Reference genes for normalization of qPCR assays in sugarcane plants under water deficit
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/31746 |
Resumo: | Background: sugarcane (Saccharum spp.) is the main raw material for sugar and ethanol production. Among the abiotic stress, drought is the main one that negatively impact sugarcane yield. Although gene expression analysis through quantitative PCR (qPCR) has increased our knowledge about biological processes related to drought, gene network that mediates sugarcane responses to water deficit remains elusive. In such scenario, validation of reference gene is a major requirement for successful analyzes involving qPCR. Results: in this study, candidate genes were tested for their suitable as reference genes for qPCR analyses in two sugarcane cultivars with varying drought tolerance. Eight candidate reference genes were evaluated in leaves sampled in plants subjected to water deficit in both field and greenhouse conditions. In addition, five genes were evaluated in shoot roots of plants subjected to water deficit by adding PEG8000 to the nutrient solution. NormFinder and RefFinder algorithms were used to identify the most stable gene(s) among genotypes and under different experimental conditions. Both algorithms revealed that in leaf samples, UBQ1 and GAPDH genes were more suitable as reference genes, whereas GAPDH was the best reference one in shoot roots. Conclusion: reference genes suitable for sugarcane under water deficit were identified, which would lead to a more accurate and reliable analysis of qPCR. Thus, results obtained in this study may guide future research on gene expression in sugarcane under varying water conditions. |
id |
UFLA_b695e0de11dbdab202d51948d92226c2 |
---|---|
oai_identifier_str |
oai:localhost:1/31746 |
network_acronym_str |
UFLA |
network_name_str |
Repositório Institucional da UFLA |
repository_id_str |
|
spelling |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficitSaccharum spp.Water-deprivationNormalizationNormFinder algorithmRefFinder algorithmWater deficitWater stressBackground: sugarcane (Saccharum spp.) is the main raw material for sugar and ethanol production. Among the abiotic stress, drought is the main one that negatively impact sugarcane yield. Although gene expression analysis through quantitative PCR (qPCR) has increased our knowledge about biological processes related to drought, gene network that mediates sugarcane responses to water deficit remains elusive. In such scenario, validation of reference gene is a major requirement for successful analyzes involving qPCR. Results: in this study, candidate genes were tested for their suitable as reference genes for qPCR analyses in two sugarcane cultivars with varying drought tolerance. Eight candidate reference genes were evaluated in leaves sampled in plants subjected to water deficit in both field and greenhouse conditions. In addition, five genes were evaluated in shoot roots of plants subjected to water deficit by adding PEG8000 to the nutrient solution. NormFinder and RefFinder algorithms were used to identify the most stable gene(s) among genotypes and under different experimental conditions. Both algorithms revealed that in leaf samples, UBQ1 and GAPDH genes were more suitable as reference genes, whereas GAPDH was the best reference one in shoot roots. Conclusion: reference genes suitable for sugarcane under water deficit were identified, which would lead to a more accurate and reliable analysis of qPCR. Thus, results obtained in this study may guide future research on gene expression in sugarcane under varying water conditions.BioMed Central (BMC)2018-11-13T11:13:41Z2018-11-13T11:13:41Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfANDRADE, L. M. de et al. Reference genes for normalization of qPCR assays in sugarcane plants under water deficit. Plant Methods, [S.l.], v. 13, p. 1-9, 2017.http://repositorio.ufla.br/jspui/handle/1/31746Plant Methodsreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAndrade, Larissa Mara deBrito, Michael dos SantosPeixoto Junior, Rafael FáveroMarchiori, Paulo Eduardo RibeiroNóbile, Paula MacedoMartins, Alexandre Palma BoerRibeiro, Rafael VasconcelosCreste, Silvanaeng2018-11-13T11:13:41Zoai:localhost:1/31746Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2018-11-13T11:13:41Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficit |
title |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficit |
spellingShingle |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficit Andrade, Larissa Mara de Saccharum spp. Water-deprivation Normalization NormFinder algorithm RefFinder algorithm Water deficit Water stress |
title_short |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficit |
title_full |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficit |
title_fullStr |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficit |
title_full_unstemmed |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficit |
title_sort |
Reference genes for normalization of qPCR assays in sugarcane plants under water deficit |
author |
Andrade, Larissa Mara de |
author_facet |
Andrade, Larissa Mara de Brito, Michael dos Santos Peixoto Junior, Rafael Fávero Marchiori, Paulo Eduardo Ribeiro Nóbile, Paula Macedo Martins, Alexandre Palma Boer Ribeiro, Rafael Vasconcelos Creste, Silvana |
author_role |
author |
author2 |
Brito, Michael dos Santos Peixoto Junior, Rafael Fávero Marchiori, Paulo Eduardo Ribeiro Nóbile, Paula Macedo Martins, Alexandre Palma Boer Ribeiro, Rafael Vasconcelos Creste, Silvana |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Andrade, Larissa Mara de Brito, Michael dos Santos Peixoto Junior, Rafael Fávero Marchiori, Paulo Eduardo Ribeiro Nóbile, Paula Macedo Martins, Alexandre Palma Boer Ribeiro, Rafael Vasconcelos Creste, Silvana |
dc.subject.por.fl_str_mv |
Saccharum spp. Water-deprivation Normalization NormFinder algorithm RefFinder algorithm Water deficit Water stress |
topic |
Saccharum spp. Water-deprivation Normalization NormFinder algorithm RefFinder algorithm Water deficit Water stress |
description |
Background: sugarcane (Saccharum spp.) is the main raw material for sugar and ethanol production. Among the abiotic stress, drought is the main one that negatively impact sugarcane yield. Although gene expression analysis through quantitative PCR (qPCR) has increased our knowledge about biological processes related to drought, gene network that mediates sugarcane responses to water deficit remains elusive. In such scenario, validation of reference gene is a major requirement for successful analyzes involving qPCR. Results: in this study, candidate genes were tested for their suitable as reference genes for qPCR analyses in two sugarcane cultivars with varying drought tolerance. Eight candidate reference genes were evaluated in leaves sampled in plants subjected to water deficit in both field and greenhouse conditions. In addition, five genes were evaluated in shoot roots of plants subjected to water deficit by adding PEG8000 to the nutrient solution. NormFinder and RefFinder algorithms were used to identify the most stable gene(s) among genotypes and under different experimental conditions. Both algorithms revealed that in leaf samples, UBQ1 and GAPDH genes were more suitable as reference genes, whereas GAPDH was the best reference one in shoot roots. Conclusion: reference genes suitable for sugarcane under water deficit were identified, which would lead to a more accurate and reliable analysis of qPCR. Thus, results obtained in this study may guide future research on gene expression in sugarcane under varying water conditions. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2018-11-13T11:13:41Z 2018-11-13T11:13:41Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
ANDRADE, L. M. de et al. Reference genes for normalization of qPCR assays in sugarcane plants under water deficit. Plant Methods, [S.l.], v. 13, p. 1-9, 2017. http://repositorio.ufla.br/jspui/handle/1/31746 |
identifier_str_mv |
ANDRADE, L. M. de et al. Reference genes for normalization of qPCR assays in sugarcane plants under water deficit. Plant Methods, [S.l.], v. 13, p. 1-9, 2017. |
url |
http://repositorio.ufla.br/jspui/handle/1/31746 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
BioMed Central (BMC) |
publisher.none.fl_str_mv |
BioMed Central (BMC) |
dc.source.none.fl_str_mv |
Plant Methods reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835060770963456 |