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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
17/05/2022 |
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Actualizado : |
02/12/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
PASSOS, J. R. S.; GUERREIRO, D. D.; OTÁVIO, K. S.; SANTOS-NETO, P. C. DOS; SOUZA-NEVES, M.; CUADRO, F.; NUÑEZ-OLIVERA, R.; CRISPO, M.; BEZERRA, M. J. B.; SILVA, R. F.; LIMA, L. F.; FIGUEIREDO, J. R.; BUSTAMANTE-FILHO, I. C.; MENCHACA, A.; MOURA, A. A. |
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Afiliación : |
JOSÉ RENATO S. PASSOS, Laboratório de Fisiologia e Ciências Ômicas, Departamento de Zootecnia, Universidade Federal do Ceará, Fortaleza, Brazil; DENISE D. GUERREIRO, Laboratório de Fisiologia e Ciências Ômicas, Departamento de Zootecnia, Universidade Federal do Ceará, Fortaleza, Brazil; KAMILA S. OTÁVIO, Laboratório de Fisiologia e Ciências Ômicas, Departamento de Zootecnia, Universidade Federal do Ceará, Fortaleza, Brazil; P. C. DOS SANTOS-NETO, Instituto de Reproducción Animal Uruguay, Fundación IRAUy, Montevideo, Uruguay; MARCELA SOUZA-NEVES, Instituto de Reproducción Animal Uruguay, Fundación IRAUy, Montevideo, Uruguay; FEDERICO CUADRO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Instituto de Reproducción Animal Uruguay, Fundación IRAUy, Montevideo, Uruguay; RICHARD NUÑEZ-OLIVERA, Instituto de Reproducción Animal Uruguay, Fundación IRAUy, Montevideo, Uruguay; MARTINA CRISPO, Unidad de Biotecnología en Animales de Laboratorio, Institut Pasteur de Montevideo, Montevideo, Uruguay; MARIA JÚLIA B. BEZERRA, Laboratório de Fisiologia e Ciências Ômicas, Departamento de Zootecnia, Universidade Federal do Ceará, Fortaleza, Brazil; RENATO F. SILVA, Laboratório de Manipulação de Oócitos e Folículos Ovarianos Pré-antrais - LAMOFOPA - Faculdade de Veterinária, Universidade Estadual do Ceará, Fortaleza, Brazil; LARITZA F. LIMA, Laboratório de Manipulação de Oócitos e Folículos Ovarianos Pré-antrais - LAMOFOPA - Faculdade de Veterinária, Universidade Estadual do Ceará, Fortaleza, Brazil; JOSÉ RICARDO FIGUEIREDO, Laboratório de Manipulação de Oócitos e Folículos Ovarianos Pré-antrais - LAMOFOPA - Faculdade de Veterinária, Universidade Estadual do Ceará, Fortaleza, Brazil; IVAN C. BUSTAMANTE-FILHO, aboratório de Biotecnologia da Reprodução Animal, Programa de Pós-graduação em Biotecnologia, Universidade do Vale do Taquari, Lajeado, Brazil; JOSE ALEJO MENCHACA BARBEITO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Instituto de Reproducción Animal Uruguay, Fundación IRAUy, Montevideo, Uruguay; ARLINDO A. MOURA, Laboratório de Fisiologia e Ciências Ômicas, Departamento de Zootecnia, Universidade Federal do Ceará, Fortaleza, Brazil. |
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Título : |
Global proteomic analysis of preimplantational ovine embryos produced in vitro. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Reproduction in Domestic Animals, 2022, Volume 57, Issue 7; pages 784-797. doi: https://doi.org/10.1111/rda.14122 |
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ISSN : |
0936-6768 |
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DOI : |
10.1111/rda.14122 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 15 February 2022; Accepted 1 April 2022. -- Funding text - The experiments presently described were conducted at the facilities of the (Fundacion IRAUy, Montevideo, Uruguay) and at the (UBAL) of the , Uruguay. Specially, the authors thank Dr. Rosario Durán and Dr. Alejandro Leyva for kindly assisting us in the proteomic experiment. Finnacial support was provided by Fundacion IRAUy; PRONEX 02/2015 (Programa de Apoio a Núcleos de Excelência Pronex/Funcap/CNPq); the Brazilian Research Council?CNPq (grants # 313160/2017‐1 and 438773/2018‐7); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil. Instituto de Reproducción Animal Uruguay Unidad de Biotecnología en Animales de Laboratorio Institut Pasteur de Montevideo. -- Corresponding author: A. Moura, A.; Laboratório de Fisiologia e Ciências Ômicas, Departamento de Zootecnia, Universidade Federal do Ceará, Fortaleza, Brazil; email:arlindo.moura@gmail.com -- Menchaca, A.; Instituto de Reproducción Animal Uruguay, Fundación IRAUy, Montevideo, Uruguay; mail:menchaca.alejo@gmail.com |
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Contenido : |
ABSTRACT. -The present study was conducted to characterize the major proteome of preimplantation (D6) ovine embryos produced in vitro. COCs were aspirated from antral follicles (2–6 mm), matured and fertilized in vitro and cultured until day six. Proteins were ex- tracted separately from three pools of 45 embryos and separately run in SDS-PAGE. Proteins from each pool were individually subjected to in-gel digestion followed by LC-MS/MS. Three ‘raw files’ and protein lists were produced by Pattern Lab software, but only proteins present in all three lists were used for the bioinformatics analyses. There were 2,262 proteins identified in the 6-day-old ovine
embryos, including al- bumin, zona pellucida glycoprotein 2, 3 and 4, peptidyl arginine deiminase 6, actin cytoplasmic 1, gamma-actin 1, pyruvate kinase, heat shock protein 90 and protein disulfide isomerase, among others. Major biological processes linked to the sheep embryo proteome were translation, protein transport and protein stabilization, and molecular functions, defined as ATP binding, oxygen carrier activity and oxygen bind- ing. There were 42 enriched functional clusters
according to the 2,147 genes (UniProt database). Ten selected clusters with potential association with embryo development included translation, structural constituent of ribosomes, ribosomes, nucleosomes, structural constituent of the cytoskeleton, microtubule-based process, translation initiation factor activity, regulation of translational initiation, cell body and nucleotide biosynthetic process. The most representative KEEG pathways were ribosome, oxida- tive phosphorylation, glutathione metabolism, gap junction, mineral absorption, DNA replication and cGMP-PKG signalling pathway. Analyses of functional clusters clearly showed differences associated
with the proteome of preimplantation (D6) sheep em- bryos generated after in vitro fertilization in comparison with in vivo counterparts (Sanchez et al., 2021; https://doi.org/10.1111/rda.13897), confirming that the quality of in vitro derived blastocysts are unlike those produced in vivo. The present study portrays the first comprehensive overview of the proteome of preimplantational ovine embryos grown in vitro.
© 2022 Wiley-VCH GmbH. MenosABSTRACT. -The present study was conducted to characterize the major proteome of preimplantation (D6) ovine embryos produced in vitro. COCs were aspirated from antral follicles (2–6 mm), matured and fertilized in vitro and cultured until day six. Proteins were ex- tracted separately from three pools of 45 embryos and separately run in SDS-PAGE. Proteins from each pool were individually subjected to in-gel digestion followed by LC-MS/MS. Three ‘raw files’ and protein lists were produced by Pattern Lab software, but only proteins present in all three lists were used for the bioinformatics analyses. There were 2,262 proteins identified in the 6-day-old ovine
embryos, including al- bumin, zona pellucida glycoprotein 2, 3 and 4, peptidyl arginine deiminase 6, actin cytoplasmic 1, gamma-actin 1, pyruvate kinase, heat shock protein 90 and protein disulfide isomerase, among others. Major biological processes linked to the sheep embryo proteome were translation, protein transport and protein stabilization, and molecular functions, defined as ATP binding, oxygen carrier activity and oxygen bind- ing. There were 42 enriched functional clusters
according to the 2,147 genes (UniProt database). Ten selected clusters with potential association with embryo development included translation, structural constituent of ribosomes, ribosomes, nucleosomes, structural constituent of the cytoskeleton, microtubule-based process, translation initiation factor activity, regulation of translational i... Presentar Todo |
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Palabras claves : |
Embryo development; In vitro fertilization; Mass spectrometry; Oocyte; Ovine; PLATAFORMA SALUD ANIMAL; Proteins. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 04571naa a2200409 a 4500
001 1063149
005 2022-12-02
008 2022 bl uuuu u00u1 u #d
022 $a0936-6768
024 7 $a10.1111/rda.14122$2DOI
100 1 $aPASSOS, J. R. S.
245 $aGlobal proteomic analysis of preimplantational ovine embryos produced in vitro.$h[electronic resource]
260 $c2022
500 $aArticle history: Received 15 February 2022; Accepted 1 April 2022. -- Funding text - The experiments presently described were conducted at the facilities of the (Fundacion IRAUy, Montevideo, Uruguay) and at the (UBAL) of the , Uruguay. Specially, the authors thank Dr. Rosario Durán and Dr. Alejandro Leyva for kindly assisting us in the proteomic experiment. Finnacial support was provided by Fundacion IRAUy; PRONEX 02/2015 (Programa de Apoio a Núcleos de Excelência Pronex/Funcap/CNPq); the Brazilian Research Council?CNPq (grants # 313160/2017‐1 and 438773/2018‐7); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil. Instituto de Reproducción Animal Uruguay Unidad de Biotecnología en Animales de Laboratorio Institut Pasteur de Montevideo. -- Corresponding author: A. Moura, A.; Laboratório de Fisiologia e Ciências Ômicas, Departamento de Zootecnia, Universidade Federal do Ceará, Fortaleza, Brazil; email:arlindo.moura@gmail.com -- Menchaca, A.; Instituto de Reproducción Animal Uruguay, Fundación IRAUy, Montevideo, Uruguay; mail:menchaca.alejo@gmail.com
520 $aABSTRACT. -The present study was conducted to characterize the major proteome of preimplantation (D6) ovine embryos produced in vitro. COCs were aspirated from antral follicles (2–6 mm), matured and fertilized in vitro and cultured until day six. Proteins were ex- tracted separately from three pools of 45 embryos and separately run in SDS-PAGE. Proteins from each pool were individually subjected to in-gel digestion followed by LC-MS/MS. Three ‘raw files’ and protein lists were produced by Pattern Lab software, but only proteins present in all three lists were used for the bioinformatics analyses. There were 2,262 proteins identified in the 6-day-old ovine embryos, including al- bumin, zona pellucida glycoprotein 2, 3 and 4, peptidyl arginine deiminase 6, actin cytoplasmic 1, gamma-actin 1, pyruvate kinase, heat shock protein 90 and protein disulfide isomerase, among others. Major biological processes linked to the sheep embryo proteome were translation, protein transport and protein stabilization, and molecular functions, defined as ATP binding, oxygen carrier activity and oxygen bind- ing. There were 42 enriched functional clusters according to the 2,147 genes (UniProt database). Ten selected clusters with potential association with embryo development included translation, structural constituent of ribosomes, ribosomes, nucleosomes, structural constituent of the cytoskeleton, microtubule-based process, translation initiation factor activity, regulation of translational initiation, cell body and nucleotide biosynthetic process. The most representative KEEG pathways were ribosome, oxida- tive phosphorylation, glutathione metabolism, gap junction, mineral absorption, DNA replication and cGMP-PKG signalling pathway. Analyses of functional clusters clearly showed differences associated with the proteome of preimplantation (D6) sheep em- bryos generated after in vitro fertilization in comparison with in vivo counterparts (Sanchez et al., 2021; https://doi.org/10.1111/rda.13897), confirming that the quality of in vitro derived blastocysts are unlike those produced in vivo. The present study portrays the first comprehensive overview of the proteome of preimplantational ovine embryos grown in vitro. © 2022 Wiley-VCH GmbH.
653 $aEmbryo development
653 $aIn vitro fertilization
653 $aMass spectrometry
653 $aOocyte
653 $aOvine
653 $aPLATAFORMA SALUD ANIMAL
653 $aProteins
700 1 $aGUERREIRO, D. D.
700 1 $aOTÁVIO, K. S.
700 1 $aSANTOS-NETO, P. C. DOS
700 1 $aSOUZA-NEVES, M.
700 1 $aCUADRO, F.
700 1 $aNUÑEZ-OLIVERA, R.
700 1 $aCRISPO, M.
700 1 $aBEZERRA, M. J. B.
700 1 $aSILVA, R. F.
700 1 $aLIMA, L. F.
700 1 $aFIGUEIREDO, J. R.
700 1 $aBUSTAMANTE-FILHO, I. C.
700 1 $aMENCHACA, A.
700 1 $aMOURA, A. A.
773 $tReproduction in Domestic Animals, 2022, Volume 57, Issue 7; pages 784-797. doi: https://doi.org/10.1111/rda.14122
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
31/01/2020 |
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Actualizado : |
31/01/2020 |
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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 : |
GASO, D.; BERGER, A.; CIGANDA, V. |
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Afiliación : |
DEBORAH VIVIANA GASO MELGAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VERONICA SOLANGE CIGANDA BRASCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Computers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026 |
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ISSN : |
0168-1699 |
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DOI : |
10.1016/j.compag.2019.02.026 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019..
This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data. |
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Contenido : |
ABSTRACT.
Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V. MenosABSTRACT.
Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to ... Presentar Todo |
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Palabras claves : |
Crop growth model; Landsat; Leaf area index; Wheat; Yield. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 02944naa a2200241 a 4500
001 1060735
005 2020-01-31
008 2019 bl uuuu u00u1 u #d
022 $a0168-1699
024 7 $a10.1016/j.compag.2019.02.026$2DOI
100 1 $aGASO, D.
245 $aPredicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images.$h[electronic resource]
260 $c2019
500 $aArticle history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019.. This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data.
520 $aABSTRACT. Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V.
653 $aCrop growth model
653 $aLandsat
653 $aLeaf area index
653 $aWheat
653 $aYield
700 1 $aBERGER, A.
700 1 $aCIGANDA, V.
773 $tComputers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026
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