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.

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.

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. The network analysis revealed hubs belonging to Proteobacteria in soil with lower CTS rates, while active degraders of recalcitrant and pollutant chemical hubs belonging to Proteobacteria and Actinobacteria were found in soils under the highest CTS rates. The majority of classified connectors belonged to Actinobacteria, but similarly to hubs taxa, they shifted from metabolic functional profile to taxa with abilities to degrade toxic compounds, revealing a soil perturbation with the CTS application on community organization, which also impacted the community modularity. Members of Actinobacteria and Acidobacteria were identified as both hub and connector suggesting their role as keystone groups. Thus, these results offered us interesting insights about crucial taxa, their response to environmental alterations, and possible implications for the ecosystem.

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.

Analysis of genomic regions of Trichoderma harzianum IOC-3844 related to biomass degradation.

Trichoderma harzianum IOC-3844 secretes high levels of cellulolytic-active enzymes and is therefore a promising strain for use in biotechnological applications in second-generation bioethanol production. However, the T. harzianum biomass degradation mechanism has not been well explored at the genetic level. The present work investigates six genomic regions (~150 kbp each) in this fungus that are enriched with genes related to biomass conversion. A BAC library consisting of 5,760 clones was constructed, with an average insert length of 90 kbp. The assembled BAC sequences revealed 232 predicted genes, 31.5% of which were related to catabolic pathways, including those involved in biomass degradation. An expression profile analysis based on RNA-Seq data demonstrated that putative regulatory elements, such as membrane transport proteins and transcription factors, are located in the same genomic regions as genes related to carbohydrate metabolism and exhibit similar expression profiles. Thus, we demonstrate a rapid and efficient tool that focuses on specific genomic regions by combining a BAC library with transcriptomic data. This is the first BAC-based structural genomic study of the cellulolytic fungus T. harzianum, and its findings provide new perspectives regarding the use of this species in biomass degradation processes.

Transcriptome profile of Trichoderma harzianum IOC-3844 induced by sugarcane bagasse.

Profiling the transcriptome that underlies biomass degradation by the fungus Trichoderma harzianum allows the identification of gene sequences with potential application in enzymatic hydrolysis processing. In the present study, the transcriptome of T. harzianum IOC-3844 was analyzed using RNA-seq technology. The sequencing generated 14.7 Gbp for downstream analyses. De novo assembly resulted in 32,396 contigs, which were submitted for identification and classified according to their identities. This analysis allowed us to define a principal set of T. harzianum genes that are involved in the degradation of cellulose and hemicellulose and the accessory genes that are involved in the depolymerization of biomass. An additional analysis of expression levels identified a set of carbohydrate-active enzymes that are upregulated under different conditions. The present study provides valuable information for future studies on biomass degradation and contributes to a better understanding of the role of the genes that are involved in this process.