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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
04/09/2019 |
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Actualizado : |
16/03/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
ROVIRA, P.J.; MCALLISTER, T.; LAKIN, S.M.; COOK, S.R.; DOSTER, E.; NOYES, N. R.; WEINROTH, M.D.; YANG, X.; PARKER, J. K.; BOUCHER, C.; BOOKER, C. W.; WOENER, D. R.; BELK, K. E.; MORLEY, P. S. |
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Afiliación : |
PABLO JUAN ROVIRA SANZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. Department of Animal Sciences, College of Agricultural Sciences, Colorado State University, USA.; TIM MCALLISTER, Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada.; STEVEN M. LAKIN, Department of Microbiology, Immunology and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, USA.; SHAUN R. COOK, Alberta Agricultural and forestry, Lethbridge, Canada.; ENRIQUE DOSTER, Department of Microbiology, Immunology and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, USA.; NOELLE R. NOYES, Veterinary Population Medicine Department, University of Minnesota, USA.; MAGGIE D. WEINROTH, Department of Animal Sciences, College of Agricultural Sciences, Colorado State University, USA.; XIANG YANG, Department of Animal Science, University of California, Davis, USA.; JENNIFER K. PARKER, Deparment of Molecular Biosciences, University of Florida, Gainesville, FL, USA.; CHRISTINA BOUCHER, Deparment of Computer and Information Science and Engineering, University of Florida, Gainessville, FL, USA.; CALVIN W. BOOKER, Feedlot Health Management Services, Okotoks, AB, Canada.; DALE R. WOEMER, Deparment of Animal and Food Sciences, College of Agricultural Sciences & Natural Resources, Texas University, TX, USA.; KEITH E. BELK, Department of Animal Sciences, College of Agricultural Sciences, Colorado State University, USA.; PAUL S. MORLEY, VERO, Veterinary Education, Research , and Outreach Program, Texas A&M University and West Texas A&M University, Canyon, TX, USA. |
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Título : |
Characterization of the microbial resistome in conventional and "raised without antibiotics" beef and dairy production systems. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Frontier in Microbiology, September 2019. v. 10, article 1980, 11 p. OPEN ACCESS. |
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DOI : |
10.3389/fmicb.2019.01980 |
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Idioma : |
Inglés |
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Notas : |
Received 18 March 2019 // Accepted 12 August 2019 // Published 4 September 2019. |
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Contenido : |
Metagenomic investigations have the potential to provide unprecedented insights into microbial ecologies, such as those relating to antimicrobial resistance (AMR). We characterized the microbial resistome in livestock operations raising cattle conventionally (CONV) or without antibiotic exposures (RWA) using shotgun metagenomics. Samples of feces, wastewater from catchment basins, and soil where wastewater was applied were collected from CONV and RWA feedlot and dairy farms. After DNA extraction and sequencing, shotgun metagenomic reads were aligned to reference databases for identification of bacteria (Kraken) and antibiotic resistance genes (ARGs) accessions (MEGARes). Differences in microbial resistomes were found across farms with different production practices (CONV vs. RWA), types of cattle (beef vs. dairy), and types of sample (feces vs. wastewater vs. soil). Feces had the greatest number of ARGs per sample (mean = 118 and 79 in CONV and RWA, respectively), with tetracycline efflux pumps, macrolide phosphotransferases, and aminoglycoside nucleotidyltransferases mechanisms of resistance more abundant in CONV than in RWA feces. Tetracycline and macrolide-lincosamide-streptogramin classes of resistance were more abundant in feedlot cattle than in dairy cow feces, whereas the b-lactam class was more abundant in dairy cow feces. Lack of congruence between ARGs and microbial communities (procrustes analysis) suggested that other factors (e.g., location of farms, cattle source, management practices, diet, horizontal ARGs transfer, and co-selection of resistance), in addition to antimicrobial use, could have impacted resistome profiles. For that reason, we could not establish a cause-effect relationship between antimicrobial use and AMR, although ARGs in feces and effluents were associated with drug classes used to treat animals according to farms' records (tetracyclines and macrolides in feedlots, b-lactams in dairies), whereas ARGs in soil were dominated by multidrug resistance.
Characterization of the "resistance potential" of animal-derived and environmental samples is the first step toward incorporating metagenomic approaches into AMR surveillance in agricultural systems. Further research is needed to assess the publichealth risk associated with different microbial resistomes. MenosMetagenomic investigations have the potential to provide unprecedented insights into microbial ecologies, such as those relating to antimicrobial resistance (AMR). We characterized the microbial resistome in livestock operations raising cattle conventionally (CONV) or without antibiotic exposures (RWA) using shotgun metagenomics. Samples of feces, wastewater from catchment basins, and soil where wastewater was applied were collected from CONV and RWA feedlot and dairy farms. After DNA extraction and sequencing, shotgun metagenomic reads were aligned to reference databases for identification of bacteria (Kraken) and antibiotic resistance genes (ARGs) accessions (MEGARes). Differences in microbial resistomes were found across farms with different production practices (CONV vs. RWA), types of cattle (beef vs. dairy), and types of sample (feces vs. wastewater vs. soil). Feces had the greatest number of ARGs per sample (mean = 118 and 79 in CONV and RWA, respectively), with tetracycline efflux pumps, macrolide phosphotransferases, and aminoglycoside nucleotidyltransferases mechanisms of resistance more abundant in CONV than in RWA feces. Tetracycline and macrolide-lincosamide-streptogramin classes of resistance were more abundant in feedlot cattle than in dairy cow feces, whereas the b-lactam class was more abundant in dairy cow feces. Lack of congruence between ARGs and microbial communities (procrustes analysis) suggested that other factors (e.g., location of farms, cattle sour... Presentar Todo |
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Palabras claves : |
ANTIBIOTIC RESISTANCE; CATTLE; CATTLE BEEF; DAIRY CATTLE; METAGENOMICA; METAGENOMICS; MICROBIOMA; MICROBIOME; RESISTENCIA A ANTIBIÓTICOS; RESISTOME. |
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Thesagro : |
BOVINOS; BOVINOS DE CARNE; GANADO LECHERO. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13237/1/Rovira-Front-microb-2019.pdf
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Marc : |
LEADER 03681naa a2200457 a 4500
001 1060137
005 2021-03-16
008 2019 bl uuuu u00u1 u #d
024 7 $a10.3389/fmicb.2019.01980$2DOI
100 1 $aROVIRA, P.J.
245 $aCharacterization of the microbial resistome in conventional and "raised without antibiotics" beef and dairy production systems.$h[electronic resource]
260 $c2019
500 $aReceived 18 March 2019 // Accepted 12 August 2019 // Published 4 September 2019.
520 $aMetagenomic investigations have the potential to provide unprecedented insights into microbial ecologies, such as those relating to antimicrobial resistance (AMR). We characterized the microbial resistome in livestock operations raising cattle conventionally (CONV) or without antibiotic exposures (RWA) using shotgun metagenomics. Samples of feces, wastewater from catchment basins, and soil where wastewater was applied were collected from CONV and RWA feedlot and dairy farms. After DNA extraction and sequencing, shotgun metagenomic reads were aligned to reference databases for identification of bacteria (Kraken) and antibiotic resistance genes (ARGs) accessions (MEGARes). Differences in microbial resistomes were found across farms with different production practices (CONV vs. RWA), types of cattle (beef vs. dairy), and types of sample (feces vs. wastewater vs. soil). Feces had the greatest number of ARGs per sample (mean = 118 and 79 in CONV and RWA, respectively), with tetracycline efflux pumps, macrolide phosphotransferases, and aminoglycoside nucleotidyltransferases mechanisms of resistance more abundant in CONV than in RWA feces. Tetracycline and macrolide-lincosamide-streptogramin classes of resistance were more abundant in feedlot cattle than in dairy cow feces, whereas the b-lactam class was more abundant in dairy cow feces. Lack of congruence between ARGs and microbial communities (procrustes analysis) suggested that other factors (e.g., location of farms, cattle source, management practices, diet, horizontal ARGs transfer, and co-selection of resistance), in addition to antimicrobial use, could have impacted resistome profiles. For that reason, we could not establish a cause-effect relationship between antimicrobial use and AMR, although ARGs in feces and effluents were associated with drug classes used to treat animals according to farms' records (tetracyclines and macrolides in feedlots, b-lactams in dairies), whereas ARGs in soil were dominated by multidrug resistance. Characterization of the "resistance potential" of animal-derived and environmental samples is the first step toward incorporating metagenomic approaches into AMR surveillance in agricultural systems. Further research is needed to assess the publichealth risk associated with different microbial resistomes.
650 $aBOVINOS
650 $aBOVINOS DE CARNE
650 $aGANADO LECHERO
653 $aANTIBIOTIC RESISTANCE
653 $aCATTLE
653 $aCATTLE BEEF
653 $aDAIRY CATTLE
653 $aMETAGENOMICA
653 $aMETAGENOMICS
653 $aMICROBIOMA
653 $aMICROBIOME
653 $aRESISTENCIA A ANTIBIÓTICOS
653 $aRESISTOME
700 1 $aMCALLISTER, T.
700 1 $aLAKIN, S.M.
700 1 $aCOOK, S.R.
700 1 $aDOSTER, E.
700 1 $aNOYES, N. R.
700 1 $aWEINROTH, M.D.
700 1 $aYANG, X.
700 1 $aPARKER, J. K.
700 1 $aBOUCHER, C.
700 1 $aBOOKER, C. W.
700 1 $aWOENER, D. R.
700 1 $aBELK, K. E.
700 1 $aMORLEY, P. S.
773 $tFrontier in Microbiology, September 2019.$gv. 10, article 1980, 11 p. OPEN ACCESS.
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Registro original : |
INIA Treinta y Tres (TT) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
03/10/2019 |
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Actualizado : |
03/10/2019 |
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Tipo de producción científica : |
Informes Agroclimáticos |
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Autor : |
GIMÉNEZ, A.; CAL, A.; TISCORNIA, G.; SCHIAVI, C. |
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Afiliación : |
AGUSTIN EDUARDO GIMÉNEZ FUREST, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ADRIAN TABARE CAL ALVAREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS IGNACIO SCHIAVI RAMPELBERG, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Informe agroclimático 2019 - Situación a Setiembre. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2019. |
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Páginas : |
4 p. |
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Serie : |
(Informe Agroclimático; 161) |
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Idioma : |
Español |
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Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICOS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; INFORME AGROCLIMÁTICO 2019; INUNDACIONES; LLUVIAS DIARIAS; MAXIMA; MEDIA; MINIMA; PANEL SOLAR; PERSPECTIVAS CLIMATICAS; PLUVIOMETRO; PRECIPITACION NACIONAL; PREVENCION HELADAS; PRONOSTICO; SENSOR; SIMETRICO; TANQUE A; TERMOCUPLAS; TERMOHIDROGRAFO; VARIABLES AGROCLIMATICAS; VELETA. |
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Thesagro : |
AGROCLIMATOLOGIA; CAMBIO CLIMATICO; CLIMA; CLIMATOLOGIA; ESTACIONES METEOROLOGICAS; ESTRES HIDRICO; EVAPORACION; EVAPOTRANSPIRACION; HUMEDAD; HUMEDAD RELATIVA; LLUVIA; METEOROLOGIA; PERSPECTIVAS; PLUVIOMETROS; PRONOSTICO DEL TIEMPO; SENSORES; SISTEMAS; SISTEMAS DE INFORMACION; SUELO; TEMPERATURA; TERMOMETROS. |
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Asunto categoría : |
P40 Meteorología y climatología |
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URL : |
http://www.inia.uy/Publicaciones/Documentos%20compartidos/Informe%20agroclimatico%20INIA-GRAS%20Setiembre%20de%202019.pdf
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Marc : |
LEADER 02130nam a2200805 a 4500
001 1060272
005 2019-10-03
008 2019 bl uuuu u0uu1 u #d
100 1 $aGIMÉNEZ, A.
245 $aInforme agroclimático 2019 - Situación a Setiembre.$h[electronic resource]
260 $aMontevideo (Uruguay): INIA$c2019
300 $a4 p.
490 $a(Informe Agroclimático; 161)
650 $aAGROCLIMATOLOGIA
650 $aCAMBIO CLIMATICO
650 $aCLIMA
650 $aCLIMATOLOGIA
650 $aESTACIONES METEOROLOGICAS
650 $aESTRES HIDRICO
650 $aEVAPORACION
650 $aEVAPOTRANSPIRACION
650 $aHUMEDAD
650 $aHUMEDAD RELATIVA
650 $aLLUVIA
650 $aMETEOROLOGIA
650 $aPERSPECTIVAS
650 $aPLUVIOMETROS
650 $aPRONOSTICO DEL TIEMPO
650 $aSENSORES
650 $aSISTEMAS
650 $aSISTEMAS DE INFORMACION
650 $aSUELO
650 $aTEMPERATURA
650 $aTERMOMETROS
653 $aAGROCLIMA
653 $aAGROCLIMATOLOGÍA
653 $aBOLETIN AGROCLIMÁTICO
653 $aCARACTERIZACIÓN AGROCLIMÁTICA
653 $aDIRECCION VIENTO
653 $aESTACIONES AGROMETEOROLOGICAS
653 $aESTACIONES AUTOMATICAS
653 $aESTACIONES INIA
653 $aESTADO DEL TIEMPO
653 $aESTRÉS HÍDRICO
653 $aGRAFICAS AGROCLIMATICOS
653 $aGRAS
653 $aHELIOFANOGRAFO
653 $aINFORMACION SATELITAL
653 $aINFORME AGROCLIMÁTICO 2019
653 $aINUNDACIONES
653 $aLLUVIAS DIARIAS
653 $aMAXIMA
653 $aMEDIA
653 $aMINIMA
653 $aPANEL SOLAR
653 $aPERSPECTIVAS CLIMATICAS
653 $aPLUVIOMETRO
653 $aPRECIPITACION NACIONAL
653 $aPREVENCION HELADAS
653 $aPRONOSTICO
653 $aSENSOR
653 $aSIMETRICO
653 $aTANQUE A
653 $aTERMOCUPLAS
653 $aTERMOHIDROGRAFO
653 $aVARIABLES AGROCLIMATICAS
653 $aVELETA
700 1 $aCAL, A.
700 1 $aTISCORNIA, G.
700 1 $aSCHIAVI, C.
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