Comparative Transcriptome Analysis Reveals Lineage- and Environment-Specific Adaptations in Cacti from the Brazilian Atlantic Forest
Planta(2024)
Universidade Federal do ABC (UFABC)
Abstract
Natural selection influenced adaptive divergence between Cereus fernambucensis and Cereus insularis, revealing key genes governing abiotic stress responses and supporting neoteny in C. insularis. Uncovering the molecular mechanisms driving adaptive divergence in traits related to habitat adaptation remains a central challenge. In this study, we focused on the cactus clade, which includes Cereus sericifer F.Ritter, Cereus fernambucensis Lem., and Cereus insularis Hemsley. These allopatric species inhabit distinct relatively drier regions within the Brazilian Atlantic Forest, each facing unique abiotic conditions. We leveraged whole transcriptome data and abiotic variables datasets to explore lineage-specific and environment-specific adaptations in these species. Employing comparative phylogenetic methods, we identified genes under positive selection (PSG) and examined their association with non-synonymous genetic variants and abiotic attributes through a PhyloGWAS approach. Our analysis unveiled signatures of selection in all studied lineages, with C. fernambucensis northern populations and C. insularis showing the most PSGs. These PSGs predominantly govern abiotic stress regulation, encompassing heat tolerance, UV stress response, and soil salinity adaptation. Our exclusive observation of gene expression tied to early developmental stages in C. insularis supports the hypothesis of neoteny in this species. We also identified genes associated with abiotic variables in independent lineages, suggesting their role as environmental filters on genetic diversity. Overall, our findings suggest that natural selection played a pivotal role in the geographic range of these species in response to environmental and biogeographic transitions.
MoreTranslated text
Key words
Cereus,Environmental filters,PhyloGWAS,Positive selection,RNA-Seq
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined