- Research article
- Open Access
Gene coexpression clusters and putative regulatory elements underlying seed storage reserve accumulation in Arabidopsis
© Peng and Weselake; licensee BioMed Central Ltd. 2011
- Received: 24 August 2010
- Accepted: 2 June 2011
- Published: 2 June 2011
In Arabidopsis, a large number of genes involved in the accumulation of seed storage reserves during seed development have been characterized, but the relationship of gene expression and regulation underlying this physiological process remains poorly understood. A more holistic view of this molecular interplay will help in the further study of the regulatory mechanisms controlling seed storage compound accumulation.
We identified gene coexpression networks in the transcriptome of developing Arabidopsis (Arabidopsis thaliana) seeds from the globular to mature embryo stages by analyzing publicly accessible microarray datasets. Genes encoding the known enzymes in the fatty acid biosynthesis pathway were found in one coexpression subnetwork (or cluster), while genes encoding oleosins and seed storage proteins were identified in another subnetwork with a distinct expression profile. In the triacylglycerol assembly pathway, only the genes encoding diacylglycerol acyltransferase 1 (DGAT1) and a putative cytosolic "type 3" DGAT exhibited a similar expression pattern with genes encoding oleosins. We also detected a large number of putative cis-acting regulatory elements in the promoter regions of these genes, and promoter motifs for LEC1 (LEAFY COTYLEDON 1), DOF (DNA-binding-with-One-Finger), GATA, and MYB transcription factors (TF), as well as SORLIP5 (Sequences Over-Represented in Light-Induced Promoters 5), are overrepresented in the promoter regions of fatty acid biosynthetic genes. The conserved CCAAT motifs for B3-domain TFs and binding sites for bZIP (basic-leucine zipper) TFs are enriched in the promoters of genes encoding oleosins and seed storage proteins.
Genes involved in the accumulation of seed storage reserves are expressed in distinct patterns and regulated by different TFs. The gene coexpression clusters and putative regulatory elements presented here provide a useful resource for further experimental characterization of protein interactions and regulatory networks in this process.
- Seed Development
- Coexpression Network
- Fatty Acid Biosynthesis
- Seed Storage Protein
- Gene Coexpression Network
Seed storage reserves accumulated during embryogenesis in higher plants are crucial for plant propagation, providing carbon and energy during germination prior to seedling establishment. In mature Arabidopsis seeds, storage lipids and proteins are the major storage compounds, each accounting for 30% - 45% of the seed dry weight . The past decade has witnessed a substantial progress in identification and characterization of genes involved in the de novo fatty acid (FA) biosynthesis and triacylglycerol (TAG) assembly pathways [[1, 4] and references therein]. This advancement is particularly evident in the model plant Arabidopsis, largely owing to the sequencing and release of its relatively compact genome in the year 2000 . Moreover, characterization of transcription factors (TFs) has led to the identification of several master regulator genes that play critical regulatory roles in this biological process, including ABI3 (ABSCISIC ACID INSENSITIVE 3), LEC1 (LEAFY COTYLEDON 1), LEC2 and FUS3 (FUSCA 3) [6–17]. These TFs interact with each other and form complex regulatory networks [18–23], regulating multiple aspects of seed development including storage reserve accumulation through interaction with cognate cis-acting DNA elements in the promoter regions of target genes. ABI3, FUS3 and LEC2 contain a plant-specific 'B3' DNA-binding domain which targets RY-repeat regulatory elements, whereas LEC1 and L1L (LEC1-LIKE) contain a NF-YB domain binding to the CCAAT boxes in the promoter region [24, 25]. Additional TFs such as WRINKLED 1 (WRI1), a member of plant-specific APETALA 2 (AP2) - ethylene response element binding factor (EREB) family, is also known to control transcription of many FA biosynthetic genes , and recent studies show it acts via binding to the AW-box motif present in the promoter region of 19 FA biosynthetic genes . Moreover, ABI4 (an AP2 family protein) and various basic-leucine zipper (bZIP) TFs including ABI5 or EEL (ENHANCED EM [EMBRYO MORPHORGENESIS] LEVEL) are known regulators of the expression of SEED STORAGE PROTEIN (SSP) genes, which act in the same signalling network but downstream of ABI3 [28, 29]. Distinct regulatory mechanisms are present in controlling the accumulation processes of oils and proteins, perhaps with cross-talk to coordinate the synthesis of seed storage compounds. This coordination could help to explain the well-documented negative correlation (correlation coefficient ranging from -0.60 to -0.90) between oil and protein content in seeds of many oleaginous species [ and references therein]. Moreover, several TFs, such as LEC1, ABI3 and FUS3, have been demonstrated to regulate many genes in the synthesis of both oils and storage proteins in developing seeds [30–32].
In contrast to the great advancement in characterizing individual genes involved in the accumulation of seed storage reserves, the relationship of their expression and regulation is not well understood. A more holistic view of this biological process at the systems level would prove beneficial in developing strategies to further enhance seed yield and oil content, as well as in the modification of oil composition. To gain insights into global transcriptional dynamics in key cellular processes, microarray is an effective method for analyzing the transcript abundance of a large number of genes simultaneously. Datasets obtained from profiling experiments can be further used to infer gene regulatory networks. In Arabidopsis, two cDNA microarrays were designed several years ago based on the expressed sequence tag (EST) sequences available at the time. One array was used for tissue-specific expression profiling to identify genes that are preferentially expressed in developing seeds compared with vegetative leaves and roots , and the other was used to study the temporal pattern of gene expression during the critical period of seed filling . These transcriptional profiling studies in Arabidopsis seeds have greatly increased our understanding of overall alterations of gene expression during seed development and storage reserve accumulation. These two early cDNA-based microarrays, however, surveyed <3500 unique Arabidopsis genes.
More recently, Schmid et al.  created a global gene expression atlas AtGenExpress (Expression Atlas of Arabidopsis development) representing the Arabidopsis life cycle using the Arabidopsis ATH1 genome array (Affymetrix, Santa Clara, CA), which can measure nearly 24,000 genes in a single assay. In AtGenExpress, 237 chips were hybridized for 79 different samples collected from various organs, growth stages and under various environmental conditions, including 24 arrays for eight stages of maturing seeds. Since its release, this exceptionally large transcriptome dataset has been a goldmine for plant biologists to identify candidate genes for molecular characterization. A number of studies have further "mined" this dataset within different contexts of plant biology. Wang et al.  extracted the expression data for several TFs experimentally determined to regulate seed development and genes that code for enzymes in the FA biosynthesis pathway. Volodarsky et al.  utilized the dataset to analyze hormone-related transcriptional activities in Arabidopsis. Vandepoele et al.  constructed coexpression networks and predicted cis-regulatory elements for the cell cycle-related TF OBP1. Recently, the identification of gene coexpression networks has emerged as a popular method for predicting gene functions and interactions [38–41], and web-based tools such as Genevestigator  and CressExpress  have been developed to facilitate such analyses at a small scale for plant biologists. Transcriptional coordination, or coexpression, of genes may be an indication of functional relatedness, based on the "guilt-by-association" principle . In a coexpression network, a vertex or node represents a gene whereas an edge is a connection inferred from the correlation coefficient calculated from the gene expression data. Although the relationship between coexpression networks and true biological networks is often not clear, it has been shown that gene groups identified from modular (cluster) analysis in coexpression networks often exhibit an enrichment of certain Gene Ontology (GO) categories , suggesting the functional association of genes connected in a coexpression network. Hence, a coexpression edge can be considered a putative interaction between two genes. Genes in a coexpression network, particularly those expressed in a specific tissue or sharing a semantic similarity in the GO 'Biological Process' aspect, might be co-regulated through common TF binding sites in their upstream regions, leading to many attempts to identify overrepresented cis-motifs in coexpressed genes [46–50].
In the current study, we took advantage of this public transcriptome dataset in Arabidopsis , analyzed the raw data thoroughly in the context of seed storage reserve accumulation during seed development, and constructed coexpression networks for seed-expressed genes. We focused on genes involved in FA biosynthesis and the accumulation of storage lipids and proteins in developing seeds. This comprehensive analysis has resulted in the identification of a large number of genes that are possibly coexpressed and function cooperatively during seed maturation. Furthermore, we predicted a large number of cis-regulatory elements for key seed-expressed genes. This information could be useful in designing experiments to probe regulatory mechanisms underlying seed storage reserve accumulation.
Association of seed transcriptome with embryo morphology in developing Arabidopsis seeds
Arabidopsis developing seed samples used for AtGenExpress microarray experiments.
Seeds stage 3 with siliques
C globular stage
Mid globular to early heart
Seeds stage 4 with siliques
D bilateral stage
Early heart to late heart
Seeds stage 5 with siliques
D bilateral stage
Late heart to mid torpedo
Seeds stage 6 without siliques
E expanded cotyledon stage
Mid torpedo to late torpedo
Seeds stage 7 without siliques
E expanded cotyledon stage
Late torpedo to early walking-stick
Seeds stage 8 without siliques
E expanded cotyledon stage
Walking-stick to early curled cotyledons
Seeds stage 9 without siliques
F mature embryo stage
Curled cotyledons to early green cotyledons
Seeds stage 10 without siliques
F mature embryo stage
Construction of gene coexpression networks in the Arabidopsis seed transcriptome
Network characteristics in the Arabidopsis seed coexpression network.
Total number of genes in the network
Mean number of connections per gene
Median number of connections per gene
Clustering coefficient a
Scale-free topology criterion b
Genes encoding fatty acid biosynthetic genes and seed storage reserve associated proteins are located in different subnetworks
In summary, our new results suggest that genes acting in a biological process (FA biosynthesis) can be indicated by their presence in the same coexpression network cluster, but genes involved in the same pathway (TAG assembly) may not necessarily exhibit expression coherence. As a result, computational approaches using coexpression network to predict gene function, such as in , will undoubtedly have limitations.
Putative regulatory elements underlying seed storage reserve accumulation
To computationally identify cis-acting regulatory elements, the upstream promoter sequences for the genes involved in storage reserve biosynthesis were extracted from the RSAT server . We included some 5'-UTR sequences as certain TF binding sites can be located within this region of a gene [27, 74]. On average, the G-C content in the promoter sequences of the gene set was found to be <35%, which is consistent with the compositional bias of nucleotides towards A-T enrichment observed in plant promoter regions [74, 75]. Two software tools, TFBS  and fdrMotif , were used to search for putative TF-binding sites on both strands. Both tools depend on TF- binding profiles (Position Weight Matrix, or PWM) derived from experimentally determined binding sites for the prediction, we thus compiled 118 PWMs from the literature [27, 74] and the JASPAR database  (Additional File 4). In the JASPAR database, we only considered the binding profiles for plant-specific TFs because of their potential critical roles in regulating the accumulation of storage reserves during seed development, a unique physiological process in higher plants.
Overrepresented motifs identified in promoters of genes involved in fatty acid synthesis, and oleosin and seed storage protein accumulation.
For the genes and isoforms in the TAG assembly pathway, no overrepresented motifs have been found. Our goal was to identify putative promoter elements that can be used for experimental studies (Additional File 5). Interestingly, promoter motifs for B3 domain TFs, such as ABI3, FUS3 and LEC2, were found to be overrepresented in promoters of genes encoding oleosins and SSPs. Motifs for bZIP factors (e.g., bZIP67) also appeared to be overrepresented in the promoter regions of these genes, but there were no binding matrices for bZIP ABI5 or EEL.
Our approach of computational promoter analysis was limited by the availability of experimentally determined TF-binding sites for deriving binding profiles of additional TFs. We compiled a list of 118 binding matrices for this analysis, but if binding profiles for other TFs can be generated from a reasonable number of known binding sites, we could identify more TFs that possibly regulate the accumulation of seed storage reserves. In addition, we only considered upstream sequences of 1000 bp plus 200 bp 5'-UTR for each gene, because the majority of cis-acting regulatory elements are located in this region . Other genomic regions including the 3'-UTR, or even introns, however, can also harbour TF binding sites.
Our analyses indicate that genes involved in the accumulation of seed storage reserves, along with known TF genes, are expressed in distinct patterns during seed maturation. Promoter motifs for CCAAT binding factors LEC1 and L1L, DOF and GATA factors, AP2 WRI1 as well as MYB factors are enriched in the promoter regions of genes involved in FA biosynthesis. Binding sites for B3-domain factors (ABI3/VP1 TF family) and bZIP factors are overrepresented in the promoter regions of genes encoding oleosins and seed storage proteins. When binding profiles for additional TFs become available, more putative regulatory elements will be detected, which in turn can be validated for functionality.
Retrieval and processing of raw hybridization data
The 24 raw hybridization intensity data files (.CEL files) for Arabidopsis seed development were retrieved from The Arabidopsis Information Resource (TAIR) gene expression data repository (http://www.arabidopsis.org/servlets/TairObject?type=hyb_descr_collection&id=1006710873) . Microarray gene expression data analyses were performed using Bioconductor packages  in the open-source statistical R environment . The raw data files were imported into Bioconductor using the Simpleaffy package . The hybridization and RNA sample qualities were assessed using a number of quality control metrics (data not shown), and the raw data were background corrected, normalized and transformed to the log2 values using the GCRMA package . This normalization method is developed on another normalization approach robust multi-array average (RMA; ), and uses probe sequence information (G-C content) for estimating hybridization affinity. The number of genes expressed in seeds was filtered using a log2 value of 6.0 as the cutoff for the binary 'present' or 'absent' calls, and any gene with 'present' calls in less than three samples (corresponding to one seed development stage) was considered as "unexpressed" in these seed samples. After filtering, 12,353 genes expressed in at least one of the eight development stages in developing Arabidopsis seeds were used for subsequent high-level analyses. Custom Perl scripts were written to find the annotation of each gene in the latest CSV file ATH1-121501.na30.annot.csv (November 15 2009) released by Affymetrix for the ATH1 Genome Array and revised in some cases through sequence analysis using BLAST . For example, the TF gene WRINKLED1 (AT3G54320) was incorrectly annotated in the Affymetrix file as an aintegumaenta-like protein or ovule development protein aintegumenta (Additional File 1).
Principal component analysis and association test of global gene expression with seed development
The normalized, log2-transformed gene expression data were used for principal component analysis (PCA) using the R prcomp function . For this analysis, expression values of the three replicates for each seed development stage were not combined in order to assess the reproducibility of biological replication. Global testing of the transcriptome with a particular variable (e.g., seed development stage) was carried out using the Globaltest package . This package tests the overall gene expression in group(s) of genes for significant association with a given variable. The test gives one P-value for the whole group instead of one P-value for each gene to avoid the issue of multiple testing corrections.
Gene expression correlation analysis and construction of coexpression networks
For the inference of gene coexpression networks in the transcriptome of developing Arabidopsis seeds, we used the 12,353 genes expressed at moderate or high levels and used the Pearson-based correlation coefficient to measure their expression coherence. We first used the median expression data of the genes in the eight samples to compute pairwise correlation coefficients in the R statistical environment, resulting in a correlation matrix of 12353 × 12353. Then we removed self-pairing and duplication, and applied a correlation cutoff of 0.90, which retained over 1.7 million gene pairs representing 11,698 distinct genes for construction of the coexpression network for the Arabidopsis seed genes. This stringent correlation threshold was chosen to eliminate potential spurious correlations in a coexpression network. Network properties were determined using custom scripts. Coexpression networks are visualized using Cytoscape . For time-course clustering analysis, the gene expression values were standardized to have a mean value of zero and a standard deviation of one for each gene profile. This standardization of data ensures that genes with similar temporal profiles are close in Euclidean space during clustering, regardless of their absolute expression levels. The transformed expressions were then clustered using the fuzzy c-means (FCM) clustering algorithm in the Bioconductor Mfuzz package . We determined six clusters can well separate the expression patterns inherent in the dataset, and another FCM parameter m = 1.75, which allows for investigation of the clustering robustness. FCM assigns a membership value in the range of 0-1 for each gene as an indicator of how representative a gene profile is for a specific cluster, and profiles with different membership values were differently coloured.
Computational analyses of transcription factor binding sites
The genomic sequences 1000 bp upstream plus 200 bp 5' untranslated regions (UTR) for the genes involved in storage reserve biosynthesis were retrieved from the RSAT server . If the intergenic region with the upstream neighbouring gene is <1000 bp long, we only retrieved upstream sequence available in order to prevent using the 3'-end sequence of the adjacent gene in the upstream. Putative TF binding sites on both strands were identified with two software tools, TFBS  and fdrMotif . Briefly, the 118 TF binding profiles (position-specific weight matrix, or PWM) were compiled from the literature [27, 74] and the JASPAR database , and converted into a format suitable for each software tool (Additional File 4). In the TFBS search, an 80% similarity cutoff was adopted. In fdrMotif search, for each input sequence 10 background sequences were generated from a 4th-order Markov model and an upper boundary of false discovery rate (FDR) of 0.15 as suggested by fdrMotif was adopted to control FDR. Only putative binding sites predicted by both tools were retained for subsequent analysis. To ascertain the predictive performance, detected motifs were compared with curated motifs in AtcisDB and AGRIS databases [90, 91]. Sequence logos for the predicted motifs for a TF binding profile were created with WebLogo .
The authors are grateful for the financial support provided by Genome Canada, Genome Alberta, Alberta Advanced Education and Technology, and the Canada Research Chairs Program. We also thank three anonymous reviewers for their helpful comments and suggestions.
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