www. 40 tributed among different species of ruminal bacteria, and that microbesonline.org/fasttree/) was used to infer Approximately-Maximum- Likelihood phylogenetic trees using default settings. The generated output file these genes are expressed by members of the microbial commu- (.tree) was visualized and annotated on the Interactive Tree of Life (iTOL) interface nity in vivo. Nonetheless, additional studies will be needed to v4 (https://iTOL.embl.de/)41. evaluate the effects of animal ageing, production systems, and To evaluate sequence conservation of the ARGs that were identified in silico as nutritional practices, including antibiotic feeding, on the expres- described above, the sequences of the most frequent genes conferring resistance were extracted from the ruminal microbial genomes, and aligned as described sion, and potential transit of the ARGs detected in this study below. To extract the sequences of the resistance genes, all genomes were annotated across the GI tract of cattle. by Prokka v1.1242 using the Galaxy platform (minimum contig size of 200 and E- Taken together, our results provide insights that characterize value < 10–6). To obtain the location of the resistance genes in the bacterial the antibiotic resistance in species of ruminal bacteria. Our genomes, the ResFinder v2.1 database was aligned against the annotated genomes fi using the BLASTn tool on the Galaxy platform. Minimum query coverage and ndings demonstrate that ARGs, in particular the genes for tet- sequence identity were 60 and 70%, respectively, with a threshold E-value < 10–6. racycline resistance, are prevalent among ruminal bacteria and The sequence of the resistance genes identified in the previous alignment were subjected to positive selection pressure. Moreover, analysis of the extracted using the SAMtools software v1.9 (http://SAMtools.sourceforge.net/)43, flanking regions of the tet(W) genes provided evidence that this and the most abundant ARGs were aligned using MUSCLE version 3.8.3144. The gene can be disseminated horizontally through ICE transfer. phylogenetic reconstruction was based on the Maximum-Likelihood method using FastTree v2.1, and iTOL v4 graphical interface was used to represent the Given the fact that the rumen ecosystem also harbor a highly phylogenetic trees, as described above. dense and diverse microbiota that maintain complex ecological associations, and the capacity of microorganisms to exchange Genetic context of ARGs. To assess potential mechanisms of ARG mobility, DNA through direct (conjugation) and indirect mechanisms the genetic elements flanking the resistance genes in a scaffold were investigated (transformation, transduction), the GIT of ruminants should be using the software Artemis45. When these flanking elements were annotated as considered as a relevant source of antibiotic-resistance genes. The hypothetical protein, sequence function was predicted on the UNIPROT website evidence of a new ICE (ICE_RbtetW_07) carrying the tet(W) gene (http://www.uniprot.org/)46, using BLASTn, UniProtKB as the target database and default settings. Putative mobile genetic elements flanking the resistance genes were emphasizes this idea and presents a risk for both animal and searched using different computational tools. ISfinder (https://www-is.biotoul.fr/)47 human health, due to the capacity of these genetic elements to was used to screen for transposons and/or integrons, and phage sequences were disseminate into the environment. This hypothesis is also sup- confirmed using PHASTER (http://phaster.ca/)48. The genomes that harbored ported by the observation of non-phylogenetically related bacteria proteins with predicted plasmid functions near the resistance genes were further analyzed against the Enterobacteriaceae and Gram-positive database of Plas- harboring the same resistance genes, and the expression of the midFinder 2.0 (https://cge.cbs.dtu.dk/services/PlasmidFinder/)49, using a threshold same ARGs in ruminal metatranscriptomes data sets from beef for minimum sequence identity of 70% and minimum coverage of 60%. Finally, and dairy cattle and sheep. In addition, our results highlight the ICEberg 2.0 (http://db-mml.sjtu.edu.cn/ICEberg/)17 was used to identify ARGs importance of using multiple computational tools to search for carried by integrative and conjugative elements (ICE). Scaffolds harboring selected ARGs in genomic data and demonstrate the relevance to validate ARGs were used as query sequences (FASTA format) in the WU-BLAST2 search tool in ICEberg, using BLASTn as the search program and ICE nucleotide sequence these results in vitro and/or in vivo. as the sequence database. All the other parameters were kept as default settings.

Conservation and in vitro confirmation of a novel ICE. To evaluate the genetic Methods conservation of the integrative and conjugative elements identified in the genomes fi fi Collection of genomic data and identi cation of ARGs. The sequence les of 435 of ruminal bacteria, the nucleotide sequences of these ICEs were aligned using ruminal microbial genomes (425 bacteria and 10 archaea), from the Hungate1000 progressiveMAUVE50. In addition, a phylogenetic tree was reconstructed in MEGA project, were downloaded in FASTA format from the NCBI (National Center for X software using Muscle to compare the sequences of the novel ICE with other Biotechnology Information, http://www.ncbi.nlm.nih.gov/genome) and the JGI ICEs available in the ICEberg database. Phylogenetic reconstruction was performed (Joint Genome Institute, http://genome.jgi.doe.gov) websites (Supplementary using the Maximum-Likelihood method with 100 replicates. Data 3). Putative ARGs in the bacterial and archaeal genomes were predicted using To confirm the presence of a novel ICE in the genome of ruminal bacteria, PCR three bioinformatics tools (ResFinder v2.1, Resfams v1.2, and ARG-ANNOT Nt amplifications were performed using Blautia schinkii DSM 10518. Primers were fi V3, see below) that vary in sensitivity and speci city and apply distinct databases designed to amplify a 4794 pb DNA fragment that included the region from gene and search methods to detect putative ARGs in nucleotide sequences. virD4 to gene tet(W). Briefly, the bacteria was grown in anaerobic media at 39 °C We initially prospected the bacterial and archaeal genomes using the ResFinder for 72 h. Genomic DNA was extracted using the Qiagen DNA extraction kit 13 v2.1 database (https://cge.cbs.dtu.dk/services/ResFinder/) against all antibiotic (Manchester, UK). PCR amplification of the region of interest was carried out in classes available for analysis of acquired ARGs, which included aminoglycosides, 25 µl volumes as follows: 12.5 µl 2x MyTaq HS Red Mix, 1 µl each of 20 µM primers fl beta-lactams, colistin, uoroquinolones, fosfomycin, fusidic acid, glycopeptides, drawn in this study (RbtetW07_F: ATGAAAAAGCAGCTTGACATCAAAAAGC MLS (macrolides, lincosamides, and streptogramins), nitroimidazole, and RbtetW07_R: TTACATTACCTTCTGAAACATATGGCGC), 8 µl of nuclease- oxazolidinone, phenicols, rifampicin, sulfonamides, tetracyclines, and free water, and 2.5 µl of 50 ng template DNA. The cycling conditions were as trimethoprim. For homology-based screening, the minimum gene identity and follows: initial denaturation step at 95 °C for 60 s, 35 cycles of denaturation at 95 °C sequence length were set to 70 and 60%, respectively, in relation to the reference- for 15 s, annealing at 58 °C for 15 s, and extension at 72 °C for 3 min, and a final resistance gene. extension step at 72 °C for 5 min. The size and integrity of PCR products were The software Resfams v1.2 was used to search for conserved structural domains visualized and assessed by agarose gel electrophoresis using a 1% (w/v) agarose in 14 of antibiotic-resistance function in protein sequences of ruminal bacteria . The TAE buffer (40 mM Tris, pH 8.0, 20 mM acetic acid, 1 mM EDTA, BioRad Ltd., protein sequences of the analyzed genomes were downloaded in the FASTA format Hemel Hempstead, UK). The PCR product was subjected to shotgun sequencing from the NCBI or JGI websites and when these sequences were not found, the performed on the MiSeq with 300 cycles using the next-Flex Illumina workflow at online interface Prodigal v1.2 (http://compbio.ornl.gov/prodigal/server.html) was the Queen’s University of Belfast Genomics Core Technology Unit (Belfast, UK) to used to convert nucleotide to protein sequences using the Standard Bacteria/ confirm the presence of the predicted integrative and conjugative element. Quality Archaea translation table code. filtering of the reads was performed using Trimmomatic software v0.2751, and Genomes of ruminal microorganisms were also analyzed using the ARG- sequences were assembled using SPAdes 3.10.152. The sequence was aligned to the ANNOT database (http://en.mediterranee-infection.com/article.php? ICE coding sequence in the genome of Blautia schinkii DSM 10518, using Clustal = = 15 laref 283&titre arg-annot-) . Sequences of the ARGs in the ARG-ANNOT Omega53. database (ARG-ANNOT Nt V3) were used for similarity analysis against the ruminal microbial genomes using BLASTn37 on the Galaxy platform38. The alignment parameters were minimum sequence identity of 70%, sequence length Selective pressure on identified ARGs. Sequences of the most prevalent ARGs cutoff of 60%, and E-value < 10–6. detected in ruminal microbial genomes were selected to evaluate if these genes are

NATURE COMMUNICATIONS | (2019) 10:5252 | https://doi.org/10.1038/s41467-019-13118-0 | www.nature.com/naturecommunications 9 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13118-0 under selection pressure. Selective pressure on the ARGs was estimated by the 2. Ventola, C. L. The antibiotic resistance crisis: part 2: management strategies number of non-synonymous and synonymous substitutions (dN/dS) using the and new agents. P. T. 40, 344–352 (2015). software JCoDA54. JCoDA uses ClustalW to align the nucleotide sequences and 3. Elliott, K. A., Kenny, C. & Madan, J. A global treaty to reduce antimicrobial Phylogenetic Analysis using Maximum Likelihood (PALM) to calculate the dN/dS use in livestock. https://www.cgdev.org/sites/default/files/global-treaty-reduce- ratio. Analyses were performed using nucleotide sequences in the FASTA format as antimicrobial-use-livestock.pdf (2017). input sequences and the universal (default) genetic translation code. The alignment 4. Aarestrup, F. M. The livestock reservoir for antimicrobial resistance: a option was set to ClustalW, and phylogenetic trees were reconstructed by the personal view on changing patterns of risks, effects of interventions and the Maximum-Likelihood method with 100 replicates using protein sequences in the way forward. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 370,1–13 (2015). Phylip-sequential format. The dN/dS was calculated by site (amino acid) using the 5. Hitch, T. C. A., Thomas, B. J., Friedersdorff, J. C. A., Ougham, H. & Creevey, Bayes Empirical Bayes (BEB) model, which takes into account the uncertainties of C. J. Deep sequence analysis reveals the ovine rumen as a reservoir of 54 model parameters, avoiding the generation of false positives . The selective antibiotic resistance genes. Environ. Pollut. 235, 571–575 (2018). fi pressure on the antibiotic-resistance genes were con rmed by two neutrality tests, 6. Huws, S. A. et al. 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