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Background: Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce., Results: Our results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors., Conclusions: The utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions., (© 2023. The Author(s).)

The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F 2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE × treatment (GE Control and GE Drought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits., (© 2022 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

Joint modeling of correlated multienvironment and multiharvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within-season multiple measurements of forage perennial ryegrass (Lolium perenne L.) traits in a reaction-norm model setup that additionally accounts for genotype × environment (G × E) interactions. Genetic parameters and accuracy of genomic estimated breeding value (gEBV) predictions were investigated by fitting three genomic random regression models (gRRMs) using Legendre polynomial functions to the data. Genomic DNA sequencing of family pools of diploid perennial ryegrass was performed using DNA nanoball-based technology and yielded 56,645 single-nucleotide polymorphisms, which were used to calculate the allele frequency-based genomic relationship matrix. Biomass yield's estimated additive genetic variance and heritability values were higher in later harvests. The additive genetic correlations were moderate to low in early measurements and peaked at intermediates with fairly stable values across the environmental gradient except for the initial harvest data collection. This led to the conclusion that complex (G × E) arises from spatial and temporal dimensions in the early season with lower reranking trends thereafter. In general, modeling the temporal dimension with a second-order orthogonal polynomial improved the accuracy of gEBV prediction for nutritive quality traits, but no gain in prediction accuracy was detected for dry matter yield (DMY). This study leverages the flexibility and usefulness of gRRM models for perennial ryegrass breeding and can be readily extended to other multiharvest crops., (© 2022 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

Brachypodium distachyon is an established model for drought tolerance. We previously identified accessions exhibiting high tolerance, susceptibility and intermediate tolerance to drought; respectively, ABR8, KOZ1 and ABR4. Transcriptomics and metabolomic approaches were used to define tolerance mechanisms. Transcriptional analyses suggested relatively few drought responsive genes in ABR8 compared to KOZ1. Linking these to gene ontology (GO) terms indicated enrichment for "regulated stress response", "plant cell wall" and "oxidative stress" associated genes. Further, tolerance correlated with pre-existing differences in cell wall-associated gene expression including glycoside hydrolases, pectin methylesterases, expansins and a pectin acetylesterase. Metabolomic assessments of the same samples also indicated few significant changes in ABR8 with drought. Instead, pre-existing differences in the cell wall-associated metabolites correlated with drought tolerance. Although other features, e.g., jasmonate signaling were suggested in our study, cell wall-focused events appeared to be predominant. Our data suggests two different modes through which the cell wall could confer drought tolerance: (i) An active response mode linked to stress induced changes in cell wall features, and (ii) an intrinsic mode where innate differences in cell wall composition and architecture are important. Both modes seem to contribute to ABR8 drought tolerance. Identification of the exact mechanisms through which the cell wall confers drought tolerance will be important in order to inform development of drought tolerant crops.

Perennial ryegrass is an outbreeding forage species and is one of the most widely used forage grasses in temperate regions. The aim of this study was to investigate the possibility of implementing genomic prediction in tetraploid perennial ryegrass, to study the effects of different sequencing depth when using genotyping-by-sequencing (GBS), and to determine optimal number of single-nucleotide polymorphism (SNP) markers and sequencing depth for GBS data when applied in tetraploids. A total of 1,515 F 2 tetraploid ryegrass families were included in the study and phenotypes and genotypes were scored on family-pools. The traits considered were dry matter yield (DM), rust resistance (RUST), and heading date (HD). The genomic information was obtained in the form of allele frequencies of pooled family samples using GBS. Different SNP filtering strategies were designed. The strategies included filtering out SNPs having low average depth (FILTLOW), having high average depth (FILTHIGH), and having both low average and high average depth (FILTBOTH). In addition, SNPs were kept randomly with different data sizes (RAN). The accuracy of genomic prediction was evaluated by using a "leave single F 2 family out" cross validation scheme, and the predictive ability and bias were assessed by correlating phenotypes corrected for fixed effects with predicted additive breeding values. Among all the filtering scenarios, the highest estimates for genomic heritability of family means were 0.45, 0.74, and 0.73 for DM, HD and RUST, respectively. The predictive ability generally increased as the number of SNPs included in the analysis increased. The highest predictive ability for DM was 0.34 (137,191 SNPs having average depth higher than 10), for HD was 0.77 (185,297 SNPs having average depth lower than 60), and for RUST was 0.55 (188,832 SNPs having average depth higher than 1). Genomic prediction can help to optimize the breeding of tetraploid ryegrass. GBS data including about 80-100 K SNPs are needed for accurate prediction of additive breeding values in tetraploid ryegrass. Using only SNPs with sequencing depth between 10 and 20 gave highest predictive ability, and showed the potential to obtain accurate prediction from medium-low coverage GBS in tetraploids.

Ryegrass single plants, bi-parental family pools, and multi-parental family pools are often genotyped, based on allele-frequencies using genotyping-by-sequencing (GBS) assays. GBS assays can be performed at low-coverage depth to reduce costs. However, reducing the coverage depth leads to a higher proportion of missing data, and leads to a reduction in accuracy when identifying the allele-frequency at each locus. As a consequence of the latter, genomic relationship matrices (GRMs) will be biased. This bias in GRMs affects variance estimates and the accuracy of GBLUP for genomic prediction (GBLUP-GP). We derived equations that describe the bias from low-coverage sequencing as an effect of binomial sampling of sequence reads, and allowed for any ploidy level of the sample considered. This allowed us to combine individual and pool genotypes in one GRM, treating pool-genotypes as a polyploid genotype, equal to the total ploidy-level of the parents of the pool. Using simulated data, we verified the magnitude of the GRM bias at different coverage depths for three different kinds of ryegrass breeding material: individual genotypes from single plants, pool-genotypes from F 2 families, and pool-genotypes from synthetic varieties. To better handle missing data, we also tested imputation procedures, which are suited for analyzing allele-frequency genomic data. The relative advantages of the bias-correction and the imputation of missing data were evaluated using real data. We examined a large dataset, including single plants, F 2 families, and synthetic varieties genotyped in three GBS assays, each with a different coverage depth, and evaluated them for heading date, crown rust resistance, and seed yield. Cross validations were used to test the accuracy using GBLUP approaches, demonstrating the feasibility of predicting among different breeding material. Bias-corrected GRMs proved to increase predictive accuracies when compared with standard approaches to construct GRMs. Among the imputation methods we tested, the random forest method yielded the highest predictive accuracy. The combinations of these two methods resulted in a meaningful increase of predictive ability (up to 0.09). The possibility of predicting across individuals and pools provides new opportunities for improving ryegrass breeding schemes.

Inhalation injury (INH) is present in one third of large burn injuries and increases oxygenation and fluid resuscitation requirements, incidences of pulmonary complications, risk for multiple organ dysfunction syndrome (MODS), and overall mortality. Previous studies have demonstrated inconsistent correlation between bronchoscopic evaluation and clinical outcomes. The authors reviewed 161 patients admitted with a diagnosis of INH or underwent diagnostic bronchoscopy for suspected INH over a period of 8.5 years. One hundred one patients had concomitant burn injury and 60 had isolated INH. Seventeen patients had abbreviated injury score (AIS) 0, 81 patients had low-grade injury (AIS 1 and 2), and 63 patients had high-grade injury (AIS 3 and 4). Patients with high-grade INH had worse pulmonary dysfunction, worse oxygenation indices (P = 0.01) and plasma carboxyhemoglobin (COHgb; P < 0.01) on admission, increased fluid requirements (P < 0.01 at 24 hours; P = 0.04 at 48 hours), MODS (P = 0.04), pneumonia (P < 0.01), acute respiratory distress syndrome (P = 0.01 at 48 hours), fewer 28-day ventilator-free days (P < 0.01), greater ventilator dependence (P = 0.03), and longer length of stay (P < 0.01). Multivariate analyses demonstrated increased risk of MODS (P = 0.03), acute respiratory distress syndrome at 48 hours (P < 0.01), pneumonia (P = 0.01), prolonged ventilator dependence (P = 0.03), and a trend toward mortality (P = 0.08) with higher AIS groups. More severe INH correlates with early oxygenation impairments and is associated with more complicated hospitalization, fluid resuscitation requirements, and ventilation demands. Severe INH is associated with and predictive of increased morbidity and mortality.

Silicon (Si) is taken up from the soil as monosilicic acid by plant roots, transported to leaves and deposited as phytoliths, amorphous silica (SiO 2 ) bodies, which are a key component of anti-herbivore defense in grasses. Silicon transporters have been identified in many plant species, but the mechanisms underpinning Si transport remain poorly understood. Specifically, the extent to which Si uptake is a passive process, driven primarily by transpiration, or has both passive and active components remains disputed. Increases in foliar Si concentration following herbivory suggest plants may exercise some control over Si uptake and distribution. In order to investigate passive and active controls on Si accumulation, we examined both genetic and environmental influences on Si accumulation in the forage grass Festuca arundinacea. We studied three F. arundinacea varieties that differ in the levels of Si they accumulate. Varieties not only differed in Si concentration, but also in increases in Si accumulation in response to leaf damage. The varietal differences in Si concentration generally reflected differences in stomatal density and stomatal conductance, suggesting passive, transpiration-mediated mechanisms underpin these differences. Bagging plants after damage was employed to minimize differences in stomatal conductance between varieties and in response to damage. This treatment eliminated constitutive differences in leaf Si levels, but did not impair the damage-induced increases in Si uptake: damaged, bagged plants still had more leaf Si than undamaged, bagged plants in all three varieties. Preliminary differential gene expression analysis revealed that the active Si transporter Lsi2 was highly expressed in damaged unbagged plants compared with undamaged unbagged plants, suggesting damage-induced Si defenses are regulated at gene level. Our findings suggest that although differences in transpiration may be partially responsible for varietal differences in Si uptake, they cannot explain damage-induced increases in Si uptake and deposition, suggesting that wounding causes changes in Si uptake, distribution and deposition that likely involve active processes and changes in gene expression.

The implementation of genomic selection (GS) in plant breeding, so far, has been mainly evaluated in crops farmed as homogeneous varieties, and the results have been generally positive. Fewer results are available for species, such as forage grasses, that are grown as heterogenous families (developed from multiparent crosses) in which the control of the genetic variation is far more complex. Here we test the potential for implementing GS in the breeding of perennial ryegrass ( L.) using empirical data from a commercial forage breeding program. Biparental F and multiparental synthetic (SYN) families of diploid perennial ryegrass were genotyped using genotyping-by-sequencing, and phenotypes for five different traits were analyzed. Genotypes were expressed as family allele frequencies, and phenotypes were recorded as family means. Different models for genomic prediction were compared by using practically relevant cross-validation strategies. All traits showed a highly significant level of genetic variance, which could be traced using the genotyping assay. While there was significant genotype × environment (G × E) interaction for some traits, accuracies were high among F families and between biparental F and multiparental SYN families. We have demonstrated that the implementation of GS in grass breeding is now possible and presents an opportunity to make significant gains for various traits., (Copyright © 2016 Crop Science Society of America.)

Keymessage: By using the genotyping-by-sequencing method, it is feasible to characterize genomic relationships directly at the level of family pools and to estimate genomic heritabilities from phenotypes scored on family-pools in outbreeding species. Genotyping-by-sequencing (GBS) has recently become a promising approach for characterizing plant genetic diversity on a genome-wide scale. We use GBS to extend the concept of heritability beyond individuals by genotyping family-pool samples by GBS and computing genomic relationship matrices (GRMs) and genomic heritabilities directly at the level of family-pools from pool-frequencies obtained by sequencing. The concept is of interest for species where breeding and phenotyping is not done at the individual level but operates uniquely at the level of (multi-parent) families. As an example we demonstrate the approach using a set of 990 two-parent F2 families of perennial ryegrass (Lolium Perenne). The families were phenotyped as a family-unit in field plots for heading date and crown rust resistance. A total of 728 K single nucleotide polymorphism (SNP) variants were available and were divided in groups of different sequencing depths. GRMs based on GBS data showed diagonal values biased upwards at low sequencing depth, while off-diagonals were little affected by the sequencing depth. Using variants with high sequencing depth, genomic heritability for crown rust resistance was 0.33, and for heading date 0.22, and these genomic heritabilities were biased downwards when using variants with lower sequencing depth. Broad sense heritabilities were 0.61 and 0.66, respectively. Underestimation of genomic heritability at lower sequencing depth was confirmed with simulated data. We conclude that it is feasible to use GBS to describe relationships between family-pools and to estimate genomic heritability directly at the level of F2 family-pool samples, but estimates are biased at low sequencing depth.

Background: Genomic selection (GS) has become a commonly used technology in animal breeding. In crops, it is expected to significantly improve the genetic gains per unit of time. So far, its implementation in plant breeding has been mainly investigated in species farmed as homogeneous varieties. Concerning crops farmed in family pools, only a few theoretical studies are currently available. Here, we test the opportunity to implement GS in breeding of perennial ryegrass, using real data from a forage breeding program. Heading date was chosen as a model trait, due to its high heritability and ease of assessment. Genome Wide Association analysis was performed to uncover the genetic architecture of the trait. Then, Genomic Prediction (GP) models were tested and prediction accuracy was compared to the one obtained in traditional Marker Assisted Selection (MAS) methods., Results: Several markers were significantly associated with heading date, some locating within or proximal to genes with a well-established role in floral regulation. GP models gave very high accuracies, which were significantly better than those obtained through traditional MAS. Accuracies were higher when predictions were made from related families and from larger training populations, whereas predicting from unrelated families caused the variance of the estimated breeding values to be biased downwards., Conclusions: We have demonstrated that there are good perspectives for GS implementation in perennial ryegrass breeding, and that problems resulting from low linkage disequilibrium (LD) can be reduced by the presence of structure and related families in the breeding population. While comprehensive Genome Wide Association analysis is difficult in species with extremely low LD, we did identify variants proximal to genes with a known role in flowering time (e.g. CONSTANS and Phytochrome C).