Biology subjects

Pimenta, R. J. G.

Publications and source records attributed to Pimenta, R. J. G..

10 recordsLinked to original sources

bioRxiv2026

Multi-omics integration reveals candidate genes and molecular mechanisms associated with resistance to sugarcane smut

Sugarcane smut, caused by the fungus Sporisorium scitamineum (Syd.), is one of the main diseases affecting sugarcane and is capable of causing severe yield losses in susceptible varieties. Given the limitations of classical breeding, mainly due to the genetic complexity of sugarcane cultivars, molecular and omics approaches have gained increasing prominence for studying the genetic basis of traits such as disease resistance. In this context, we investigated sugarcane resistance to smut through the integration of association mapping methods and transcriptomics. A panel of 155 genotypes was genotyped with 33,335 SNP markers and inoculated with S. scitamineum generating three phenotypic variables. These data were jointly analyzed through genome-wide association analyses to identify marker-trait associations. In addition, transcriptomic analyses were performed using publicly available datasets from four studies evaluating sugarcane responses to smut. The integration of these multi-omics data enabled the identification of candidate genes involved in structural reinforcement, immune signaling, redox homeostasis, genome maintenance, and cellular adaptation. These findings provide valuable molecular resources for functional validation and breeding strategies aimed at developing sugarcane cultivars with enhanced smut resistance.

genomics↗

bioRxiv2024

Sugarcane yellow leaf virus impairs the transcriptomic defense responses of sugarcane to its new aphid vector Melanaphis sorghi

Sugarcane (Saccharum spp.) is a preeminent sugar and bioenergy crop and has great economic importance in tropical countries. A major disease affecting this crop is yellow leaf disease, which is caused by sugarcane yellow leaf virus (SCYLV, Polerovirus SCYLV, Solemoviridae). The sugarcane aphid Melanaphis sacchari is considered the main vector of SCYLV, and the closely related sorghum aphid Melanaphis sorghi, which has recently emerged as a pest of great relevance in sorghum, has also been suspected as a vector. Genetic resistance is an important resource for preventing yield losses caused by SCYLV and its vectors, but knowledge on the underlying molecular mechanisms is lacking. Therefore, the present work was aimed at investigating the transcriptomic responses of sugarcane to SCYLV and M. sorghi, which was reported to transmit this virus for the first time herein. Two sugarcane cultivars, one susceptible and one tolerant to SCYLV, were fed upon by aviruliferous and viruliferous aphids. The transcriptome of the plants was assessed by RNA-Seq using differential gene expression analyses and a gene coexpression network. The susceptible cultivar showed an incipient reaction to both M. sorghi and SCYLV, with very few differentially expressed genes (DEGs) identified in comparison with aphid-free plants. The response of the tolerant cultivar to aviruliferous M. sorghi involved pathways typically associated with defense against herbivory, which were also enriched in coexpression network modules in which DEGs were overrepresented. Some of these genes were hubs in their respective modules, indicating they are potential key regulators of the defense responses. However, these responses were diminished when viruliferous aphids were used, and other processes linked to infection with SCYLV were altered. These results indicated that SCYLV could affect sugarcane defense responses to its vector, similar to other viruses of the same genus. Some possible implications for the epidemiology and impact of SCYLV and M. sorghi are discussed.

plant 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↗

bioRxiv2024

Identifying Candidate Genes for Sugar Accumulation in Sugarcane Cultivars: From a Syntenic Genomic Region to a Gene Coexpression Network

Elucidating the intricacies of the sugarcane genome is essential for breeding superior cultivars. This economically important crop originates from hybridizations of highly polyploid Saccharum species. However, the large size (10 Gb), high polyploidy, and aneuploidy of the sugarcane genome pose significant challenges to complete genome sequencing, assembly, and annotation. One successful strategy for identifying candidate genes linked to agronomic traits, particularly those associated with sugar accumulation, leverages synteny and potential collinearity with related species. In this study, we explored synteny between sorghum and sugarcane. Genes from a sorghum Brix QTL were used to screen bacterial artificial chromosome (BAC) libraries from two Brazilian sugarcane varieties (IACSP93-3046 and SP80-3280). The entire region was successfully recovered, confirming synteny and collinearity between the species. Manual annotation identified 51 genes in the hybrid varieties that were subsequently confirmed to be present in Saccharum spontaneum. To identify candidate genes for sugar accumulation, this study employed a multifaceted approach, including retrieving the genomic region of interest, performing gene-by-gene analysis, analyzing RNA-seq data of internodes from Saccharum officinarum and S. spontaneum accessions, constructing a coexpression network to examine the expression patterns of genes within the studied region and their neighbors, and finally identifying differentially expressed genes (DEGs). This comprehensive approach led to the discovery of three candidate genes potentially involved in sugar accumulation: an ethylene-responsive transcription factor (ERF), an ABA 8-hydroxylase, and a prolyl oligopeptidase (POP). These findings could be valuable for identifying additional candidate genes for other important agricultural traits and directly targeting candidate genes for further work in molecular breeding.

genomics↗

bioRxiv2022

Multiomic investigation of sugarcane mosaic virus resistance in sugarcane

Sugarcane mosaic virus (SCMV) is the main etiological agent of sugarcane mosaic disease, which affects sugarcane, maize and other economically important grass species. Despite the extensive characterization of quantitative trait loci controlling resistance to SCMV in maize, the genetic basis of this trait is largely unexplored in sugarcane. Here, a genome-wide association study was performed and machine learning coupled to feature selection was used for the genomic prediction of resistance to SCMV in a diverse panel of sugarcane accessions. This ultimately led to the identification of nine single nucleotide polymorphisms (SNPs) explaining up to 29.9% of the phenotypic variance and a 73-SNP set that predicted resistance with high accuracy, precision, recall, and F1 scores. Both marker sets were validated in additional sugarcane genotypes, in which the SNPs explained up to 23.6% of the phenotypic variation and predicted resistance with a maximum accuracy of 69.1%. Synteny analyses showed that the gene responsible for the major SCMV resistance in maize is probably absent in sugarcane, explaining why such a major resistance source is thus far unknown in this crop. Lastly, using sugarcane RNA sequencing data, markers associated with the resistance to SCMV in sugarcane were annotated and a gene coexpression network was constructed to identify the predicted biological processes involved in SCMV resistance. This allowed the identification of candidate resistance genes and confirmed the involvement of stress responses, photosynthesis and regulation of transcription and translation in the resistance to this virus. These results provide a viable marker-assisted breeding approach for sugarcane and identify target genes for future molecular studies on resistance to SCMV.

plant biology↗

bioRxiv2022

Genome-wide characterization of the common bean kinome: catalog and insights into expression patterns and genetic organization

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and is the core regulator of cellular signaling. Even considering this substantial importance, the kinome of common bean (Phaseolus vulgaris) has not been profiled yet. Here, we identified and characterised the complete set of kinases of common bean, performing an in-depth investigation with phylogenetic analyses and measurements of gene distribution, structural organization, protein properties, and expression patterns over a large set of RNA-Sequencing data. Being composed of 1,203 PKs distributed across all P. vulgaris chromosomes, this set represents 3.25% of all predicted proteins for the species. These PKs could be classified into 20 groups and 119 subfamilies, with a more pronounced abundance of subfamilies belonging to the receptor-like kinase (RLK)-Pelle group. In addition to provide a vast and rich reservoir of data, our study supplied insights into the compositional similarities between PK subfamilies, their evolutionary divergences, highly variable functional profile, structural diversity, and expression patterns, modeled with coexpression networks for investigating putative interactions associated with stress response.

plant biology↗

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

The sugarcane and sorghum kinomes: insights into evolutionary expansion and diversification

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and is the core regulator of cellular signaling. Even considering this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, which were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between these species, this study offers insights about Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq, identifying significant similarities between Sbi and Ssp. Moreover, through coexpression networks, we inferred a core structure of kinase interactions with specific key elements. This study is the first to categorize the allele specificity of a kinome and provides a wide reservoir of molecular and genetic information, enhancing the understanding of the evolutionary history of Sbi and Ssp PKs. HighlightThis study describes the catalog of kinase gene family in Saccharum spontaneum and Sorghum bicolor, providing a reservoir of molecular features and expression patterns based on RNA-Seq and co-expression networks.

genomics↗

bioRxiv2020

Genome-Wide Association Mapping of Sugarcane Yellow Leaf Virus Resistance

A major disease affecting sugarcane, a leading sugar and energy crop, is sugarcane yellow leaf (SCYL), caused by the sugarcane yellow leaf virus (SCYLV). Despite damages caused by SCYLV, the genetic basis of resistance to this virus remains largely unknown. Several methodologies have arisen to identify molecular markers associated with SCYLV resistance, which are crucial for marker-assisted selection and understanding response mechanisms to this virus. We investigated the genetic basis of SCYLV resistance using dominant and codominant markers and genotypes of interest for breeding. A sugarcane panel inoculated with SCYLV was analyzed for SCYL symptoms, and viral titer was estimated by RT-qPCR. This panel was genotyped with 662 dominant markers and 70,888 SNPs and indels with allele proportion information. We used polyploid-adapted genome-wide association analyses and machine-learning algorithms coupled with feature selection methods to establish marker-trait associations. While each approach identified unique marker sets associated with phenotypes, convergences were observed between them, demonstrating their complementarity. Lastly, we annotated these markers, identifying genes encoding emblematic participants in virus resistance mechanisms and previously unreported candidates involved in viral responses. Our approach could accelerate sugarcane breeding targeting SCYLV resistance and facilitate studies on biological processes leading to this trait.

plant biology↗

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↗