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      Multi-omics analysis of cellular pathways involved in different rapid growth stages of moso bamboo

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          Abstract

          Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau) is a rapidly growing grass of industrial and ecological importance. However, the molecular mechanisms of its remarkable growth are not well understood. In this study, we investigated the early-stage growth of moso bamboo shoots and defined three different growth stages based on histological and biochemical analyses, namely, starting of cell division (SD), rapid division (RD) and rapid elongation (RE). Further analyses on potentially relevant cellular pathways in these growth stages using multi-omics approaches such as transcriptomics and proteomics revealed the involvement of multiple cellular pathways, including DNA replication, repair and ribosome biogenesis. A total of 8045 differentially expressed genes (DEGs) and 1053 differentially expressed proteins (DEPs) were identified in our analyses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of detected DEGs identified several key biological pathways such as phytohormone metabolism, signal transduction, cell wall development and carbohydrate metabolism. The comparative analysis of proteins displayed that a total of 213 DEPs corresponded with DEGs and 3 significant expression profiles that could be promoting the fast growth of bamboo internodes. Moreover, protein–protein interaction network prediction analysis is suggestive of the involvement of five major proteins of signal transduction, DNA synthesis and RNA transcription, and may act as key elements responsible for the rapid shoot growth. Our work exploits multi-omics and bioinformatic approaches to unfurl the complexity of molecular networks involved in the rapid growth of moso bamboo and opens up questions related to the interactions between the functions played by individual molecular pathway.

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          Most cited references76

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

          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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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

              High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Tree Physiology
                Oxford University Press (OUP)
                1758-4469
                November 2020
                October 29 2020
                July 24 2020
                November 2020
                October 29 2020
                July 24 2020
                : 40
                : 11
                : 1487-1508
                Affiliations
                [1 ]State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin’an, Hangzhou 311300, Zhejiang, China
                [2 ]Zhejiang Provincial Collaborative Innovation Centre for Bamboo Resources and High-efficiency Utilization, Zhejiang A&F University, Lin’an, Hangzhou 311300, Zhejiang, China
                [3 ]The State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Xiangshan, Haidian District, Beijing, China
                [4 ]Research Institute of Forestry, Chinese Academy of Forestry, Xiangshan, Haidian District, Beijing, China
                [5 ]Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
                [6 ]Department of Forest Sciences, University of Helsinki, Helsinki P.O. Box 27 00014, Finland
                [7 ]Shanghai Center for Plant Stress Biology, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, 200032, China
                [8 ]National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
                [9 ]CAS-JIC Centre of Excellence for Plant and Microbial Science, Chinese Academy of Sciences, Shanghai 200032, China
                Article
                10.1093/treephys/tpaa090
                006d69dc-394a-42ab-803f-486a6469105e
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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