Biology subjects

Ferreira, R. C. U.

Publications and source records attributed to Ferreira, R. C. U..

6 recordsLinked to original sources

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

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↗

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↗

bioRxiv2021

An 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. 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.

genomics↗

bioRxiv2019

High-resolution Linkage Map with Allele Dosage Allows the Identification of an Apomixis Region and Complex Traits in Guinea Grass (Megathyrsus maximus)

Forage grasses are mainly used in animal feed to fatten cattle and dairy herds. Among tropical forage crops that reproduce by seeds, guinea grass (Megathyrsus maximus) is considered one of the most productive. This species has several genomic complexities, such as autotetraploidy and apomixis, due to the process of domestication. Consequently, approaches that relate phenotypic and genotypic data are incipient. In this context, we built a linkage map with allele dosage and generated novel information about the genetic architecture of traits that are important for the breeding of M. maximus. From a full-sib progeny, a linkage map containing 858 single nucleotide polymorphism (SNP) markers with allele dosage information expected for an autotetraploid was obtained. The high genetic variability of the progeny allowed us to map ten quantitative trait loci (QTLs) related to agronomic traits, such as regrowth capacity and total dry matter, and 36 QTLs related to nutritional quality, which were distributed among all homology groups (HGs). Various overlapping regions associated with the quantitative traits suggested QTL hotspots. In addition, we were able to map one locus that controls apospory (apo-locus) in HG II. A total of 55 different gene families involved in cellular metabolism and plant growth were identified from markers adjacent to the QTLs and apomixis locus by using the Panicum virgatum genome as a reference in comparisons with the genomes of Arabidopsis thaliana and Oryza sativa. Our results provide a better understanding of the genetic basis of reproduction by apomixis and traits important for breeding programs that considerably influence animal productivity as well as the quality of meat and milk.

plant biology↗

bioRxiv2018

Genetic mapping with allele dosage information in tetraploid Urochloa decumbens (Stapf) R.D. Webster reveals insights into spittlebug (Notozulia entreriana Berg) resistance

Urochloa decumbens (Stapf) R.D. Webster is one of the most important African forage grasses in Brazilian beef production. Currently available genetic-genomic resources for this species are restricted mainly due to polyploidy and apomixis. Therefore, crucial genomic-molecular studies such as the construction of genetic maps and the mapping of quantitative trait loci (QTLs) are very challenging and consequently affect the advancement of molecular breeding. The objectives of this work were to (i) construct an integrated U. decumbens genetic map for a full-sibling progeny using GBS-based markers with allele dosage information, (ii) detect QTLs for spittlebug (Notozulia entreriana) resistance, and (iii) seek putative candidate genes involved in resistance/defense against pathogens. We used the Setaria viridis genome as reference to align GBS reads and selected 4,240 high-quality SNPs markers with allele dosage information. Of these markers, 1,000 were distributed throughout nine homologous groups with a cumulative map length of 1,335.09 cM and an average marker density of 1.33 cM. We detected QTLs for resistance to spittlebug, an important pasture insect pest, that explained between 4.66% and 6.24% of the phenotypic variation. These QTLs are in regions containing putative candidate genes related to resistance/defense against pathogens. Because this is the first genetic map with SNP autotetraploid dosage data and QTL detection in U. decumbens, it will be useful for future evolutionary studies, genome assembly, and other QTL analyses in Urochloa spp. Moreover, the results might facilitate the isolation of spittlebug-related candidate genes and help clarify the mechanism of spittlebug resistance. These approaches will improve selection efficiency and accuracy in U. decumbens molecular breeding and shorten the breeding cycle.

plant biology↗