Biology subjects

Costa, E. A.

Publications and source records attributed to Costa, E. A..

5 recordsLinked to original sources

bioRxiv2025

Transmission potential of Culex and Aedes species for Madariaga virus, a member of the eastern equine encephalitis virus complex

Madariaga virus (MADV), widely distributed in Latin America, can cause severe disease in humans and equids, yet, key aspects of its transmission cycle remain unclear. To identify mosquitoes that could act as MADV vectors, we assessed the vector competence of Aedes aegypti, Ae. albopictus, Ae. taeniorhynchus, Culex tarsalis, Cx. coronator, and Cx. quinquefasciatus, following oral exposure to MADV isolated in Panama (all species) or Brazil (Ae. taeniorhynchus only). MADV RNA and infectious virus were quantified from mosquito bodies, legs, and saliva. At 14 days post-exposure, five species had virus in all biological sample types. Culex quinquefasciatus was susceptible to infection and dissemination but had no positive saliva samples. Aedes taeniorhynchus showed higher infection probabilities with MADV-BR. Time-course analysis revealed distinct dynamics in Ae. aegypti and Ae. albopictus. Our findings indicate MADV may be compatible with mosquito species present in endemic regions and areas at risk of virus introduction.

molecular biology↗

bioRxiv2024

Multiomic insights into sucrose accumulation in sugarcane

Sugarcane (Saccharum spp.) holds significant economic importance in sugar and biofuel production. Despite extensive research, understanding highly quantitative traits, such as sucrose content, remains challenging due to the complex genomic landscape of the crop. In this study, we conducted a multiomic investigation to elucidate the genetic architecture and molecular mechanisms governing sucrose accumulation in sugarcane. Using a biparental cross (IACSP95-3018 x IACSP93-3046) and a genetically diverse collection of sugarcane genotypes, we evaluated the soluble solids (Brix) and sucrose content (POL) across various years and environments. Both populations were genotyped using a genotyping-by-sequencing (GBS) approach, with single nucleotide polymorphisms (SNPs) identified via bioinformatics pipelines. Genotype-phenotype associations were established using a combination of traditional linear mixed-effect models and machine learning algorithms. Furthermore, we conducted an RNA sequencing (RNA-Seq) experiment on genotypes exhibiting distinct Brix and POL profiles across different developmental stages. Differentially expressed genes (DEGs) potentially associated with variations in sucrose accumulation were identified. All findings were integrated through a comprehensive gene coexpression network analysis. Strong correlations among the evaluated characteristics were observed, with estimates of modest to high heritabilities. By leveraging a broad set of SNPs identified for both populations, we identified several SNPs potentially linked to phenotypic variance. Our examination of genes close to these markers facilitated the association of such SNPs with DEGs in genotypes with contrasting sucrose levels. Through the integration of these results with a gene coexpression network, we delineated a set of genes potentially involved in the regulatory mechanisms of sucrose accumulation in sugarcane, collectively contributing to the definition of this critical phenotype. Our findings constitute a significant resource for biotechnology and plant breeding initiatives. Furthermore, our genotype-phenotype association models hold promise for application in genomic selection, offering valuable insights into the molecular underpinnings governing sucrose accumulation in sugarcane.

genetics↗

bioRxiv2022

A joint learning approach for genomic prediction in polyploid grasses

Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.

bioinformatics↗

bioRxiv2020

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

bioinformatics↗

bioRxiv2019

Direct genome editing of patient-derived xenografts using CRISPR-Cas9 enables rapid in vivo functional genomics

Patient-derived xenografts (PDXs) constitute a powerful set of preclinical models for in vivo cancer research, reflecting the spectrum of genomic alterations and therapeutic liabilities of human cancers1-4. In contrast to either cancer cell lines or genetically engineered mouse models, the utility of PDXs has been limited by the inability to perform targeted genome editing of these tumors. To address this limitation, we have generated a lentiviral platform for CRISPR-Cas9 editing of PDXs using a tightly regulated, inducible Cas9 vector that does not require in vitro culture for selection of transduced cells. We demonstrate the utility of this platform in PDXs (1) to analyze genetic dependencies by targeted gene disruption and (2) to analyze mechanisms of acquired drug resistance by site-specific gene editing using templated homology-directed repair. This flexible system has broad application to other explant models and substantially augments the utility of PDXs as genetically programmable models of human cancer.

cancer biology↗