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      Comparative Transcriptome Combined with Morphophysiological Analyses Revealed Carotenoid Biosynthesis for Differential Chilling Tolerance in Two Contrasting Rice ( Oryza sativa L.) Genotypes

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          Abstract

          Early spring cold spells can lead to leaf chlorosis during the rice seedling greening process. However, the physiological and molecular mechanisms underlying the rice greening process under low-temperature conditions remain unknown. In this study, comparative transcriptome and morphophysiological analyses were performed to investigate the mechanisms mediating the responses of the Koshihikari (Kos) and Kasalath (Kas) rice cultivars to chilling stress. According to their growth-related traits, electrolyte leakage, and chlorophyll fluorescence parameters, Kos was more tolerant to low-temperature stress than Kas. Moreover, chloroplast morphology was more normal (e.g., oval) in Kos than in Kas at 17 °C. The comparative transcriptome analysis revealed 610 up-regulated differentially expressed genes that were common to all four comparisons. Furthermore, carotenoid biosynthesis was identified as a critical pathway for the Kos response to chilling stress. The genes in the carotenoid biosynthesis pathway were expressed at higher levels in Kos than in Kas at 17 °C, which was in accordance with the higher leaf carotenoid content in Kos than in Kas. The lycopene β-cyclase and lycopene ε-cyclase activities increased more in Kos than in Kas. Additionally, the increases in the violaxanthin de-epoxidase and carotenoid hydroxylase activities in Kos seedlings resulted in the accumulation of zeaxanthin and lutein and mitigated the effects of chilling stress on chloroplasts. These findings have clarified the molecular mechanisms underlying the chilling tolerance of rice seedlings during the greening process.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12284-023-00669-6.

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          A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding

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            RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

            Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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              Differential expression analysis for sequence count data

              High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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                Author and article information

                Contributors
                liliangtao@hebeu.edu.cn
                chenhuizhe@163.com
                yikaizhang168@163.com
                Journal
                Rice (N Y)
                Rice (N Y)
                Rice
                Springer US (New York )
                1939-8425
                1939-8433
                25 November 2023
                25 November 2023
                2023
                : 16
                : 52
                Affiliations
                [1 ]State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, ( https://ror.org/05szcn205) Hangzhou, 310006 Zhejiang People’s Republic of China
                [2 ]College of Landscape and Ecological Engineering, Hebei University of Engineering, ( https://ror.org/036h65h05) Handan, 056009 Hebei People’s Republic of China
                [3 ]Guizhou Rice Research Institute, Guiyang, 550009 Guizhou People’s Republic of China
                Article
                669
                10.1186/s12284-023-00669-6
                10676345
                38006430
                465c3f06-93f8-4a06-a9f1-7391ba63c0a4
                © The Author(s) 2023

                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/.

                History
                : 7 September 2023
                : 16 November 2023
                Funding
                Funded by: Special Funds for the Construction of Modern Agricultural Technology System
                Award ID: CARS-01-22
                Funded by: “Leading Geese, Pioneer” Research and Development Project of Zhejiang Province
                Award ID: 2022C02008
                Award ID: 2023C02004
                Award ID: 2022C02034
                Funded by: the National Key Research and Development Plan of China
                Award ID: 2022YFD1500404
                Categories
                Research
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2023

                Agriculture
                rice,carotenoid biosynthesis,chloroplast development,cold tolerance,greening process
                Agriculture
                rice, carotenoid biosynthesis, chloroplast development, cold tolerance, greening process

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