Bibliographic record
Abstract
Daily newspapers abound with stories of the latest discoveries of the interplay between human genetics and nutrition and its role in human health and development. For example, recent evidence has provided an incredibly strong link between genetic variation in human populations, vitamin D concentrations and the occurrence of multiple sclerosis, where genetic predisposition to the disorder is strongly influenced by diet and environment (Ramagopalan et al., 2009). Similar examples in human genetics are plentiful, with some recent reviews focusing on the combinatorial effects of genotype and diet on human health and development from a systems biology perspective (Daniel et al., 2008; van Ommen et al., 2008). Like their human counterparts, plant phenotypes are also shaped by the interaction between genotype and the availability of essential nutrients, as recent reviews focusing on a variety of crop species illustrate (e.g. Fageria et al., 2008; Rengel & Damon, 2008). Given the increasing global demand for resources derived from plants, such as food and fuel, there is a palpable urgency to establish sustainable and highly productive crops. To achieve this, it is critical that efforts are continually focused on deepening our understanding of the combined effects of genotype and nutrient availability on the growth and productivity of plant species – especially those species earmarked as having the potential to mitigate future resource crises. Identification of genotype-specific and genotype-independent responses to nutritional cues will inform future crop improvement efforts and production regimes. ‘The ability to substantially alter the quantity or quality of lignocellulosic material simply by manipulating the environment in which a feedstock species is grown is an exciting prospect.’ In the current issue of New Phytologist, Novaes et al. (pp. 878–890) investigate the relationship between genotype, nutrient availability and growth in poplar, an ecologically and economically significant woody perennial plant. Specifically, the authors dissect the interplay between genotype and nitrogen (N) availability as it relates to the accumulation of both above-ground and below-ground biomass, as well as wood quality. These are key traits in poplar trees, which have historically been used for the generation of pulp, paper and wood products, but which have recently received much attention as biofuel feedstocks, as well as potential sinks for below-ground carbon sequestration. To facilitate the study, Novaes et al. employed a pedigree of pseudo-backcrossed hybrid poplar (Populus trichocarpa×Populus deltoides), which provided the basis for a detailed quantitative trait locus (QTL) analysis exploring the effect of N fertilization on phenotypic plasticity. A population of glasshouse-grown poplar was established from cuttings under modest fertilization before N was either removed or increased for the remainder of the growth trial. Following further growth, the resulting phenotypes of both subgroups (N deficient vs N fertilized) of the pedigree were assessed for growth parameters (e.g. height, diameter and above-ground biomass) and wood quality traits (e.g. cellulose, hemicellulose and lignin content). The authors demonstrated that N fertilization significantly increased all growth traits with the exception of root biomass, which significantly decreased. Taken together, the data clearly demonstrated an increased above-ground to below-ground biomass ratio in the fertilized trees compared with those experiencing N deficiency. Additionally, N fertilization statistically increased the amount of cellulose and hemicellulose in the wood with a concomitant decrease in lignin. This typical carbon trade-off between the cellulose and lignin components of wood was further highlighted by the near-perfect negative genetic correlation (−0.99) between cellulose and lignin observed by Novaes et al. A number of other strong correlations within and between growth parameters and wood chemistry traits were also reported, such as the ratio of above-ground biomass with the relative proportions of lignin monomers (−0.59). In addition to correlative analysis, a genetic linkage map constructed primarily from microsatellite markers was also employed to define a number of loci that were statistically linked to the various growth and wood chemistry traits. Using this approach, just six QTL were identified irrespective of N status, while 51 QTL were defined based on the presence or absence of N fertilization. Naturally, this prompted the authors to conclude that ‘in Populus a large fraction of the interspecific variance in growth, carbon allocation and carbon partitioning traits is highly responsive to the level of nitrogen available in the environment’. In-depth analyses revealed that a number of traits co-localized to the same QTL within the same N treatment, including growth and wood chemistry traits. For example, the authors defined one region within linkage group 13 (LG13) as a ‘hot spot of co-localized QTL’ under N-fertilized conditions, with co-localized traits such as above-ground biomass, diameter, cellulose and lignin. Plants grown under N deficiency had similar ‘hot spots’ with co-localizations of both growth and wood chemistry traits. A key aspect of the work by Novaes et al. was their ability to identify QTL within defined regions of specific linkage groups. In the past, similar research has been unable to define QTL beyond broad regions within poplar linkage groups, making it difficult, if not impossible, to mine QTL regions for genes and regulatory elements that may be of subsequent interest (e.g. Street et al., 2006). By contrast, the QTL identified by Novaes et al. are well defined and, as such, provide a solid foundation for future gene exploration. For example, within a ‘hot spot’ QTL on LG13, the authors identified a gene encoding cinnamate-4-hydroxylase (C4H) – a key enzyme involved in lignin biosynthesis. Similarly, key QTL on linkage group 1 (LG1) contain a number of genes encoding enzymes of the lignin and cellulose biosynthetic pathways, as well as putative MYB regulatory sequences. Further mining of the QTL clusters generated by Novaes et al. will undoubtedly resolve additional, previously characterized, genes relating to growth and wood chemistry, as well as illuminate uncharacterized genes or regulatory sequences that are directly or indirectly involved in biomass accumulation and/or wood quality. These genes and regulatory sequences are of particular interest to researchers focused on manipulating phenotypic traits in poplar. To that end, researchers have demonstrated the ability to alter the wood chemistry of poplar via the manipulation of key enzymes involved in lignin biosynthesis (e.g. Franke et al., 2000). In this case, overexpression of an Arabidopsis ferulate-5-hydroxylase changed the proportion of lignin monomers, resulting in poplar wood that was more amenable to pulp and paper processing (Huntley et al., 2003). Poplar has been identified as a key perennial species of interest with respect to sustainable second-generation biofuel production in North America, along with Miscanthus and switchgrass (Kintisch, 2008). Recent evidence suggests that some annual crop species, such as maize, are unsuitable biofuel feedstocks, primarily because of competition with food markets, energy-inefficient biofuel conversion and notoriously high fertilization requirements (e.g. Patzek, 2008). By examining the link between N fertilization and above-ground biomass in poplar, Novaes et al. have further contributed to the understanding of the impact that fertilization has on poplar with respect to its use as a sustainable bioenergy crop. Importantly, Novaes et al. examined not only wood quantity (i.e. biomass) but also the effects of fertilization on wood quality (i.e. wood chemistry). This is of key interest because gains in biomass have the potential to be negatively offset by diminished wood quality. This concept is especially relevant when considering industrial applications of wood, which often rely on extensive enzymatic pretreatments to liberate the valuable sugar-rich components from lignin and other residual cell-wall components. These treatments can often be cost prohibitive and are highly dependent on the relative proportion of cell-wall constituents, such as lignin monomers. In this regard, findings from genetic and phenotypic correlation analyses by Novaes et al. prompted the following statement: ‘when more carbon is partitioned into cellulose relative to hemicellulose the total amount of lignin decreases and a disproportionate reduction in syringyl relative to guaiacyl monomers is observed’. This suggests that while N fertilization positively influenced biomass in poplar, the digestibility of the wood may have been compromised because of an increase in the relative abundance of the more recalcitrant form of lignin monomer. This finding, and others detailed by Novaes et al., further underscores the importance of considering wood and fibre quality while attempting to increase or maximize biomass for industrial processes such as biofuel production. Novaes et al. have also shown that the phenotype of poplar – notably biomass – can be substantially affected solely by changing the local environment, instead of employing more invasive transgenic-based biotechnology approaches. The ability to substantially alter the quantity or quality of lignocellulosic material simply by manipulating the environment in which a feedstock species is grown is an exciting prospect. Naturally, studies of this nature may become increasingly pertinent as a result of the apparent delay in public support for the widespread use of transgenic technology. The article by Novaes et al. provides further insight with respect to the interplay between environment and genotype and regarding the effects of subsequent genetic ‘decision making’ on phenotypic plasticity. In particular, the authors were able to illustrate that N fertilization has a significant effect on traits relating to growth, biomass and wood chemistry in poplar. Moreover, Novaes et al. were able to pinpoint key QTL within specific regions of poplar linkage groups, enabling future gene and regulatory sequence mining for subsequent marker-aided selection or transgenic-based plant-improvement strategies. Broadly, this study highlights the importance of nutrient availability with respect to plant growth and emphasizes the value of systems biology-level approaches in the dissection of genotype-dependent effects of diet and nutrition across a wide variety of organisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".