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Manechini JRV, Santos PHDS, Romanel E, Brito MDS, Scarpari MS, Jackson S, Pinto LR, Vicentini R. Transcriptomic Analysis of Changes in Gene Expression During Flowering Induction in Sugarcane Under Controlled Photoperiodic Conditions. FRONTIERS IN PLANT SCIENCE 2021; 12:635784. [PMID: 34211482 PMCID: PMC8239368 DOI: 10.3389/fpls.2021.635784] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 04/12/2021] [Indexed: 05/11/2023]
Abstract
Flowering is of utmost relevance for the agricultural productivity of the sugarcane bioeconomy, but data and knowledge of the genetic mechanisms underlying its photoperiodic induction are still scarce. An understanding of the molecular mechanisms that regulate the transition from vegetative to reproductive growth in sugarcane could provide better control of flowering for breeding. This study aimed to investigate the transcriptome of +1 mature leaves of a sugarcane cultivar subjected to florally inductive and non-inductive photoperiodic treatments to identify gene expression patterns and molecular regulatory modules. We identified 7,083 differentially expressed (DE) genes, of which 5,623 showed significant identity to other plant genes. Functional group analysis showed differential regulation of important metabolic pathways involved in plant development, such as plant hormones (i.e., cytokinin, gibberellin, and abscisic acid), light reactions, and photorespiration. Gene ontology enrichment analysis revealed evidence of upregulated processes and functions related to the response to abiotic stress, photoprotection, photosynthesis, light harvesting, and pigment biosynthesis, whereas important categories related to growth and vegetative development of plants, such as plant organ morphogenesis, shoot system development, macromolecule metabolic process, and lignin biosynthesis, were downregulated. Also, out of 76 sugarcane transcripts considered putative orthologs to flowering genes from other plants (such as Arabidopsis thaliana, Oryza sativa, and Sorghum bicolor), 21 transcripts were DE. Nine DE genes related to flowering and response to photoperiod were analyzed either at mature or spindle leaves at two development stages corresponding to the early stage of induction and inflorescence primordia formation. Finally, we report a set of flowering-induced long non-coding RNAs and describe their level of conservation to other crops, many of which showed expression patterns correlated against those in the functionally grouped gene network.
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Affiliation(s)
- João Ricardo Vieira Manechini
- Laboratório de Biologia de Sistemas, Departamento de Genética, Evolução, Microbiologia e Imunologia, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | - Paulo Henrique da Silva Santos
- Departamento de Genética e Melhoramento de Plantas, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual de São Paulo (UNESP), Jaboticabal, Brazil
| | - Elisson Romanel
- Laboratório de Genômica de Plantas e Bioenergia (PGEMBL), Departamento de Biotecnologia, Escola de Engenharia de Lorena (EEL), Universidade de São Paulo (USP), Lorena, Brazil
| | - Michael dos Santos Brito
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo (UNIFESP), São José dos Campos, Brazil
| | | | - Stephen Jackson
- School of Life Sciences, The University of Warwick, Coventry, United Kingdom
| | - Luciana Rossini Pinto
- Departamento de Genética e Melhoramento de Plantas, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual de São Paulo (UNESP), Jaboticabal, Brazil
- Centro de Cana, Instituto Agronômico de Campinas (IAC), Ribeirão Preto, Brazil
| | - Renato Vicentini
- Laboratório de Biologia de Sistemas, Departamento de Genética, Evolução, Microbiologia e Imunologia, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
- *Correspondence: Renato Vicentini,
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Aono AH, Pimenta RJG, Garcia ALB, Correr FH, Hosaka GK, Carrasco MM, Cardoso-Silva CB, Mancini MC, Sforça DA, dos Santos LB, Nagai JS, Pinto LR, Landell MGDA, Carneiro MS, Balsalobre TW, Quiles MG, Pereira WA, Margarido GRA, de Souza AP. The Wild Sugarcane and Sorghum Kinomes: Insights Into Expansion, Diversification, and Expression Patterns. FRONTIERS IN PLANT SCIENCE 2021; 12:668623. [PMID: 34305969 PMCID: PMC8294386 DOI: 10.3389/fpls.2021.668623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 03/17/2021] [Indexed: 05/11/2023]
Abstract
The protein kinase (PK) superfamily is one of the largest superfamilies in plants and the core regulator of cellular signaling. Despite this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here, we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, and these were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between the species, this study offers insights into Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq and identified significant similarities between Sbi and Ssp. Moreover, coexpression networks allowed inference of a core structure of kinase interactions with specific key elements. This study provides the first categorization of the allelic specificity of a kinome and offers a wide reservoir of molecular and genetic information, thereby enhancing the understanding of Sbi and Ssp PK evolutionary history.
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Affiliation(s)
- Alexandre Hild Aono
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | - Ricardo José Gonzaga Pimenta
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | - Ana Letycia Basso Garcia
- Department of Genetics, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, Brazil
| | - Fernando Henrique Correr
- Department of Genetics, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, Brazil
| | - Guilherme Kenichi Hosaka
- Department of Genetics, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, Brazil
| | - Marishani Marin Carrasco
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | | | - Melina Cristina Mancini
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | - Danilo Augusto Sforça
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | - Lucas Borges dos Santos
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | - James Shiniti Nagai
- Faculty of Medicine, Institute for Computational Genomics, RWTH Aachen University, Aachen, Germany
| | - Luciana Rossini Pinto
- Advanced Center of Sugarcane Agrobusiness Technological Research, Agronomic Institute of Campinas (IAC), Ribeirão Preto, Brazil
| | | | - Monalisa Sampaio Carneiro
- Departamento de Biotecnologia e Produção Vegetal e Animal, Centro de Ciências Agrárias, Universidade Federal de São Carlos (UFSCar), São Carlos, Brazil
| | - Thiago Willian Balsalobre
- Departamento de Biotecnologia e Produção Vegetal e Animal, Centro de Ciências Agrárias, Universidade Federal de São Carlos (UFSCar), São Carlos, Brazil
| | - Marcos Gonçalves Quiles
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (Unifesp), São José dos Campos, Brazil
| | | | | | - Anete Pereira de Souza
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
- Department of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
- *Correspondence: Anete Pereira de Souza,
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Aono AH, Costa EA, Rody HVS, Nagai JS, Pimenta RJG, Mancini MC, Dos Santos FRC, Pinto LR, Landell MGDA, de Souza AP, Kuroshu RM. Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance. Sci Rep 2020; 10:20057. [PMID: 33208862 PMCID: PMC7676261 DOI: 10.1038/s41598-020-77063-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 08/24/2020] [Indexed: 12/18/2022] Open
Abstract
Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.
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Affiliation(s)
- Alexandre Hild Aono
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Estela Araujo Costa
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil
| | - Hugo Vianna Silva Rody
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil
| | - James Shiniti Nagai
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil
| | - Ricardo José Gonzaga Pimenta
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Melina Cristina Mancini
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil
| | | | - Luciana Rossini Pinto
- Advanced Center of Sugarcane Agrobusiness Technological Research, Agronomic Institute of Campinas (IAC), Ribeirão Preto, SP, Brazil
| | | | - Anete Pereira de Souza
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil.
- Department of Plant Biology, Institute of Biology (IB), University of Campinas (UNICAMP), Campinas, SP, Brazil.
| | - Reginaldo Massanobu Kuroshu
- Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil.
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Manimekalai R, Suresh G, Govinda Kurup H, Athiappan S, Kandalam M. Role of NGS and SNP genotyping methods in sugarcane improvement programs. Crit Rev Biotechnol 2020; 40:865-880. [PMID: 32508157 DOI: 10.1080/07388551.2020.1765730] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Sugarcane (Saccharum spp.) is one of the most economically significant crops because of its high sucrose content and it is a promising biomass feedstock for biofuel production. Sugarcane genome sequencing and analysis is a difficult task due to its heterozygosity and polyploidy. Long sequence read technologies, PacBio Single-Molecule Real-Time (SMRT) sequencing, the Illumina TruSeq, and the Oxford Nanopore sequencing could solve the problem of genome assembly. On the applications side, next generation sequencing (NGS) technologies played a major role in the discovery of single nucleotide polymorphism (SNP) and the development of low to high throughput genotyping platforms. The two mainstream high throughput genotyping platforms are the SNP microarray and genotyping by sequencing (GBS). This paper reviews the NGS in sugarcane genomics, genotyping methodologies, and the choice of these methods. Array-based SNP genotyping is robust, provides consistent SNPs, and relatively easier downstream data analysis. The GBS method identifies large scale SNPs across the germplasm. A combination of targeted GBS and array-based genotyping methods should be used to increase the accuracy of genomic selection and marker-assisted breeding.
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Affiliation(s)
- Ramaswamy Manimekalai
- Crop Improvement Division, ICAR - Sugarcane Breeding Institute, Indian Council of Agricultural Research (ICAR), Coimbatore, Tamil Nadu, India
| | - Gayathri Suresh
- Crop Improvement Division, ICAR - Sugarcane Breeding Institute, Indian Council of Agricultural Research (ICAR), Coimbatore, Tamil Nadu, India
| | - Hemaprabha Govinda Kurup
- Crop Improvement Division, ICAR - Sugarcane Breeding Institute, Indian Council of Agricultural Research (ICAR), Coimbatore, Tamil Nadu, India
| | - Selvi Athiappan
- Crop Improvement Division, ICAR - Sugarcane Breeding Institute, Indian Council of Agricultural Research (ICAR), Coimbatore, Tamil Nadu, India
| | - Mallikarjuna Kandalam
- Business Development, Asia Pacific Japan region, Thermo Fisher Scientific, Waltham, MA, USA
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Hoang NV, Furtado A, Perlo V, Botha FC, Henry RJ. The Impact of cDNA Normalization on Long-Read Sequencing of a Complex Transcriptome. Front Genet 2019; 10:654. [PMID: 31396260 PMCID: PMC6664245 DOI: 10.3389/fgene.2019.00654] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 06/20/2019] [Indexed: 11/13/2022] Open
Abstract
Normalization of cDNA is widely used to improve the coverage of rare transcripts in analysis of transcriptomes employing next-generation sequencing. Recently, long-read technology has been emerging as a powerful tool for sequencing and construction of transcriptomes, especially for complex genomes containing highly similar transcripts and transcript-spliced isoforms. Here, we analyzed the transcriptome of sugarcane, a highly polyploidy plant genome, by PacBio isoform sequencing (Iso-Seq) of two different cDNA library preparations, with and without a normalization step. The results demonstrated that, while the two libraries included many of the same transcripts, many longer transcripts were removed, and many new generally shorter transcripts were detected by normalization. For the same input cDNA and data yield, the normalized library recovered more total transcript isoforms and number of predicted gene families and orthologous groups, resulting in a higher representation for the sugarcane transcriptome, compared to the non-normalized library. The non-normalized library, on the other hand, included a wider transcript length range with more longer transcripts above ∼1.25 kb and more transcript isoforms per gene family and gene ontology terms per transcript. A large proportion of the unique transcripts comprising ∼52% of the normalized library were expressed at a lower level than the unique transcripts from the non-normalized library, across three tissue types tested including leaf, stalk, and root. About 83% of the total 5,348 predicted long noncoding transcripts was derived from the normalized library, of which ∼80% was derived from the lowly expressed fraction. Functional annotation of the unique transcripts suggested that each library enriched different functional transcript fractions. This demonstrated the complementation of the two approaches in obtaining a complete transcriptome of a complex genome at the sequencing depth used in this study.
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Affiliation(s)
- Nam V. Hoang
- College of Agriculture and Forestry, Hue University, Hue, Vietnam
| | - Agnelo Furtado
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | - Virginie Perlo
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | - Frederik C. Botha
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
- Sugar Research Australia, Indooroopilly, QLD, Australia
| | - Robert J. Henry
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
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Sforça DA, Vautrin S, Cardoso-Silva CB, Mancini MC, Romero-da Cruz MV, Pereira GDS, Conte M, Bellec A, Dahmer N, Fourment J, Rodde N, Van Sluys MA, Vicentini R, Garcia AAF, Forni-Martins ER, Carneiro MS, Hoffmann HP, Pinto LR, Landell MGDA, Vincentz M, Berges H, de Souza AP. Gene Duplication in the Sugarcane Genome: A Case Study of Allele Interactions and Evolutionary Patterns in Two Genic Regions. FRONTIERS IN PLANT SCIENCE 2019; 10:553. [PMID: 31134109 PMCID: PMC6514446 DOI: 10.3389/fpls.2019.00553] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 04/11/2019] [Indexed: 05/25/2023]
Abstract
Sugarcane (Saccharum spp.) is highly polyploid and aneuploid. Modern cultivars are derived from hybridization between S. officinarum and S. spontaneum. This combination results in a genome exhibiting variable ploidy among different loci, a huge genome size (~10 Gb) and a high content of repetitive regions. An approach using genomic, transcriptomic, and genetic mapping can improve our knowledge of the behavior of genetics in sugarcane. The hypothetical HP600 and Centromere Protein C (CENP-C) genes from sugarcane were used to elucidate the allelic expression and genomic and genetic behaviors of this complex polyploid. The physically linked side-by-side genes HP600 and CENP-C were found in two different homeologous chromosome groups with ploidies of eight and ten. The first region (Region01) was a Sorghum bicolor ortholog region with all haplotypes of HP600 and CENP-C expressed, but HP600 exhibited an unbalanced haplotype expression. The second region (Region02) was a scrambled sugarcane sequence formed from different noncollinear genes containing partial duplications of HP600 and CENP-C (paralogs). This duplication resulted in a non-expressed HP600 pseudogene and a recombined fusion version of CENP-C and the orthologous gene Sobic.003G299500 with at least two chimeric gene haplotypes expressed. It was also determined that it occurred before Saccharum genus formation and after the separation of sorghum and sugarcane. A linkage map was constructed using markers from nonduplicated Region01 and for the duplication (Region01 and Region02). We compare the physical and linkage maps, demonstrating the possibility of mapping markers located in duplicated regions with markers in nonduplicated region. Our results contribute directly to the improvement of linkage mapping in complex polyploids and improve the integration of physical and genetic data for sugarcane breeding programs. Thus, we describe the complexity involved in sugarcane genetics and genomics and allelic dynamics, which can be useful for understanding complex polyploid genomes.
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Affiliation(s)
| | - Sonia Vautrin
- Centre National de Ressources Genomiques Vegetales (CNRGV), Institut National de la Recherche Agronomique (INRA), Castanet Tolosan, France
| | | | | | | | | | - Mônica Conte
- Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | - Arnaud Bellec
- Centre National de Ressources Genomiques Vegetales (CNRGV), Institut National de la Recherche Agronomique (INRA), Castanet Tolosan, France
| | - Nair Dahmer
- Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | - Joelle Fourment
- Centre National de Ressources Genomiques Vegetales (CNRGV), Institut National de la Recherche Agronomique (INRA), Castanet Tolosan, France
| | - Nathalie Rodde
- Centre National de Ressources Genomiques Vegetales (CNRGV), Institut National de la Recherche Agronomique (INRA), Castanet Tolosan, France
| | | | | | | | | | | | - Hermann Paulo Hoffmann
- Centro de Ciências Agrárias, Universidade Federal de São Carlos (UFSCAR), Araras, Brazil
| | | | | | - Michel Vincentz
- Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | - Helene Berges
- Centre National de Ressources Genomiques Vegetales (CNRGV), Institut National de la Recherche Agronomique (INRA), Castanet Tolosan, France
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