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

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Characterization of Antibiotic Resistance Genes in the Species of the Rumen Microbiota ARTICLE 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 pres- sure. 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 L a c h L L 8 n a 9 L 3 7 a 3 0 o c 7 4 a 0 3 c 1 Lachnosp s h Lachnos 1 3 c h 0 0 Eubacte p 196 Lachnos n h n 1 A 2 i L G70 L Lachnos o n C r o Lachnos 4 B 1006 G277 D C134 a a a s - o s - K -zl-G2 Lachnospirace P B c c S Tree scale: 0.1 c p p s C242 -zl- -zl- e E h N h X Lachnos i - -zl p Y i -zl r Anae E Lachnoc r E a n n a i a E r AGR2157 e o m Lachnos o m p c 8 m a c ATCC 255 rium ce irace p s e s u 6 e iracea u 1 s DSM 54 u Lachnoc e c m XB E-zl b i i i p e p irace p a r 4 n r 5 e piracea r a u Lachnosp a A m r e NLA i irace i e e 1 e 0 a e Lachnos r e ovib c r m t t t AGR2154 e NLA a a 2 1 e t c c a b c ae NLA pirace e b c c i m lostridi R a a e bacteri a a b a e a e bacteri r e iofor us M nse NLA i b b l c tum NLAE tolera r b pirace a G a e bacteri a c iofor luloso u a d io lipo r S t o t c ioform l e bacteri e d Butyriv m e e e A e bacteri e e ostridi e a t D d iofor r a 0 a e a r e bacteri b e e bacteri b ticum NL Butyriv a i i d 3217 Sarc N i p u i iracea e 5 e r e u a u a a a e bacteriu - i k irace c c m r c u M L m ami m c a c m citron Butyriv l Butyriv
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