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Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data
BACKGROUND: Jatropha curcas L. is an important non-edible oilseed crop with a promising future in biodiesel production. However, little is known about the molecular biology of oil biosynthesis in this plant when compared with other established oilseed crops, resulting in the absence of agronomically...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375076/ https://www.ncbi.nlm.nih.gov/pubmed/34407772 http://dx.doi.org/10.1186/s12859-021-04319-w |
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author | Zhang, Xuan Li, Jing Pan, Bang-Zhen Chen, Wen Chen, Maosheng Tang, Mingyong Xu, Zeng-Fu Liu, Changning |
author_facet | Zhang, Xuan Li, Jing Pan, Bang-Zhen Chen, Wen Chen, Maosheng Tang, Mingyong Xu, Zeng-Fu Liu, Changning |
author_sort | Zhang, Xuan |
collection | PubMed |
description | BACKGROUND: Jatropha curcas L. is an important non-edible oilseed crop with a promising future in biodiesel production. However, little is known about the molecular biology of oil biosynthesis in this plant when compared with other established oilseed crops, resulting in the absence of agronomically improved varieties of Jatropha. To extensively discover the potentially novel genes and pathways associated with the oil biosynthesis in J. curcas, new strategy other than homology alignment is on the demand. RESULTS: In this study, we proposed a multi-step computational framework that integrates transcriptome and gene interactome data to predict functional pathways in non-model organisms in an extended process, and applied it to study oil biosynthesis pathway in J. curcas. Using homologous mapping against Arabidopsis and transcriptome profile analysis, we first constructed protein–protein interaction (PPI) and co-expression networks in J. curcas. Then, using the homologs of Arabidopsis oil-biosynthesis-related genes as seeds, we respectively applied two algorithm models, random walk with restart (RWR) in PPI network and negative binomial distribution (NBD) in co-expression network, to further extend oil-biosynthesis-related pathways and genes in J. curcas. At last, using k-nearest neighbors (KNN) algorithm, the predicted genes were further classified into different sub-pathways according to their possible functional roles. CONCLUSIONS: Our method exhibited a highly efficient way of mining the extended oil biosynthesis pathway of J. curcas. Overall, 27 novel oil-biosynthesis-related gene candidates were predicted and further assigned to 5 sub-pathways. These findings can help better understanding of the oil biosynthesis pathway of J. curcas, as well as paving the way for the following J. curcas breeding application. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04319-w. |
format | Online Article Text |
id | pubmed-8375076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83750762021-08-19 Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data Zhang, Xuan Li, Jing Pan, Bang-Zhen Chen, Wen Chen, Maosheng Tang, Mingyong Xu, Zeng-Fu Liu, Changning BMC Bioinformatics Research BACKGROUND: Jatropha curcas L. is an important non-edible oilseed crop with a promising future in biodiesel production. However, little is known about the molecular biology of oil biosynthesis in this plant when compared with other established oilseed crops, resulting in the absence of agronomically improved varieties of Jatropha. To extensively discover the potentially novel genes and pathways associated with the oil biosynthesis in J. curcas, new strategy other than homology alignment is on the demand. RESULTS: In this study, we proposed a multi-step computational framework that integrates transcriptome and gene interactome data to predict functional pathways in non-model organisms in an extended process, and applied it to study oil biosynthesis pathway in J. curcas. Using homologous mapping against Arabidopsis and transcriptome profile analysis, we first constructed protein–protein interaction (PPI) and co-expression networks in J. curcas. Then, using the homologs of Arabidopsis oil-biosynthesis-related genes as seeds, we respectively applied two algorithm models, random walk with restart (RWR) in PPI network and negative binomial distribution (NBD) in co-expression network, to further extend oil-biosynthesis-related pathways and genes in J. curcas. At last, using k-nearest neighbors (KNN) algorithm, the predicted genes were further classified into different sub-pathways according to their possible functional roles. CONCLUSIONS: Our method exhibited a highly efficient way of mining the extended oil biosynthesis pathway of J. curcas. Overall, 27 novel oil-biosynthesis-related gene candidates were predicted and further assigned to 5 sub-pathways. These findings can help better understanding of the oil biosynthesis pathway of J. curcas, as well as paving the way for the following J. curcas breeding application. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04319-w. BioMed Central 2021-08-18 /pmc/articles/PMC8375076/ /pubmed/34407772 http://dx.doi.org/10.1186/s12859-021-04319-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhang, Xuan Li, Jing Pan, Bang-Zhen Chen, Wen Chen, Maosheng Tang, Mingyong Xu, Zeng-Fu Liu, Changning Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data |
title | Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data |
title_full | Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data |
title_fullStr | Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data |
title_full_unstemmed | Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data |
title_short | Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data |
title_sort | extended mining of the oil biosynthesis pathway in biofuel plant jatropha curcas by combined analysis of transcriptome and gene interactome data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375076/ https://www.ncbi.nlm.nih.gov/pubmed/34407772 http://dx.doi.org/10.1186/s12859-021-04319-w |
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