A multiscale approach to understanding the shared blue-orange flower color polymorphism in two <i>Lysimachia</i> species

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Abstract

Background

Polymorphisms are common in nature, but they are rarely shared among closely related species. Polymorphisms could originate through convergence, ancestral polymorphism, or introgression. Although shared neutral genomic variation across species is commonplace, few examples of shared functional traits exist. The blue-orange petal color polymorphisms in two closely related species, Lysimachia monelli and L. arvensis were investigated with UV-vis reflectance spectra, flavonoid biochemistry, and transcriptome comparisons followed by climate niche analysis.

Results

Similar color morphs between species have nearly identical reflectance spectra, flavonoid biochemistry, and ABP gene expression patterns. Transcriptome comparisons reveal two orange-specific genes directly involved in both blue-orange color polymorphisms: DFR-2 specificity redirects flux from the malvidin to the pelargonidin while BZ1-2 stabilizes the pelargonidin with glucose, producing the orange pelargonidin 3-glucoside. Moreover, a reduction of F3’5’H expression in orange petals also favors pelargonidin production. The climate niches for each color morph are the same between the two species for three temperature characteristics but differ for four precipitation variables.

Conclusions

The similarities in reflectance spectra, biochemistry, and ABP genes suggest that a single shift from blue-to-orange shared by both lineages is the most plausible explanation. Our evidence suggests that this persistent flower color polymorphism may represent an ancestrally polymorphic trait that has transcended speciation, yet future analyses are necessary to confidently reject the alternative hypotheses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-024-05481-y.

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Most cited references 73

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

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

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

Bo Li, Colin Dewey (2011)

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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Author and article information

Contributors

Mercedes Sánchez-Cabrera: mercesc@us.es

Journal

Journal ID (nlm-ta): BMC Plant Biol

Journal ID (iso-abbrev): BMC Plant Biol

Title: BMC Plant Biology

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2229

Publication date (Electronic): 30 September 2024

Publication date PMC-release: 30 September 2024

Publication date Collection: 2024

Volume: 24

Electronic Location Identifier: 905

Affiliations

[1 ]Departmento de Biología Vegetal y Ecología, Facultad de Biología, Universidad de Sevilla, ( https://ror.org/03yxnpp24) Sevilla, 41012 España

[2 ]Departamento de Biología Molecular e Ingeniería Bioquímica, Universidad Pablo de Olavide, ( https://ror.org/02z749649) Sevilla, 41013 España

[3 ]Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos (URJC), ( https://ror.org/01v5cv687) Móstoles, 28933 España

[4 ]GRID grid.28479.30, ISNI 0000 0001 2206 5938, Instituto de Investigación en Cambio Global (IICG-URJC), Universidad Rey Juan Carlos, ; Móstoles, 28933 España

[5 ]Department of Chemistry and Biochemistry, Santa Clara University, ( https://ror.org/03ypqe447) Santa Clara, CA 95053 USA

[6 ]Department of Biological Sciences, San Jose State University, ( https://ror.org/04qyvz380) San Jose, CA 95182 USA

[7 ]Department of Biology, Santa Clara University, ( https://ror.org/03ypqe447) Santa Clara, CA 95053 USA

Article

Publisher ID: 5481

DOI: 10.1186/s12870-024-05481-y

PMC ID: 11441164

PubMed ID: 39350020

SO-VID: 36885093-e692-47b5-92b9-8995a3e4af28

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/.

History

Date received : 24 April 2024

Date accepted : 2 August 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100004837, Ministerio de Ciencia e Innovación;

Award ID: BES-C-2016-0023

Award ID: PID2020-116222GB-100

Award ID: CGL2015-63827

Award ID: BES-2013-062859

Funded by: FundRef http://dx.doi.org/10.13039/501100004837, Ministerio de Ciencia, Innovación y Universidades;

Award ID: FJC2020-044244-I

Custom metadata

ScienceOpen disciplines: Plant science & Botany

Keywords: flower color polymorphism,anthocyanin,flavonoid,lysimachia arvensis,lysimachia monelli,petal transcriptome

Data availability:

ScienceOpen disciplines: Plant science & Botany

Keywords: flower color polymorphism, anthocyanin, flavonoid, lysimachia arvensis, lysimachia monelli, petal transcriptome

A multiscale approach to understanding the shared blue-orange flower color polymorphism in two Lysimachia species

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Abstract

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Supplementary Information

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The Biology of Macroalgae

Most cited references 73

Trimmomatic: a flexible trimmer for Illumina sequence data

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

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

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