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      Genome-wide analysis of the MADS-box gene family in Lonicera japonica and a proposed floral organ identity model

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          Abstract

          Background

          Lonicera japonica Thunb. is widely used in traditional Chinese medicine. Medicinal L. japonica mainly consists of dried flower buds and partially opened flowers, thus flowers are an important quality indicator. MADS-box genes encode transcription factors that regulate flower development. However, little is known about these genes in L. japonica.

          Results

          In this study, 48 MADS-box genes were identified in L. japonica, including 20 Type-I genes (8 Mα, 2 Mβ, and 10 Mγ) and 28 Type-II genes (26 MIKC c and 2 MIKC *). The Type-I and Type-II genes differed significantly in gene structure, conserved domains, protein structure, chromosomal distribution, phylogenesis, and expression pattern. Type-I genes had a simpler gene structure, lacked the K domain, had low protein structure conservation, were tandemly distributed on the chromosomes, had more frequent lineage-specific duplications, and were expressed at low levels. In contrast, Type-II genes had a more complex gene structure; contained conserved M, I, K, and C domains; had highly conserved protein structure; and were expressed at high levels throughout the flowering period. Eleven floral homeotic MADS-box genes that are orthologous to the proposed Arabidopsis ABCDE model of floral organ identity determination, were identified in L. japonica. By integrating expression pattern and protein interaction data for these genes, we developed a possible model for floral organ identity determination.

          Conclusion

          This study genome-widely identified and characterized the MADS-box gene family in L. japonica. Eleven floral homeotic MADS-box genes were identified and a possible model for floral organ identity determination was also developed. This study contributes to our understanding of the MADS-box gene family and its possible involvement in floral organ development in L. japonica.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-023-09509-9.

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          Most cited references91

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)
          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Highly accurate protein structure prediction with AlphaFold

            John Jumper, Richard Evans, Alexander Pritzel … (2021)
            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              TBtools - an integrative toolkit developed for interactive analyses of big biological data

              Chengjie Chen, Hao Chen, Yi Zhang … (2020)
              The rapid development of high-throughput sequencing techniques has led biology into the big-data era. Data analyses using various bioinformatics tools rely on programming and command-line environments, which are challenging and time-consuming for most wet-lab biologists. Here, we present TBtools (a Toolkit for Biologists integrating various biological data-handling tools), a stand-alone software with a user-friendly interface. The toolkit incorporates over 130 functions, which are designed to meet the increasing demand for big-data analyses, ranging from bulk sequence processing to interactive data visualization. A wide variety of graphs can be prepared in TBtools using a new plotting engine ("JIGplot") developed to maximize their interactive ability; this engine allows quick point-and-click modification of almost every graphic feature. TBtools is platform-independent software that can be run under all operating systems with Java Runtime Environment 1.6 or newer. It is freely available to non-commercial users at https://github.com/CJ-Chen/TBtools/releases.
              Article 0 comments Cited 3251 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Chengyuan Liang: liangcy618@cnbg.net
                Journal
                Journal ID (nlm-ta): BMC Genomics
                Journal ID (iso-abbrev): BMC Genomics
                Title: BMC Genomics
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2164
                Publication date (Electronic): 8 August 2023
                Publication date PMC-release: 8 August 2023
                Publication date Collection: 2023
                Volume: 24
                Electronic Location Identifier: 447
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, , Chinese Academy of Sciences, ; Nanjing, 210014 Jiangsu Province China
                [2 ]GRID grid.410745.3, ISNI 0000 0004 1765 1045, Nanjing University of Chinese Medicine, ; Nanjing, 210023 China
                Article
                Publisher ID: 9509
                DOI: 10.1186/s12864-023-09509-9
                PMC ID: 10408238
                SO-VID: 8381714d-33ed-4e85-b36c-d62c3851dd87
                Copyright © © BioMed Central Ltd., part of Springer Nature 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 : 1 December 2022
                Date accepted : 8 July 2023
                Funding
                Funded by: Science and Technology Support Project Fund of Changzhou
                Award ID: CE20202006
                Funded by: National Natural Science Foundation of China
                Award ID: 32000262
                Funded by: Independent Research Fund of Public Research Institutes in Jiangsu Province
                Award ID: BM2018021-4
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © BioMed Central Ltd., part of Springer Nature 2023

                ScienceOpen disciplines: Genetics
                Keywords: lonicera japonica,mads-box genes,expression pattern,abcde model,floral organ identity
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: lonicera japonica, mads-box genes, expression pattern, abcde model, floral organ identity

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