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      Oil candidate genes in seeds of cotton ( Gossypium hirsutum L.) and functional validation of GhPXN1

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          Abstract

          Background

          Cottonseed oil is a promising edible plant oil with abundant unsaturated fatty acids. However, few studies have been conducted to explore the characteristics of cottonseed oil. The molecular mechanism of cottonseed oil accumulation remains unclear.

          Results

          In the present study, we conducted comparative transcriptome and weighted gene co-expression network (WGCNA) analysis for two G. hirsutum materials with significant difference in cottonseed oil content. Results showed that, between the high oil genotype 6053 (H6053) and the low oil genotype 2052 (L2052), a total of 412, 507, 1,121, 1,953, and 2,019 differentially expressed genes (DEGs) were detected at 10, 15, 20, 25, and 30 DPA, respectively. Remarkably, a large number of the down-regulated DEGs were enriched in the phenylalanine metabolic processes. Investigation into the dynamic changes of expression profiling of genes associated with both phenylalanine metabolism and oil biosynthesis has shed light on a significant competitive relationship in substrate allocation during cottonseed development. Additionally, the WGCNA analysis of all DEGs identified eight distinct modules, one of which includes GhPXN1, a gene closely associated with oil accumulation. Through phylogenetic analysis, we hypothesized that GhPXN1 in G. hirsutum might have been introgressed from G. arboreum. Overexpression of the GhPXN1 gene in tobacco leaf suggested a significant reduction in oil content compared to the empty-vector transformants. Furthermore, ten other crucial oil candidate genes identified in this study were also validated using quantitative real-time PCR (qRT-PCR).

          Conclusions

          Overall, this study enhances our comprehension of the molecular mechanisms underlying cottonseed oil accumulation.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13068-023-02420-1.

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          Most cited references58

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          Kenneth Livak, Thomas D. Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            WGCNA: an R package for weighted correlation network analysis

            Peter Langfelder, Steve Horvath (2008)
            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              HISAT: a fast spliced aligner with low memory requirements.

              Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)
              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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                Author and article information

                Contributors
                Zongming Xie: xiezmchy@163.com
                Jiwen Yu: yujw666@hotmail.com
                Zhibin Zhang: zhibinzhang90@163.com
                Journal
                Journal ID (nlm-ta): Biotechnol Biofuels Bioprod
                Journal ID (iso-abbrev): Biotechnol Biofuels Bioprod
                Title: Biotechnology for Biofuels and Bioproducts
                Publisher: BioMed Central (London )
                ISSN (Electronic): 2731-3654
                Publication date (Electronic): 6 November 2023
                Publication date PMC-release: 6 November 2023
                Publication date Collection: 2023
                Volume: 16
                Electronic Location Identifier: 169
                Affiliations
                [1 ]Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University, ( https://ror.org/04ypx8c21) Zhengzhou, 450000 China
                [2 ]GRID grid.410727.7, ISNI 0000 0001 0526 1937, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, , Chinese Academy of Agricultural Sciences, ; Anyang, 455000 China
                [3 ]GRID grid.469620.f, ISNI 0000 0004 4678 3979, Key Laboratory of Cotton Biology and Genetic Breeding in the Northwest Inland Cotton Production Region of the Ministry of Agriculture and Rural Affairs, Institute of Cotton Research, , Xinjiang Academy of Agricultural and Reclamation Science, ; Shihezi, 832000 China
                [4 ]Shijiazhuang Academy of Agriculture and Forestry Sciences, ( https://ror.org/0201w0c54) Shijiazhuang, 050000 China
                [5 ]Jiangsu Academy of Agricultural Sciences, ( https://ror.org/001f9e125) Nanjing, 210000 China
                Article
                Publisher ID: 2420
                DOI: 10.1186/s13068-023-02420-1
                PMC ID: 10629180
                PubMed ID: 37932798
                SO-VID: fbc87659-bc26-4e02-bff5-43dc15a73567
                Copyright © © The Author(s) 2023
                License:

                Open Access This 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/. The Creative Commons Public Domain Dedication waiver ( http://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.

                History
                Date received : 10 July 2023
                Date accepted : 26 October 2023
                Funding
                Funded by: the Natural Science Foundation of Henan
                Award ID: No.232300421010
                Award Recipient :
                Funded by: the Fundamental Research Funds of State Key Laboratory of Cotton Biology
                Award ID: CB2021E03
                Award Recipient :
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © BioMed Central Ltd., part of Springer Nature 2023

                Keywords: gossypium hirsutum l.,cottonseed oil content,comparative transcriptome,fatty acid biosynthesis,gene introgression
                Data availability:
                Keywords: gossypium hirsutum l., cottonseed oil content, comparative transcriptome, fatty acid biosynthesis, gene introgression

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