Harnessing genetic diversity: The genomic and transcriptomic insights of Eugenia uniflora for environmental resilience

Pitanga (Eugenia uniflora L.), a member of the Myrtaceae family, is native to the Brazilian Atlantic Forest and distributed across various ecological environments, including regions with contrasting edaphoclimatic conditions. Known for its production of secondary metabolites with significant biological activity, pitanga holds considerable pharmacological potential. Genomic and transcriptomic resources for this species are therefore valuable for understanding the genetic mechanisms that enable its adaptation to diverse ecosystems and for identifying candidate genes relevant for crop improvement and bioprospection. To explore whether genetic diversity is associated with population adaptation to environmental conditions, we first generated a draft genome of E. uniflora, totaling 385.1 Mbp with an N50 value of 26,199 bp, assembled de novo from Illumina-sequence reads. Likewise, gene prediction, based on Viridiplantae protein references, identified 30,663 protein-coding genes. Comparative genomics revealed 2,219 orthologous clusters, 40% of which were functionally annotated and encompassing 1,772 gene ontology terms. This draft genome also facilitated the identification of microsatellite markers, whose variation was analyzed across pitanga samples from two contrasting natural environments: Restinga and Riparian forest. The microsatellite profile showed a natural bias towards monomeric repeats, with genetic diversity differences across both populations that could be used as molecular markers for phenotype selection and plant breeding. Furthermore, RNA sequencing coupled with a Transcriptogram approach revealed significant differences in gene expression between the two populations. Pitangas from the Restinga ecosystem exhibited a stronger stress response, with distinct gene expression patterns associated with osmoprotection, cell wall modification, detoxification, nutrient balance, and epigenetic regulation. These patterns are likely linked to enhanced adaptation to the water and osmotic stress conditions, characteristic of this environment. Together, these findings enhance our knowledge of genetic diversity within E. uniflora populations and the molecular basis of their environmental adaptability. Such insights are critical for understanding how plants rapidly adapt to climate change and how these adaptations affect population dynamics, important for conservation strategies, population management, and the development of resilient cultivars.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-11-04
DOI
https://doi.org/10.1371/journal.pone.0333437
Primary Topic
Genetic diversity and population structure
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Harnessing genetic diversity: The genomic and transcriptomic insights of Eugenia uniflora for environmental resilience

Rogério Margis, Rita M. C. de Almeida, Dámaris Adelaida Esquén Bayona, J. Flores et al.
PLoS ONE
Genetic diversity and population structure
article

Harnessing genetic diversity: The genomic and transcriptomic insights of Eugenia uniflora for environmental resilience

Rogério Margis, Rita M. C. de Almeida, Dámaris Adelaida Esquén Bayona, J. Flores, Frank Guzmán, Inés Sánchez, Edgar Luis Waschburger, Andreia Carina Turchetto‐Zolet, Guilherme Loss de Morais, Alexandre Nascimento de Vargas
article en

Abstract

Pitanga (Eugenia uniflora L.), a member of the Myrtaceae family, is native to the Brazilian Atlantic Forest and distributed across various ecological environments, including regions with contrasting edaphoclimatic conditions. Known for its production of secondary metabolites with significant biological activity, pitanga holds considerable pharmacological potential. Genomic and transcriptomic resources for this species are therefore valuable for understanding the genetic mechanisms that enable its adaptation to diverse ecosystems and for identifying candidate genes relevant for crop improvement and bioprospection. To explore whether genetic diversity is associated with population adaptation to environmental conditions, we first generated a draft genome of E. uniflora, totaling 385.1 Mbp with an N50 value of 26,199 bp, assembled de novo from Illumina-sequence reads. Likewise, gene prediction, based on Viridiplantae protein references, identified 30,663 protein-coding genes. Comparative genomics revealed 2,219 orthologous clusters, 40% of which were functionally annotated and encompassing 1,772 gene ontology terms. This draft genome also facilitated the identification of microsatellite markers, whose variation was analyzed across pitanga samples from two contrasting natural environments: Restinga and Riparian forest. The microsatellite profile showed a natural bias towards monomeric repeats, with genetic diversity differences across both populations that could be used as molecular markers for phenotype selection and plant breeding. Furthermore, RNA sequencing coupled with a Transcriptogram approach revealed significant differences in gene expression between the two populations. Pitangas from the Restinga ecosystem exhibited a stronger stress response, with distinct gene expression patterns associated with osmoprotection, cell wall modification, detoxification, nutrient balance, and epigenetic regulation. These patterns are likely linked to enhanced adaptation to the water and osmotic stress conditions, characteristic of this environment. Together, these findings enhance our knowledge of genetic diversity within E. uniflora populations and the molecular basis of their environmental adaptability. Such insights are critical for understanding how plants rapidly adapt to climate change and how these adaptations affect population dynamics, important for conservation strategies, population management, and the development of resilient cultivars.

PLoS ONEVol. 20(11)
Universidade Federal do Rio Grande do Sul (BR), Peruvian University of Applied Sciences (PE), Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul (BR), Centro Universitário Curitiba (BR), Universidade Federal do Rio Grande do Norte (BR)
Openalex Percentile: Top 65%
Genetic diversity and population structure
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.