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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
08/07/2020 |
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Actualizado : |
10/08/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
UMPIÉRREZ, A.; ERNST, D.; FERNÁNDEZ, M.; OLIVER, M; CASAUX, M.L.; CAFFARENA, D.; SCHILD, C.; GIANNITTI, F.; FRAGA, M.; ZUNINO, P. |
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Afiliación : |
ANA UMPIÉRREZ, Departamento de Microbiología, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay.; DÉBORAH ERNST, Departamento de Microbiología, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay.; MAGALÍ FERNÁNDEZ, Departamento de Microbiología, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay.; MARTIN OLIVER, Departamento de Microbiología, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay; MARÍA LAURA CASAUX, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RUBEN DARÍO CAFFARENA LEDESMA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS SCHILD, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO GIANNITTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN FRAGA COTELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO ZUNINO, Departamento de Microbiología, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay. |
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Título : |
Virulence genes of Escherichia coli in diarrheic and healthy calves. [Genes de virulencia de Escherichia coli en terneros con diarrea neonatal y asintomáticos]. |
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Complemento del título : |
BRIEF REPORT. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Revista Argentina de Microbiologia, Volume 53, Issue 1, Pages 34-38, January-March 2021. OPEN ACCESS. Doi: https://doi.org/10.1016/j.ram.2020.04.004 |
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DOI : |
10.1016/j.ram.2020.04.004 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 30 September 2019// Accepted 6 April 2020// Available online 16 June 2020. This work was funded by the project PL-15 from Instituto Nacional de Investigación Agropecuaria (INIA), and the project FMV-104922 from the Uruguayan Agencia Nacional de Investigación e Innovación (ANII) |
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Contenido : |
Abstract:Escherichia coli ETEC, EPEC, NTEC and STEC/EHEC pathotypes are often isolatedfrom bovine feces. The objective of this study was to detect 21 E. coli virulence genes in fecesfrom 252 dairy calves in Uruguay (149 with neonatal diarrhea --- NCD --- and 103 asymptomatic).Genes iucD, f17A, afa8E, papC, clpG and f17G(II) were the most prevalent (81.3%; 48.4%; 37.3%;35.7%; 34.1%; 31.3%, respectively). Genes eae, stx1and stx2 were poorly represented; 13/252animals harbored one or a combination of these genes. The prevalence of the cnf gene was4.4%, while that of cdt-IV and cdt-III genes was 24.2% and 12.7% respectively. This study reportsupdated data about the virulence profiles of E. coli in dairy calves in Uruguay. A large number ofadhesins and toxin genes were detected. Our results demonstrate that E. coli from bovine feceshas diarrheagenic and extraintestinal profiles although other NCD risks factors may contributeto the disease outcome.
Resumen:Los patotipos de Escherichia coli ETEC, EPEC, NTEC y STEC/EHEC son frecuentemente aislados de heces bovinas. El objetivo del presente estudio fue detectar 21 genes de virulencia de E. coli en las heces de 252 terneros de leche en Uruguay, 149 de ellos con síntomas de diarrea neonatal (DNT) y 103 asintomáticos. Los genes iucD, f17A, afa8E, papC, clpG y f17G(II) fueron los más prevalentes (81,3; 48,4; 37,3; 35,7; 34,1 y 31,3%, respectivamente). Los genes eae, stx1 y stx2 estuvieron poco representados: 13/252 animales presentaron uno o una combinación de dichos genes. La prevalencia del gen cnf fue del 4,4%, mientras que la de los genes cdt-IV y cdt-III fue del 24,2 y 12,7%, respectivamente. Este trabajo aporta datos actualizados sobre el perfil de virulencia de E. coli en terneros en Uruguay. Fueron detectados un alto número de genes de adherencia y de toxinas. Se demuestra que los aislamientos de E. coli recuperados de heces de terneros presentan perfiles diarreogénicos y extraintestinales, aunque otros factores de riesgo de DNT podrán contribuir al desarrollo de la enfermedad. MenosAbstract:Escherichia coli ETEC, EPEC, NTEC and STEC/EHEC pathotypes are often isolatedfrom bovine feces. The objective of this study was to detect 21 E. coli virulence genes in fecesfrom 252 dairy calves in Uruguay (149 with neonatal diarrhea --- NCD --- and 103 asymptomatic).Genes iucD, f17A, afa8E, papC, clpG and f17G(II) were the most prevalent (81.3%; 48.4%; 37.3%;35.7%; 34.1%; 31.3%, respectively). Genes eae, stx1and stx2 were poorly represented; 13/252animals harbored one or a combination of these genes. The prevalence of the cnf gene was4.4%, while that of cdt-IV and cdt-III genes was 24.2% and 12.7% respectively. This study reportsupdated data about the virulence profiles of E. coli in dairy calves in Uruguay. A large number ofadhesins and toxin genes were detected. Our results demonstrate that E. coli from bovine feceshas diarrheagenic and extraintestinal profiles although other NCD risks factors may contributeto the disease outcome.
Resumen:Los patotipos de Escherichia coli ETEC, EPEC, NTEC y STEC/EHEC son frecuentemente aislados de heces bovinas. El objetivo del presente estudio fue detectar 21 genes de virulencia de E. coli en las heces de 252 terneros de leche en Uruguay, 149 de ellos con síntomas de diarrea neonatal (DNT) y 103 asintomáticos. Los genes iucD, f17A, afa8E, papC, clpG y f17G(II) fueron los más prevalentes (81,3; 48,4; 37,3; 35,7; 34,1 y 31,3%, respectivamente). Los genes eae, stx1 y stx2 estuvieron poco representados: 13/252 animales presentaron u... Presentar Todo |
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Palabras claves : |
ADHESION GENES; ESCHERICHIA COLI; GENES DE ADHESION; NCD; NTEC; PATHOGENIC ESCHERICHIA COLI; PLATAFORMA DE SALUD ANIMAL; ZOONOSES; ZOONOSIS. |
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Thesagro : |
TERNEROS. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15935/1/Revista-Argentina-de-Microbiologia-Volume-53-Issue-1-Pages-34-38.pdf
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Marc : |
LEADER 03546naa a2200373 a 4500
001 1061210
005 2021-08-10
008 2021 bl uuuu u00u1 u #d
024 7 $a10.1016/j.ram.2020.04.004$2DOI
100 1 $aUMPIÉRREZ, A.
245 $aVirulence genes of Escherichia coli in diarrheic and healthy calves. [Genes de virulencia de Escherichia coli en terneros con diarrea neonatal y asintomáticos].$h[electronic resource]
260 $c2021
500 $aArticle history: Received 30 September 2019// Accepted 6 April 2020// Available online 16 June 2020. This work was funded by the project PL-15 from Instituto Nacional de Investigación Agropecuaria (INIA), and the project FMV-104922 from the Uruguayan Agencia Nacional de Investigación e Innovación (ANII)
520 $aAbstract:Escherichia coli ETEC, EPEC, NTEC and STEC/EHEC pathotypes are often isolatedfrom bovine feces. The objective of this study was to detect 21 E. coli virulence genes in fecesfrom 252 dairy calves in Uruguay (149 with neonatal diarrhea --- NCD --- and 103 asymptomatic).Genes iucD, f17A, afa8E, papC, clpG and f17G(II) were the most prevalent (81.3%; 48.4%; 37.3%;35.7%; 34.1%; 31.3%, respectively). Genes eae, stx1and stx2 were poorly represented; 13/252animals harbored one or a combination of these genes. The prevalence of the cnf gene was4.4%, while that of cdt-IV and cdt-III genes was 24.2% and 12.7% respectively. This study reportsupdated data about the virulence profiles of E. coli in dairy calves in Uruguay. A large number ofadhesins and toxin genes were detected. Our results demonstrate that E. coli from bovine feceshas diarrheagenic and extraintestinal profiles although other NCD risks factors may contributeto the disease outcome. Resumen:Los patotipos de Escherichia coli ETEC, EPEC, NTEC y STEC/EHEC son frecuentemente aislados de heces bovinas. El objetivo del presente estudio fue detectar 21 genes de virulencia de E. coli en las heces de 252 terneros de leche en Uruguay, 149 de ellos con síntomas de diarrea neonatal (DNT) y 103 asintomáticos. Los genes iucD, f17A, afa8E, papC, clpG y f17G(II) fueron los más prevalentes (81,3; 48,4; 37,3; 35,7; 34,1 y 31,3%, respectivamente). Los genes eae, stx1 y stx2 estuvieron poco representados: 13/252 animales presentaron uno o una combinación de dichos genes. La prevalencia del gen cnf fue del 4,4%, mientras que la de los genes cdt-IV y cdt-III fue del 24,2 y 12,7%, respectivamente. Este trabajo aporta datos actualizados sobre el perfil de virulencia de E. coli en terneros en Uruguay. Fueron detectados un alto número de genes de adherencia y de toxinas. Se demuestra que los aislamientos de E. coli recuperados de heces de terneros presentan perfiles diarreogénicos y extraintestinales, aunque otros factores de riesgo de DNT podrán contribuir al desarrollo de la enfermedad.
650 $aTERNEROS
653 $aADHESION GENES
653 $aESCHERICHIA COLI
653 $aGENES DE ADHESION
653 $aNCD
653 $aNTEC
653 $aPATHOGENIC ESCHERICHIA COLI
653 $aPLATAFORMA DE SALUD ANIMAL
653 $aZOONOSES
653 $aZOONOSIS
700 1 $aERNST, D.
700 1 $aFERNÁNDEZ, M.
700 1 $aOLIVER, M
700 1 $aCASAUX, M.L.
700 1 $aCAFFARENA, D.
700 1 $aSCHILD, C.
700 1 $aGIANNITTI, F.
700 1 $aFRAGA, M.
700 1 $aZUNINO, P.
773 $tRevista Argentina de Microbiologia, Volume 53, Issue 1, Pages 34-38, January-March 2021. OPEN ACCESS. Doi: https://doi.org/10.1016/j.ram.2020.04.004
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INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
14/09/2023 |
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Actualizado : |
14/09/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. |
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Afiliación : |
MARÍA INÉS REBOLLO PANUNCIO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO MOLINA CASELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZEPARTMENT OF STATISTICS, UNIVERSITY DE LA REPÚBLICA, COLLEGE OF AGRICULTURE, GARZÓN 780, MONTEVIDEO, MONTEVIDEO, URUGUAY DEPARTMENT OF AGRONOMY, UNIVERSITY OF WISCONSIN–MADISON, 1575 LINDEN DRIVE, MADISON, WI, UNITED STATES, Department of Statistics, University de la República, College of Agriculture, Montevideo, Uruguay; Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, WI, United States; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay. |
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Título : |
Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. |
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Complemento del título : |
Original article. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. |
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ISSN : |
0011-183X (print); 1435-0653 (electronic). |
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DOI : |
10.1002/csc2.21029 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ ) |
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Contenido : |
ABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America. MenosABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiati... Presentar Todo |
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Palabras claves : |
Genotype by environment interaction (GEI); Random regression models (RRMs); Rice (Oryza sativa L.). |
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Asunto categoría : |
-- |
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URL : |
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21029
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Marc : |
LEADER 03749naa a2200253 a 4500
001 1064311
005 2023-09-14
008 2023 bl uuuu u00u1 u #d
022 $a0011-183X (print); 1435-0653 (electronic).
024 7 $a10.1002/csc2.21029$2DOI
100 1 $aREBOLLO, I.
245 $aGenotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ )
520 $aABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America.
653 $aGenotype by environment interaction (GEI)
653 $aRandom regression models (RRMs)
653 $aRice (Oryza sativa L.)
700 1 $aAGUILAR, I.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aMOLINA, F.
700 1 $aGUTIÉRREZ, L.
700 1 $aROSAS, J.E.
773 $tCrop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS.
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