Biology subjects

Silva, J. C. F.

Publications and source records attributed to Silva, J. C. F..

7 recordsLinked to original sources

bioRxiv2026

SpaceBio Knowledge Hub: A LiteratOmics Platform for Microgravity and Space Biology Research

Space biology literature is growing exponentially. Existing infrastructure has not kept pace with organizing, synthesizing, and disseminating this knowledge. We present SpaceBio SpaceBio Knowledge Hub (www.spacebio.space), an integrated digital ecosystem that combines artificial intelligence, real-time data integration, and open-access infrastructure to advance research, education, and collaboration in microgravity, space biology and space exploration. The platform applies AI-driven approaches including natural language processing, machine learning, and automated content generation to construct a semantic atlas of the field. The atlas reveals the hierarchical thematic organization underlying microgravity-induced biological responses, space mission infrastructure, planetary science, and astrobiology. As part of this effort, SpaceBio is moving toward the construction of a LiteratOmics framework for microgravity, and space biology a systematic, AI-enabled approach to mining, integrating, and structuring the primary literature generated by omics-driven spaceflight research, treating the scientific literature itself as a navigable data layer alongside genomic, transcriptomic, and proteomic datasets. Built on a scalable, cloud-based architecture with a user-centered interface, SpaceBio supports literature exploration, data integration, and knowledge discovery for researchers, educators, students, industry partners, and citizen scientists. The platform also functions as a community-building ecosystem. It integrates hands-on research initiatives, AI-generated educational content, pilot data science projects, and social responsibility programs that broaden participation without compromising scientific rigor. AI-enabled digital environments can transform fragmented literature into a navigable knowledge landscape. SpaceBio accelerates research productivity, strengthens STEM education, and supports the global space life sciences community as human space exploration enters in the most ambitious era.

scientific communication and education↗

bioRxiv2026

Explainable AI identifies recombination and chromatin environment as key predictors of subgenome evolution in maize and Brassica

Polyploidy, or whole genome duplication, reshapes genomes through biased gene loss and regulatory rewiring, yet the drivers of biased fractionation among subgenomes remain unclear. Using maize and Brassica rapa as model allopolyploids, we compiled 60 genomic and epigenomic features in maize and 45 in B. rapa and constructed supervised machine-learning models to classify genes by subgenome identity. In both species, eXplainable Artificial Intelligence (XAI) approaches identified recombination rate as the most influential and highly interconnected feature despite nonsignificant mean differences between subgenome groups. Chromosome location, transposon density, and chromatin-associated features, including the proximity of accessible chromatin regions to genes and active histone marks such as H3K9ac and H3K27ac, consistently ranked among the top contributors to subgenome classification. XAI-derived co-variation and interaction networks further revealed recombination rate as the central node connecting significant features. Together, these results highlight recombination and chromatin environment as major predictors of subgenome divergence in polyploid genomes. TeaserRecombination and chromatin environment are key predictors of unequal gene loss and subgenome dominance after polyploidy.

plant biology↗

bioRxiv2025

Evolution of ssDNA plant viruses in the natural environment - a journey through time

Begomoviruses pose a major threat to food security, particularly in developing countries. These small ssDNA viruses exhibit substitution rates comparable to those of RNA viruses. The temporal dynamics of begomoviruses in non-agricultural environments have been largely overlooked, and little is known about how these viruses evolve in the absence of anthropogenic influence. In this study, we investigated the temporal dynamics of begomoviruses in a small fragment of regenerating Atlantic Forest area in the state of Minas Gerais, Brazil. Samples of Sida acuta, a wild plant native from South America, were collected at the same location over a 12-year period (2011-2022). Five distinct begomoviruses were detected infecting this host: OxYVV, SiYLCV, SimMV, MaYVV, and SiMV. OxYVV and SimMV were subdivided into multiple variants, revealing their potential as reservoirs of viral biodiversity. Several shifts in species and variant composition were observed in the viral community over time, with the most drastic change occurring in 2016, when SiYLCV outnumbered OxYVV. The reasons behind this turnover remain uncertain, but the most compelling clues point to a population expansion of SiYLCV. We detected a strong temporal signal in two of the most abundant viruses (OxYVV and SiYLCV), which allowed us to calibrate molecular clocks and estimate substitution rates for both of them. Our results indicate that, even in the natural environment, begomoviruses can evolve at rates similar to those reported in agricultural systems.

microbiology↗

bioRxiv2025

InteracTor: Feature Engineering and Explainable AI for Profiling Protein Structure-Interaction-Function Relationships

Characterizing protein families structural and functional diversity is essential for understanding their biological roles. Traditional analyses often focus on primary and secondary structures, which may not fully capture complex protein interactions. Here we introduce InteracTor, a novel toolkit that extracts multimodal features from protein three-dimensional (3D) structures, including interatomic interactions like hydrogen bonds, van der Waals forces, and hydrophobic contacts. By integrating Explainable AI (XAI) techniques, we quantified the importance of the extracted features in the classification of protein structural and functional families. InteracTors interpretable features enable mechanistic insights into the determinants of protein structure, function, and dynamics, offering a transparent means to assess their predictive power within machine learning models. Interatomic interaction features extracted by InteracTor demonstrated superior predictive power for protein family classification compared to features based solely on primary or secondary structure, revealing the importance of considering specific tertiary contacts in computational protein analysis. This work provides a robust framework for future studies aiming to enhance the capabilities of models for protein function prediction and drug discovery. AUTHOR SUMMARYInteracTor is a computational toolkit designed to enhance our understanding of protein structure and function by focusing on three-dimensional (3D) structural interactions. Unlike traditional approaches that primarily rely on sequence or secondary structure data, InteracTor extracts biologically meaningful features such as hydrogen bonds, van der Waals forces, and hydrophobic contacts, which are critical for protein stability and dynamics. By integrating these features into machine learning models alongside explainable AI methods, InteracTor provides interpretable insights into how specific structural interactions influence protein behavior. Our results demonstrate that tertiary structure features significantly improve the accuracy of protein family classification compared to sequence-based methods alone, underscoring the importance of considering 3D interactions in computational protein analyses. The toolkits modular design makes it adaptable for diverse applications, including drug discovery and protein engineering. In a broader context, InteracTor bridges the gap between computational biology and practical applications in medicine and biotechnology by offering a transparent and robust framework for analyzing proteins at a molecular level. This work represents a step forward in leveraging structural data to advance predictive modeling and biological discovery.

bioinformatics↗

bioRxiv2024

InteracTor: A new integrative feature extraction toolkit for improved characterization of protein structural properties

Understanding the structural and functional diversity of protein families is crucial for elucidating their biological roles. Traditional analyses often focus on primary and secondary structures, which include amino acid sequences and local folding patterns like alpha helices and beta sheets. However, primary and secondary structures alone may not fully represent the complex interactions within proteins. To address this limitation, we developed a new algorithm (InteracTor) to analyze proteins by extracting features from their three-dimensional (3D) structures. The toolkit extracts interatomic interaction features such as hydrogen bonds, van der Waals interactions, and hydrophobic contacts, which are crucial for understanding protein dynamics, structure, and function. Incorporating 3D structural data and interatomic interaction features provides a more comprehensive understanding of protein structure and function, potentially enhancing downstream predictive modeling capabilities. By using the extracted features in Mutual Information scoring (MI), Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and hierarchical clustering analysis as use cases, we identified clear separations among protein structural families, highlighting distinct functional aspects. Our analysis revealed that interatomic interaction features were more informative than protein secondary structure features, providing insights into potential structural and functional properties. These findings underscore the significance of considering tertiary structure in protein analysis, offering a robust framework for future studies aiming at enhancing the capabilities of models for protein function prediction and drug discovery.

bioinformatics↗

bioRxiv2021

Photoperiod shapes aluminium tolerance in plants

Aluminium is a limiting factor for crop productivity in acidic soils (pH [≤] 5.5). Since acid soil distribution on Earth cannot adequately explain the differential Al tolerance across the plant kingdom, we investigated photoperiod effects on plant Al tolerance. We observed that with increasing distance from the equator, Al tolerance disappears, suggesting a relationship with the photoperiod. Long-day (LD) species are generally more Al-sensitive than short-day (SD) species, whereas genetic conversion of tomato for SD growth boosts Al tolerance. Reduced Al tolerance correlates with DNA-checkpoint activation under LD. DNA-checkpoint-related genes are under positive selection in Arabidopsis accessions from regions with shorter days, suggesting photoperiod acts as a selective barrier for Al tolerance. Our findings revealed that diel regulation and genetic diversity affect Al tolerance, suggesting that day-length orchestrates Al tolerance. One-Sentence SummaryAluminum is a major constraint for crop yield worldwide. We reveal that photoperiod acts as a barrier for Al tolerance in plants.

plant biology↗

bioRxiv2020

A new plant-specific syntaxin-6 protein may define an intracytoplasmic route for begomoviruses

Due to limited free diffusion in the cytoplasm, viruses must use active transport mechanisms to move intracellularly. Nevertheless, how the plant ssDNA begomoviruses hijacks the host intracytoplasmic transport machinery to move from the nucleus to the plasmodesmata remains enigmatic. Here, we identified nuclear shuttle protein (NSP)-interacting proteins from Arabidopsis by probing a protein microarray and demonstrated that the Cabbage leaf curl virus (CabLCV) NSP, a facilitator of the nucleocytoplasmic trafficking of viral (v)DNA, interacts with an endosomal vesicle-localized plant-specific syntaxin-6 protein, designated NSP-interacting syntaxin-6 domain-containing protein (NISP) in planta. NISP displays a pro-viral function, but not the syntaxin-6 paralog AT2G18860 that failed to interact with NSP. Consistent with these findings, nisp-1 mutant plants were less susceptible to begomovirus infection, a phenotype reversed by NISP complementation. NISP-overexpressing lines accumulated higher levels of viral DNA than wild-type. Furthermore, NISP interacted with NIG, an NSP-interacting GTPase involved in NSP-vDNA nucleocytoplasmic translocation. The NISP-NIG interaction was enhanced by NSP. We also showed that NISP associates with vDNA and might assemble a NISP-NIG-NSP-vDNA-complex. NISP may function as a docking site for recruiting NIG and NSP into endosomes, providing a mechanism for the intracytoplasmic translocation of the NSP-vDNA complex towards to and from the cell periphery.Author Summary As viruses must use an active and directed intracellular movement, they hijack the intracellular host transport system for their own benefits. Therefore, the identification of interactions between host proteins and begomovirus movement proteins should target the intracellular transport machinery. This work focused on the identification of these protein-protein interactions; it addressed the molecular bases for the intracellular transport of begomoviruses. We used a protein microarray to identify cellular partners for the movement protein (MP) and the viral nuclear shuttle protein (NSP), which is a facilitator of the nucleocytoplasmic trafficking of viral (v)DNA. We identified relevant protein-protein interaction (PPI) hubs connecting host and viral proteins. We revealed a novel NSP-interacting protein, which functions in the intracytoplasmic transport of proteins and DNA from begomoviruses and was designated NSP-interacting syntaxin domain-containing protein (NISP). Our data suggest an intracellular route connecting the release of newly synthesized begomoviral DNA in the cytosol with the cell surface. Resolving viral DNA-host protein complexes led to the identification of a novel class of components of the cell machinery and a representative member, NISP, that functions as a susceptibility gene against begomoviruses. As geminiviruses pose a severe threat to agriculture and food security, this recessive gene can now be exploited as a target for engineering resistance by gene editing in crops.Competing Interest StatementThe authors have declared no competing interest.View Full Text

plant biology↗