Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism
© Mentzen et al; licensee BioMed Central Ltd. 2008
Received: 11 March 2008
Accepted: 11 July 2008
Published: 11 July 2008
Elucidating metabolic network structures and functions in multicellular organisms is an emerging goal of functional genomics. We describe the co-expression network of three core metabolic processes in the genetic model plant Arabidopsis thaliana: fatty acid biosynthesis, starch metabolism and amino acid (leucine) catabolism.
These co-expression networks form modules populated by genes coding for enzymes that represent the reactions generally considered to define each pathway. However, the modules also incorporate a wider set of genes that encode transporters, cofactor biosynthetic enzymes, precursor-producing enzymes, and regulatory molecules. We tested experimentally the hypothesis that one of the genes tightly co-expressed with starch metabolism module, a putative kinase AtPERK10, will have a role in this process. Indeed, knockout lines of AtPERK10 have an altered starch accumulation. In addition, the co-expression data define a novel hierarchical transcript-level structure associated with catabolism, in which genes performing smaller, more specific tasks appear to be recruited into higher-order modules with a broader catabolic function.
Each of these core metabolic pathways is structured as a module of co-expressed transcripts that co-accumulate over a wide range of environmental and genetic perturbations and developmental stages, and represent an expanded set of macromolecules associated with the common task of supporting the functionality of each metabolic pathway. As experimentally demonstrated, co-expression analysis can provide a rich approach towards understanding gene function.
Biological systems are characterized by their capacity to achieve net metabolic inter-conversions while maintaining homeostasis in the face of environmental and developmental cues. This capacity is hard-wired into the genetic blueprint of an organism, and is manifested by the controlled expression of the genetic potential of the organism's genome as it responds to divergent signals and prompts. Mechanisms that control the expression of an organism's genetic potential include those that regulate gene transcription, RNA processing, stability and translation, and processing of polypeptides and their assembly into complexes. Some of these complexes are enzyme catalysts, whereas others are structural or regulatory. A metabolic network in its broadest sense can be defined as encompassing the collection of catalytic, structural and regulatory genes, which are expressed as mRNAs, proteins and metabolites that work in coordination to achieve net metabolic conversions.
Advances made over the last decade in the area of functional genomics have provided an increasing ability to globally profile genome expression at the level of RNAs, proteins and metabolites. These data define the transcriptome, proteome and metabolome, respectively. It is conceptually possible to identify metabolic networks from experimental data that reveal correlations in abundance of sub-group of molecules (mRNAs, proteins/protein complexes, or metabolites). Given that the behavior of an organism can be regulated by multiple mechanisms that impact the transcriptome, proteome and metabolome, it is significant to ask the extent to which the transcriptome can reveal metabolic networks. In the unicellular eukaryote Saccharomyces cerevisiae, which offers the advantage of cell populations that are homogenous and a relatively simple genome with only 6,608 genes (SGD project "Saccharomyces Genome Database", January 13, 2008 ), the operation of metabolic networks, including glycolysis and purine metabolism, has been revealed from transcriptome datasets alone [2–4]. But can such metabolic networks be detected in organisms with a larger, more complex genome, or in multicellular organisms, where evidence for metabolic networks can be swamped by noise associated with cellular differentiation?
The pathway-guided approach to examine the correspondence between the known metabolic pathway and the organization of metabolic processes learned from expression data is to select sets of genes for pathways and search for significant co-expression within each pathway. This method has been used to reveal transcriptional co-expression of genes belonging to the same metabolic pathway across different tissues in complex organisms, e.g., Krebs cycle enzymes in frog  or lactose biosynthesis enzymes in mouse . Another, module-guided, approach is to classify a set of genes according to their expression patterns, and study the correspondence of these clusters with identifiable biological processes. This approach was used to reveal 44 modules that correspond to several metabolic pathways in C. elegans, including lipid and amino acid biosynthesis .
In recent years, wealth of expression data emerged and several online repositories for microarray data have been created, including NASCArrays , Genevestigator , PLEXdb , MetaOmGraph , ArrayExpress , Vanted , Virtual Plant , ATTED-II , Arabidopsis Coexpression Data Mining Tool , MapMan , and PageMan . The availability of the data querying the transcriptome across the diverse conditions allows for the creation of the compendium of the biological responses to many perturbations, that may guide the interpretation of the microarray experiments and the annotation of the new genes [19–22]. Because it has become technically possible to profile the entire transcriptome of an organism (it is still a technical challenge to determine the proteome and metabolome), we tested the feasibility of using only transcriptomics data to reveal the operation of metabolic networks in Arabidopsis. A study by Gachon et al. has shown that genes for enzymes of indole, phenylpropanoid and flavonoid biosynthesis in Arabidopsis are co-expressed . Similar co-expression has been shown for enzymes for synthesis of cellulose and other cell wall components [24–26] and in isoprenoid biosynthesis pathways . An analysis of the transcription coordination of 1,330 Arabidopsis genes implicated in metabolic pathways in AraCyc revealed that the co-expression among genes from the same pathway is higher, than among genes from different pathways . Several metabolic processes could be also identified as coherent subnetworks in the recent analysis of Arabidopsis gene network, based on the compendium of the expression profiles .
Here, we focus on three core metabolic pathways of Arabidopsis: fatty acid biosynthesis, starch metabolism and leucine catabolism, and analyze their expression in the context of the 22,746 genes represented on the Affymetrix ATH1 chip. Our approach unites the pathway-guided and module-guided methods in that we group the pre-selected set of known and putative genes from pathways into modules, and subsequently assign the function to those putative candidates that are within the module. We exploit the assumption that a node in a network can be hypothesized to have a similar attribute (e.g., function, or location, if nodes represent genes) as the majority of the nodes in its neighborhood of some radius. This assumption has recently gained popularity as the way of assigning function to otherwise unknown genes [28, 30, 31]. The co-expression network created from among the genes from these pathways forms modules that include enzymes for reactions within each pathway, suggest extensions of catalytic steps in the pathway, and also allow for identification of transporters, cofactors and regulatory molecules associated with the module. Indeed, one of the genes identified in the starch module had been shown independently  to be involved in regulation of starch metabolism. We used knock out mutants to experimentally evaluate a second putative regulatory gene in the starch module.
Results and discussion
Fatty Acid Biosynthesis, Starch Metabolism and Leucine Catabolism form Comprehensive Modules
Starch is a complex molecule, whose metabolism comprises a network with many genes of known catalytic function but unclear physiological function [46, 47]. Indeed, several genes may function both in the catabolism and biosynthesis of starch (e.g., pullulanase ). At the 0.6 correlation level, the starch metabolism module contains 28 genes encoding all the known enzymes of starch metabolism. The module incorporates genes encoding proteins, for which, to date, there is no experimental evidence of involvement in starch metabolism, for example beta-amylase At2g32290 (BAM2), and three phosphoglucomutase-like proteins (At4g11570, PGM-like2; At1g70730, PGM-like3 and At1g70820, PGM-like4). On the other hand, alpha-glucosidase, an enzyme which historically had been considered to be involved in starch degradation, although recent experimental data from potato indicate otherwise , is not correlated with the Arabidopsis starch metabolic module (Fig. 2B). Although the processes of starch synthesis and degradation are temporarily separated over diurnal cycles in Arabidopsis leaves (reviewed in ; ), as is the transcription of many of the starch metabolic genes ([52, 53]; Foster and Wurtele, unpublished data), this is not reflected in the co-expression network of Arabidopsis; rather the two processes cluster together in a single module. Two factors in particular may contribute to starch synthesis and degradation transcripts being in a common module. First, Arabidopsis doesn't have a major starch-storage organ such as a tuber, thus an organ of Arabidopsis that is highly capable of starch synthesis may also be capable of a rapid shift to starch degradation, and the overall levels of all the relevant transcripts may be high. Indeed, this is the case for the diurnal fluctuations of the transcripts. Second, as several of the starch metabolic enzymes appear to function in both starch degradation and synthesis  (indeed the full extent to which this is true is not yet known), expression of such genes may be associated with both starch degradation and starch synthesis.
Modules are Identifiable in the Space of All Gene Transcripts
Genes from 22 k Arabidopsis ATH1 array that have the highest correlation across 965 chips with the hub genes.
Hub Gene: MCCB, At4g34030, methylcrotonyl-CoA carboxylase, subunit B, leucine catabolism
MCCA, methylcrotonyl-CoA carboxylase, subunit A
BCE2, E2 subunit of branched chain alpha-ketoacid dehydrogenase
DIN4, E1 beta subunit of branched-chain alpha-keto acid dehydrogenase
electron transfer flavoprotein: ubiquinone oxidoreductase family protein
IVD, isovaleryl-CoA dehydrogenase
Senescence-associated protein (SEN1)
AMP-activated protein kinase
BCAT2, branched-chain amino acid transaminase
nodulin MtN21 family protein
glutamine-dependent asparagine synthetase 1 (ASN1)
E1 alpha subunit of branched-chain alpha-keto acid dehydrogenase, BCDH_E1a2
ketose-bisphosphate aldolase class-II family protein
bZIP family transcription factor
BCDH BETA1, E1 beta subunit of branched-chain alpha-keto acid dehydrogenase
Hub Gene: DE2, At2g40840, disproportionating enzyme, starch metabolism
PHS2, starch phosphorylase
GWD1, glucan water dikinase
SEX4, protein tyrosine phosphatase/kinase
ISA2, isoamylase DB
ISA3, isoamylase DB
31.2 kDa small heat shock family protein/hsp20 family protein
GWD2, glucan water dikinase
PHS1, starch phosphorylase
subtilase family protein
acid phosphatase class B family protein
similar to endonuclease/exonuclease/phosphatase family protein
ATP synthase protein I-related
Hub Gene: MOD1, At2g05990, enoyl reductase, fatty acid biosynthesis
PDHC_E2, acetyltransferase subunit of pyruvate dehydrogenase
HD, 3-hydroxyacyl-[ACP] dehydratase
CAC2, biotin carboxylase subunit of heteromeric acetyl-CoA carboxylase
KASI, 3-ketoacyl-[ACP] synthase I
MAT, malonyl-CoA:ACP transacylase
LPD1, lipoamide dehydrogenase subunit of pyruvate dehydrogenase
KAS III, 3-ketoacyl-[ACP] synthase III
LTA2, acetyltransferase subunit of pyruvate dehydrogenase
BCCP1, biotin carboxyl carrier subunit of heteromeric acetyl-CoA carboxylase
LIP1-like, lipoate synthase
HD-like, 3-hydroxyacyl-[ACP] dehydratase
PDHC_E1beta2, E1 beta subunit of plastidic pyruvate dehydrogenase
BCCP2, biotin carboxyl carrier subunit of heteromeric acetyl-CoA carboxylase
PDHC_E1alpha, E1 alpha subunit of plastidic pyruvate dehydrogenase
pentatricopeptide (PPR) repeat-containing protein
Despite the complexities associated with a genome of about 32,000 genes (TAIR7 Genome Release, April 23, 2007 ), and the challenge associated with the fact that the RNAs used for the microarray analyses came from plant samples that mix metabolically distinct cellular and tissue compartments, it was still possible to find expression correlations among genes of common metabolic pathways. Specifically, the majority of the genes that show the highest correlation with the fatty acid biosynthesis hub gene, the starch metabolism hub-gene, and the leucine catabolism hub-gene are the structural genes that code for the enzymes required in each of these metabolic pathways (Table 1). Similarly, for every gene in the module, we found genes correlated with it (above the set threshold) among 22,746 genes on ATH1 array and checked how many of those correlated genes are also in the same module ("in" links) and how many are not in module ("out" links). We found that at 0.7 threshold, for 19 genes out of 22 in the fatty acid biosynthesis module, the number of "in" links is higher than the number of "out" links, and the same is true for 7 out of 8 leucine catabolism genes, and 9 out of 16 starch metabolism genes at the threshold of 0.8. Thus, these genes are preferentially interconnected among themselves with fewer connections to other genes, which defines them as a module in the context of the Arabidopsis genome.
Analysis of co-expression also provides means of querying the consequence of genome duplications. As with other plant genomes, Arabidopsis has undergone whole-genome duplications (polyploidization), followed by radical gene silencing, gene turnover, and expansion of gene families, the most recent of which is thought to have occurred about 25 million years ago [63, 64]. In our analysis, At5g52920, one of the fourteen Arabidopsis genes that code for pyruvate kinase, is highly correlated with the fatty acid biosynthesis hub-gene (Table 1). Pyruvate kinase catalyzes the conversion of phosphoenolpyruvate to pyruvate, the immediate precursor of the acetyl-CoA used for fatty acid biosynthesis . These data, therefore, help to constrain the complexity associated with the polyploidization, and indicate that of the fourteen pyruvate kinase genes that occur in the genome, At5g52920 is most likely the one predominantly utilized for fatty acid biosynthesis.
In addition, the co-expression of pyruvate dehydrogenase and pyruvate kinase with the fatty acid biosynthesis genes implies that the functional pathway commonly thought of as "fatty acid synthesis" (acetyl-CoA to fatty acids) could be expanded to begin with phosphoenolopyruvate (PEP), the substrate for pyruvate kinase (Fig. 4A).
Potential Regulatory Genes Identified as Coexpressing with Metabolic Pathways
A set of potential regulatory genes whose expression patterns highly correlate with each of the pathway-specific hub-gene was identified in this analysis (genes marked with an asterisk, Table 1). Although these correlations cannot be extrapolated to imply a causative role, identification of candidate regulatory genes delineates hypotheses and suggests experimental approaches for identifying the regulatory function of these genes.
Interestingly, the starch hub-gene shows a high negative correlation with two Rab-like, GTPase-coding genes (At5g47200; RAB1A and At4g17530; RAB1C; Fig. 6B). In eukaryotes, proteins from this family of small GTP-ases are regulatory and act as signaling molecules in diverse processes . The Arabidopsis protein that is phylogenetically the closest homolog to Rab-like GTPase is RAB1B (At1g02130), which is localized to the ER, and as in mammalian systems, it is important for Golgi apparatus development . RAB1A and RAB1C are predicted to be secretory and might have function similar to that of RAB1B, or some novel plant-specific regulatory function, which appears to be negatively correlated with starch metabolism.
The fatty acid synthesis module contains a pentatricopeptide repeat-containing protein (encoded by At5g50390). Because such proteins are thought to participate in modulating the stability of organelle transcripts , this correlation may indicate a role for this gene in coordinating nuclear-plastidic interactions for regulating fatty acid biosynthesis, via, for example, the control of the plastid-encoded subunit gene of acetyl-CoA carboxylase (accD, AtCg00500). This is the plastid-encoded biochemical function that is most directly involved in fatty acid biosynthesis.
The leucine catabolism module contains a putative transcriptional regulator, At5g49450, which may code for a bZIP family transcription factor.
Hierarchical Modularity of Catabolism
Hierarchical organization of modules has been observed for metabolic and transcriptional networks of E. coli [70–72] and metabolic networks of 42 other organisms [73, 74]. Hierarchical modular organization has been postulated as the topology explaining the observed features of biological networks, like scale-free character, high clustering coefficient and short path lengths [74, 75]. Such architecture may enable better adaptation of the metabolic fluxes to the changing conditions .
For Arabidopsis, the incorporation of leucine catabolism into an overarching catabolic module is observed both on the topological and functional level. From the topological point of view, genes participating in the leucine catabolism form a fully connected subnetwork, while they also exhibit weaker connection to the common larger set of genes. Thus, a smaller, more strongly connected module is nested within the bigger and less interconnected one. From the functional point of view, one function (leucine catabolism) forms a separable part of the larger, more encompassing biological process. The supermodule contains genes that appear to have a broader but common biological task of maintaining cellular energy balance via catabolism. Specifically, the 26 genes most highly correlated with the eight genes in the leucine catabolic module (defined in Fig. 2B) include genes that might be involved in protein turnover (At1g76410), amino acid catabolism (At1g06570, At1g08630, At2g14170, At3g47340, At5g63620, in addition to eight genes from leucine catabolism pathway), carbohydrate catabolism (At1g18270, At5g20250, At5g49360), lipid breakdown (At3g51840, At5g41080) and cell wall degradation (At4g30270). Five genes are annotated as associated with aging, senescence, or response to light or sucrose stimuli (At2g43400, At3g47340, At4g30270, At4g35770, At5g20250). Because plants are photoautotrophs, such catabolic processes may be expected to become critical when photosynthesis cannot maintain cellular energy balance, for example during senescence, seed germination, carbon deprivation or autophagy [37, 39, 42, 76]. Consistent with this concept, we find that the genes within this supermodule are co-induced to highest levels of expression under experimental conditions that are expected to limit photosynthetic carbon fixation and induce catabolic processes for maintaining energy balance (e.g., oxidative, cold, heat and drought stress – experiments from NASCArrays: NASCARRAYS-28, NASCARRAYS-79, NASCARRAYS-70; disruption of plastome-nucleome communication, NASCARRAYS-89; mutations and illumination conditions that affect the phytochrome response, NASCARRAYS-89, NASCARRAYS-124; sucrose starvation, Contento et al.  and NASCARRAYS-14; and hexose signaling, NASCARRAYS-40 ).
This catabolic supermodule also contains genes with well-defined or partially defined molecular functions, but undefined physiological functionality (e.g. At1g76410, zinc finger protein; At5g21170, protein kinase; At5g16340, AMP-binding protein). The inclusion of these genes within this catabolic supermodule may indicate their broader physiological function in catabolic processes; a hypothesis that can be experimentally tested by new genetic and biochemical analyses.
This study reveals that transcriptomics data can divulge hitherto unsuspected aspects of metabolic networks in a complex eukaryotic organism. Each of these core metabolic pathways that were targeted for analysis is structured as a co-expressed module whose transcripts co-accumulate over a wide range of environmental and genetic perturbations and developmental stages. These analyses further indicate that modules can be recruited into hierarchical organization (supermodules), hinting at the possibility that supermodules are regulated by common signals that coordinate net metabolic changes within highly interactive networks. The fact that such hierarchical organization is detected from the transcriptome data implies that at least a subset of the higher-order metabolic network may be regulated at the transcript level.
The experimental and meta-data was obtained from online microarray depositories: NASCArrays  and PLEXdb . The data obtained from NASCArrays, from 72 experiments comprising of 956 Affymetrix ATH1 microarray slides, had already been normalized with the Affymetrix MAS 5.0 algorithm to the common mean value of 100. We scaled the data from PLEXdb to set the mean to 100.
The reproducibility of experiments was qualitatively assessed on scatter plots by visual inspection and chips with poor biological replicates were discarded. The remaining biological replicates were averaged to yield 424 samples. Determination of biological replicates (replicates of the same treatment within the experiments) was done manually, based on the slide name and experiment information. Most slides have descriptive names that summarize treatment and the replicate status. Thus, slides that are biological replicates of the same treatment have the same slide name, differing only in "Repx" suffix, e.g.,
The choice of genes involved in each pathway was guided by: Arabidopsis Lipid Gene Database for fatty acid synthesis genes [80, 81]; Arabidopsis Starch Metabolism Network project  for starch metabolism; and original references for leucine catabolism [42, 83, 84]. We further considered each candidate gene based on original references.
Network of Coexpressed Genes
The values of Pearson correlations above 0.3 are statistically highly significant (p-value < 0.00001 after Bonferroni correction for multiple testing ). We additionally assessed the validity of correlations higher than 0.6 by performing 10,000 permutations of the corresponding data vectors. Correlations that had a permutation-based p-value < 0.0001 (in our case, all pairs of genes examined) were considered for further analysis [see Additional file 4]. The networks in Fig. 2 were constructed by placing an edge between any pair of genes correlated above the threshold. Modules in the network were defined by the grouping produced by the network visualization software.
Enrichment of Within-Pathway Edges in the Coexpression Network
To test whether there are more correlations between the genes from the same metabolic pathway (i.e. from fatty acid biosynthesis, starch metabolism, or leucine catabolism category) than could be expected by chance, we compared the co-expression network from experimental data (Fig. 2B) to 10,000 computationally generated random networks. In these random networks the total number of the edges and the number of neighbors for each node was set to be the same as in the original graph. A function for generating random graphs was implemented in R. It constructed graphs by random filling of the adjacency matrix, where nodes' degrees were constrained and self-loops not allowed. Indeed, the proportion of within-pathway edges is significantly higher in our coexpression network compared to such proportions in random graphs (245/444 versus a mean of 112/444, which is equal to the difference of ~20 standard deviations, Fig. 3).
Correlation with Hub Genes
The calculation of Pearson correlations between the three hub-genes (At2g40840, At4g34030, At2g05990) and all other genes on the chip (Table 1) was conducted in our publicly available software, MetaOmGraph . MetaOmGraph is able to handle large datasets with an efficient use of memory and is integrated with other publicly available bioinformatics tools in MetNet Exchange suit [86, 87].
The genes belonging to catabolic supermodule [see Fig. 8 and Additional file 3] were identified by intersection of lists of all genes that are correlated above 0.5 threshold with each of eight genes from leucine catabolism module.
Three independent alleles named Atperk10-1, Atperk10-2, and Atperk10-3 corresponding to T-DNA insertions at gene AtPERK10 locus were found in the mutant collections generated at INRA-Versailles [88, 89], GABI-KAT [90, 91] and SALK [92, 93]. The T-DNA insertions are located in 5'-UTR, exon 1 and intron 6, respectively.
Two individual plants (100–400 mg fresh weight) were boiled in 50 ml 80% (v/v) ethanol. The decolored plants were then ground in 80% ethanol with a 2 ml Konte tissue grinder, centrifuged for 10 min at 3,000 g at room temperature. The pellet was washed twice with 80% ethanol. After centrifugation, the insoluble material was suspended in 1 ml of distilled water and boiled for 30 min. Total starch was quantified using a commercial Glc assay kit (catalog no. E0207748; R-Biopharm, Darmstadt, Germany), according to instructions in the kit protocol. The assay employs amyloglucosidase to achieve complete digestion of the starch sample to Glc, and measurement of the Glc by application of a Glc oxidase reagent. The average of six biological replicates, using two individual plants for each replicate, was calculated.
The computations (normalization, correlation matrix, permutation-based p-values, random graphs generation, hypergeometric distribution) were conducted in R software . The RNA profiles were plotted in MetaOmGraph. The network of metabolic genes in Fig. 2 was visualized using GraphExplore . The pathway data is from MetNetDB database. The R code is available upon request.
Authors would like to express their gratitude to the Arabidopsis scientific community for making their microarray expression data available through NASC and PLEXdb databases. We thank D. Cook and D. Nettleton for assistance with normalizing the microarray data, MetNet group for advice and stimulating discussions and Susan Blauth for helpful discussion and initial characterization of the Atperk10-3 homozygous mutant. We are particularly grateful to A. Myers, L. Li, C. Foster and M. James for their very helpful expertise on starch metabolism.
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