Biology subjects

Aono, A. H.

Publications and source records attributed to Aono, A. H..

At least 19 recordsLinked to original sources

bioRxiv2025

Optimizing Soybean Breeding: High-Throughput Phenotyping Technologies for Stink Bug Resistance and High Yields

The stink bug complex is a major agricultural pest for soybean crops, significantly reducing productivity. Genetic resistance is the most effective control strategy, but its quantitative nature and labor-intensive phenotyping make its implementation in breeding programs challenging. This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and identify stink bug resistance by correlating image-derived features and machine learning (ML) models. Using an alpha-lattice design with three replications, we phenotyped 304 soybean lines over two seasons under natural stink bug infestations. We manually evaluated five traits associated with stink bug resistance and correlated them with color, texture, and histogram features from aerial images. Three ML models--AdaBoost, SVM, and MLP-- were tested to predict these traits. VIs, especially the Visible Atmospherically Resistant Index at the first percentile (VARI_P25) and texture-based indices at 45{degrees} and 135{degrees}, effectively predicted traits in stressed environments, particularly during flights near maturation. While ML models showed good predictive ability for yield, healthy seed weight, and maturity, they were less effective for stink bug resistance. Increasing the number of UAV flights modestly improved predictive accuracy, though predicting traits across different seasons remained challenging. Despite this, indices like VARI_25P were valuable for screening and excluding less promising genotypes, optimizing breeding program resources. This pioneering work offers valuable insights and highlights the need for further research to optimize resistance selection, promising significant advances in soybean breeding for stink bug resistance.

genetics↗

bioRxiv2024

Sugarcane yellow leaf virus impairs the transcriptomic defense responses of sugarcane to its new aphid vector Melanaphis sorghi

Sugarcane (Saccharum spp.) is a preeminent sugar and bioenergy crop and has great economic importance in tropical countries. A major disease affecting this crop is yellow leaf disease, which is caused by sugarcane yellow leaf virus (SCYLV, Polerovirus SCYLV, Solemoviridae). The sugarcane aphid Melanaphis sacchari is considered the main vector of SCYLV, and the closely related sorghum aphid Melanaphis sorghi, which has recently emerged as a pest of great relevance in sorghum, has also been suspected as a vector. Genetic resistance is an important resource for preventing yield losses caused by SCYLV and its vectors, but knowledge on the underlying molecular mechanisms is lacking. Therefore, the present work was aimed at investigating the transcriptomic responses of sugarcane to SCYLV and M. sorghi, which was reported to transmit this virus for the first time herein. Two sugarcane cultivars, one susceptible and one tolerant to SCYLV, were fed upon by aviruliferous and viruliferous aphids. The transcriptome of the plants was assessed by RNA-Seq using differential gene expression analyses and a gene coexpression network. The susceptible cultivar showed an incipient reaction to both M. sorghi and SCYLV, with very few differentially expressed genes (DEGs) identified in comparison with aphid-free plants. The response of the tolerant cultivar to aviruliferous M. sorghi involved pathways typically associated with defense against herbivory, which were also enriched in coexpression network modules in which DEGs were overrepresented. Some of these genes were hubs in their respective modules, indicating they are potential key regulators of the defense responses. However, these responses were diminished when viruliferous aphids were used, and other processes linked to infection with SCYLV were altered. These results indicated that SCYLV could affect sugarcane defense responses to its vector, similar to other viruses of the same genus. Some possible implications for the epidemiology and impact of SCYLV and M. sorghi are discussed.

plant biology↗

bioRxiv2024

Multiomic insights into sucrose accumulation in sugarcane

Sugarcane (Saccharum spp.) holds significant economic importance in sugar and biofuel production. Despite extensive research, understanding highly quantitative traits, such as sucrose content, remains challenging due to the complex genomic landscape of the crop. In this study, we conducted a multiomic investigation to elucidate the genetic architecture and molecular mechanisms governing sucrose accumulation in sugarcane. Using a biparental cross (IACSP95-3018 x IACSP93-3046) and a genetically diverse collection of sugarcane genotypes, we evaluated the soluble solids (Brix) and sucrose content (POL) across various years and environments. Both populations were genotyped using a genotyping-by-sequencing (GBS) approach, with single nucleotide polymorphisms (SNPs) identified via bioinformatics pipelines. Genotype-phenotype associations were established using a combination of traditional linear mixed-effect models and machine learning algorithms. Furthermore, we conducted an RNA sequencing (RNA-Seq) experiment on genotypes exhibiting distinct Brix and POL profiles across different developmental stages. Differentially expressed genes (DEGs) potentially associated with variations in sucrose accumulation were identified. All findings were integrated through a comprehensive gene coexpression network analysis. Strong correlations among the evaluated characteristics were observed, with estimates of modest to high heritabilities. By leveraging a broad set of SNPs identified for both populations, we identified several SNPs potentially linked to phenotypic variance. Our examination of genes close to these markers facilitated the association of such SNPs with DEGs in genotypes with contrasting sucrose levels. Through the integration of these results with a gene coexpression network, we delineated a set of genes potentially involved in the regulatory mechanisms of sucrose accumulation in sugarcane, collectively contributing to the definition of this critical phenotype. Our findings constitute a significant resource for biotechnology and plant breeding initiatives. Furthermore, our genotype-phenotype association models hold promise for application in genomic selection, offering valuable insights into the molecular underpinnings governing sucrose accumulation in sugarcane.

genetics↗

bioRxiv2024

Identifying Candidate Genes for Sugar Accumulation in Sugarcane Cultivars: From a Syntenic Genomic Region to a Gene Coexpression Network

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 polyploidy, and aneuploidy of the sugarcane genome pose significant challenges to complete genome sequencing, assembly, and annotation. One successful strategy for identifying candidate genes linked to agronomic traits, particularly those associated with sugar accumulation, leverages synteny and potential collinearity with related species. In this study, we explored synteny between sorghum and sugarcane. Genes from a sorghum Brix QTL were used to screen bacterial artificial chromosome (BAC) libraries from two Brazilian sugarcane varieties (IACSP93-3046 and SP80-3280). The entire region was successfully recovered, confirming synteny and collinearity between the species. Manual annotation identified 51 genes in the hybrid varieties that were subsequently confirmed to be present in Saccharum spontaneum. To identify candidate genes for sugar accumulation, this study employed a multifaceted approach, including retrieving the genomic region of interest, performing gene-by-gene analysis, analyzing RNA-seq data of internodes from Saccharum officinarum and S. spontaneum accessions, constructing a coexpression network to examine the expression patterns of genes within the studied region and their neighbors, and finally identifying differentially expressed genes (DEGs). This comprehensive approach led to the discovery of three candidate genes potentially involved in sugar accumulation: an ethylene-responsive transcription factor (ERF), an ABA 8-hydroxylase, and a prolyl oligopeptidase (POP). These findings could be valuable for identifying additional candidate genes for other important agricultural traits and directly targeting candidate genes for further work in molecular breeding.

genomics↗

bioRxiv2024

Molecular attributes of the tropical tree Avicennia schaueriana involved in the response and tolerance to low temperatures

Mangroves are coastal ecosystems of great socioenvironmental importance that are highly threatened by human activities. Mangrove trees live under harsh environmental conditions, which makes them sensitive to extreme weather events, particularly freezing events. Such events are unpredictable and have catastrophic consequences for mangrove trees; therefore, understanding and anticipating the impacts of such events are essential for directing future mitigation measures. Freezing cold currently limits the distribution of mangroves to tropical and subtropical latitudes worldwide. Mangrove trees are seriously affected by freezing conditions and suffer severe metabolic fluctuations due to photosystem and cellular structure damage. However, land plants more broadly have developed sophisticated mechanisms of resistance to freezing during their evolution, and the central molecular mechanisms involved in this process are consistent. However, the known information is restricted to models of herbaceous plants, such as Arabidopsis thaliana, that are native to temperate habitats, and there is a research gap regarding tropical trees such as mangroves. This work aimed to improve the understanding of the molecular aspects of the response and tolerance to freezing in mangrove trees using Avicennia schaueriana as a model. This species occurs within the colder range limits of South American mangroves and shows evidence of the existence of two functional groups that are locally adapted to the equatorial (EQ) and subtropical (ST) portions of the Brazilian coast. We investigated the transcriptional profiles of seedlings from both functional groups under freezing shock (-4{degrees}C) in a time series. We analyzed transcriptomic data by combining differential expression, coexpression network and protein interaction data. Our results allowed us to describe the profile of the molecular response of A. schaueriana to freezing and the divergence in the behavior of the EQ and ST functional groups. In EQ plants, the response strongly depended on the action of abscisic acid (ABA) and stress signals throughout the experiment. Notably, ABA negatively affects plant growth and promotes the accumulation of carotenoids, anthocyanins and lipids through chlorophyll degradation. On the other hand, in the ST, there were fewer hormones active in the process of primary growth maintenance and metabolic normalization. The accumulation of substances is mainly based on sucrose, anthocyanin and lipid levels, and lipid synthesis is not dependent on chlorophyll degradation. Based on these results, we hypothesize that susceptibility to freezing damage is greater in EQ mangroves than in ST mangroves. Therefore, we recommend that this fact be considered when managing this species, especially at higher latitudes, which are more prone to lower temperatures and extreme freezing events.

plant biology↗

bioRxiv2023

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

Tropical forage grasses, especially species of the genus Urochloa, play an important role in cattle production and are the main food source for animals in tropical/subtropical regions. Most of the species are apomictic and tetraploid, which gives special importance to U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to assist in breeding programs, this study investigates the applicability of genome-wide family prediction (GWFP) in U. ruziziensis half-sibling families to predict growth and biomass production. Machine learning and feature selection algorithms were used to reduce the necessary number of markers for prediction and to enhance the predictive ability across the phenotypes. Beyond that, to investigate the regulation of agronomic traits, the positions of the markers with more importance for the prediction were considered putatively associated to quantitative trait loci (QTLs), and in a multiomic approach, genes obtained in the species transcriptome were mapped and linked to those markers. Furthermore, a gene coexpression network was modeled, enabling the investigation of not only the mapped genes but also their coexpressed genes. The functional annotation showed that the mapped genes are mainly associated with auxin transport and biosynthesis of lignin, flavonol and folic acid, while the coexpressed genes are associated with DNA metabolism, stress response and circadian rhythm. The results provide a viable marker-assisted breeding approach for tropical forages and identify target regions for future molecular studies on these agronomic traits.

genetics↗

bioRxiv2023

Advances in genomic characterization of Urochloa humidicola: exploring polyploid inheritance and apomixis

Tropical forage grasses are an important food source for animal feeding, with Urochloa humidicola, also known as Koronivia grass, being one of the main pasture grasses for poorly drained soils in the tropics. However, genetic and genomic resources for this species are lacking due to its genomic complexity, including high heterozygosity, evidence of segmental allopolyploidy, and reproduction by apomixis. These complexities hinder the application of marker-assisted selection (MAS) in breeding programs. Here, we developed the highest-density linkage map currently available for the hexaploid tropical forage grass U. humidicola. This map was constructed using a biparental F1 population generated from a cross between the female parent H031 (CIAT 26146), the only known sexual genotype for the species, and the apomictic male parent H016 (BRS cv. Tupi). The linkage analysis included 4,873 single nucleotide polymorphism (SNP) markers with allele dosage information. It allowed mapping of the apospory locus and phenotype to linkage group 3, in a region syntenic with chromosome 3 of Urochloa ruziziensis and chromosome 1 of Setaria italica. We also identified hexaploid haplotypes for all individuals, assessed the meiotic configuration, and estimated the level of preferential pairing in parents during the meiotic process, which revealed the autopolyploid origin of sexual H031 in contrast to H016, which presented allopolyploid behavior in preferential pairing analysis. These results provide new information regarding the genetic organization, mode of reproduction, and allopolyploid origin of U. humidicola, potential SNPs markers associated to apomixes for MAS and resources for research on polyploids and tropical forage grasses. Key messageWe present the highest-density genetic map for the hexaploid Urochloa humidicola. SNP markers expose genetic organization, reproduction, and species origin, aiding polyploid and tropical forage research.

genomics↗

bioRxiv2023

Lignin, glutathione and kinases play important roles in Paspalum regnellii defense against spittlebug (Mahanarva spectabilis) attack

Spittlebugs cause large production losses that affect agribusiness worldwide. Understanding plant-herbivore interactions at the molecular level may be the key to developing resistant cultivars. After a nymph survival experiment, root transcriptomes were assembled from two Paspalum regnellii genotypes (BGP 248 and 344) with different first-line defense strategies, with no infestation and at two times after the initial attack of the spittlebug (Mahanarva spectabilis) nymph, thus integrating differential expression analysis and biological network modeling supplemented by root anatomical analysis. Gene Ontology terms related to different stress responses, such as salicylic acid catabolic process, were enriched in BGP 248, while some specific to spittlebugs, such as response to herbivores, were enriched in BGP 344. Enriched pathways were related to structural differences between genotypes, such as those related to cutin, suberin and wax biosynthesis. BGP 344 also presented pathways related to induced defense, such as glutathione metabolism. Metabolic networks highlighted kinases, and coexpression networks demonstrated a complex cascade response that included lncRNAs. This study provides the first molecular insights into the defense mechanisms of P. regnellii against M. spectabilis. The genotype with the fastest response to insect attack and highest nymph mortality (BGP 344) presented kinases and an enriched glutathione pathway, in addition to constitutive barriers, such as lignin, which can make it difficult for the insect to colonize the plant.

plant biology↗

bioRxiv2023

Uncovering the molecular signatures of russet skin formation in Niagara grapevine (Vitis vinifera x 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 x 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. Histological sections of the berries were also performed to understand anatomical and chemical composition differences of the berry skin between the genotypes. An RNA-Seq pipeline was implemented, followed by global coexpression network modeling. Niagara Steck, an intensified russet mutant with the most extreme phenotype, showed the largest difference in expression and showed selection of coexpressed network modules involved in the development of its russet-like characteristics. Enrichment analysis of differently expressed genes and hub network modules revealed differences in transcription regulation, auxin signaling and cell wall and plasmatic membrane biogenesis. Cutin- and suberin-related genes were also differently expressed, supporting the anatomical differences observed with microscopy.

bioinformatics↗

bioRxiv2023

The Cucumber Kinome: genome-wide, identification, characterisation and expression analysis of the Cucumis sativus protein kinases in response to Powdery Mildew, Alternaria Leaf Spot and Root-Knot Nematode

It is widely known that protein kinases (PKs) play a fundamental role in regulating various metabolic processes in plants, from development to response to the environment. However, a detailed characterization of this superfamily is still lacking for several species, such as cucumber (Cucumis sativus), especially regarding their involvement in the response to Powdery Mildew (PM) caused by Podosphaera xanthii. This study aimed to characterize the cucumber PK family, shedding light on its genomic distribution, classification, and expression patterns triggered by P. xanthii. The hidden Markov models (HMMs) analysis uncovered 835 PKs in the cucumber kinome, distributed across its seven chromosomes, and categorized into 20 distinct groups and 123 families, with the RLK group being the most abundant. Evidence of tandem duplication of PK genes was also observed, enriching our understanding of cucumber PKs. To investigate the expression profiles of PK genes in cucumber, we analyzed the transcription levels of all 835 PK genes in RNA-seq data from leaves of resistant and susceptible cultivars of cucumber to P. xanthii, which were artificially inoculated. Depending on the treatment, DEGs ranged from 319 to 1,690, with PK DEGs ranging from 8 to 105. The number of PK DEGs varied between the different contrasts analyzed. Notably, we observed a greater number of PK DEGs in susceptible genotypes when challenged by the pathogen. Our findings indicate the role of specific cucumber PKs in regulating metabolic processes in the context of plant-pathogen interactions and pave the way for further research into the intricate mechanisms underlying cucumber responses to Powdery Mildew. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/532963v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@11340b8org.highwire.dtl.DTLVardef@fe1896org.highwire.dtl.DTLVardef@5de8org.highwire.dtl.DTLVardef@1564d0_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2022

Unraveling growth molecular mechanisms in Pinus taeda with GWAS, machine learning and gene coexpression networks

Pinus taeda (loblolly pine [LP]) is a long-lived tree species and one of the most economically significant forest species. Among growth traits, volume is the most widely considered trait in tree improvement programs. However, deciphering the genetic variants responsible for growth trait variations in conifers, such as LP, is particularly challenging due to the vast size and intricate complexity of Pinus genomes. We present a comprehensive genetic analysis of LP, focusing on markers associated with stem volume variation, to elucidate the molecular mechanisms governing high-performance phenotypes. We used a population of 1,692 individuals phenotyped for stem volume and genotyped these individuals using sequence capture probes. To conduct genome-wide associations, we utilized both genome-wide association study (GWAS) analysis and machine learning (ML) approaches. The markers identified in association with volume were found to be linked with the genes assembled from three distinct transcriptomes. These genes were subsequently used to construct gene coexpression networks, and through topological evaluations, we identified key genes with potential regulatory roles within stem volume configurations. Using a set of 31,589 SNPs, we defined 7 GWAS-associated SNPs and 128 ML-associated markers, all of which were correlated with multiple genes involved in diverse biological functions. Gene coexpression analysis revealed a group of 270 genes potentially associated with the regulation of genetic material. Key genes directly implicated in the regulation of growth and response to stress were identified, and inferences about their impact on pine development were subsequently elucidated. Our study not only offers insights into SNPs associated with stem volume but also elucidates a subset of genes characterized by unique regulatory features. These findings significantly advance our understanding of the genetic factors influencing growth traits, reveal candidate genes for future functional studies, and contribute to a broader comprehension of the genetic architecture underlying volume traits in LP.

genetics↗

bioRxiv2022

A novel image-based approach for soybean seed phenotyping using machine learning techniques

Soybean is one of the most important sources of protein and vegetable oil in the world. Given its increasing demand, the increment in yield has only been possible due to investments in research and production technology, especially in breeding programs. One of the main factors influencing soybean yield is the seed morphology; however, its analyses are hampered by the lack of efficient computational approaches with not only accurate results, but also a high flexibility to user preferences and needs. In this context, the present work provides a methodological framework for: (i) seed segmentation in soybean images; (ii) seed morphological evaluation; and (iii) image-based prediction of the hundred-seed weight trait. We used genotypes from a partial diallel cross design, which aimed at obtaining genotypes with high agronomic performance. In addition to the measurement of the hundred-seed weight, we collected RGB images of seeds of each plot. For image segmentation, we created an in-house image processing pipeline, which enabled a full morphological seed evaluation. For predicting the hundred-seed weight, we compared different machine learning algorithms using as input the morphological characteristics obtained, and also features from state-of-the-art convolutional neural network (CNN) architectures. The image segmentation methodology showed to be highly efficient, as more than 98% of the seeds in the images were correctly identified. Even if the seeds were close, the segmentation strategy could separate them into independent image components. In addition to supplying a highly accurate decision support system for soybean breeders, we verified the morphological phenotyping adaptability in other plant species, fully assessing the pipeline generalization. We consider the use of this methodology highly advantageous, as the method is entirely based on widely used morphological operations, which results in an easy implementation and low computational costs. Using these morphological measures, we could estimate machine learning models for predicting the hundred-seed weight, achieving considerable predictive accuracy. The same results were observed for CNN-obtained features, showing the efficiency of the morphological measurements as feature extractors. The possibility of obtaining seed morphological characteristics provides a valuable tool for the continuous and efficient development of new soybean cultivars in breeding programs aimed at long-term genetic gain. Additionally, through a faster seed image acquisition workflow, with less chance of errors and low cost, it is also possible to make predictions of important soybean characteristics. The work conducted has the potential to help future research and the industry to develop automated phenotyping tools, incorporating the proposed analytical workflows.

plant biology↗

bioRxiv2022

Genome-wide analysis and characterization of theLRR-RLK gene family provides insights intoanthracnose 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 Leucine-rich repeat kinase receptor protein (LRR-RLK) family is a diverse group of transmembrane receptors, which potentially recognizes pathogen-associated molecular patterns and activates an immune response. In this study, we performed in silico analyses to identify, classify, and characterize common bean LRR-RLKs, also evaluating their expression profile in response to the infection by C. lindemuthianum. By analyzing the entire genome of Phaseolus vulgaris, we could identify and classify 230 LRR-RLKs into 15 different subfamilies. The analyses of gene structures, conserved domains and motifs suggest that LRR-RLKs from the same subfamily are consistent in their exon/intron organization and composition. LRR-RLK genes were found along the 11 chromosomes of the species, including regions of proximity with anthracnose resistance markers. By investigating the duplication events within the LRR-RLK family, we associated the importance of such a family with an expansion resulting from a strong stabilizing selection. Promoter analysis was also performed, highlighting cis elements associated with the plant response to biotic stress. With regard to the expression pattern of LRR-RLKs in response to the infection by C. lindemuthianum, we could point out several differentially expressed genes in this subfamily, which were associated to specific molecular patterns of LRR-RLKs. Our work provides a broad analysis of the LRR-RLK family in P. vulgaris, allowing an in-depth structural and functional characterization of genes and proteins of this family. From specific expression patterns related to anthracnose response, we could infer a direct participation of RLK-LRR genes in the mechanisms of resistance to anthracnose, highlighting important subfamilies for further investigations.

genomics↗

bioRxiv2022

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 Hevea 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 esculenta and Ricinus 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.

plant biology↗

bioRxiv2022

Multiomic investigation of sugarcane mosaic virus resistance in sugarcane

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.

plant biology↗

bioRxiv2022

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.

plant biology↗

bioRxiv2022

Taxonomically Restricted Genes are Associated with Responses to Biotic and Abiotic Stresses in Sugarcane (Saccharum 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 function 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 Saccharum group, which may have contributed to the origin and diversification of OGs in the sugarcane genome. Here, we identified and characterized OGs in sugarcane, examined their expression profiles across tissues and genotypes, and investigated their regulation under varying conditions. We identified 319 OGs in the Saccharum spontaneum genome without detected homology to protein-coding genes in green plants, except those belonging to Saccharinae. Transcriptomic analysis showed 288 sugarcane OGs with detectable expression levels in at least one tissue or genotype. We observed similar expression patterns of OGs in sugarcane genotypes originating from the closest geographical locations. We also observed tissue-specific expression of some OGs, possibly indicating a complex regulatory process for maintaining diverse functional activity of these genes across sugarcane tissues and genotypes. Sixty-six OGs were differentially expressed under stress conditions, especially cold and osmotic stresses. Gene co-expression network and functional enrichment analyses suggested that sugarcane OGs may be involved in several biological mechanisms, including stimulus response and defence mechanisms. These findings provide a valuable genomic resource for sugarcane researchers, especially those interested in selecting stress-responsive genes.

plant biology↗

bioRxiv2022

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.

bioinformatics↗