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      Integrative iTRAQ-based proteomic and transcriptomic analysis reveals the accumulation patterns of key metabolites associated with oil quality during seed ripening of Camellia oleifera

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          Abstract

          Camellia oleifera ( C. oleifera) is one of the four major woody oil-bearing crops in the world and has relatively high ecological, economic, and medicinal value. Its seeds undergo a series of complex physiological and biochemical changes during ripening, which is mainly manifested as the accumulation and transformation of certain metabolites closely related to oil quality, especially flavonoids and fatty acids. To obtain new insights into the underlying molecular mechanisms, a parallel analysis of the transcriptome and proteome profiles of C. oleifera seeds at different maturity levels was conducted using RNA sequencing (RNA-seq) and isobaric tags for relative and absolute quantification (iTRAQ) complemented with gas chromatography-mass spectrometry (GC-MS) data. A total of 16,530 transcripts and 1228 proteins were recognized with significant differential abundances in pairwise comparisons of samples at various developmental stages. Among these, 317 were coexpressed with a poor correlation, and most were involved in metabolic processes, including fatty acid metabolism, α-linolenic acid metabolism, and glutathione metabolism. In addition, the content of total flavonoids decreased gradually with seed maturity, and the levels of fatty acids generally peaked at the fat accumulation stage; these results basically agreed with the regulation patterns of genes or proteins in the corresponding pathways. The expression levels of proteins annotated as upstream candidates of phenylalanine ammonia-lyase (PAL) and chalcone synthase (CHS) as well as their cognate transcripts were positively correlated with the variation in the flavonoid content, while shikimate O-hydroxycinnamoyltransferase (HCT)-encoding genes had the opposite pattern. The increase in the abundance of proteins and mRNAs corresponding to alcohol dehydrogenase (ADH) was associated with a reduction in linoleic acid synthesis. Using weighted gene coexpression network analysis (WGCNA), we further identified six unique modules related to flavonoid, oil, and fatty acid anabolism that contained hub genes or proteins similar to transcription factors (TFs), such as MADS intervening keratin-like and C-terminal (MIKC_MADS), type-B authentic response regulator (ARR-B), and basic helix-loop-helix (bHLH). Finally, based on the known metabolic pathways and WGCNA combined with the correlation analysis, five coexpressed transcripts and proteins composed of cinnamyl-alcohol dehydrogenases (CADs), caffeic acid 3-O-methyltransferase (COMT), flavonol synthase (FLS), and 4-coumarate: CoA ligase (4CL) were screened out. With this exploratory multiomics dataset, our results presented a dynamic picture regarding the maturation process of C. oleifera seeds on Hainan Island, not only revealing the temporal specific expression of key candidate genes and proteins but also providing a scientific basis for the genetic improvement of this tree species.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            WGCNA: an R package for weighted correlation network analysis

            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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              Universal sample preparation method for proteome analysis.

              We describe a method, filter-aided sample preparation (FASP), which combines the advantages of in-gel and in-solution digestion for mass spectrometry-based proteomics. We completely solubilized the proteome in sodium dodecyl sulfate, which we then exchanged by urea on a standard filtration device. Peptides eluted after digestion on the filter were pure, allowing single-run analyses of organelles and an unprecedented depth of proteome coverage.
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                Author and article information

                Contributors
                wygeng2003@163.com
                hxl@njau.edu.cn
                Journal
                Hortic Res
                Hortic Res
                Horticulture Research
                Nature Publishing Group UK (London )
                2662-6810
                2052-7276
                1 July 2021
                1 July 2021
                2021
                : 8
                : 157
                Affiliations
                [1 ]GRID grid.428986.9, ISNI 0000 0001 0373 6302, College of Horticulture, , Hainan University, ; Haikou, China
                [2 ]State Key Laboratory of Crop Genetics & Germplasm Enhancement, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (East China), Ministry of Agriculture and Rural Affairs of the P.R. China, Engineering Research Center of Germplasm Enhancement and Utilization of Horticultural Crops, Ministry of Education of the P.R. China, Institute of Plasma Engineering, Nanjing, China
                Author information
                http://orcid.org/0000-0002-0997-7163
                http://orcid.org/0000-0003-0776-2959
                Article
                591
                10.1038/s41438-021-00591-2
                8245520
                34193845
                b3c95f44-b23f-4b46-893e-2b1f88fb8b5c
                © The Author(s) 2021

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 December 2020
                : 28 April 2021
                : 4 May 2021
                Funding
                Funded by: Demonstration funds for the Promotion of Forestry science and Technology from the Central Government ([2020]TG 02)
                Funded by: Hainan Province Innovative Scientific Research Projects of Postgraduates (No. Hyb2019-01)
                Categories
                Article
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                © The Author(s) 2021

                transcriptomics,plant molecular biology
                transcriptomics, plant molecular biology

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