Identifying candidate genes for sugar accumulation in sugarcane: an integrative approach.

Background: 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 degree of polyploidy, and aneuploidy of the sugarcane genome pose significant challenges to complete genome sequencing, assembly, and annotation.

Uncovering the molecular mechanisms of russet skin formation in Niagara grapevine (Vitis vinifera × Vitis labrusca).

Grape breeding programs are mostly focused on developing new varieties with high production volume, sugar contents, and phenolic compound diversity combined with resistance and tolerance to the main pathogens under culture and adverse environmental conditions. The 'Niagara' variety (Vitis labrusca Vitis vinifera) is one of the most widely produced and commercialized table grapes in Brazil. In this work, we selected three Niagara somatic variants with contrasting berry phenotypes and performed morphological and transcriptomic analyses of their berries.

Correction: A stomata classification and detection system in microscope images of maize cultivars.

[This corrects the article DOI: 10.1371/journal.pone.

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in .

Tropical forage grasses, particularly those belonging to the genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of , a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in families to predict agronomic traits.

Genome-wide analysis and characterization of the LRR-RLK gene family provides insights into anthracnose resistance in common bean.

Anthracnose, caused by the hemibiotrophic fungus Colletotrichum lindemuthianum, is a damaging disease of common beans that can drastically reduce crop yield. The most effective strategy to manage anthracnose is the use of resistant cultivars. There are many resistance loci that have been identified, mapped and associated with markers in common bean chromosomes.

The rubber tree kinome: Genome-wide characterization and insights into coexpression patterns associated with abiotic stress responses.

The protein kinase (PK) superfamily constitutes one of the largest and most conserved protein families in eukaryotic genomes, comprising core components of signaling pathways in cell regulation. Despite its remarkable relevance, only a few kinase families have been studied in . A comprehensive characterization and global expression analysis of the PK superfamily, however, is currently lacking.

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.

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning.

Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection.

The rate and role of pseudogenes of the complex.

Whole-genome sequence analyses have significantly contributed to the understanding of virulence and evolution of the complex (MTBC), the causative pathogens of tuberculosis. Most MTBC evolutionary studies are focused on single nucleotide polymorphisms and deletions, but rare studies have evaluated gene content, whereas none has comprehensively evaluated pseudogenes. Accordingly, we describe an extensive study focused on quantifying and predicting possible functions of MTBC and pseudogenes.

Taxonomically Restricted Genes Are Associated With Responses to Biotic and Abiotic Stresses in Sugarcane ( spp.).

Orphan genes (OGs) are protein-coding genes that are restricted to particular clades or species and lack homology with genes from other organisms, making their biological functions difficult to predict. OGs can rapidly originate and become functional; consequently, they may support rapid adaptation to environmental changes. Extensive spread of mobile elements and whole-genome duplication occurred in the group, which may have contributed to the origin and diversification of OGs in the sugarcane genome.

Network Analysis Reveals Different Cellulose Degradation Strategies Across Strains Associated With XYR1 and CRE1.

whose gene expression is tightly controlled by the transcription factors (TFs) XYR1 and CRE1, is a potential candidate for hydrolytic enzyme production. Here, we performed a network analysis of IOC-3844 and CBMAI-0179 to explore how the regulation of these TFs varies between these strains. In addition, we explored the evolutionary relationships of XYR1 and CRE1 protein sequences among spp.

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches.

(rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics.

Trends in biological data integration for the selection of enzymes and transcription factors related to cellulose and hemicellulose degradation in fungi.

Fungi are key players in biotechnological applications. Although several studies focusing on fungal diversity and genetics have been performed, many details of fungal biology remain unknown, including how cellulolytic enzymes are modulated within these organisms to allow changes in main plant cell wall compounds, cellulose and hemicellulose, and subsequent biomass conversion. With the advent and consolidation of DNA/RNA sequencing technology, different types of information can be generated at the genomic, structural and functional levels, including the gene expression profiles and regulatory mechanisms of these organisms, during degradation-induced conditions.

A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses.

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies.

A stomata classification and detection system in microscope images of maize cultivars.

Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses.

Genome-wide approaches for the identification of markers and genes associated with sugarcane yellow leaf virus resistance.

Sugarcane yellow leaf (SCYL), caused by the sugarcane yellow leaf virus (SCYLV) is a major disease affecting sugarcane, a leading sugar and energy crop. Despite damages caused by SCYLV, the genetic base 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.

The Wild Sugarcane and Sorghum Kinomes: Insights Into Expansion, Diversification, and Expression Patterns.

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and the core regulator of cellular signaling. Despite this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here, we identified and profiled the complete kinomes of the polyploid (Ssp) and (Sbi), a close diploid relative.

Microbial co-occurrence network and its key microorganisms in soil with permanent application of composted tannery sludge.

Soil microbial communities act on important environmental processes, being sensitive to the application of wastes, mainly those potential contaminants, such as tannery sludge. Due to the microbiome complexity, graph-theoretical approaches have been applied to represent model microbial communities interactions and identify important taxa, mainly in contaminated soils. Herein, we performed network and statistical analyses into microbial 16S rRNA gene sequencing data from soil samples with the application of different levels of composted tannery sludge (CTS) to assess the most connected nodes and the nodes that act as bridges to identify key microbes within each community.

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.