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Biology subjects

Sandoval, J.

Publications and source records attributed to Sandoval, J..

3 recordsLinked to original sources

Climate drives plasticity and genetic adaptation in apple trees

BackgroundPerennial crops respond to climate change through phenotypic plasticity and local adaptation, yet how these responses are orchestrated at the molecular level and reshaped by domestication remains poorly understood. ResultsWe grew 1,249 seedlings from five populations of M. domestica, M. orientalis, and M. sylvestris under four simulated European climates, quantified 12 phenotypic traits, performed RNA sequencing on 96 individuals, and integrated these data with previously published genome-wide polymorphism data from M. sylvestris to disentangle conserved climate responses from local adaptation. Climate was the dominant driver of phenotypic and transcriptional variation, revealing 344 conserved climate-responsive genes enriched for stress responses, nutrient metabolism, and cell wall biosynthesis. These genes are under strong purifying selection and carry fewer deleterious mutations. In contrast, genome-environment association analyses identified 217 loci associated with temperature and precipitation gradients, only a subset of which showed climate-responsive expression and elevated genomic differentiation. Population-level expression divergence closely mirrored neutral genetic differentiation, indicating that most transcriptomic divergence reflects demographic history rather than adaptive regulatory evolution. Domestication reshaped molecular diversity and mutation load without disrupting the conserved climate-response program. ConclusionsClimate responses in apple are primarily mediated by an evolutionarily conserved transcriptional program maintained by strong purifying selection, whereas local adaptation relies on largely distinct, population-specific loci. These findings reveal that conserved plasticity constitutes the dominant molecular response to climatic variation, whereas local adaptation contributes a secondary, population-specific layer of evolutionary change.

evolutionary biology↗

Genome wide DNA methylation profiling identifies specific epigenetic features in high-risk cutaneous squamous cell carcinoma.

Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer. Although most cSCCs have good prognosis, a subgroup of high-risk cSCC has a higher frequency of recurrence and mortality. Therefore, the identification of molecular risk factors associated with this aggressive subtype is of major interest. In this work we carried out a global-scale approach to investigate the DNA-methylation profile in patients at different stages, from premalignant actinic keratosis to low-risk invasive and high-risk non-metastatic and metastatic cSCC. The results showed massive non-sequential changes in DNA-methylome and identified a minimal methylation signature that discriminates between stages. Importantly, a direct comparison of low-risk and high-risk stages revealed epigenetic traits characteristic of high-risk tumours. Finally, a prognostic prediction model in cSCC patients identified a methylation signature able to predict the overall survival of patients. Thus, the analysis of DNA-methylation in cSCC revealed changes during the evolution of the disease through the different stages that can be of great value not only in the diagnosis but also in the prognosis of the disease.

cancer biology↗

GenPipes: an open-source framework for distributed and scalable genomic analyses

With the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing. Here we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for High Performance Computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA-Seq, ChIP-Seq, DNA-Seq, Methyl-Seq, Hi-C, capture Hi-C, metagenomics and PacBio long read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has been already configured on several servers and a docker image is also available to facilitate additional installations. In summary, GenPipes offers genomic researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.

bioinformatics↗