Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Exploring the midgut physiology of the non-haematophagous mosquito Toxorhynchites theobaldi

Toxorhynchites mosquitoes have an exclusively phytophagous feeding habit as adults, which leads to significant differences in their morphophysiology compared with haematophagous mosquitoes. However, the molecular mechanisms of digestion in this mosquito are not well understood. In this study, RNA sequencing of the posterior midgut (PMG) of the mosquito Toxorhynchites theobaldi was undertaken, highlighting its significance in mosquito digestion. Subsequently, a comparison was made between the differential gene expression of the PMG and that of the anterior midgut. It was found that the most abundant proteases in the PMG were trypsin and chymotrypsin, and the level of gene expression for enzymes essential for digestion (such as serine protease, α-amylase and pancreatic triacylglycerol lipase) and innate immune response (including catalase, cecropin-A2 and superoxide dismutase) was like that of haematophagous mosquitoes. Peritrophin-1 was detected in the entire midgut, with an elevated expression level in the PMG. Based on our findings, it is hypothesized that a non-haematophagic habit might have been exhibited by the ancestor of Tx. theobaldi, and this trait may have been retained. This study represents a pioneering investigation at the molecular level of midgut contents in a non-haematophagous mosquito. The findings offer valuable insights into the evolutionary aspects of feeding habits in culicids.

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

Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.

Analysis of Growth Trajectories and Verification of Related SNPs in Populus deltoides

As an important timber genus with high economic and ecological values, Populus is a model for dissecting the genetic architecture of growth traits in perennial forest trees. However, the genetic mechanisms of longitudinal growth traits in poplar remain incompletely understood. In this study, we conducted longitudinal genetic analysis of height and diameter at breast height (DBH) in eleven-year poplar clones using ultra-deep sequencing datasets. We compared four S-shaped growth models, including asymptotic, Gompertz, logistic, and Richard, on eleven-year height and DBH records in terms of five metrics. We constructed the best-fitting growth model (Richard) and determined poplar ontogenetic stages by virtue of growth curve fitting and likelihood ratio testing. This study provides some scientific clues for temporal variation of longitudinal growth traits in Populus species.

Genome-wide association study of multiyear dynamic growth traits in hybrid Liriodendron identifies robust genetic loci associated with growth trajectories

The genetic factors underlying growth traits differ over time points or stages. However, most current studies of phenotypes at single time points do not capture all loci or explain the genetic differences underlying growth trajectories. Hybrid Liriodendron exhibits obvious heterosis and is widely cultivated, although its complex genetic mechanism underlying growth traits remains unknown. A genome-wide association study (GWAS) is an effective method for elucidating the genetic architecture by identifying genetic loci underlying complex quantitative traits. In the present study, using a GWAS, we identified robust loci associated with growth trajectories in hybrid Liriodendron populations. We selected 233 hybrid progenies derived from 25 crosses for resequencing, and measured their tree height (H) and diameter at breast height (DBH) for 11 consecutive years; 192 972 high-quality single nucleotide polymorphisms (SNPs) were obtained. The dynamics of the multiyear single-trait GWAS showed that year-specific SNPs predominated, and only five robust SNPs for DBH were identified in at least three different years. Multitrait GWAS analysis with model parameters as latent variables also revealed 62 SNPs for H and 52 for DBH associated with the growth trajectory, displaying different biomass accumulation patterns, among which four SNPs exerted pleiotropic effects. All identified SNPs also exhibited temporal variations in effect sizes and inheritance patterns potentially related to different growth and developmental stages. The haplotypes resulting from these significant SNPs might pyramid favorable loci, benefitting the selection of superior genotypes. The present study provides insights into the genetic architecture of dynamic growth traits and lays a basis for future molecular-assisted breeding.

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. 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. 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. 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.

Analysis of Genetic Diversity and Resistance to Foliar Pathogens Based on Genotyping-by-Sequencing of a Para Rubber Diversity Panel and Progeny of an Interspecific Cross

Para rubber trees (Hevea brasiliensis) are the largest major source of natural rubber in the world. Its major pathogens are Phytophthora spp., Corynespora cassiicola, and Colletotrichum spp. A rubber diversity panel of 116 clones using over 12,000 single nucleotide polymorphisms (SNPs) from DArTSeq genotyping revealed clear phylogenetic differences in clones that originated from different geographical regions of the world. An integrated linkage map constructed with an F₁ progeny of 86 from an interspecific cross between H. brasiliensis and H. benthamiana using 23,978 markers [10,323 SNPs and 13,655 SilicoDArTs] spanned 3947.83 cM with 0.83 cM average marker-interval. The genome scaffolds that were anchored to the linkage map, covering 1.44 Gb of H. brasiliensis reference genome, revealed a high level of collinearity between the genetic map and reference genome. Association analysis identified 12 SNPs significantly associated with the resistance against Phytophthora, Corynespora, and Colletotrichum in six linkage groups: 2, 6, 12, 14, 17, and 18. Kompetitive Allele-Specific PCR marker assays were developed for those 12 SNPs, screened with 178 individuals, and detected clear separation between two genotypes. Within the proximity to those SNPs, 41 potentially key genes that have previously been reported to associate with plant disease resistance were predicted with high confidence.

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.