Search

Copyright of Applications in Plant Sciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Debido a que el conocimiento de la diversidad genética existente en las variedades modernas de caña de azúcar es de vital importancia para los procesos de mejoramiento, el presente estudio examinó 33 variedades usadas por los mejoradores en CENICAÑA, y cinco clones de Saccharum officinarum mediante la técnica de los microsatélites. Se evaluaron 63 iniciadores los cuales produjeron 263 fragmentos polimórficos. Los patrones electroforéticos generados mediante esta técnica fueron analizados usando los paquetes estadísticos SAS (Análisis de Correspondencia Múltiple) y NTSYS-pc (dendrograma y matriz de distancias genéticas). Los alelos generados por cada iniciador oscilaron entre 1 y 16 (media de 5). Las agrupaciones generadas mediante estos análisis lograron diferenciar las variedades cultivadas de caña de los clones de S. officinarum, y a su vez determinaron que la similitud promedio de todos los individuos fue 0.664. El análisis de diversidad genética mostró un grupo bastante diverso (Ht: 0.973) y logro identificar 38 genotipos en toda la población. Dentro de los resultados más sobresalientes se destaca la ubicación de la variedad CC 91-1880 muy cerca de las variedades Q provenientes de Australia, proponiendo a esta variedad como un buen candidato para ser analizado por los mejoradores. Los resultados de este trabajo son muy importantes, pues deja claro que a pesar de la homogeneidad genética presente en las variedades modernas de caña de azúcar, existen variantes alélicas que podrían ser utilizadas en los nuevos proyectos de mejoramiento de CENICAÑA. Palabras clave: Caña de azúcar, diversidad genética, microsatélites, marcadores moleculares, Saccharum officinarum.

Recent developments in High Throughput Sequencing (HTS) technologies and bioinformatics, including improved read lengths and genome assemblers allow the reconstruction of complex genomes with unprecedented quality and contiguity. Sugarcane has one of the most complicated genomes among grassess with a haploid length of 1Gbp and a ploidies between 8 and 12. In this work, we present a genome assembly of the Colombian sugarcane hybrid CC 01-1940. Three types of sequencing technologies were combined for this assembly: PacBio long reads, Illumina paired short reads, and Hi-C reads. We achieved a median contig length of 34.94 Mbp and a total genome assembly of 903.2 Mbp. We annotated a total of 63,724 protein coding genes and performed a reconstruction and comparative analysis of the sucrose metabolism pathway. Nucleotide evolution measurements between orthologs with close species suggest that divergence between Saccharum officinarum and Saccharum spontaneum occurred <2 million years ago. Synteny analysis between CC 01-1940 and the S. spontaneum genome confirms the presence of translocation events between the species and a random contribution throughout the entire genome in current sugarcane hybrids. Analysis of RNA-Seq data from leaf and root tissue of contrasting sugarcane genotypes subjected to water stress treatments revealed 17,490 differentially expressed genes, from which 3,633 correspond to genes expressed exclusively in tolerant genotypes. We expect the resources presented here to serve as a source of information to improve the selection processes of new varieties of the breeding programs of sugarcane. [ABSTRACT FROM AUTHOR]

Sugarcane, a prominent global crop utilized for sugar, bioethanol, and renewable bioenergy production, holds significant importance in Colombia. In 2022, it contributed to the production of 2.1 million tons of sugar, 347 million liters of bioethanol, and 1745 GWh of electrical energy. The cultivation of sugarcane worldwide faces vulnerability to drought stress induced by climate change, which significantly affects yields. Understanding how plants respond to drought involves complex interactions among genes, morphology, physiology, and biochemistry. However, these factors are often analyzed separately using methods such as Differentially Expressed Genes, comparative physiology, or metabolomics, thereby restricting the potential for broader insights that could arise from their further integration. This paper uses an improved version of Control-Stress data Integration with Overlapping Clustering (CSI-OC), a methodology that provides a comprehensive perspective by integrating diverse data types, in which a Lasso-based network helps in pinpointing stress-responsive genes. The objective of this study is to utilize CSI-OC to identify genes relevant to drought stress in Colombian sugarcane cultivars. To accomplish this goal, the study analyzes both leaf and root expression data, alongside four physiological parameters associated with leaf responses across different levels of drought stress. Computational evaluation indicates that the datasets are effectively processed using the CSI-OC workflow. This methodology demonstrates good performance in identifying genes strongly correlated with both the stress condition and the considered phenotypic traits. As stress levels increase, the number of genes selected by CSI-OC displays a contrasting pattern between leaves and roots. This observation implies a coordinated cascade of gene responses from leaves to roots with escalating stress levels, indicating a holistic adaptation strategy within the plant. Overall, the findings of this study underscore the effectiveness of CSI-OC as a comprehensive approach for identifying pertinent genes linked to drought stress in sugarcane. [Display omitted] • Stress genes were found via interactions in overlapping modules influencing phenotype. • Genes can be stress-relevant even without differential expression or hub node status. • Merging control-stress data for Lasso-based networks boosts large dataset efficiency. • Findings suggest coordinated gene response from leaves to roots with increased stress. [ABSTRACT FROM AUTHOR]