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Silva, S. R.

Publications and source records attributed to Silva, S. R..

2 recordsLinked to original sources

Genomic decoding of Theobroma grandiflorum (cupuassu) at chromosomal scale: Evolutionary insights for horticultural innovation

BackgroundTheobroma grandiflorum (Malvaceae), known as cupuassu, is a tree indigenous to the Amazon Basin, valued for its large fruits and seed-pulp, contributing notably to the Amazonian bioeconomy. The seed-pulp is utilized in desserts and beverages, and its seed butter is used in cosmetics. Here, we present the sequenced telomere-to-telomere cupuassu genome, disclosing features of the genomic structure, evolution, and phylogenetic relationships within the Malvaceae. ResultsThe cupuassu genome spans 423 Mb, encodes 31,381 genes distributed in the ten chromosomes, and it exhibits approximately 65% gene synteny with the T. cacao genome, reflecting a conserved evolutionary history, albeit punctuated with unique genomic variations. The main changes are pronounced by bursts of long-terminal repeats retrotransposons expansion at post-species divergence, retrocopied and singleton genes, and gene families displaying distinctive patterns of expansion and contraction. Furthermore, positively selected genes are evident, particularly among retained and dispersed, tandem and proximal duplicated genes associated to general fruit and seed traits and defense mechanisms, supporting the hypothesis of potential episodes of subfunctionalization and neofunctionalization following duplication, and impact from distinct domestication process. These genomic variations may underpin the differences observed in fruit and seed morphology, ripening, and disease resistance between cupuassu and the other Malvaceae species. ConclusionsSequencing the cupuassu genome offers a foundational resource for both breeding and conservation efforts, yielding insights into the evolution and diversity within the genus Theobroma. Core ideasO_LITelomere-to-telomere sequencing of the Theobroma grandiflorum genome elucidates a 65% synteny with T. cacao. C_LIO_LIRetrotransposon expansion identified as a pivotal factor in post-divergence genomic evolution between Theobroma species. C_LIO_LIComparative genomics has revealed genes associated with key agronomic traits, providing evolutionary insights. C_LIO_LIPositive selection pressure in retained duplicated genes implicated in adaptive functions and fruit-seed traits diversity. C_LIO_LICupuassu genome as a genetic resource for breeding and to boosts Brazillian Amazonian bioeconomy. C_LI AUTHOR SUMMARYCupuassu, a fruit-bearing tree from the Amazon, is prized for its nutritious fruits and seeds, widely used in food and cosmetics. In this study, we sequenced the complete genome of cupuassu to understand its development, unique traits, and genetic relationship with cacao and other related species. The cupuassu genome shares a high similarity with cacao, but it also exhibits distinctive features. Notably, repetitive DNA elements have significantly influenced its genomic structure. Furthermore, specific genes responsible for its fruit and seed characteristics, as well as disease resistance, were identified. Overall, this research not only deepens our knowledge of cupuassu genetics but also illuminates broader aspects of plant evolution and diversity in the Amazon. It lays the groundwork for advanced breeding programs and promises to contribute significantly to the Amazonian bioeconomy. Ultimately, these findings have important implications for agriculture and conservation, highlighting the intricate processes of plant adaptation and evolution.

genomics↗

Lira: Rotational Invariant Shape and ElectrostaticDescriptors for Small Molecules and ProteinPockets based on Real Spherical Harmonics

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSModern AI-based tools are increasing the number of protein structures available, creating an opportunity and a challenge for automated high-throughput drug discovery pipelines. The amount of data is overwhelming for the current methods, thus demanding new high-performance approaches for Machine Learning-based rational drug design. As shape and electrostatics are the main components for understanding protein-ligand interaction; they are the primary targets for efficient AI-compatible descriptors and their associated comparison methods. ResultsThe Lira toolbox is a set of components devised for describing, comparing and analysing shape and electrostatics for small ligands, peptides and protein pockets. It can generate databases with descriptors for tens of millions of shapes in a few hours, which can then be queried in seconds. The Lira design, focused on performance and reliability, makes its integration into AI-driven rational drug design pipelines simple. Availability and implementationLira packages, available for download at https://pinheirolab.com/, are free to use for research and educational purposes.

bioinformatics↗