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.

Hybrid zone of a tree in a Cerrado/Atlantic Forest ecotone as a hotspot of genetic diversity and conservation.

The Cerrado, the largest Neotropical savanna, and the Brazilian Atlantic Forest form large ecotonal areas where savanna and forest habitats occupy adjacent patches with closely related species occurring side by side, providing opportunities for hybridization. Here, we investigated the evolutionary divergence between the savanna and forest ecotypes of the widely distributed tree Plathymenia reticulata (n = 233 individuals). Genetic structure analysis of P. reticulata was congruent with the recognition of two ecotypes, whose divergence captured the largest proportion of genetic variance in the data (F CT = 0.222 and F ST = 0.307). The ecotonal areas between the Cerrado and the Atlantic Forest constitute a hybrid zone in which a diversity of hybrid classes was observed, most of them corresponding to second-generation hybrids (F2) or backcrosses. Gene flow occurred mainly toward the forest ecotype. The genetic structure was congruent with isolation by environment, and environmental correlates of divergence were identified. The observed pattern of high genetic divergence between ecotypes may reflect an incipient speciation process in P. reticulata. The low genetic diversity of the P. reticulata forest ecotype indicate that it is threatened in areas with high habitat loss on Atlantic Forest. In addition, the high divergence from the savanna ecotype suggests it should be treated as a different unit of management. The high genetic diversity found in the ecotonal hybrid zone supports the view of ecotones as important areas for the origin and conservation of biodiversity in the Neotropics.

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.

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. We investigated the genetic base of SCYLV resistance using dominant and codominant markers and genotypes of interest for sugarcane 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 and demonstrated 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.

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 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, and these 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 the species, this study offers insights into Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq and identified significant similarities between Sbi and Ssp. Moreover, coexpression networks allowed inference of a core structure of kinase interactions with specific key elements. This study provides the first categorization of the allelic specificity of a kinome and offers a wide reservoir of molecular and genetic information, thereby enhancing the understanding of Sbi and Ssp PK evolutionary history.

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% when 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. As a result of this approach, we achieved an 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.

Selected to survive and kill: Tityus serrulatus, the Brazilian yellow scorpion.

Annually, more than 1.2 million scorpion stings and more than 3,000 deaths occur worldwide. Tityus serrulatus Lutz and Mello, 1922 (Scorpiones, Buthidae) is the most medically relevant species in Brazil where it is spreading rapidly and causing over 90,000 cases of envenomation yearly. We monitored T. serrulatus longevity and ability to reproduce under conditions of food and/or water deprivation. We found that T. serrulatus is highly tolerant to food deprivation, with individuals enduring up to 400 days without food. On the other hand, access to water played a pivotal role in T. serrulatus survival. Food and water deprived scorpions showed weight reduction. Reproduction occurred throughout the year for food-deprived scorpions and controls, but not in the water-deprived groups. Remarkably, food-deprived animals were able to give birth after 209 days of starvation. Tityus serrulatus resistance to food and water deprivation is likely to be an additional factor underlying this species' geographic expansion and the difficulties encountered in controlling it.

Many unique characteristics revealed by the complete mitochondrial genome of the scorpion Tityus serrulatus (Lutz e Mello 1922) (Chelicerata; Arachnida).

This is the first complete mitochondrial genome of a Tityus species, although it is the most medically important genus in South America. Tityus serrulatus (Brazilian yellow scorpion) mtDNA revealed the same gene arrangement of three out of four other mitogenomes published by now for the same family (Centruroides limpidus, Mesobuthus gibbosus, M. martensii and Buthus occitanus). However, it presented many unique characteristics such as possession of Cox1 gene, different from all other protein-coding genes of scorpion mtDNA, starts with an atypical start codon (CTG). Moreover, no tRNA gene have complete typical secondary structure and the Tytius genome presented three non-coding regions longer than 100bp. Also, it contains the smallest scorpion 16S gene reported by now. Phylogenetic analysis using concatenated homologous genes confirmed Buthidae as a monophyletic clade and supports a monophyletic group including T. serrulatus and the other American species, C. limpidus.

Complete mitochondrial genome of Salminus brasiliensis (Characiformes, Characidae).

We report the complete mitochondrial genome of the fish Salminus brasiliensis, popularly known as dourado. It is a circular, 17,721 bp long DNA molecule, containing 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes and a non-coding Control Region of 2128 bp, a relatively large molecule when compared to other closely related fishes. All protein-coding genes are on the heavy strand, except for Nd6, and all have ATG as the start codon, except for Cox1 gene which has a GTG start codon. Seven protein-coding genes have incomplete stop codons (Nd2, Cox2, Atp6, Nd4, and Cytb have T- -, and Cox3 and Nd3 have TA-). TAG is the stop codon for Nd6 and AGG is the stop codon for Cox1.