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First published online August 13, 2008; 10.1104/pp.108.124271 Plant Physiology 148:1117-1127 (2008) © 2008 American Society of Plant Biologists Combined Genetic and Modeling Approaches Reveal That Epidermal Cell Area and Number in Leaves Are Controlled by Leaf and Plant Developmental Processes in Arabidopsis1,[W]Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux UMR759, INRA-SUPAGRO, F–34060 Montpellier, France (S.T., D.V., J.F., M.D., C.G.); Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany (M.R., M.K.); and Laboratory of Genetics, Wageningen University, 6703 BD Wageningen, The Netherlands (M.K.)
Both leaf production and leaf expansion are tightly linked to cell expansion and cell division, but the functional relationships between all these variables are not clearly established. To get insight into these relationships, a quantitative genetic analysis was performed in 118 recombinant inbred lines derived from a cross between the Landsberg erecta and Antwerp accessions and was combined with a structural equation modeling approach. Main effects and epistatic interactions at the quantitative trait locus (QTL) level were detected for rosette area, rosette leaf number, leaf 6 area, epidermal cell area and number. A QTL at ERECTA marker (ER) controlled cell expansion and cell division, in interaction with two other QTLs at SNP295 and SNP21 markers. Moreover, both the screening for marker association involved in the variation of the relationships between leaf growth variables and the test of alternative functional models by structural equation modeling revealed that the allelic value at ER controlled epidermal cell area and epidermal cell number in a leaf. These effects are driven both by a whole plant mechanism associated with leaf production and by a single leaf mechanism associated with leaf expansion. The complex effects of the QTL at ER were validated in selected heterogeneous inbred families. The ERECTA gene, which is mutated in the Landsberg erecta parental line, was found to be a putative candidate responsible for these mapped effects by phenotyping mutants of this gene at the cellular level. Together, these results give insight into the complex determination of leaf epidermal cell number and area.
Final leaf area in a plant is an integrated variable depending on many different elementary processes, such as cell production and cell expansion, duration and rate of expansion of each individual leaf, leaf production rate, and duration of the phase of leaf production. As a consequence, leaf growth can be studied through various variables at different organizational levels, such as cellular, individual leaf, and whole plant. As a first step toward a modeling approach of whole plant leaf growth, it is necessary to elucidate how the different leaf growth variables are connected to one another. Until now, the causal or functional links between underlying leaf growth variables have not been clearly identified. Even the coordination of the cellular processes controlling leaf growth is not yet established.
The traditional view is that leaf development is driven by cell cycle-associated processes leading to an accumulation of cells in particular regions of the leaf, thereby driving morphogenesis and determining the size of the leaf (Fleming, 2007
Evidence that this cellular theory is insufficient in some situations comes from specific manipulation of the cell cycle and cell division during leaf development. Overexpression of CYCLIN-D2 leads to an increase in cell division rate in tobacco (Nicotiana tabacum) leaves, an increase in leaf growth rate but without significant changes in final leaf shape and size (Cockcroft et al., 2000
The research presented here aimed to identify functional links between (1) leaf cellular growth processes themselves (namely, cell division and cell expansion); (2) leaf cellular growth processes and individual leaf expansion; and (3) leaf cellular growth processes and rosette leaf production. As a first step, we phenotyped leaf growth from the cellular level to the whole plant level in a set of recombinant inbred lines (RILs) and we searched for relationships between cellular leaf growth variables and variables at other organizational levels. Then, colocalizations of quantitative trait loci (QTLs) for cellular leaf growth variables and other leaf growth variables were identified and interpreted. Moreover, QTLs for correlations between the leaf growth variables were also detected with a systematic automated analysis of the bivariate correlations. These three steps revealed possible sets of functional links between leaf growth variables, which were tested further with structural equation modeling (Shipley, 2000
Variation in Leaf Growth Variables of the Ler x An-1 RIL Population Rosette area, leaf number, leaf 6 area, and epidermal cell number in leaf 6 were significantly lower in Antwerp (An-1) compared with Landsberg erecta (Ler; Table I ). In contrast, epidermal cell area in leaf 6 was significantly higher in An-1 compared with Ler. A large phenotypic variation in the population of RILs derived from a cross Ler x An-1 was observed for all variables including those for which parental values hardly differed (Fig. 1 ; Table I). The broad sense heritabilities ranged from 0.79 to 0.91 for leaf 6 area and leaf number, respectively (Fig. 1).
Genotypic Correlations among Leaf Growth Variables and Colocalization of QTLs in the Ler x An-1 RIL Population Epidermal cell number in leaf 6 was not significantly correlated to rosette area, but was negatively correlated to leaf number, indicating that, in this population, plants having a higher number of leaves have a lower number of cells in leaf 6 (Table II ). The absence of an overall correlation between rosette area and epidermal cell number can be explained by colocalization of QTLs with similar or opposite allelic effects. A QTL for rosette area, on chromosome IV around SNP295 marker, collocated with a QTL for epidermal cell number both with the same allelic effect (Fig. 2; Table III), whereas two other QTL clusters were detected on chromosome V and have opposite allelic effects for rosette area and epidermal cell number (Fig. 2 ; Table III ). The negative correlations found between epidermal cell number in leaf 6 and leaf production variables (Table II) could also be explained by colocalizations of QTLs with opposite allelic effects on chromosome V (Fig. 2; Table III).
Epidermal cell area in leaf 6 was positively correlated to rosette area and leaf 6 area, but not to leaf number (Table II). This is explained by a QTL for rosette area, which coincided with QTLs for leaf 6 area and for epidermal cell area in leaf 6 in the middle of chromosome 4 at SNP295 (Fig. 2; Table III). The Ler alleles increased the values of all the variables at this marker. A QTL for cell area centered on ER accounted for 34.9% of the phenotypic variance (Fig. 2) and colocalized with a QTL for epidermal cell number, which accounted for 8.4% of the phenotypic variance (Fig. 2). At this chromosomal position, only these QTLs were detected with opposite allelic effects. In addition, a cluster of QTLs at SNP295 included QTLs for epidermal cell area and number accounting for 14.2% and 11.3%, respectively, of the phenotypic variance. At this position, QTLs for both variables had the same allelic effect (Fig. 2). This cluster included also a QTL for leaf 6 area, accounting for 36.2% of the phenotypic variance (Fig. 2).
The interaction between the QTLs at markers SNP295 and ER was revealed by using EPISTAT (Table III). ANOVAs were performed using SPSS to further analyze this interaction (see "Materials and Methods"), which is presented in Figure 3, A to C . Epidermal cell area in leaf 6 was systematically higher in lines carrying the An-1 allele at the ER marker whatever the allele at SNP295 (Fig. 3B). Leaf 6 area and epidermal cell area in leaf 6 were both systematically higher when lines contained the Ler allele at SNP295 independently of the allelic value at ER (Fig. 3, A and B), indicating additive effects of both loci for these traits. However, for epidermal cell number in leaf 6, both QTLs showed strong interaction as epidermal cell number was increased by a factor of 2 only when lines had Ler alleles both at ER and at SNP295 (Fig. 3C) compared to the other three combinations of alleles at these markers.
The phenotypic effect explained by the QTL at ER for epidermal cell area was increased when this QTL was considered in interaction with the QTL at SNP21 (Fig. 3E; Table III). The An-1 allele at SNP21 caused a slight, but significant, increase in epidermal cell area in leaf 6 without changing significantly leaf 6 area and epidermal cell number (Fig. 3, D–F). However, this same allele caused a significant increase in epidermal cell area with a decrease in epidermal cell number when plants had the An-1 allele at ER (Fig. 3, D–F). The Ler allele at ER therefore reduced the positive effect of the An-1 allele at SNP21 on epidermal cell area (Fig. 3E).
We tested whether the relationships between leaf growth variables were affected by allelic segregation at specific loci. For this, a script was developed in R (R Development Core Team, 2007
Structural Equation Models
We further used structural equation modeling to investigate the functional relationships among leaf growth variables. The purpose of structural equation modeling is to quantify the relative contributions of correlated causal sources of variance once a certain network of interrelated variables with biological significance has been accepted (Shipley, 2000 The analysis of the interactions between QTLs controlling epidermal cell area and/or cell number was subsequently combined with the detection of QTLs modifying correlations between cellular leaf growth variables and others. This allowed us to set up new hypotheses for the functional links between leaf growth variables: (1) Epidermal cell area is positively determined by the number of leaves (locus at ER) and also by the expansion of leaf 6 itself (locus at SNP295); (2) epidermal cell number is negatively determined by the number of leaves produced by the rosette (locus at CIW10 and SNP77) and, to some extent, positively by the expansion of leaf 6 itself (interaction between a locus at ER and a locus at SNP295); (3) both epidermal cell area and cell number contribute to leaf 6 area; (4) both the individual leaf area reflected by leaf 6 and the number of leaves contribute to rosette area; and (5) epidermal cell area in leaf 6 contributes to rosette area. These hypotheses were integrated into a new structural equation model. The model provided a very good fit to the data (P = 0.79; root mean square error approximation [RMSEA] <0.05; comparative fit index [CFI] = 0.99) in the subpopulation with an An-1 allele at ER (Fig. 5A ). All the path coefficients were significantly different from zero. Additionally, a substantial proportion of variance of the response variables, namely, epidermal cell area, epidermal cell number, and rosette area, was explained by the model (r2 = 0.43, 0.38, and 0.96, respectively).
This model was rejected in the subpopulation with a Ler allele at ER (P < 0.001). However, genetic results described above permitted the establishment of different hypotheses in this case and a new model was constructed for this second subpopulation. We have shown that Ler allele at ER (1) abolished the relationship between leaf number and epidermal cell area; (2) abolished the relationship between epidermal cell area and rosette area; (3) affected the relationship between leaf number and epidermal cell number in leaf 6 in interaction with SNP295; and (4) affected the relationship between epidermal cell area in leaf 6 and leaf 6 area. This second model (Fig. 5B) with deletion of four arrows compared to the first one was not rejected in the subpopulation carrying a Ler allele at ER (P = 0.22) and provided a good fit to the data (RMSEA = 0.09; CFI = 0.99). All the path coefficients were significantly different from zero and a substantial proportion of variance of the response variables, namely, epidermal cell area and rosette area, was explained by the model (r2 = 0.59 and 0.92, respectively). This model was strongly rejected in the subpopulation carrying an An-1 allele at ER (P < 0.001).
Measurements of final epidermal cell area and number in HIF derived from the RIL 35 (heterozygous line at ER) confirmed the effect of the QTLs clustered at ER marker (Fig. 6 ). Epidermal cell area was twice as large in HIF-35/9 carrying the An-1 allele at this marker compared with HIF-35/1, which remained heterozygous at this marker (Fig. 6B). In addition, epidermal cell number was twice as low in HIF-35/9 as in HIF-35/1 (Fig. 6C). As a consequence, final leaf area did not differ significantly between the two lines (Fig. 6A).
Considering the traits analyzed, these QTLs could be attributable to the erecta mutation that is carried by the Ler line and segregates in the mapping population. Single gene mutants confirmed that this candidate gene could be responsible of the mapped cellular effects by measuring epidermal cell area and epidermal cell number in leaves of two erecta mutants with two different genetic backgrounds. The two mutants showed significantly reduced epidermal cell area and increased epidermal cell number without changes in final leaf 6 area (Fig. 6, A–C), revealing complete compensation between the two growth variables measured at the cellular level.
Both Epidermal Cell Number and Cell Area Are Controlled by Whole Plant Processes Associated with Leaf Production
Short-day conditions, increasing the number of leaves on the rosette, cause a decrease in epidermal cell number in each individual leaf (Cookson et al., 2007
Both epidermal cell number and area vary in leaves depending on the rank of their emergence. A decline in cell area with increasing leaf position is commonly observed in plants, whereas epidermal cell number is generally increased as reported in Ipomoea (Ashby, 1948
Many studies have shown that the final area for a leaf at a given rank on the plant was more related to its final epidermal cell number than to its final epidermal cell area (Dale, 1992
As described in the introduction of this article, it is often shown that there is a balance between both cell area and cell number in plants (Ter Steege et al., 2005
Arabidopsis Ler is one of the most popular laboratory strains that have been widely used as a wild-type background for collections of mutants (Berná et al., 1999
The ERECTA gene was cloned (Torii et al., 1996 Our QTL analysis shows that QTLs at ER act in regulatory pathways of cell expansion and cell division, by interaction with at least two other genes or groups of genes around marker SNP295 and SNP21 on chromosomes IV and V, respectively. The interaction involving SNP295 and ER for epidermal cell number is noteworthy because its effect on epidermal cell number results from a unique combination of alleles. The analyses performed here with partial correlations at ER present evidence that this QTL drastically alters relationships between leaf growth variables at all organizational levels. For example, an An-1 allele at ER gives a negative correlation between the cell number and cell size, whereas a Ler allele at ER gives a positive correlation. In addition, an An-1 allele at ER confers a strong positive correlation between epidermal cell area and rosette leaf number, which vanishes when plants have the allele from Ler at ER. Even if a causal connection between ERECTA and the QTLs detected in its region cannot been firmly established in our study, results presented here with the erecta mutants (both in Col and Ler backgrounds) indicate a role of ERECTA on epidermal cell expansion in the leaf. Further analyses will be needed to determine whether ERECTA itself is responsible of the polymorphism between Ler and An-1 in the interval of the QTL at ER and then regulates the timing of cell expansion and cell division in a leaf, integrating both signals at the leaf level and signals at the plant level.
Combination of quantitative genetics and statistical modeling approaches allowed us to show that both epidermal cell area and number depend on growth at the leaf level and at the plant level via leaf production. This finding is particularly important because many attempts to increase leaf size by modifying cell division or expansion have failed. Our results indicate that these two variables are, to some extent, retrocontrolled by whole leaf and whole plant processes, therefore limiting their impact on leaf growth itself with maybe a role for the ERECTA gene in the second control. In a more general way, our data show that functional models relating leaf growth variables, as formalized here by path models and tested using structural equation modeling, can strongly depend on the genetic makeup of the plant materials that are tested. The complete genetic analysis performed in this study revealed that crucial relationships between variables combined in a model can differ significantly, depending on the allelic variation at a specific locus. Correlative analyses in unstructured populations of a species would not detect such genetic differences in the correlation structure and this would result in incorrect or poor-fitting models.
Plant Material
For the QTL mapping, 120 RILs were previously generated from a cross between Ler and An-1 (El-Lithy et al., 2006 An additional experiment was performed to confirm the QTLs mapped at ER and analyze the effect of ERECTA on the measured variables. Eleven lines derived from the progeny of RIL-35, heterozygous only in the region with the ERECTA gene, were selected for this experiment. Five replicates of each line were grown and the visible phenotype due to the mutation of ERECTA was noted for each plant (observing the compact typical inflorescence). Two lines were identified as having the same inflorescence phenotype for each of the five replicates: HIF-35/1 had a Ler inflorescence phenotype and HIF-35/9 an An-1 inflorescence phenotype. These two lines were genotyped in the ER region (markers Msat2-17, 10.7 Mb and nga1126, 11.7 Mb; data not shown) and HIF-35/1 and HIF-35/9 were heterozygous and homozygous for the An-1 allele at the ER marker, respectively. Leaf and cellular variables were measured on these two lines.
Moreover, two different genetic backgrounds (Ler and Col-5 ER) and their erecta mutant (Ler and Col-5er) have also been phenotyped during the same experiment in 10 replicates (Godiard et al., 2003 For each experiment, seeds were stored at 4°C and were imbibed with water 30 min before sowing. They were sown in cylindrical pots (9-cm height and 4.5-cm diameter) filled with a mixture (1:1 [v/v]) of a loamy soil and organic compost.
The two experiments were performed in a growth chamber equipped with the automated phenotyping platform PHENOPSIS (Granier et al., 2006
Micrometeorological Conditions
Control of Soil Water Content
PHENOPSIS took digital pictures of all individual pots on a daily basis during the experiments. On these pictures, stages of leaf development were scored for each individual plant three times a week as described in Boyes et al. (2001)
Leaf Area
Leaf Production
Cellular Development
All statistical analyses were done using the computer package SPSS 11.0.1 for Windows (SPSS) and R software (R Development Core Team, 2007 Correlations between leaf growth variables were tested using mean value of each leaf growth variable for each RIL. Both the Pearson and Spearman correlation coefficients were computed and their significance was tested. These two tests of correlations gave similar results. In a second step, a script was developed on R to detect loci affecting the slopes of the correlation between two variables when the correlation was considered as linear. The effect of each marker on the correlation between each couple of variables was determined by analysis of covariance with generalized linear model (GLM). The marker was considered to affect the slope of the linear regression between two variables only when the P value given by GLM was below a threshold of 0.001. This analysis was done both on nontransformed and transformed variables without any changes in the interpretation of the results.
Structural equation modeling is a generalized method for the analysis of covariance relationships and is used to evaluate the fit of data to a priori causal hypotheses about the functioning of a system (Shipley, 2000
QTLs were first identified using single interval mapping with the software package MapQTL 5 (Van Ooijen, 2004
The following materials are available in the online version of this article.
We thank Jean-Jacques Thioux and Crispulo Balsera for help with plant growth measurements, the R community for software development, and Sarah Cookson for English corrections. Received June 6, 2008; accepted August 6, 2008; published August 13, 2008.
1 This work was supported by GENOPLANTE (grant no. GPLA–06014G to S.T.), an European Integrated Project in the 6th Framework Program (AGRON-OMICS grant no. LSHG–CT–2006–037704 to J.F.), as well as the PROCOPE and ARABRAS (ERAPG–003–03) program (financial support and exchange visits between the Montpellier and Cologne groups).  The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Christine Granier (granier{at}supagro.inra.fr).
[W] The online version of this article contains Web-only data. www.plantphysiol.org/cgi/doi/10.1104/pp.108.124271 * Corresponding author; e-mail granier{at}supagro.inra.fr.
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2 statistic. Here, the aim was to impose a theoretical structure relating the direct and indirect relationships between leaf growth traits taking into account the results of QTL analyses and bivariate correlations. Transformed variables were used for rosette area and leaf number, to achieve linearity of the bivariate relationships and normal distribution of the residuals. This also helped to satisfy the assumption of univariate as well as multivariate normality. Structural equation models were tested in R using the structural equation modeling package (Fox, 2006
