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NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encodi...

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Autores principales: Shami, Anam Amin, Akhtar, Muhammad Tayyab, Mumtaz, Muhammad Waseem, Mukhtar, Hamid, Tahir, Amna, Shahzad-ul-Hussan, Syed, Chaudhary, Safee Ullah, Muneer, Bushra, Iftikhar, Hafsa, Neophytou, Marios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966674/
https://www.ncbi.nlm.nih.gov/pubmed/36838756
http://dx.doi.org/10.3390/molecules28041763
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author Shami, Anam Amin
Akhtar, Muhammad Tayyab
Mumtaz, Muhammad Waseem
Mukhtar, Hamid
Tahir, Amna
Shahzad-ul-Hussan, Syed
Chaudhary, Safee Ullah
Muneer, Bushra
Iftikhar, Hafsa
Neophytou, Marios
author_facet Shami, Anam Amin
Akhtar, Muhammad Tayyab
Mumtaz, Muhammad Waseem
Mukhtar, Hamid
Tahir, Amna
Shahzad-ul-Hussan, Syed
Chaudhary, Safee Ullah
Muneer, Bushra
Iftikhar, Hafsa
Neophytou, Marios
author_sort Shami, Anam Amin
collection PubMed
description Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.
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spelling pubmed-99666742023-02-26 NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches Shami, Anam Amin Akhtar, Muhammad Tayyab Mumtaz, Muhammad Waseem Mukhtar, Hamid Tahir, Amna Shahzad-ul-Hussan, Syed Chaudhary, Safee Ullah Muneer, Bushra Iftikhar, Hafsa Neophytou, Marios Molecules Article Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies. MDPI 2023-02-13 /pmc/articles/PMC9966674/ /pubmed/36838756 http://dx.doi.org/10.3390/molecules28041763 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shami, Anam Amin
Akhtar, Muhammad Tayyab
Mumtaz, Muhammad Waseem
Mukhtar, Hamid
Tahir, Amna
Shahzad-ul-Hussan, Syed
Chaudhary, Safee Ullah
Muneer, Bushra
Iftikhar, Hafsa
Neophytou, Marios
NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches
title NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches
title_full NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches
title_fullStr NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches
title_full_unstemmed NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches
title_short NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches
title_sort nmr-based metabolomics: a new paradigm to unravel defense-related metabolites in insect-resistant cotton variety through different multivariate data analysis approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966674/
https://www.ncbi.nlm.nih.gov/pubmed/36838756
http://dx.doi.org/10.3390/molecules28041763
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