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As a growing body of evidence supports the negative effects of accumulation of CO2 and other greenhouse gases in the atmosphere (Lenoir et al., 2008; Rosenzweig et al., 2008), society is increasingly turning to forests and forest management for the mitigation of atmospheric CO2 (Canadell & Raupach, 2008). Forests store c. 45% of terrestrial carbon (Bonan, 2008) and cellulosic ethanol production from wood has great potential to diminish the need for fossil fuels, limiting atmospheric CO2 accumulation (Sticklen, 2008). Therefore, increasing the productivity of plantation forests could have a significant impact on society by simultaneously enhancing carbon sequestration and meeting greater demands for renewable wood and bioenergy products.
The chemical composition of wood, cellulose (45–50%), hemicellulose (25%) and lignin (25–35%) (Plomion et al., 2001), is important for its conversion into products and for carbon sequestration. Because lignin is richest in carbon and the most recalcitrant component of wood, higher proportions can translate into more carbon stored for longer periods of time. However, higher lignin contents may be undesirable for the production of pulp/paper and cellulosic ethanol. For these applications, lignin needs to be extracted from wood with relatively harsh chemical treatments and high energy inputs (Li et al., 2003; Chen & Dixon, 2007; Sticklen, 2008). One strategy for increasing carbon sequestration and improving pulp, paper and cellulosic biofuel productivity is to raise the lignin content in nonharvested roots while reducing lignin in the woody stem. Therefore, the development of tree germplasm that is optimal for carbon sequestration and biomass conversion requires an understanding of the genetic regulation of growth, carbon allocation among plant organs and carbon partitioning into lignin, cellulose and hemicellulose within organs.
In woody plants, studies have identified a consistent, significant correlation between biomass growth and wood composition that is genetically regulated (Hu et al., 1999; Wu et al., 1999; Kirst et al., 2004; Yu et al., 2006). Pleiotropic genetic loci coordinating wood composition and stem growth are important targets for the enhancement of wood products. Populus is an excellent model species to identify these genetic elements, given the availability of several segregating pedigrees, easy clonal propagation, well-established transformation protocols and the genome sequence of a P. trichocarpa genotype (Tuskan et al., 2006). Previous studies have identified quantitative trait loci (QTLs) for biomass accumulation and allocation in segregating Populus families (Bradshaw & Stettler, 1995; Wu, 1998; Wu et al., 1998; Wullschleger et al., 2005; Rae et al., 2008). Above- and below-ground growth QTLs have also been mapped in poplar grown under ambient and elevated CO2 (Rae et al., 2007). However, few studies have attempted to map QTLs for wood chemical composition in poplar (Zhang et al., 2006) or, more importantly, have dissected the coordinate regulation of stem growth and wood chemistry.
In addition to genetics, environmental cues at the level of nutrient availability have a tremendous impact on tree growth, biomass allocation and wood composition. Nitrogen is generally the most limiting nutrient for tree growth and carbon sequestration (Oren et al., 2001; Finzi et al., 2007). Poplar trees exhibit extensive phenotypic plasticity in response to nitrogen. Nitrogen fertilization of young (< 1 yr old) poplar trees increases shoot biomass and wood cellulose content, while decreasing lignin content and the ratio of S- and G-lignin (S/G ratio) (Cooke et al., 2005; Pitre et al., 2007a,b). Nitrogen supply has also been observed to increase the photosynthetic rate in mature leaves (Cooke et al., 2005) and to alter xylem fiber anatomy by thickening and shortening (Pitre et al., 2007a). Changes in mRNA abundance have also been observed in poplar in response to nitrogen treatments (Cooke et al., 2003). Although these studies have contributed to a better understanding of the effects of nitrogen on tree anatomy and physiology, little is known about how genotypes interact with nitrogen and how these interactions are regulated in forest species.
The objective of this study was to define the genetic loci controlling the phenotypic variation in biomass accumulation, carbon allocation and partitioning in an interspecific Populus pedigree grown under two different nitrogen conditions. To identify these regions, a QTL mapping experiment with 396 clonally replicated genotypes was completed. Extensive phenotypic plasticity in response to nitrogen availability was observed. The experimental design allowed the analysis of genotype by nitrogen interaction for the first time in a tree QTL mapping population, resulting in the identification of 51 loci that control traits only under one nitrogen condition. Biomass accumulation, cellulose and lignin levels in wood were strongly correlated, genetically and phenotypically. More importantly, some QTLs for biomass growth and wood composition mapped to the same genomic region, delineating potential pleiotropic regulators that coordinate these traits.
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In this article, we report the widespread effects of nitrogen fertilization on the genetic regulation of growth and wood chemistry traits in the progeny of an interspecific pseudo-backcross of P. deltoides and P. trichocarpa. Nitrogen supply affected all growth and wood chemistry traits, confirming the extensive phenotypic plasticity of poplar in response to this essential nutrient. Studies have shown that nitrogen positively influences poplar tree height, diameter, number of leaves, number of sylleptic branches and shoot biomass (Pregitzer et al., 1990; Ibrahim et al., 1997; Kleiner et al., 1998; Cooke et al., 2005). However, contrary to our results, the same studies reported an increase in root biomass in response to nitrogen supply. Although statistical significance was either not observed (Cooke et al., 2005) or not tested (Pregitzer et al., 1990; Ibrahim et al., 1997; Kleiner et al., 1998), this disparity might reflect differences in experimental conditions. For example, in our trial, nutrients were supplied with a flood irrigation system in ebb-and-flow benches, whereas, in previous work, plants were fertilized with irrigation on the top of the pots. Despite differences in total root biomass, inarguably nitrogen fertilization caused significant changes in biomass allocation, decreasing root/shoot biomass ratio in poplar. Wood chemistry also responded to different nitrogen availability. Confirming previous studies with poplar trees (Pitre et al., 2007a,b), we observed that nitrogen fertilization significantly increased wood cellulose content and decreased lignin content and its extractability, measured as the S/G ratio. We also found that nitrogen supply increased hemicelluloses at a significantly lower rate (5%) when compared with the increase in cellulose sugars (8%).
The extensive phenotypic plasticity in response to nitrogen availability observed in Populus may be triggered by changes in gene regulation (Sultan, 2000). Supporting this hypothesis in poplar, nitrogen significantly changed the transcript abundance of 52 cDNA clones identified by differential display (Cooke et al., 2003). Consistent with a nitrogen-mediated decrease in wood lignin, two of the differentially expressed cDNA clones were from putative lignin biosynthesis genes, and were down-regulated in response to nitrogen. Similarly, studies that assessed the effects of nitrogen fertilization on the whole transcriptome of Arabidopsis also demonstrated that nitrogen represses most of the genes from the phenylpropanoid pathway (Scheible et al., 2004; Gutierrez et al., 2008). Some genes involved in cell wall growth and modification, including expansins and xyloglucan:xyloglucosyl transferase, were induced in response to nitrogen. Nitrogen induction was also detected in most genes involved with photosynthesis, the Calvin cycle and photorespiration (Scheible et al., 2004). Changes in gene expression in response to nitrogen fertilization may be the result of regulatory signals that balance carbon and nitrogen metabolism (Koch, 1997; Palenchar et al., 2004; Weigelt et al., 2008). The assimilation of nitrogen into amino acids depends on the availability of carbon skeletons, and carbon is stored in starch if nitrogen is deprived (Calenge et al., 2006). When nitrogen is luxuriant (relatively low carbon to nitrogen ratio), plant metabolism shifts resources towards the absorption of carbon through photosynthesis and develops more shoot than root. Conversely, when nitrogen is limited (high carbon to nitrogen ratio), metabolism favors the allocation of biomass to root over shoot (Koch, 1997).
Consistent with the widespread phenotypic and gene expression plasticity of plants in response to nitrogen, our study detected extensive changes in the genetic control of carbon allocation and partitioning with nitrogen supply. Most of the QTLs identified in our study (51 of 63) are nitrogen treatment specific. In other plant species, significant QTL by nitrogen interactions have been reported for disease resistance and root traits in rice (Talukder et al., 2005; MacMillan et al., 2006), carbohydrate content and plant biomass in Arabidopsis (Rauh et al., 2002; Calenge et al., 2006), and grain yield and quality in wheat (Laperche et al., 2007). Significant QTL by nitrogen treatment interaction indicates that fast- and slow-growing genotypes are not the same under limiting and luxuriant nitrogen conditions. Spearman correlation of phenotypic values under the two nitrogen treatments indicates extensive changes in rank among genotypes (Table S2, see Supporting Information). The even lower Spearman correlations between nitrogen treatments for wood chemistry traits indicate that most of these traits are more responsive to nitrogen supply than are biomass and growth traits. This agrees with the lower clonal repeatability observed for most wood components when compared with phenotypic traits (Tables 1, 2), which was not observed when clonal repeatability was estimated for each nitrogen treatment separately (Table S3, see Supporting Information). Supporting the idea that wood chemistry traits are more reactive to nitrogen availability, no QTLs for wood traits were consistently mapped in both nitrogen treatments, whereas six QTLs for phenotypic traits were mapped independently of nitrogen level.
Our results support the hypothesis that growth co-varies with wood chemistry traits in trees. As demonstrated previously with mutants of Populus (Hu et al., 1999) and Pinus (Wu et al., 1999; Yu et al., 2006), and in a segregating pedigree of Eucalyptus (Kirst et al., 2004), we observed that fast-growing poplar trees have higher cellulose and lower lignin content when compared with slow-growing trees. To genetically dissect the association between growth and wood quality traits, we searched for common pleiotropic loci that regulate variation in both trait groups. We identified two common QTLs for growth and wood chemistry. One QTL cluster detected in our study occurs on LG13, between markers G2577 and G2218, and regulates growth, biomass and wood composition traits. Specifically, trees that inherited the P. trichocarpa allele at this locus grew faster, contained more cellulose and less lignin when compared with those that inherited the P. deltoides allele. Genomic regions in the vicinity of this LG13 QTL cluster control the abundance of metabolites derived from the phenylpropanoid pathway in an independent pedigree of Populus (Morreel et al., 2006). The other QTL cluster coordinately controls the height of the live crown and wood composition under nitrogen deficiency and is located on LG1, between markers G3205 and G834. In this same region of LG1, QTLs have been mapped previously for height, stem circumference, stem volume, root density and root growth in family 331, which shares a common grand-parent (clone 93-968) with family 52–124 analyzed here (Rae et al., 2007, 2008).
Other QTLs detected in our study were also detected in family 331. For example, both QTLs for the height of the live crown (LG1 and LG2), mapped under limiting nitrogen treatment, co-localized with the QTL for stem height identified by Rae et al. (2008). The QTL identified for sylleptic branch count under high nitrogen (LG10) from our experiment (Ma et al., 2008) was also mapped in family 331 (Rae et al., 2008). Two QTLs for above-ground biomass from our experiment (LG1 under limiting nitrogen and LG12 under high nitrogen) co-localized with QTLs for stem height and diameter in family 331 growing under elevated (LG1) or ambient (LG12) CO2 (Rae et al., 2007). Two of our QTLs for total leaf biomass (LG3 under limiting nitrogen and LG12 under luxuriant nitrogen) mapped in the same region of previously identified QTLs controlling leaf number (Rae et al., 2006).
A number of interesting a priori candidate genes are physically positioned within the marker intervals flanking potential pleiotropic quantitative loci on LG1 and LG13. An immediately apparent candidate gene underlying the QTL cluster on LG13 is cinnamate-4-hydroxylase, encoding an enzyme that regulates the conversion of cinnamate to 4-coumarate in the phenylpropanoid biosynthesis pathway, and a transcript for which we previously identified a strong correlation with growth in the Eucalyptus segregating population (Kirst et al., 2004). Similarly, the QTL cluster for growth and wood composition on LG1 harbors a cluster of three tandemly duplicated genes encoding putative cinnamoyl-CoA reductases, which regulate the conversion of feruloyl-CoA to coniferaldeyhyde in the phenylpropanoid pathway. Additional components (chalcone synthase, 4-coumarate:CoA ligase) and potential regulators [AtMYB12 homolog (Mehrtens et al., 2005) and other uncharacterized R2R3-MYB-type transcription factors] of phenylpropanoid metabolism are also conceivable candidate genes within these intervals. Furthermore, several C5/C6 metabolic enzymes, including xyloglucan fucosyltransferase, glucose-6-phosphate-1-dehydrogenase, xyloglucan endotransglycosylase and cellulose synthase, are encoded by genes within these intervals. Finally, numerous genes of unknown function, or with no homology to known Arabidopsis or Oryza coding sequences, are prevalent among genes within the intervals, leaving open the possibility that some of these uncharacterized elements might coordinately regulate growth and biomass composition in woody plants. The gene models within marker intervals that flank most of our QTLs, including those clusters on LG1 and LG13, are available as Supporting Information (Table S4). Gene models within some QTL intervals (eight of 29) could not be found, because at least one of the flanking markers either did not have a BLAST hit in the genome or hit an unmapped scaffold.
The number of candidate genes within these QTLs is certainly too large to be tested with functional analysis – our average QTL interval is 31 cM wide and contains approximately 426 genes, 45% of which have unknown function (Table S4). Although a reduction in the size of the candidate gene pool is necessary, the traditional positional cloning-based isolation of the regulatory locus would be challenging given the breadth of the QTL intervals, low heritability of quantitative traits and the typical low resolution of maps in forest species. Nevertheless, the availability of the P. trichocarpa genome sequence (Tuskan et al., 2006), allied to gene expression data that are being assayed with microarrays from three tissues (root, xylem and leaf) of our experiment, will form an excellent foundation to narrow down the candidate genes within QTLs. Integration of gene expression with genotypic and phenotypic data in a genetical genomics setting (Jansen & Nap, 2001) will uncover regions containing regulators of gene expression (eQTL) and of phenotypes. The identification of candidate genes for an eQTL/QTL hot spot can be achieved by elucidating genetic networks controlled by the region, as has been demonstrated in mammals (Mehrabian et al., 2005; Meng et al., 2007). Pinpointing genetic regulators linking tree growth to wood quality will ultimately enhance our ability to breed for and engineer genotypes improved for pulp, paper, below-ground carbon sequestration and cellulosic ethanol production.