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Pinus taeda(loblolly pine [LP]), a long-lived tree species, is one of the world’s most economically important forest trees. Among growth traits, volume is the most widely considered in tree improvement programs. The challenge of revealing the genetic variants involved in growth trait variation is even greater in conifers such as LP because of the extremely large size and high complexity ofPinusgenomes. Here, we present a comprehensive genetic analysis of LP involving genetic markers associated with volume (via genome-wide association studies [GWAS] and machine learning [ML]) to reveal pathways involved in high-performance phenotypes. We used a population of LP in the 2nd cycle of breeding composed of full-sib progenies established in seven sites. A total of 1,692 individuals were phenotyped and genotyped using sequence capture probes. 31,589 SNPs were identified and used to perform a GWAS through a multilocus mixed model. A precision core marker set with 7,864 SNPs selected through ML, i.e., feature selection (FS), was used for genomic selection (GS), providing a predictive accuracy of approximately 0.79. For genomic annotation and the construction of gene coexpression networks, three transcriptomes were assembled. We selected genes associated with the target trait and assessed the cascade of related molecular mechanisms within gene coexpression networks. These results advance the understanding of the genetics influencing growth traits, reveal candidate genes for future functional studies in LP, and increase the understanding of quantitative genetics and the genomics of complex phenotypic variations in LP.Key MessageThis manuscript leverages the power of genomic selection, traditional GWAS, machine learning, transcriptome data, and gene coexpression networks to better understand pine’s complex trait of volume growth.

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 inHevea brasiliensis. A comprehensive characterization and global expression analysis of the PK superfamily, however, is currently lacking. In this study, with the aim of providing novel inferences about the mechanisms associated with the stress response developed by PKs and retained throughout evolution, we identified and characterized the entire set of PKs, also known as the kinome, present in the Hevea genome. A total of 1,809 PK genes were identified using the current reference genome assembly at the scaffold level, and 1,379 PK genes were identified using the latest chromosome-level assembly and combined into a single set of 2,842 PKs. These proteins were further classified into 20 different groups and 122 families, exhibiting high compositional similarities among family members and with two phylogenetically close species (Manihot esculentaandRicinus communis). Different RNA-sequencing datasets were employed to identify tissue-specific expression patterns and potential correspondences between different rubber tree genotypes. In addition, coexpression networks under several abiotic stress conditions, such as cold, drought and latex overexploitation, were employed to elucidate associations between families and tissues/stresses. Through the joint investigation of tandemly duplicated kinases, transposable elements, gene expression patterns, and coexpression events, we provided insights into the understanding of the cell regulation mechanisms in response to several conditions, which can often lead to a significant reduction in rubber yield.

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.

Machine learning (ML) has been a major driver in complex data analysis in recent decades, allowing the mining of large databases. ML techniques allow the creation of computational models for prediction, pattern extraction and recognition, considering the premise that the computer acquires learning skills to perform a given task without being explicitly programmed for such a purpose. Driven by the efficiency of these techniques, several studies have demonstrated their wide range of applications and high potential for maize breeding. From the prediction of genetic values by omic data to applications of high-throughput phenotyping data, ML models have promoted advances in the species comprehension and assisted in the development of more effective tools for its breeding, driving expressive yield gains. In this context, this work presents the main contributions of ML in maize breeding, providing a broad view of the main studies and methodological perspectives in the area.

BackgroundHevea brasiliensis, a tree species from the Amazon rainforest, is the main global natural rubber source. Due to high pressure from fungal diseases in hot, humid regions, rubber plantations have been moved to dryer “scape areas” with lower temperatures. We studied GT1 and RRIM 600 rubber tree clones with different cold tolerance strategies to analyze gene expression regulation during 24 hours of cold exposure.ResultsThe RNA-seq gene co-expression network (GCN) was developed with 27,220 genes grouped into 205 gene clusters. Through the GCN, we related predominant rubber tree molecular responses to cold stress to 31 clusters, divided into three GCN modules: a downregulated group with 16 clusters and two upregulated groups with twelve and three clusters. Hub genes of cold-responsive modules were also identified and analyzed. Considering the three modules and hub genes identified, we concluded that the generalHevearesponse to short-term cold exposure involves complex jasmonic acid stress response and programmed cell death regulation, ethylene-responsive gene upregulation and relaxation of florigen gene inhibition. The GCN strategy not only identified single DEGs related toHeveacold responses but also provided insights into a more extensive cascade of associated mechanisms involved in the young rubber tree cold stress response.ConclusionsOur findings may represent the genetic stress responses developed during species evolution. UnderstandingH. brasiliensiscold response mechanisms could improve breeding strategies for this crop with a narrow genetic base being impacted by climate change that is the only large-scale rubber production source.Key MessageDifferent cold tolerance strategies were revealed through gene expression analysis during cold exposure in rubber tree. This approach identified genes and provided insights into molecular pathways involved in such resistance.

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. This increase in data generation made rapid computational development necessary to deal with the large amounts of data generated. In this context, the origination of bioinformatics, a hybrid science integrating biological data with various techniques for information storage, distribution and analysis, was a fundamental step toward the current state-of-the-art in the postgenomic era. The possibility of integrating biological big data has facilitated exciting discoveries, including identifying novel mechanisms and more efficient enzymes, increasing yields, reducing costs and expanding opportunities in the bioprocess field. In this review, we summarize the current status and trends of the integration of different types of biological data through bioinformatics approaches for biological data analysis and enzyme selection.

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. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones, self-fertilized individuals, half-siblings and full contaminants, in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation and clustering analysis (CA). The combination of these methods allowed the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

Made available in DSpace on 2022-05-01T07:58:54Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Secretaría Nacional de Ciencia, Tecnología e Innovación Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 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. Centre of Molecular Biology and Genetic Engineering (CBMEG) University of Campinas (UNICAMP) Gustavo Galindo Velasco Campus Littoral Polytechnic Superior School (ESPOL) São Paulo State University (UNESP) Advanced Centre for Technological Research in Sugarcane Agribusiness Agronomic Institute of Campinas (IAC/APTA) Plant Protection Research Centre Biological Institute (IB/APTA) Department of Plant Biology Institute of Biology University of Campinas (UNICAMP) São Paulo State University (UNESP) FAPESP: 2015/24346-9 FAPESP: 2018/18588-8 FAPESP: 2019/03232-6 FAPESP: 2019/21682-9 CNPq: 424050/2016-1

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 polyploidSaccharum spontaneum(Ssp) andSorghum 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 inSaccharum spontaneumandSorghum bicolor, providing a reservoir of molecular features and expression patterns based on RNA-Seq and co-expression networks.

Trichoderma harzianum, 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 T. harzianum IOC-3844 and T. harzianum 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 Trichoderma spp. The results of the T. harzianum strains were compared with those of Trichoderma atroviride CBMAI-0020, a mycoparasitic species. Although transcripts encoding carbohydrate-active enzymes (CAZymes), TFs, transporters, and proteins with unknown functions were coexpressed with cre1 or xyr1, other proteins indirectly related to cellulose degradation were identified. The enriched GO terms describing the transcripts of these groups differed across all strains, and several metabolic pathways with high similarity between both regulators but strain-specific differences were identified. In addition, the CRE1 and XYR1 subnetworks presented different topology profiles in each strain, likely indicating differences in the influences of these regulators according to the fungi. The hubs of the cre1 and xyr1 groups included transcripts not yet characterized or described as being related to cellulose degradation. The first-neighbor analyses confirmed the results of the profile of the coexpressed transcripts in cre1 and xyr1. The analyses of the shortest paths revealed that CAZymes upregulated under cellulose degradation conditions are most closely related to both regulators, and new targets between such signaling pathways were discovered. Although the evaluated T. harzianum strains are phylogenetically close and their amino acid sequences related to XYR1 and CRE1 are very similar, the set of transcripts related to xyr1 and cre1 differed, suggesting that each T. harzianum strain used a specific regulation strategy for cellulose degradation. More interestingly, our findings may suggest that XYR1 and CRE1 indirectly regulate genes encoding proteins related to cellulose degradation in the evaluated T. harzianum strains. An improved understanding of the basic biology of fungi during the cellulose degradation process can contribute to the use of their enzymes in several biotechnological applications and pave the way for further studies on the differences across strains of the same species.

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.

Made available in DSpace on 2022-05-01T04:26:38Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-10-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 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. Institute of Science and Technology Universidade Federal de São Paulo Department of Agricultural Engineering and Soil Science Federal University of Piauí Laboratório de Ecologia Microbiana e Biotecnologia Federal University of Ceara Center for Nuclear Energy in Agriculture University of Sao Paulo University of Sao Paulo West, Campus II Universidade Estadual Paulista, Campus de Jaboticabal Universidade Brasil Universidade Estadual Paulista, Campus de Jaboticabal CNPq: 305069/2018-1 CNPq: 305102/2014-1 CNPq: 471347/2013-2

A utilização de marcadores moleculares em conjunto com as principais metodologias de diversidade genética permitem inferir o quão próximo determinados organismos se encontram em uma escala evolutiva. No presente trabalho, foram realizadas otimizações de desempenho no cálculo de medidas de distância genética em um pacote do R, amplamente utilizado pela comunidade científica com diferentes formatos de dados. Com uma redução média de 90% no tempo gasto para esses cálculos usando diferentes medidas de diver- sidade, os resultados mostraram grande potencial de otimização em métodos de uma área que se encontra em constante evolução e que tem produzido uma quantidade massiva de dados biológicos.

Fundamental questions such as what are the genes that are really necessary for the survival of cells have motivated many studies to investigate the essentiality of genes in different species. Initial efforts have attempted to address this problem through exhaustive knockout experiments in simple bacteria. Recently, results obtained in these studies have also been applied to the emerging field of synthetic biology with possible implications in many other fields such as health and energy. Motivated by the evolution of DNA sequencing technology and high-throughput biological data generation, many recent efforts have also been made for building and understanding biological networks. In particular, metabolic networks represent the set of known biochemical reactions within a cell. Essential genes are expected to play a key role in these networks, as they must be involved in vital metabolic pathways. Even though some studies investigated the correlation between essential genes and biological network information, different types of networks and other biological information were usually combined and the effect of each of them in the obtained results was not stressed. This paper describes an attempt to predict essential genes using solely topological features from metabolic networks. The networks were built from a common repository, the KEGG database, ensuring data uniformity. Experimentally, considering different prediction scenarios and reference organisms, the use of topological features from metabolic networks achieved mean AUC of about 70% in the prediction of gene essentiality. This reveals that more factors affect essentiality and should indeed be considered in order to obtain more accurate predictions.

Este artigo apresenta uma iniciativa de ensino de pensamento computacional para estudantes de uma escola de ensino fundamental com a utilização de uma metodologia expositiva aliada à utilização do Scratch como um ambiente de aprendizado intuitivo e acessível. O método de aula adotado consistiu em uma explicação teórica sobre um tópico relacionado à aquisição de pensamento computacional seguida de atividades práticas de programação nas quais os alunos construíram partes de um jogo que posteriormente foram reunidas em um projeto final. Este experimento permitiu avaliar metodologias para ensino de programação e utilização de games como ferramenta para incentivo pedagógico naárea.