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In the search for renewable energy sources, genetic engineering is a promising strategy to improve plant cell wall composition for biofuel and bioproducts generation. Lignin is a major factor determining saccharification efficiency and, therefore, is a prime target to engineer. Here, lignin content and composition were modified in poplar (Populus tremula 3 Populus alba) by specifically down-regulating CINNAMYL ALCOHOL DEHYDROGENASE1 (CAD1) by a hairpin-RNA-mediated silencing approach, which resulted in only 5% residual CAD1 transcript abundance. These transgenic lines showed no biomass penalty despite a 10% reduction in Klason lignin content and severe shifts in lignin composition. Nuclear magnetic resonance spectroscopy and thioacidolysis revealed a strong increase (up to 20-fold) in sinapaldehyde incorporation into lignin, whereas coniferaldehyde was not increased markedly. Accordingly, ultra-high-performance liquid chromatography-mass spectrometry-based phenolic profiling revealed a more than 24,000-fold accumulation of a newly identified compound made from 8-8 coupling of two sinapaldehyde radicals. However, no additional cinnamaldehyde coupling products could be detected in the CAD1-deficient poplars. Instead, the transgenic lines accumulated a range of hydroxycinnamate-derived metabolites, of which the most prominent accumulation (over 8,500-fold) was observed for a compound that was identified by purification and nuclear magnetic resonance as syringyl lactic acid hexoside. Our data suggest that, upon down-regulation of CAD1, coniferaldehyde is converted into ferulic acid and derivatives, whereas sinapaldehyde is either oxidatively coupled into S'(8-8) S' and lignin or converted to sinapic acid and derivatives. The most prominent sink of the increased flux to hydroxycinnamates is syringyl lactic acid hexoside. Furthermore, low-extent saccharification assays, under different pretreatment conditions, showed strongly increased glucose (up to +81%) and xylose (up to +153%) release, suggesting that down-regulating CAD1 is a promising strategy for improving lignocellulosic biomass for the sugar platform industry.

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an [i]Arabidopsis thaliana[/i] data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.

Genome-wide association studies have been successful in identifying genes involved in polygenic traits and are valuable for crop improvement. Tomato ([i]Solanum lycopersicum[/i]) is a major crop and is highly appreciated worldwide for its health value. We used a core collection of 163 tomato accessions composed of [i]S. lycopersicum,[/i] [i]S. lycopersicum var cerasiforme[/i], and [i]Solanum pimpinellifolium[/i] to map loci controlling variation in fruit metabolites. Fruits were phenotyped for a broad range of metabolites, including amino acids, sugars, and ascorbate. In parallel, the accessions were genotyped with 5,995 single-nucleotide polymorphism markers spread over the whole genome. Genome-wide association analysis was conducted on a large set of metabolic traits that were stable over 2 years using a multilocus mixed model as a general method for mapping complex traits in structured populations and applied to tomato. We detected a total of 44 loci that were significantly associated with a total of 19 traits, including sucrose, ascorbate, malate, and citrate levels. These results not only provide a list of candidate loci to be functionally validated but also a powerful analytical approach for finding genetic variants that can be directly used for crop improvement and deciphering the genetic architecture of complex traits

The water transport pipeline in herbs is assumed to be more vulnerable to drought than in trees due to the formation of frequent embolisms (gas bubbles), which could be removed by the occurrence of root pressure, especially in grasses. Here, we studied hydraulic failure in herbaceous angiosperms by measuring the pressure inducing 50% loss of hydraulic conductance (P50) in stems of 26 species - mainly European grasses (Poaceae).Our measurements show a large range in P50 from -0.5 to -7.5MPa, which overlaps with 94% of the woody angiosperm species in a worldwide, published dataset, and which strongly correlates with an aridity 52 index. Moreover, the P50 values obtained were substantially more negative than the midday water potentials for five grass species monitored throughout the entire growing season, suggesting that embolism formation and repair are not routine and mainly occur under water deficits. These results show that both herbs and trees share the ability to withstand very negative water potentials without embolism formation in their xylem conduits during drought stress. In addition, structure function trade-offs in grass stems reveal that more resistant species are more lignified which was confirmed for herbaceous and closely related woody species of the daisy group (Asteraceae). Our findings could imply that herbs with more lignified stems will become more abundant in future grasslands under more frequent and severe droughts, potentially resulting in lower forage digestibility.

Plant metabolites are important to world food security due to their roles in crop yield andnutritional quality. Here we report the metabolic profile of 300 tomato accessions (Solanum lycopersicum and related wild species) by quantifying 60 primary and secondary metabolites, including volatileorganic compounds, over a period of 2 yr. Metabolite content and genetic inheritance of metabolites varied broadly, both within and between different genetic groups. Using genotype information gained from 10 000 single nucleotide polymorphism markers, we performed a metabolite genome-wide association mapping (GWAS) study. We identified 79 associations influencing 13 primary and 19 secondary metabolites with large effects at high resolution. Four genome regions were detected, highlighting clusters of associations controlling the variation of several metabolites. Local linkage disequilibrium analysis and allele miningidentified possible candidate genes which may modulate the content of metabolites that areof significant importance for human diet and fruit consumption. We precisely characterized two associations involved in fruit acidity and phenylpropanoid volatile production. Taken together, this study reveals complex and distinct metabolite regulation in tomato subspecies and demonstrates that GWAS is a powerful tool forgene-metabolite annotation and identification, pathways elucidation, and further cropimprovement.

Many recent studies have emphasized the important role of structural variation (SV) in determining human genetic and phenotypic variation. In plants, studies aimed at elucidating the extent of SV are still in their infancy. Evidence has indicated a high presence and an active role of SV in driving plant genome evolution in different plant species.With the aim of characterizing the size and the composition of the poplar pan-genome, we performed a genome-wide analysis of structural variation in three intercrossable poplar species: Populus nigra, Populus deltoides, and Populus trichocarpa. We detected a total of 7,889 deletions and 10,586 insertions relative to the P. trichocarpa reference genome, covering respectively 33.2 Mb and 62.9 Mb of genomic sequence, and 3,230 genes affected by copy number variation (CNV). The majority of the detected variants are inter-specific in agreement with a recent origin following separation of species.Insertions and deletions (INDELs) were preferentially located in low-gene density regions of the poplar genome and were, for the majority, associated with the activity of transposable elements. Genes affected by SV showed lower-than-average expression levels and higher levels of dN/dS, suggesting that they are subject to relaxed selective pressure or correspond to pseudogenes.Functional annotation of genes affected by INDELs showed over-representation of categories associated with transposable elements activity, while genes affected by genic CNVs showed enrichment in categories related to resistance to stress and pathogens. This study provides a genome-wide catalogue of SV and the first insight on functional and structural properties of the poplar pan-genome.

Lignin is one of the main factors determining recalcitrance to enzymatic processing of lignocellulosic biomass. Poplars (Populus tremula x Populus alba) down-regulated for cinnamoyl-CoA reductase (CCR), the enzyme catalyzing the first step in the monolignol-specific branch of the lignin biosynthetic pathway, were grown in field trials in Belgium and France under short-rotation coppice culture. Wood samples were classified according to the intensity of the red xylem coloration typically associated with CCR down-regulation. Saccharification assays under different pretreatment conditions (none, two alkaline, and one acid pretreatment) and simultaneous saccharification and fermentation assays showed that wood from the most affected transgenic trees had up to 161% increased ethanol yield. Fermentations of combined material from the complete set of 20-mo-old CCR-down-regulated trees, including bark and less efficiently down-regulated trees, still yielded ∼ 20% more ethanol on a weight basis. However, strong down-regulation of CCR also affected biomass yield. We conclude that CCR down-regulation may become a successful strategy to improve biomass processing if the variability in down-regulation and the yield penalty can be overcome.

ContextSince the 2003 drought and heat wave, there have been many reports of Douglas-fir decline and dieback in France. Given the climate change that is predicted, more frequent drought episodes could induce recurrent decline processes. The nature and background of this threat requires a careful assessment.AimsThe objectives of this study were: (1) to test the hypothesis that the major climate hazard that accounts for the decline in Douglas-fir growth is drought, and (2) to identify the main vulnerability factors involved.MethodsDecline and recovery of radial growth were quantified of Douglas-fir in two particularly affected regions with a dendroecological approach; 899 trees were sampled on 58 plots. Mean climate data, ecological and dendrometric stand characteristics were tested in order to identify potential vulnerability factorsResultsA clear relationship was evidenced between growth and soil water deficit. A severe decline of radial growth was induced by the 2003 drought and was maintained during subsequent years due to recurrent drought episodes. Growth recovery at the stand level was enhanced by soil nutrient fertility.ConclusionRadial growth of Douglas-fir is clearly driven by the intensity of drought. In a context of climate change, an adaptive silvicultural strategy is required in order to cope with more frequent drought events. To mitigate the frequency and the intensity of water shortage episodes, site selection must take into account not only local climate characteristic but also soil properties like maximum extractable water content. Soil nitrogen fertility was found to play a key role for an efficient recovery of radial growth after drought episodes and thus must be preserved.

Arabidopsis thaliana is an important model organism for understanding the genetics and molecular biology of plants. Its highly selfing nature, small size, short generation time, small genome size, and wide geographic distribution make it an ideal model organism for understanding natural variation. Genome-wide association studies (GWAS) have proven a useful technique for identifying genetic loci responsible for natural variation in A. thaliana. Previously genotyped accessions (natural inbred lines) can be grown in replicate under different conditions and phenotyped for different traits. These important features greatly simplify association mapping of traits and allow for systematic dissection of the genetics of natural variation by the entire A. thaliana community. To facilitate this, we present GWAPP, an interactive Web-based application for conducting GWAS in A. thaliana. Using an efficient implementation of a linear mixed model, traits measured for a subset of 1386 publicly available ecotypes can be uploaded and mapped with a mixed model and other methods in just a couple of minutes. GWAPP features an extensive, interactive, and user-friendly interface that includes interactive Manhattan plots and linkage disequilibrium plots. It also facilitates exploratory data analysis by implementing features such as the inclusion of candidate polymorphisms in the model as cofactors.