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      Expression of a maize SOC1 gene enhances soybean yield potential through modulating plant growth and flowering

      research-article
      1 , 2 , 1 ,
      Scientific Reports
      Nature Publishing Group UK
      Biotechnology, Plant sciences

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          Abstract

          Yield enhancement is a top priority for soybean ( Glycine max Merr.) breeding. SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 ( SOC1) is a major integrator in flowering pathway, and it is anticipated to be capable of regulating soybean reproductive stages through its interactions with other MADS-box genes. Thus, we produced transgenic soybean for a constitutive expression of a maize SOC1 ( ZmSOC1). T 1 transgenic plants, in comparison with the nontransgenic plants, showed early flowering, reduced height of mature plants, and no significant impact on grain quality. The transgenic plants also had a 13.5–23.2% of higher grain weight per plant than the nontransgenic plants in two experiments. Transcriptome analysis in the leaves of 34-day old plants revealed 58 differentially expressed genes (DEGs) responding to the expression of the ZmSOC1, of which the upregulated FRUITFULL MADS-box gene, as well as the transcription factor VASCULAR PLANT ONE-ZINC FINGER1, contributed to the promoted flowering. The downregulated gibberellin receptor GID1B could play a major role in reducing the plant height. The remaining DEGs suggested broader effects on the other unmeasured traits (e.g., photosynthesis efficiency and abiotic tolerance), which could contribute to yield increase. Overall, modulating expression of SOC1 in soybean provides a novel and promising approach to regulate plant growth and reproductive development and thus has a potential either to enhance grain yield or to change plant adaptability.

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            De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis.

            De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.
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                Author and article information

                Contributors
                songg@msu.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                17 June 2021
                17 June 2021
                2021
                : 11
                : 12758
                Affiliations
                [1 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Plant Biotechnology Resource and Outreach Center, Department of Horticulture, , Michigan State University, ; East Lansing, MI 48824 USA
                [2 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Plant Soil and Microbial Sciences, , Michigan State University, ; East Lansing, MI 48824 USA
                Article
                92215
                10.1038/s41598-021-92215-x
                8211702
                34140602
                023a86fe-b751-4ea1-b5da-eed2dcf999e8
                © 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 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
                : 22 January 2021
                : 7 June 2021
                Funding
                Funded by: This research was partially supported by MTRAC Program at Michigan State University, East Lansing, MI
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                © The Author(s) 2021

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                biotechnology,plant sciences
                Uncategorized
                biotechnology, plant sciences

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