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First published online February 8, 2008; 10.1104/pp.107.115220 Plant Physiology 146:1482-1500 (2008) © 2008 American Society of Plant Biologists OPEN ACCESS ARTICLE
New Connections across Pathways and Cellular Processes: Industrialized Mutant Screening Reveals Novel Associations between Diverse Phenotypes in Arabidopsis1,[W],[OA]Department of Biochemistry and Molecular Biology (Y.L., L.J.S., I.A., K.M.I., C.B., D.D.P., C.G.W., R.L.L.), and Department of Plant Biology (D.W.Y., J.B.O., K.W.O., A.P.W., C.G.W., R.L.L.), Michigan State University, East Lansing Michigan 48824
In traditional mutant screening approaches, genetic variants are tested for one or a small number of phenotypes. Once bona fide variants are identified, they are typically subjected to a limited number of secondary phenotypic screens. Although this approach is excellent at finding genes involved in specific biological processes, the lack of wide and systematic interrogation of phenotype limits the ability to detect broader syndromes and connections between genes and phenotypes. It could also prevent detection of the primary phenotype of a mutant. As part of a systems biology approach to understand plastid function, large numbers of Arabidopsis thaliana homozygous T-DNA lines are being screened with parallel morphological, physiological, and chemical phenotypic assays (www.plastid.msu.edu). To refine our approaches and validate the use of this high-throughput screening approach for understanding gene function and functional networks, approximately 100 wild-type plants and 13 known mutants representing a variety of phenotypes were analyzed by a broad range of assays including metabolite profiling, morphological analysis, and chlorophyll fluorescence kinetics. Data analysis using a variety of statistical approaches showed that such industrial approaches can reliably identify plant mutant phenotypes. More significantly, the study uncovered previously unreported phenotypes for these well-characterized mutants and unexpected associations between different physiological processes, demonstrating that this approach has strong advantages over traditional mutant screening approaches. Analysis of wild-type plants revealed hundreds of statistically robust phenotypic correlations, including metabolites that are not known to share direct biosynthetic origins, raising the possibility that these metabolic pathways have closer relationships than is commonly suspected.
Identification and analysis of mutants has played an important role in understanding biological processes of all types and in a wide variety of organisms. Traditionally this approach involves screening through large numbers of individuals for the small subset that have a change in a specific class of phenotype. A common approach is to use visual identification of variants with altered morphology under standard conditions (Bowman et al., 1989  ; Pyke and Leech, 1991), or following growth under altered environment (Glazebrook et al., 1996; Landry et al., 1997). Mutant screens can also be conducted using more specific molecular phenotypic outputs, ranging from changes in expression of specific genes (Susek et al., 1993) to direct analysis of metabolites (Benning, 2004; Jander et al., 2004; Valentin et al., 2006).
Once mutants are identified from a narrow screen detailed studies typically are performed to reveal secondary phenotypes. This deeper analysis is useful for several reasons. First, it can separate mutants into different classes and suggest novel relationships between the genes responsible for the phenotypic traits. Second, these studies can lead to a deeper understanding of the gene(s) responsible for the first phenotype discovered, and can reveal the underlying mechanism for the original phenotype (Conklin et al., 1996
Until recently, mutant identification was performed either by ‘forward’ or ‘reverse’ genetic analysis (Alonso and Ecker, 2006 As biology moves increasingly away from reductionism to systems thinking, there are several reasons why one phenotype or one gene/gene family at a time reverse genetic approaches hamper creation of large and durable genetic data sets. First, a limited number of genes are tested and phenotypes assayed in any given study, and protocols for screens are rarely consistent within or across laboratory groups. Second, the lack of common germplasm across different studies hampers comparisons. Finally, the tried and true approaches to data analysis and presentation in published articles, on laboratory Web sites, and community databases, with inconsistent descriptions of experiments and other metadata, make it difficult to discover all relevant data sets and to mine the data once discovered.
With the sequencing of an increasing number of plant genomes, accurately and efficiently assessing the function of the tens of thousands of genes that are annotated of unknown function or whose annotation is based upon similarity to genes from other organisms becomes an increasingly high priority. Tools for genome-wide analysis of mRNA and proteins have advanced very rapidly in recent years, enabling facile placement of genes into regulatory networks (Li et al., 2004
Changes in technology are creating new opportunities to perform systematic phenotypic studies. Eukaryotic model organisms offer an increasing number of mutants defective in known genes identified through classical genetic screening and collections of sequenced insertion mutants (Winzeler et al., 1999 We describe a pilot study performed to create a high-throughput and parallel-mutant screening and analysis pipeline (www.plastid.msu.edu). This study employed approximately 100 wild-type Arabidopsis (Arabidopsis thaliana) plants and three to six replicates each of 13 previously characterized mutants (Table I ). These plants were analyzed using 10 phenotypic screens, many of which provided multiple phenotypic outputs (for example, a liquid chromatography-tandem mass spectrometry [LC-MS/MS] assay that captured data for 25 protein amino acids and related compounds), for a total of 85 data points per plant line. Analysis of the data permitted assessment of phenotypic variability within a genotype and evaluation of statistical and data display methods. It also revealed unexpected phenotypic signatures and relationships for the characterized mutants, which would not have been detected if fewer mutants and phenotypic characteristics were assessed.
Analysis of Mutants and Wild Type with High-Throughput Screens Because the long-term goal of the project is to identify functions for genes involved in chloroplast physiology, the project incorporated a variety of efficient phenotypic assays that interrogate chloroplast function as well as the general growth and development of the plant from our laboratories or the literature. Chloroplast morphology and chlorophyll fluorescence screens were included as direct measures of the development and function of the chloroplast. Three classes of metabolites were assayed because they include pathways operating entirely or partly within the plastid: qualitative assays were performed for leaf and seed starch whereas quantitative assays were done for leaf and seed amino acids and leaf fatty acids. Finally, vegetative-stage plant morphology, seed morphology, and a quantitative assay for seed total carbon (C) and nitrogen (N) composition were chosen to assess the overall health of the plants and to look for correlations between leaf and seed physiology. The phenotypic assays were adapted from established methods to a pipeline process, with the goal of minimizing variability in growth conditions and assays, and discovering a wide variety of relevant morphological and physiological traits. Leaf tissues were harvested in a set process, with each assay (morning starch, amino acids, fatty acids, etc.) sampled in the identical order, on the equivalent leaf (judged by order of leaf emergence) starting at the same time of day after the same number of days of growth. Biological replicates of mutants were grown in separate flats along with large numbers of each wild-type ecotype. A laboratory information management system was designed to increase the speed and accuracy of each planting and harvesting step. Whenever possible, phenotypic data were captured directly to the database. All sample collection and processing was performed with anonymous bar code identifiers, and the technicians who recorded the data did not know the genotype of the plants.
One goal of the pilot study was to assess how well the phenotypic assays were working in the relatively high-throughput environment of the project. Three related issues were addressed: the ability of the assays to detect phenotypic changes, the variability of the assays, and the accuracy of plant and sample tracking. To this end, eight known mutants of ecotype Columbia of Arabidopsis (Col) and five known mutants of ecotype Wassilewskija of Arabidopsis (Ws; Table I) were planted in 6-fold replication along with 114 wild-type plants (72 Col and 42 Ws ecotypes). Seeds were harvested from the plants that survived to maturity and these were assayed for seed phenotypes and plants were grown to assay vegetative traits. The majority of the quantitative data from Col and Ws wild-type samples were found to be normally distributed (Shapiro-Wilk test, p > 0.01; Shapiro and Wilk, 1965
As detailed below, in every case relevant phenotypes described in the literature were identified in this blind study (Table I), validating that the mutants were correct and that our assays can accurately track large numbers of samples and discover a wide variety of targeted phenotypes. Dunnett's test, a method developed for multiple comparisons involving a control (Dunnett, 1955
The data on amino acids in leaves and seeds of the previously described mutants confirmed that the LC-MS/MS assay accurately reported levels of these metabolites (Tables II–V
Leaf samples from the ats1-1 and fatb-ko mutants (deficient in glycerol-3-P acyltransferase and acyl-acyl carrier protein thioesterase, respectively) were used to validate the fatty acid screening method. In ats1-1 mutants both the mol % of 16:3 (carbons in chain:number of double bonds) and overall proportion of C16 (Cnumber of carbons) relative to C18 chains were significantly reduced (Tables VI and VII ), as described previously (Kunst et al., 1988 ; Xu et al., 2006). The fatb-ko leaves had significantly higher mol % of the unsaturated fatty acids cis-16:1, 16:2, 18:1d9, 18:1d11, and 18:2, and significantly lower mol % of saturated fatty acids, 16:0 and 18:0 (all p < 0.001; Table VI), as reported by Bonaventure et al. (2003). The fatb-ko mutant also showed a strongly significant (p < 0.001) reduction in seed C/N ratio, consistent with the fatty acid biosynthetic defect in seeds (Bonaventure et al., 2003).
We also confirmed the phenotypes of mutants included to validate the qualitative assays. The arc10 and arc12 mutants (deficient in chloroplast division proteins AtFtsZ1 and AtMinE1, respectively) had fewer chloroplasts in the mesophyll cells from expanded leaf tips: the arc10 mutant often contained one greatly enlarged chloroplast and some smaller chloroplasts (Fig. 1, F and N) and the arc12 mutant had a single giant chloroplast (Fig. 1, B and J), as reported (Glynn et al., 2007 ; Yoder et al., 2007). The glucan-water dikinase-deficient sex1-1 mutant, laforin-like carbohydrate phosphatase-deficient sex4-5 mutant, and disproportionating enzyme-deficient dpe2-1 mutant had higher leaf starch (Fig. 2
, clusters 6 and 10), as previously published (Yu et al., 2001; Lu and Sharkey, 2004; Niittylä et al., 2006; Sokolov et al., 2006; Gentry et al., 2007). The violaxanthin deepoxidase mutant npq1-2 had lower nonphotochemical quenching (NPQ; Fig. 2, cluster 1), as described (Niyogi et al., 1998). Finally, the tt7-3 and tt7-1 mutants, carrying mutations in the gene encoding flavonoid 3'-hydroxylase, had pale brown seed coats as expected (Schoenbohm et al., 2000; Abrahams et al., 2002; Salaita et al., 2005).
Parallel Assays Reveal Phenotypic Networks Typical forward genetics and reverse genetics strategies suffer from the interrogation of each mutant with a limited number of phenotypic assays. This has two related consequences: it limits the likelihood that the full effects of a mutation will be discovered, and blinds us from discovering unexpected relationships between genes. The mutants included in this study were previously characterized (and except for pig1-1, the affected gene published), and have diverse primary physiological defects. This allowed us to look for unexpected secondary phenotypes and syndromes of effects.
The 5-fcl mutant is defective in an enzyme that recycles 5-formyltetrahydrofolate, which is implicated as an inhibitor of mitochondrial Ser hydroxymethyltransferase, a key enzyme in photorespiration (Goyer et al., 2005
In addition to these striking seed phenotypes, the 5-fcl mutant has previously unreported changes in leaf biochemistry and physiology. First, there are modest, but statistically significantly higher contents (in both mol % and nmol/g FW) of the unsaturated fatty acids cis-16:1 and 18:1d11 in total leaf lipids (Table VI; Supplemental Table S5). After high light treatment, all six 5-fcl mutant plants tested had lower maximum photochemical efficiency of PSII (Fv/Fm) than Col wild type (displayed in red in false-color image in Fig. 1W). The only other mutant to show this chlorophyll fluorescence phenotype is npq1-2 (Fig. 1X). This mutant was previously shown to be defective in NPQ due to an inability to convert violaxanthin to zeaxanthin under conditions of excessive light (Niyogi et al., 1998 Because of the central role of starch in chloroplast biochemistry, three previously characterized excess leaf starch mutants, sex1-1, sex4-5, and dpe2-1, were phenotypically analyzed, and each was found to have pleiotropic phenotypes. The accumulation of starch resulted in wrinkled chloroplasts in the leaf tips of each mutant (Fig. 1, C, D, and G), presumably due to excess amounts of starch stored in the chloroplast. In contrast, wrinkled petiole cell chloroplasts were only seen in sex1-1 mutant (Fig. 1, compare K to L and O). This is unlike the arc10 and arc12 mutants, which have dramatically altered leaf tip and petiole cell chloroplast morphology (Fig. 1, J and N).
An interesting example of phenotypic diversity was seen for leaf starch-excess mutants. Our iodine-staining assay indicates that mature and dried sex1-1 and sex4-5 seeds have excess starch in their seed coats (Fig. 1, R and S). In contrast, dried seeds of the leaf starch-excess mutants dpe2-1 and dpe2-2 did not stain positive with iodine solution (Fig. 2, cluster 10). In Ws wild-type Arabidopsis seeds, starch accumulates in the outer integument during the early stage of development, and is degraded later in development (Baud et al., 2002
A variety of other metabolic differences were seen in the three starch mutants, although the changes from wild type and from one another were small compared with the dramatic changes in C metabolism and chloroplast morphology. Although sex1-1 had altered seed C/N ratio (p < 0.001), the other two high-starch mutants were unaffected for seed C/N ratio. There were statistically significant differences in mol % levels of leaf and seed free amino acids in each of the three mutants compared with wild type, though in only two cases was the change 3-fold or more (Tables II–V
Two classes of mutants altered in amino acid homeostasis were chosen for this study. The first, originally found to have changes in metabolism of specific amino acids, is represented by the lkr-sdh and tha1-1 mutants, which are deficient in seed Lys catabolism (Zhu et al., 2001
The pig1-1 mutant was chosen for this study because it was found to have more global changes in amino acid homeostasis; it was reported to have abnormal levels of multiple free amino acids and an approximately 2-fold increase in total soluble amino acids in 2-week-old plate-grown seedlings (Voll et al., 2004
The tt7-3 mutant, deficient in flavonoid 3'-hydroxylase, represents another example of strong pleiotropy in seed phenotypes without dramatic effects in the leaf. It was originally included in the study because it has a subtle pale brown seed coat and smaller seeds than Col wild type. Surprisingly the seeds stain very dark purple-black with iodine solution, suggesting that the line may have excess seed coat starch (Fig. 1T). Consistent with their pleiotropic seed morphology and iodine staining, tt7-3 had statistically significant increases in nine amino acids (p < 0.001 for eight; Supplemental Table S3) and seed C/N ratio (p < 0.001; Table VII). These abnormalities are confined to the seed because tt7-3 has relatively normal leaf amino acid (Supplemental Table S1) and fatty acid (Supplemental Table S5) content. It is unclear whether the tt7-3 lesion is responsible for the pleiotropic phenotypes in this mutant because tt7-1 ecotype Landsberg erecta of Arabidopsis (Ler) seeds did not stain dark with iodine solution (Fig. 1U), and unstained seeds were rounder, lighter, and more evenly colored than tt7-3. Both lines have the expected mutations, and each should produce a protein truncated within the first half of the coding sequence, as previously published (Schoenbohm et al., 2000
Although examination of differences between individual mutants and the progenitor wild-type ecotype was useful in looking for specific phenotypes or syndromes of changes in the mutant, other approaches are necessary to reveal more complex relationships between genotype and phenotype inherent in the data set. Two general approaches were followed: clustering and principal component analysis (PCA; Quackenbush, 2001
A variety of data transformations and tests were performed to make meaningful comparisons between qualitative and quantitative phenotypes, as detailed in "Materials and Methods". For example, the controlled vocabulary text descriptions associated with individual morphological or qualitative traits were systematically coded into numerical form as summarized in Table VIII
. Before raw quantitative data from different flats of plants and assay plates were merged, O'Brien's test was conducted to confirm the homogeneity of variance across flats and plates (O'Brien, 1979
Classification of Mutants via Clustering Analysis and PCA
Hierarchical clustering analysis (HCA) was performed using Ward's minimum variance method to systematically analyze and visualize the full set of qualitative data and z-scores from the quantitative assays. As shown in Figure 2, this method resulted in 12 clusters and, in the vast majority of cases, biological replicates of each genotype clustered together. Notably, 29/32 Ws and 60/63 Col plants were in the same clusters, showing that the biological and process variations were substantially lower than the phenotypic differences between genotypes. The mutants clustered with or near the wild-type lines from which they were derived, indicating that the general clustering pattern was influenced by a suite of phenotypic traits, and was not simply caused by the strong outlier phenotypes associated with the mutations. For example, npq1-2, tha1-1, ats1-1, and tt7-3 clustered near Col wild-type lines whereas lkr-sdh, dpe2-1, and pig1-1 clustered near Ws wild-type lines (Fig. 2). The clustering of these mutants with their wild-type ecotypes extends the results described by Fiehn et al. (2000) The robustness of these clusters was tested in several ways. To study the impact of individual variables (i.e. phenotypes) on clustering, individual phenotypic variables were removed one by one, and the remaining data reclustered using HCA. Removal of most variables individually and reclustering with HCA did not dramatically alter the groupings (Y. Lu, unpublished data). The npq1-2 mutant was an exception, consistent with the hypothesis that decreased Fv/Fm after high light and NPQ are the only traits distinguishing it from Col. The three Col and three Ws wild-type plants that initially did not cluster with the majorities were sometimes relocated to a different cluster when one variable was removed (Y. Lu, unpublished data). This indicates that these unusually behaving wild-type samples (in clusters 1, 6, 11, and 12 of Fig. 2) were at cluster boundaries. To test the contribution of qualitative versus quantitative data to the discrimination of genotypes, HCA was performed after removing each full set of phenotypes individually. Removal of all the qualitative variables changed the groupings for half of the clusters, in ways not seen when individual traits were removed. The two subclusters containing large numbers of wild-type samples became less well differentiated from the arc and lkr-sdh knockout individuals. This emphasizes the importance of chloroplast morphology in creating the clusters containing these mutants. The npq1-2 subcluster also became unresolved from the Col cluster because of removal of the chlorophyll fluorescence phenotypes. Reclustering without the quantitative z-score data also changed the groupings for about half of the clusters, whereas six clusters did not change: tt7-3, arc10 and arc12, sex1-1 and sex4-5, fatb-ko, 5-fcl, and dpe2-1. Three clusters in Figure 2 had some substantial changes: Col wild type and npq1-2 (cluster 1), ats1-1 (cluster 3), and pig1-1 (cluster 12). Four ats1-1 plants, four pig1-1 plants, and one Ws wild-type plant became mixed with Col wild-type plants. Taken together, these results strongly reinforce the value of using a combination of qualitative and quantitative traits to detect phenotypic relationships and differences. To facilitate graphical interpretation of the differences and the similarities among the mutants and wild-type plants and to look for variables with significant impacts on clustering results, the same data set was analyzed by PCA. Eighty-one principal components were extracted and, as expected, clustering with the entire set of 81 principal components resulted in clusters identical to that shown in Figure 2. Although the first, second, and third principal components together explained only 35% of the variation within the entire data set (Fig. 3E ), the overall similarity of mutants in the same background to each other and to their isogenic wild type was well reflected in these dimensions (Fig. 3, A and B), consistent with the clustering results of HCA (Fig. 2). When plotting the dimensions of the first and second principal components or the first and third principal components, ats1-1, npq1-2, and tt7-3 clustered around Col wild-type plants whereas dpe2-1 and lkr-sdh mutants clustered around Ws wild-type plants (Fig. 3, A and B). Six of the 12 mutants formed distinct clusters in one or both of the graphs. Many variables have significant weightings in PCA (Fig. 3, C and D), indicating that the clustering of biological replicates of the same genotype is due to changes in many phenotypic traits, consistent with the results from HCA. The top 18 variables with significant weightings (>0.19 or <–0.19) include six leaf amino acids (Arg, Gly, Lys, Met, Tyr, and Val), seven seed amino acids (Gly, His, Leu, Phe, Ser, Trp, and Tyr), and five qualitative traits.
Correlations among Traits in Wild-Type Plants
Having a large set of phenotypic observations on multiple wild-type plant and seed samples permits the detection of minor phenotypic changes that are due to small differences in the physiological state of each plant. We took advantage of this biological variability to look for associations between the various phenotypic and morphological traits. The data set of 63 Col and 32 Ws samples assayed for the full set of 85 variables was analyzed by nonparametric Spearman's
To ask whether any of the identified correlations were due to the large differences in phenotypic patterns observed between the Col and Ws ecotypes (Figs. 2 and 3), the data set of 63 Col wild-type samples was analyzed separately by Spearman's correlation. Only 429 significant correlations (p < 0.05) were identified: approximately one-third as many as those identified in the dataset with both Col and Ws wild-type samples. Among the 364 correlations listed in Supplemental Table S6, 161 were still significant with the Col-only data set (|
The impact of using mol % on correlation analysis was investigated by merging z-scores calculated from nmol/g FW of amino acids and fatty acids with numeric codes from qualitative assays. Spearman's
To identify correlations reflecting intrinsic mechanisms of metabolic pathways, we sought strong and significant correlations (|
Fatty acids 16:0, 18:0, 18:1d9, and 18:2 showed strong positive correlation with each other (Table IX). This is consistent with our understanding that 16:0, 18:0, and 18:1d9 are consecutive intermediates in fatty acid biosynthesis and precursors to the most abundant fatty acid, 18:3 (Somerville et al., 2000 ). Specific lipid classes in leaf subcellular organelles have distinct fatty acid compositions. For example, 16:3 and trans-16:1d3 are almost exclusively found in plastidial galactolipids and phosphatidylglycerol whereas 18:0 and 18:2 are enriched in extraplastidial membrane lipids. Therefore, the two ratios (16:3+trans-16:1d3)/(18:0+18:2) and 16:3/18:2 provide a representation of the abundance of thylakoid and extraplastidial membrane fatty acids. Both ratios are negatively correlated with 16:0 and 18:1 and these correlations could be indicative of altered ratios of thylakoid to extraplastidial membranes across the sample set. Further work would be required to study the significance of these correlations.
The data in Table IX contain examples of metabolically related amino acids that show positive correlations using both nmol/g FW and mol % data (Coruzzi and Last, 2000
Of greater interest is the number of strongly correlated metabolites that are not known to share a direct biosynthetic origin. For example, the branched chain amino acid Leu is correlated with the aromatic amino acids Phe and Tyr in leaf, whereas seed Phe is correlated with Leu and Val. His, which is derived from the relatively unusual precursor 5-phosphoribosyl-1-pyrophosphate, shows correlation to a variety of biosynthetically unrelated amino acids in leaf (Leu, Lys, Tyr, and Val) and seed (Ile and Val).
To go beyond one-mutant-at-a-time analysis of complex biological processes requires systematic analysis of genomes and the networks that operate within complex organisms. This project had multiple goals aimed at enabling systematic analysis of Arabidopsis mutants. The first was to set up a relatively high-throughput plant growth and phenotypic assay process facilitated by a laboratory information management system. Second was evaluation of how well this pipeline could be used to identify mutants altered in a variety of phenotypes. A third goal was to explore the extent to which unknown mutant phenotypes could be discovered by parallel phenotypic analysis and to assess the level of pleiotropy in previously characterized mutants. Finally, we analyzed the large data set to look for correlations between phenotypes, both in mutant and wild-type plants.
Previously unknown phenotypes were detected by subjecting the mutants to a large number of phenotypic assays. The 5-fcl mutant is an example of a mutant with a far more complex phenotype than previously reported (Goyer et al., 2005
The theme of differences in leaf and seed phenotypes was seen in other mutants. The pig1-1 mutant was altered in 12 seed amino acids (six with very large changes) and had a >70% increase in total free seed amino acids (Supplemental Table S4), whereas leaf amino acid changes were fewer and smaller in magnitude (Supplemental Table S2). Although the tha1-1 mutant was found to have an increase in seed Cys levels not previously reported (due to use of an improved analytical assay; Gu et al., 2007
As this and other parallel multiphenotype data are accumulated for a larger set of mutants, it should be possible to discover emergent patterns associated with different classes of mutants. For example, our results show that all three starch-excess mutants tested have similar chloroplast abnormalities. Now that this is known, high-starch mutants could not only be found by screening directly for leaf or seed starch, but could also be identified by analysis of data from screens for changes in chloroplast morphology or leaf free amino acids. The fact that the detailed phenotypic patterns vary across mutants (in this case sex1, sex4, and dpe2) will also be very useful in detailed studies of gene function. For instance, assembly of such a data set for all high-starch mutants (or any other set of mutants of interest that have multiple phenotypes) would help place the gene products into pathways of action and may allow the deduction of functions for unknown genes (Messerli et al., 2007 Although strong pleiotropy was observed for some mutants, others showed remarkably restricted phenotypic changes. Despite impressive changes in chloroplast number and morphology (Fig. 1, B, F, J, and N), arc10 and arc12 mutants were wild type for all other phenotypes measured, including chlorophyll fluorescence and metabolite accumulation (Fig. 2, compare to Col and Ws, respectively). This indicates that Arabidopsis has a remarkable resilience to large changes in chloroplast morphology, and that the pleiotropy observed for starch-excess mutants is not the default condition when chloroplast function is impaired. Because such a large number of phenotypic traits were measured, we regard the small number of defined phenotypes for mutants such as arc10, arc12, lkr-sdh, npq1-2, and tha1-1 as noteworthy.
Inclusion of a large number of wild-type lines allowed evaluation of the variability of each assay and discovery of traits that covaried; 126 strong correlations were identified when Spearman's
The identified correlations allow the creation of hypotheses about regulatory and biosynthetic relationships that might exist between seemingly disparate metabolic pathways. One set of examples is the positive correlations between branched chain amino acids Leu and Val and aromatic amino acids Phe and Tyr. A plausible explanation is that the branched-chain amino acids are derived from pyruvate, whereas aromatic amino acid synthesis requires phosphoenolpyruvate. Recently published work indicates that phosphoenolpyruvate conversion to pyruvate by plastidial pyruvate kinase disrupts seed oil accumulation (Andre et al., 2007
This study demonstrates the strong utility of parallel phenotypic measurements on mutant and wild-type plants, and argues that this mode of mutant analysis has strong advantages over the traditional one-phenotype-at-a-time approach. The study benefited from participation of a large group of collaborators with complementary technical expertise in biology, chemistry, informatics, and statistics. This diverse know-how allowed us to create a robust experimental pipeline and to interpret the complex phenotypic results. Similar industrial scale mutant analysis approaches have been proposed and performed for gene discovery in industry and academia, reinforcing the general utility of this approach (Boyes et al., 2001 ; Fernie et al., 2004; Schauer et al., 2006).
For functional genomics to maximally impact systems biology will require extension of this idea to a larger germplasm (for instance, a broader set of sequence indexed insertion mutants or ethylmethanesulfonate (EMS) mutants, ecotypes, and recombinant inbred or introgression lines) and more diverse sets of phenotypic assays under a broader set of environmental conditions. Because of the clear value of creation of a vast phenotypic data set that would be of long-term utility (similar to GenBank for DNA sequence and AtGenExpress for gene expression; Schmid et al., 2005
Plant Materials and Growth Conditions Arabidopsis (Arabidopsis thaliana) mutants used in the study are summarized in Table I. Seeds were sown in 3.5-inch deep 2.5- x 2.5-inch pots in 1- x 2-foot flats (32 pots per flat) using Redi-earth plug and seedling mix (Hummert International) topped with a thin layer of vermiculite. One pot of each mutant, 12 pots of wild-type Col, and seven pots of wild-type Ws were randomly placed in each flat. Sown seeds were stratified at 4°C in the dark for 3 to 4 d before they were moved to the same controlled environment chamber at a 16-h light/8-h dark photoperiod. The first set of 96 pots was moved to the growth chamber on the third day and the last set on the fourth day to facilitate rapid harvesting of tissue. The irradiance was 100 µmol m–2 s–1 photosynthetic photon flux density (PPFD) using a mix of cool-white fluorescent and incandescent bulbs, the temperature was 21°C, and the relative humidity was set to 50%. After 7 d in the growth chamber, seedlings were thinned to one plant per pot. Seeds harvested from plants under the 16-/8-h photoperiod were used for seed assays and were sown for growth in a 12-/12-h photoperiod, under the same light conditions as for seed bulk-up. These plants were used for leaf assays when they were 4 to 5 weeks old. Full sets of assays were obtained for leaf and seed from 148 lines; these constitute samples in our analyses as described in "Results" and in "Materials and Methods" below. Plants for chlorophyll fluorescence analysis were grown separately, as described below. To maximize accuracy in data tracking, every seed stock, flat, pot, and sample container was bar-coded and the associations among them and the phenotypic data tracked in a relational database. Leaf samples for different assays were harvested in the following order: morning starch assay (for high-starch mutants), amino acid assay, fatty acid assay, afternoon starch assay (for low-starch phenotype), and chloroplast morphology.
Plant, chloroplast, and seed morphology were assessed using controlled vocabulary descriptions (detailed in Table VIII), and captured by photography (see Fig. 1 for examples), with both types of data stored in the database. Plants under the 16-/8-h photoperiod and the 12-/12-h photoperiod were photographed after 23 and 30 d in the growth chamber, respectively. Morphology data from plants grown under the 12-/12-h photoperiod were used in this study.
Chloroplast morphology was assessed by harvesting petioles and tips from mature expanded leaves at the beginning of the light period. Leaf tissues were fixed and macerated as previous described (Osteryoung et al., 1998 Seeds were visually inspected with a MZ12.5 high-performance stereomicroscope (Leica Microsystems), using a polarizing lens. Images were captured by computer using a SPOT Insight Color 3.2.0 digital camera and SPOT advanced imaging software (Diagnostic Instruments).
Leaf discs (5.5-mm diameter) were harvested from leaf numbers 8 and 9 (counting from the newest visible leaf) at the beginning of the light period and 8 h after the light period began, respectively. Leaf discs obtained with a number 2 cork borer were harvested into a chilled microtiter plate and stained with iodine solution as previously described (Yu et al., 2001 Aliquots of seeds were placed into a 96-well microtiter plate and stained with iodine solution with 0.67% (w/v) iodine and 3.33% (w/v) of potassium iodide using the same protocol as with leaf discs.
To prepare leaf samples for amino acid analysis, leaf number 7 (counting from the newest visible leaf) was harvested beginning 1 h after the light period started, weighed, placed into 2-mL microfuge tubes containing a single 3-mm stainless steel ball. Leaf samples were immediately frozen with dry ice and then ground frozen to a fine powder for 1 min on a S2200 paint shaker (Hero Products Group). Samples were suspended in 0.4 mL of extraction solution containing 1 µM of L-Phe- To prepare seed samples for the amino acid assay, approximately 7 mg aliquots of seeds were placed into a deep-well microplate (VWR International) containing a single 3-mm stainless steel ball in each well. The same extraction solution as used for leaf was added to the seeds and the samples were ground for 5 min on the paint shaker. Further processing of the seed samples was the same as described above for leaf samples except that the seed samples were centrifuged at 2,000g at 4°C for 50 min to remove insoluble materials prior to filtration.
Leaf and seed extracts were analyzed with HPLC coupled with tandem mass spectrometry as described (Gu et al., 2007
For measurement of leaf fatty acid contents, two leaves (numbers 5 and 6 counting from the newest visible leaf) were harvested from each plant beginning 5 h after the light period started. Fatty acids were transmethylated in 1 mL of 1 N methanolic HCl containing 5 µg/mL pentadecanoic acid (15:0) standard and 10 µg/mL butylated hydroxytoluene at 80°C for 30 min. One milliliter of 0.9% NaCl and 0.15 mL of heptane were added to the methylated samples. One microliter of the heptane phase were separated using a J & W DB-23 capillary column on an Agilent 6890 series gas chromatography system with a flame ionization detector (Agilent; Bonaventure et al., 2003
In Arabidopsis leaves, 16:3 mainly exists in plastidial lipids, such as monogalactosyldiacylglycerol, and trans-16:1d3 is exclusively present in plastidial phosphatidylglycerol, whereas 18:0 and 18:2 are more abundant in extraplastidial lipids, such as phosphatidylethanolamine (Bonaventure et al., 2003
Seeds harvested from plants grown under the 16-/8-h photoperiod were desiccated under vacuum for 48 h, weighed with an AP110 Analytical Plus balance (Ohaus), and packed into tin capsules (CE Elantech). Approximately 10 to 12 mg of desiccated seeds was analyzed by the Duke Environmental Stable Isotope Laboratory (http://www.biology.duke.edu/jackson/devil/). The C and N contents in the seeds were quantified by combusting the seeds at 1,200°C in an elemental analyzer in the presence of chemical catalysts. Seed C/N ratio was calculated to estimate the relative abundance of storage oil and storage protein in seeds because, in Arabidopsis seeds, approximately 90% of N is in protein and more than 50% of C exists in oil (Baud et al., 2002
Plants used for chlorophyll fluorescence assay were grown for 3 weeks in one flat of eight 13- x 13-cm subflats (12 pots per subflat, 1 plant per pot) so that each subflat could be analyzed with the MAXI version of the IMAGING-PAM M-Series chlorophyll fluorescence system (Heinz-Walz Instruments). The system was equipped with an AVT Dolphin camera (Allied Vision Technologies). The growth conditions were the same as those for leaf assays (see above). The plants were dark-adapted for 20 min before measurement. Maximum photochemical efficiency of PSII (Fv/Fm) before high light, and NPQ, i.e. (F°m – F'm)/F'm, were determined at the beginning of the light period according to Maxwell and Johnson (2000)
All statistical analyses were performed with JMP 6.0 statistical software (SAS Institute).
Before data from different assays were combined for analysis, morphological traits and qualitative traits were systematically coded into numeric form (Bucciarelli et al., 2006
Before raw quantitative data from different flats or plates were merged, O'Brien's test was conducted to test the homogeneity of variance across flats and plates (O'Brien, 1979
The Shapiro-Wilk test (Shapiro and Wilk, 1965
To minimize the problem of multiple comparisons involving a control, Dunnett's test, instead of the more commonly employed Student's t test, was used to compare means between the mutants and their corresponding Col or Ws wild type (Dunnett, 1955
To allow comparisons of results between plants grown in the microenvironments of different flats, quantitative data (mol % of fatty acids, mol % of amino acids, and C/N ratio) were converted to z-scores (Schmid et al., 2005 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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ABSTRACT
RESULTS
correlation. A total of 1,327 significant Spearman's correlations (p < 0.05) were identified, nearly equally divided between negative and positive correlations (Supplemental Fig. S1). Data from mutants were not included to avoid correlations influenced by phenotypes of outlier individuals. The 364 pairs of variables with correlation coefficient | 
-Aminobutyric acid (GABA), which is synthesized from Glu and thought to be involved in N-homeostasis, N-transport, and stress responses (Bouché and Fromm, 2004
,β,β,2,3,4,5,6-d8 (Phe-d8; Cambridge Isotope Laboratories) and 10 µM of 1,4-dithiothreitol (Roche Applied Science) in dH2O. The extracts were incubated at 85°C for 5 min and were centrifuged at 3,220g at 4°C for 5 min. The supernatant was transferred to a prewetted MultiScreen Solvinert filter plate (Millipore) and centrifuged at 2,000g at 4°C for 5 min to remove insoluble materials. Ninety microliters of filtrate were transferred to a 96-well plate and mixed with 10 µL of 9 µM of L-Val-2,3,4,4,4,5,5,5-d8 (Val-d8; Cambridge Isotope Laboratories). Phe-d8 and Val-d8 were used to normalize extraction and loading accuracy, respectively (Jander et al., 2004