Variability of cell wall recalcitrance and composition in genotypes of Miscanthus from different genetic groups and geographical origin.

Miscanthus is a promising crop for bioenergy and biorefining in Europe. The improvement of Miscanthus as a crop relies on the creation of new varieties through the hybridization of germplasm collected in the wild with genetic variation and suitable characteristics in terms of resilience, yield and quality of the biomass. Local adaptation has likely shaped genetic variation for these characteristics and is therefore important to quantify. A key biomass quality parameter for biorefining is the ease of conversion of cell wall polysaccharides to monomeric sugars. Thus far, the variability of cell wall related traits in Miscanthus has mostly been explored in accessions from limited genetic backgrounds. Here we analysed the soil and climatic conditions of the original collection sites of 592 Miscanthus genotypes, which form eight distinct genetic groups based on discriminant analysis of principal components of 25,014 single-nucleotide polymorphisms. Our results show that species of the genus Miscanthus grow naturally across a range of soil and climate conditions. Based on a detailed analysis of 49 representative genotypes, we report generally minor differences in cell wall characteristics between different genetic groups and high levels of genetic variation within groups, with less investigated species like M. floridulus showing lower recalcitrance compared to the other genetic groups. The results emphasize that both inter- and intra- specific variation in cell wall characteristics and biomass recalcitrance can be used effectively in Miscanthus breeding programmes, while also reinforcing the importance of considering biomass yield when quantifying overall conversion efficiency. Thus, in addition to reflecting the complexity of the interactions between compositional and structural cell wall features and cell wall recalcitrance to sugar release, our results point to traits that could potentially require attention in breeding programmes targeted at improving the Miscanthus biomass crop.

Genomic index selection provides a pragmatic framework for setting and refining multi-objective breeding targets in Miscanthus.

BACKGROUND: Miscanthus has potential as a biomass crop but the development of varieties that are consistently superior to the natural hybrid M. × giganteus has been challenging, presumably because of strong G × E interactions and poor knowledge of the complex genetic architectures of traits underlying biomass productivity and climatic adaptation. While linkage and association mapping studies are starting to generate long lists of candidate regions and even individual genes, it seems unlikely that this information can be translated into effective marker-assisted selection for the needs of breeding programmes. Genomic selection has emerged as a viable alternative, and prediction accuracies are moderate across a range of phenological and morphometric traits in Miscanthus, though relatively low for biomass yield per se. METHODS: We have previously proposed a combination of index selection and genomic prediction as a way of overcoming the limitations imposed by the inherent complexity of biomass yield. Here we extend this approach and illustrate its potential to achieve multiple breeding targets simultaneously, in the absence of a priori knowledge about their relative economic importance, while also monitoring correlated selection responses for non-target traits. We evaluate two hypothetical scenarios of increasing biomass yield by 20 % within a single round of selection. In the first scenario, this is achieved in combination with delaying flowering by 44 d (roughly 20 %), whereas, in the second, increased yield is targeted jointly with reduced lignin (-5 %) and increased cellulose (+5 %) content, relative to current average levels in the breeding population. KEY RESULTS: In both scenarios, the objectives were achieved efficiently (selection intensities corresponding to keeping the best 20 and 4 % of genotypes, respectively). However, the outcomes were strikingly different in terms of correlated responses, and the relative economic values (i.e. value per unit of change in each trait compared with that for biomass yield) of secondary traits included in selection indices varied considerably. CONCLUSIONS: Although these calculations rely on multiple assumptions, they highlight the need to evaluate breeding objectives and explicitly consider correlated responses in silico, prior to committing extensive resources. The proposed approach is broadly applicable for this purpose and can readily incorporate high-throughput phenotyping data as part of integrated breeding platforms.

Genetic relationships between spring emergence, canopy phenology, and biomass yield increase the accuracy of genomic prediction in Miscanthus.

Miscanthus has potential as a bioenergy crop but the rapid development of high-yielding varieties is challenging. Previous studies have suggested that phenology and canopy height are important determinants of biomass yield. Furthermore, while genome-wide prediction was effective for a broad range of traits, the predictive ability for yield was very low. We therefore developed models clarifying the genetic associations between spring emergence, consequent canopy phenology and dry biomass yield. The timing of emergence was a moderately strong predictor of early-season elongation growth (genetic correlation >0.5), but less so for growth later in the season and for the final yield (genetic correlation <0.1). In contrast, early-season canopy height was consistently more informative than emergence for predicting biomass yield across datasets for two species in Miscanthus and two growing seasons. We used the associations uncovered through these models to develop selection indices that are expected to increase the response to selection for yield by as much as 21% and improve the performance of genome-wide prediction by an order of magnitude. This multivariate approach could have an immediate impact in operational breeding programmes, as well as enable the integration of crop growth models and genome-wide prediction.

Multiscale digital Arabidopsis predicts individual organ and whole-organism growth.

Understanding how dynamic molecular networks affect whole-organism physiology, analogous to mapping genotype to phenotype, remains a key challenge in biology. Quantitative models that represent processes at multiple scales and link understanding from several research domains can help to tackle this problem. Such integrated models are more common in crop science and ecophysiology than in the research communities that elucidate molecular networks. Several laboratories have modeled particular aspects of growth in Arabidopsis thaliana, but it was unclear whether these existing models could productively be combined. We test this approach by constructing a multiscale model of Arabidopsis rosette growth. Four existing models were integrated with minimal parameter modification (leaf water content and one flowering parameter used measured data). The resulting framework model links genetic regulation and biochemical dynamics to events at the organ and whole-plant levels, helping to understand the combined effects of endogenous and environmental regulators on Arabidopsis growth. The framework model was validated and tested with metabolic, physiological, and biomass data from two laboratories, for five photoperiods, three accessions, and a transgenic line, highlighting the plasticity of plant growth strategies. The model was extended to include stochastic development. Model simulations gave insight into the developmental control of leaf production and provided a quantitative explanation for the pleiotropic developmental phenotype caused by overexpression of miR156, which was an open question. Modular, multiscale models, assembling knowledge from systems biology to ecophysiology, will help to understand and to engineer plant behavior from the genome to the field.

Genome-wide analysis of longevity in nutrient-deprived Saccharomyces cerevisiae reveals importance of recycling in maintaining cell viability.

Although typically cosseted in the laboratory with constant temperatures and plentiful nutrients, microbes are frequently exposed to much more stressful conditions in their natural environments where survival and competitive fitness depend upon both growth rate when conditions are favourable and on persistence in a viable and recoverable state when they are not. In order to determine the role of genetic heterogeneity in environmental fitness we present a novel approach that combines the power of fluorescence-activated cell sorting with barcode microarray analysis and apply this to determining the importance of every gene in the Saccharomyces cerevisiae genome in a high-throughput, genome-wide fitness screen. We have grown > 6000 heterozygous mutants together and exposed them to a starvation stress before using fluorescence-activated cell sorting to identify and isolate those individual cells that have not survived the stress applied. Barcode array analysis of the sorted and total populations reveals the importance of cellular recycling mechanisms (autophagy, pexophagy and ribosome breakdown) in maintaining cell viability during starvation and provides compelling evidence for an important role for fatty acid degradation in maintaining viability. In addition, we have developed a semi-batch fermentor system that is a more realistic model of environmental fitness than either batch or chemostat culture. Barcode array analysis revealed that arginine biosynthesis was important for fitness in semi-batch culture and modelling of this regime showed that rapid emergence from lag phase led to greatly increased fitness. One hundred and twenty-five strains with deletions in unclassified proteins were identified as being over-represented in the sorted fraction, while 27 unclassified proteins caused a haploinsufficient phenotype in semi-batch culture. These methods thus provide a screen to identifying other genes and pathways that have a role in maintaining cell viability.

Multivariate data analysis methods for the interpretation of microbial flow cytometric data.

Flow cytometry is an important technique in cell biology and immunology and has been applied by many groups to the analysis of microorganisms. This has been made possible by developments in hardware that is now sensitive enough to be used routinely for analysis of microbes. However, in contrast to advances in the technology that underpin flow cytometry, there has not been concomitant progress in the software tools required to analyse, display and disseminate the data and manual analysis, of individual samples remains a limiting aspect of the technology. We present two new data sets that illustrate common applications of flow cytometry in microbiology and demonstrate the application of manual data analysis, automated visualisation (including the first description of a new piece of software we are developing to facilitate this), genetic programming, principal components analysis and artificial neural nets to these data. The data analysis methods described here are equally applicable to flow cytometric applications with other cell types.