Transcriptome characterization and differentially expressed genes under flooding and drought stress in the biomass grasses Phalaris arundinacea and Dactylis glomerata.

BACKGROUND AND AIMS: Perennial grasses are a global resource as forage, and for alternative uses in bioenergy and as raw materials for the processing industry. Marginal lands can be valuable for perennial biomass grass production, if perennial biomass grasses can cope with adverse abiotic environmental stresses such as drought and waterlogging. METHODS: In this study, two perennial grass species, reed canary grass (Phalaris arundinacea) and cocksfoot (Dactylis glomerata) were subjected to drought and waterlogging stress to study their responses for insights to improving environmental stress tolerance. Physiological responses were recorded, reference transcriptomes established and differential gene expression investigated between control and stress conditions. We applied a robust non-parametric method, RoDEO, based on rank ordering of transcripts to investigate differential gene expression. Furthermore, we extended and validated vRoDEO for comparing samples with varying sequencing depths. KEY RESULTS: This allowed us to identify expressed genes under drought and waterlogging whilst using only a limited number of RNA sequencing experiments. Validating the methodology, several differentially expressed candidate genes involved in the stage 3 step-wise scheme in detoxification and degradation of xenobiotics were recovered, while several novel stress-related genes classified as of unknown function were discovered. CONCLUSIONS: Reed canary grass is a species coping particularly well with flooding conditions, but this study adds novel information on how its transcriptome reacts under drought stress. We built extensive transcriptomes for the two investigated C3 species cocksfoot and reed canary grass under both extremes of water stress to provide a clear comparison amongst the two species to broaden our horizon for comparative studies, but further confirmation of the data would be ideal to obtain a more detailed picture.

Genomic prediction of crown rust resistance in Lolium perenne.

BACKGROUND: Genomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability. RESULTS: Using these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set. CONCLUSION: Using a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.

Markers associated with heading and aftermath heading in perennial ryegrass full-sib families.

BACKGROUND: Heading and aftermath heading are important traits in perennial ryegrass because they impact forage quality. So far, genome-wide association analyses in this major forage species have only identified a small number of genetic variants associated with heading date that overall explained little of the variation. Some possible reasons include rare alleles with large phenotypic affects, allelic heterogeneity, or insufficient marker density. We established a genome-wide association panel with multiple genotypes from multiple full-sib families. This ensured alleles were present at the frequency needed to have sufficient statistical power to identify associations. We genotyped the panel via partial genome sequencing and performed genome-wide association analyses with multi-year phenotype data collected for heading date, and aftermath heading. RESULTS: Genome wide association using a mixed linear model failed to identify any variants significantly associated with heading date or aftermath heading. Our failure to identify associations for these traits is likely due to the extremely low linkage disequilibrium we observed in this population. However, using single marker analysis within each full-sib family we could identify markers and genomic regions associated with heading and aftermath heading. Using the ryegrass genome we identified putative orthologs of key heading genes, some of which were located in regions of marker-trait associations. CONCLUSION: Given the very low levels of LD, genome wide association studies in perennial ryegrass populations are going to require very high SNP densities. Single marker analysis within full-sibs enabled us to identify significant marker-trait associations. One of these markers anchored proximal to a putative ortholog of TFL1, homologues of which have been shown to play a key role in continuous heading of some members of the rose family, Rosaceae.

Epichloë seed transmission efficiency is influenced by plant defense response mechanisms.

Asexual Epichloë are endophytic fungi that form mutualistic symbioses with cool-season grasses, conferring to their hosts protection against biotic and abiotic stresses. Symbioses are maintained between grass generations as hyphae are vertically transmitted from parent to progeny plants through seed. However, endophyte transmission to the seed is an imperfect process where not all seeds become infected. The mechanisms underpinning the varying efficiencies of seed transmission are poorly understood. Host gene expression in response to Epichloë sp. LpTG-3 strain AR37 was examined within inflorescence primordia and ovaries of high and low endophyte transmission genotypes within a single population of perennial ryegrass. A genome-wide association study was conducted to identify population-level single nucleotide polymorphisms (SNPs) and associated genes correlated with vertical transmission efficiency. For low transmitters of AR37, upregulation of perennial ryegrass receptor-like kinases and resistance genes, typically associated with phytopathogen detection, comprised the largest group of differentially expressed genes (DEGs) in both inflorescence primordia and ovaries. DEGs involved in signaling and plant defense responses, such as cell wall modification, secondary metabolism, and reactive oxygen activities were also abundant. Transmission-associated SNPs were associated with genes for which gene ontology analysis identified "response to fungus" as the most significantly enriched term. Moreover, endophyte biomass as measured by quantitative PCR of Epichloë non-ribosomal peptide synthetase genes, was significantly lower in reproductive tissues of low-transmission hosts compared to high-transmission hosts. Endophyte seed-transmission efficiency appears to be influenced primarily by plant defense responses which reduce endophyte colonization of host reproductive tissues.

Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield.

Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass.

Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = -0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

Feeding Bugs to Bugs: Edible Insects Modify the Human Gut Microbiome in an in vitro Fermentation Model.

We here report a study characterizing the potential for edible insects to act as a prebiotic by altering the bacterial composition of the human fecal microbiome, using batch cultures inoculated with fecal adult human donors. Black field cricket nymphs, grass grub larvae, and wax moth larvae were subjected to an in vitro digestion to simulate the oral, gastric, and small intestinal stages of digestion. The digested material was then dialyzed to remove small molecules such as amino acids and free sugars to simulate removal of nutrients through upper gastrointestinal tract digestion. The retentate, representing the digestion resistant constituents, was then fermented in fecal batch cultures for 4, 7, and 15 h to represent rapid and longer fermentation times. Batch cultures without any added substrates were also set up to act as controls. Additionally, phosphate-buffered saline was used as a no-protein control and milk powder as "standard" protein control. At the end of the incubation period, the bacterial pellets were collected for microbiome analysis by 16S rRNA gene amplicon sequencing. Analysis of fecal cultures showed striking differences in community composition. Each substrate led to significant differences across a wide range of taxa compared to each other and PBS controls. Among the differences observed, digested grass grub larvae increased proportions of Faecalibacterium and the Prevotella 2 group. Black field crickets increased the prevalence of the Escherichia-Shigella group, Dialister genus, and a group of unclassified Lachnospiraceae. Wax moth larvae promoted the expansion of the same group of unclassified Lachnospiraceae and the Escherichia/Shigella group. The increased Faecalibacterium observed in the cultures with grass grub larvae represents a noteworthy finding as this bacterium is widely thought to be beneficial in nature, with demonstrated anti-inflammatory properties and associations with gut health. We conclude that insects can differentially modulate the microbiome composition in batch cultures inoculated with adult fecal material after simulated in vitro digestion. Although the physiological impact in vivo remains to be determined, this study provides sound scientific evidence that investigating the potential for consuming insects for gut health is warranted.

Using variable importance measures to identify a small set of SNPs to predict heading date in perennial ryegrass.

Prior knowledge on heading date enables the selection of parents of synthetic cultivars that are well matched with respect to time of heading, which is essential to ensure plants put together will cross pollinate. Heading date of individual plants can be determined via direct phenotyping, which has a time and labour cost. It can also be inferred from family means, although the spread in days to heading within families demands roguing in first generation synthetics. Another option is to predict heading date from molecular markers. In this study we used a large training population consisting of individual plants to develop equations to predict heading date from marker genotypes. Using permutation-based variable selection measures we reduced the marker set from 217,563 to 50 without impacting the predictive ability. Opportunities exist to develop a cheap assay to sequence a small number of regions in linkage disequilibrium with heading date QTL in thousands of samples. Simultaneous use of these markers in non-linkage based marker-assisted selection approaches, such as paternity testing, should enhance the utility of such an approach.