Background and Aims

A key challenge in biology is to systematically investigate and integrate the different levels of information available at the global and single-cell level. Recent studies have elucidated spatiotemporal expression patterns of root cell types in Arabidopsis thaliana, and genome-wide quantification of polysome-associated mRNA levels, i.e. the translatome, has also been obtained for corresponding cell types. Translational control has been increasingly recognized as an important regulatory step in protein synthesis. The aim of this study was to investigate coupled transcription and translation by use of publicly available root datasets.

Methods

Using cell-type-specific datasets of the root transcriptome and translatome of arabidopsis, a systematic assessment was made of the degree of co-ordination and divergence between these two levels of cellular organization. The computational analysis considered correlation and variation of expression across cell types at both system levels, and also provided insights into the degree of co-regulatory relationships that are preserved between the two processes.

Key Results

The overall correlation of expression and translation levels of genes resemble an almost bimodal distribution (mean/median value of 0·08/0·12), with a second, less strongly pronounced ‘mode’ for negative Pearson's correlation coefficient values. The analysis conducted also confirms that previously identified key transcriptional activators of secondary cell wall development display highly conserved patterns of transcription and translation across the investigated cell types. Moreover, the biological processes that display conserved and divergent patterns based on the cell-type-specific expression and translation levels were identified.

Conclusions

In agreement with previous studies in animal cells, a large degree of uncoupling was found between the transcriptome and translatome. However, components and processes were also identified that are under co-ordinated transcriptional and translational control in plant root cells.

INTRODUCTION

Arabidopsis thaliana has become one of the most widely used plant model organisms in basic research, largely due to the availability of resources (Mochida and Shinozaki, 2010; Hamilton and Robin Buell, 2012). Recently, efforts have been made to monitor gene expression at the level of specific cell types and across different developmental stages in arabidopsis to obtain a deeper and systematic understanding of the underlying cellular processes (Edwards and Coruzzi, 1990; Birnbaum et al., 2005; Brandt, 2005; Shen-Orr et al., 2010; Wang and Jiao, 2011). These studies have resulted in the accumulation of distinct data types which provide a different, partly independent and complementary view of the whole genome. Nevertheless, to bring our understanding about plant biology to a systems level, integration of different types of data are needed (Sauer et al., 2007). However, integrating data from heterogeneous sources brings many challenges due to experimental, computational or statistical complexities. It is therefore also important to provide robust statistical and computational means to integrate the data (Joyce and Palsson, 2006; Zhang et al., 2010; Arakawa and Tomita, 2013).

The relationship between information elements (genes/transcripts) and functional elements (metabolites) has been studied by integrated transcriptomic and metabolomic analyses (Tohge et al., 2005; Hannah et al., 2010; Osorio et al., 2012; Brink-Jensen et al., 2013). In addition, there are numerous integrative studies of transcriptomic and proteomic datasets (Kleffmann et al., 2004; Baerenfaller et al., 2008; Baginsky et al., 2010; Vogel and Marcotte, 2012; Pan et al., 2012). However, these mass spectrometry-based approaches only reveal a limited coverage of the proteome and metabolome, i.e. allowing identification of a few hundred metabolites (Schauer et al., 2006) and a few thousand proteins in plants (Petricka et al., 2012). Despite the limited coverage of the proteome, recent combined transcriptomic and proteomic analyses have reported weak correlation of protein and mRNA abundances (Hack, 2004; Wang et al., 2010).

As an intermediate step in the flow of genetic information in a biological system, the level of translational control determines quantitative variation of the proteome together with protein degradation (Tebaldi et al., 2012). In particular, the composition of the translatome is based primarily on translation initiation, i.e. the loading of ribosomes on messenger ribonucleoprotein particles (mRNPs) to form polysomes, and secondarily on translation elongation (Tebaldi et al., 2012). Finally, correlation of levels of transcripts and polysomal-bound mRNA abundances allow inferences about gene activities and the conversion of its mRNA into a protein.

Stress-dependent coordination studies in yeast and mammalian cells integrating these two system levels have been undertaken (Halbeisen and Gerber, 2009; Tebaldi et al., 2012; Ventoso et al., 2012). While a relatively high degree of coordination of transcription and translation could be observed upon different conditions of cellular stress in yeast, divergent responses of transcriptome and translatome were found in mammalian cells. Interestingly, in yeast, harsh stresses which lead to an arrested cell growth display conserved transcriptional and translational responses, whereas relatively mild stresses also display a divergence of transcription and translation. In contrast, experiments with mammalian cells that are treated with a growth stimulus in general display predominantly divergence of responses between the two system levels.

Here, we performed a comparative study of cell-specific transcripts in plants. The relative simplicity of arabidopsis root anatomy and availability of cell-specific expression profiling data from developmental zones has made it appropriate for this study (Bevan and Walsh, 2005; Benfey et al., 2010). Arabidopsis also serves as a powerful model system for plant cell wall research, such as the identification of cell wall biosynthesis-related genes. Moreover, arabidopsis has also been extensively used to study the root cell wall biology and understand how cell walls are developmentally controlled in different cells (Milioni et al., 2002; Liepman et al., 2010). To complement these studies, we analysed two datasets, one transcriptome and the other of the translatome, of arabidopsis root cells. We elucidated the genome-wide correlation of cell-specific transcription and translation for the majority of genes in the arabidopsis genome. We present evidence for translational prioritization of transcripts of cell-wall-related gene families and root-related biological processes.

MATERIALS AND METHODS

Arabidopsis root transcriptome and translatome gene expression datasets

In this study, two microarray datasets of A. thaliana root samples were employed characterizing gene expression and translation levels of tissues and cell types. While the first dataset comprises a discrete global map of total mRNA levels, i.e. the transcriptome (Birnbaum et al., 2005; Brady et al., 2007), the latter measured polysome-associated mRNAs, i.e. the translatome (Mustroph et al., 2009), in a variety of cell types and developmental stages of the root.

The two datasets were generated using different experimental protocols and originate from two different laboratories. For the transcriptome dataset, fluorescence activated cell sorting (FACS; Iyer-Pascuzzi and Benfey, 2010) of arabidopsis radial sections of root samples under the control of cell-type-specific promoters was used to profile transcript levels. Affymetrix ATH1 microarrays were used as the platform for gene expression profiling. Genome-wide transcriptomic profiles of eight green fluorescent protein (GFP)-marked cell populations with 2–3 replicates each were obtained, that in combination with a previous study of 11 microarray expression experiments yielded a transcriptomic expression atlas. The expression atlas profiles the expression of 14 non-overlapping arabidopsis root cell types targeted by 19 promoters (Birnbaum et al., 2003; Brady et al., 2007). For the transcriptome data available from Birnbaum et al. (2003), cell-type and tissue-specific expression was obtained by protoplasting of plant roots expressing GFP in specific cell types. The raw data [accession number GSE ID 8934 available via Gene Expression Omnibus (GEO; Barrett and Edgar, 2006)] from the radial root sections were downloaded in the form of CEL files for further analysis.

The translatome dataset was obtained by the immunopurification of ribosome-associated/loaded transcripts from arabidopsis root cells and the immunopurified mRNAs were hybridized to the Affymetrix ATH1 microarray platform. In this study, the immunopurification was extended by using developmentally regulated promoters to drive the expression of FLAG-tagged RPL18 lines allowing the generation of 21 cell-specific populations in root and shoot (Mustroph et al., 2009). While the complete translatome data additionally include a stress condition (hypoxia), for the following computational analysis, raw data with an accession number GSE ID 14502 comprising only root control samples were used. An overview of all promoters used in the transcriptome and translatome data together with their intended tissue specificity can be found in Supplementary Data Table S1.

Furthermore, to avoid artefacts arising from different normalization techniques, the raw data from these two datasets were pre-processed using the same normalization strategy. Here, the robust multichip average (RMA) method was used to conduct pre-processing, e.g. removal of background noise and quality control, subsequent probe summarization and adjustment by quantile normalization (Irizarry et al., 2003). Applying the same normalization strategy, both datasets were jointly and independently normalized to study the influence of the normalization to the final results.

Lastly, given the availability of 19 promoters in the transcriptome and 10 different promoters in the translatome dataset (see Table S1), a common set of identical cell types corresponding to identical promoters (see Supplementary Data Promoter Sequences for the nucleotide sequences of the promoters) in both datasets – namely the phloem companion cells, root vasculature, quiescent centre, cortex and non-hair cells/root atrichoblast epidermis – were identified. Moreover, because in some cases different promoters were used to drive gene expression of the same cell type on the two system levels, a mapping of promoter and target cell type was conducted using a literature survey (cf. Results).

Analysis of similarity of cell-specific mRNA levels on the level of transcriptome and translatome in arabidopsis root cells

Genome-scale system-level comparisons between the transcriptome and translatome were conducted by quantifying the similarity of expression (total mRNA) and translation (polysome-associated mRNA) levels of genes across the same set of cell types. Here, Pearson's correlation coefficient (PCC) was used to assess this similarity (Stigler, 1989). The statistical significance of observed PCC values was further assessed by creating 1000 bootstrapped datasets of the transcriptome and the translatome, respectively. Using the available data for all 19 and ten promoters for transcriptome and translatome as background, a bootstrapped dataset of equal size was randomly selected without replacement. As each bootstrapped group comprises a random mixture of cell types, any variations in deriving PCCs would exist mainly as a result of cell-type-specific expression rather than differences in translatome and transcriptome expression levels, thus resembling an adequate null model. For each of these bootstrapped data, and for all genes, PCC of translatome and transcriptome was calculated. Z-scores were calculated for each observed PCC value by subtracting the mean and dividing by the standard deviation of the corresponding gene's PCC value obtained from the bootstrapping analysis (Fig. 1). Those genes which exhibit a high positive PCC value that corresponds to a Z-score ≥1·96 comprise the set of genes that exhibit a high degree of correlation between transcription and translation. Likewise, PCCs of high negative value, i.e. a corresponding Z-score of ≤−1·96, correspond to genes that display a high degree of uncoupling of cell-specific transcription and translation. Note that an absolute Z-score of 1·96 corresponds to a statistical significance level of 5 % in the case of a two-tailed test (Sokal and Rohlf, 1995). Additionally, following suggestions of Huttenhower et al. (2006), PCC values were adjusted using Fisher transformation, resulting in normal distributions of PCC values irrespective of the dataset analysed, further allowing for cross-dataset comparisons.

Fig. 1.

Analysis of similarity of cell-specific mRNA levels on the level of transcriptome and translatome in arabidopsis root cells using identical and common cell types for both datasets. The Pearson correlation coefficient (PCC) between expression and translation levels for each gene was computed. Eight promoters were identified that drive gene expression in common cell types in both datasets. By comparing the observed PCC value of each gene with PCC values obtained from bootstrapped data, Z-scores were computed for each of the corresponding genes.

Fig. 1.

Analysis of similarity of cell-specific mRNA levels on the level of transcriptome and translatome in arabidopsis root cells using identical and common cell types for both datasets. The Pearson correlation coefficient (PCC) between expression and translation levels for each gene was computed. Eight promoters were identified that drive gene expression in common cell types in both datasets. By comparing the observed PCC value of each gene with PCC values obtained from bootstrapped data, Z-scores were computed for each of the corresponding genes.

Finally, a characterization of the biological processes of these genes was conducted by gene set enrichment analysis (GSEA; Subramanian et al., 2005). Here, the gene ontology (GO) was used to obtain functional gene annotation used for GSEA (Ashburner et al., 2000). Specifically, the sub-ontology Biological Process (GO-BP) was used to derive overrepresented GO terms and further pre-processed following the considerations given by Klie and Nikoloski (2012). For statistical testing, the hypergeometric distribution was used to test for the probability that a specific set of genes is annotated with the same GO term by considering the background distribution of GO terms (Rivals et al., 2007).

Identification of altered regulation of gene expression on both system levels

In addition to the analysis of cell-type-specific gene expression/translation similarity by means of PCC, the genome-wide co-expression structure for each gene was investigated by deriving co-expression networks (Butte and Kohane, 2003). Specifically, both a co-expression and a co-translation network were constructed based on the common cell-type-specific transcriptome and translatome data to further identify the co-expression/co-translation relationships of genes which differ between the two networks. In the co-expression and co-translation networks, nodes correspond to genes and edges (connections) are present between any two nodes that are significantly connected resulting in a fully connected network (Chartrand, 1985). As a consequence, the similarity of a gene's neighbourhood within the co-expression and co-translation in the network reflects the extent to which expression and translation relationships of groups of genes are coupled. Accordingly, changed network topology suggests altered regulation or regulatory uncoupling of co-expression and co-translation relationships.

All edges were weighted, where the weight of an edge adjacent to two nodes/genes corresponds to the value of the PCC between the corresponding mRNA levels of both genes. For the co-expression network this weight is defined by the PCC of expression levels; likewise, in the co-translation network, this weight is defined by the PCC of translation levels in the cell types of interest. Furthermore, the concept of expression conservation (EC) was used to assess the similarity of co-expression relationships for all genes contrasting the translatome and transcriptome networks (Dutilh et al., 2006). Both networks can be represented by an adjacency matrix that can then be compared. Technically, this procedure simplifies the computation of the PCC of the same row corresponding to a gene in both matrices (cf. Fig. 2).

Fig. 2.

Co-expression/co-translation networks for five cell types using the common eight promoters. Two fully connected co-expression/co-translation networks were generated separately using the transcriptome and translatome datasets. An expression conservation score (EC score) was then defined as the PCC between the co-expression and co-translation networks. Further, the same analysis was performed on a set of 1000 bootstrapped co-expression/co-translation networks generated from bootstrapped datasets. Rewired genes were identified using Z-scores.

Fig. 2.

Co-expression/co-translation networks for five cell types using the common eight promoters. Two fully connected co-expression/co-translation networks were generated separately using the transcriptome and translatome datasets. An expression conservation score (EC score) was then defined as the PCC between the co-expression and co-translation networks. Further, the same analysis was performed on a set of 1000 bootstrapped co-expression/co-translation networks generated from bootstrapped datasets. Rewired genes were identified using Z-scores.

The statistical significance of the difference of a gene's EC score was assessed by creating a series of n = 1000 random co-expression networks for translatome and transcriptome by selecting two random equal-sized sets of cell-type-specific translatome and transcriptome data. Based on this procedure, an empirical null distribution of random EC scores, genes that exhibit a statistically significant low EC score can be derived by calculation of Z-scores: genes with a positive EC score and a Z-score ≥1·96 comprise genes with highly conserved co-expression relationships. Correspondingly, genes with a low EC score (matching to Z-scores ≤–1·96) exhibit low or no conservation of co-expression/co-translation. Again, to characterize processes overrepresented within those genes with low and high EC scores, a subsequent characterization of biological processes, GO-BP by GSEA, was conducted.

To investigate co-expression and co-translation relationships on a global scale, we computed the correlation of the adjacency matrices of both networks. The similarity of the co-expression and co-translation network is defined by their topology: as the set of nodes, i.e. genes, is the same, we compared the distribution of edges and edge-weights. As with PCC, the value of this full matrix (or network) correlation falls in the interval [–1,1] (Swanson-Wagner et al., 2012). Again, the statistical significance of the observed value of the full matrix correlation was assessed by selecting random cell-type expression and translation data to generate 1000 pairs of networks. Subsequently, the observed similarities were then compared with values obtained from these bootstrapped networks.

Analysis of cell type specificity of gene expression in transcriptome and translatome of arabidopsis root cells

Differentially expressed/translated genes (for simplicity jointly referred to as DE genes) displaying statistically significant mean differences in expression levels across the common cell types were identified by performing an analysis of variance (ANOVA; Kerr et al., 2000). The ANOVA was performed independently for the transcriptome and translatome data. Moreover, while an ANOVA identifies genes exhibiting significant mean expression differences across all cell types, post-hoc tests, such as Tukey's honest significant difference (HSD) were applied to further derive statistical significance of pairwise mean differences (Tukey and Braun, 1994). In this study, a series of Tukey's HSD tests were performed for those genes determined to be differentially expressed by ANOVA after a Benjamini and Hochberg false discovery rate (FDR) correction (Benjamini and Hochberg, 1995). The significance level was set to 5 % for both ANOVA and Tukey's HSD.

To summarize the pairwise differences of cell type expression levels on translatome and transcriptome, cell type similarity networks were constructed for each gene. In this cell type similarity network, nodes correspond to the respective cell type of the arabidopsis root. An edge between two nodes indicates no significant mean difference of expression values of the investigated gene. Accordingly, the absence of an edge indicates a significant difference and thus dissimilarity of the adjacent nodes. Given a certain number, n, of cell types, a total of m = n × (n – 1)/2 edges can be placed between nodes resulting in 2m possible network topologies or configurations, which are defined as network motifs (Milo et al., 2002). Furthermore, the statistical significance of a particular number of occurrences, i.e. the number of genes that coincide with any particular network motif, was assessed empirically by permutating the data obtained for pairwise cell type mean expression differences (i.e. the results of the Tukey HSD tests) for all DE genes.

All genes identified at any step of the analysis are available in the Supplementary Data Gene List.

RESULTS

Promoter/cell type mapping and normalization of common transcriptome/translatome datasets

In this study, we attempted to compare and assess the relationship between cell-type-specific transcriptome and translatome data of arabidopsis roots. In particular, we were interested in testing to what degree gene expression and translation patterns were conserved in these samples. The obtained raw data were jointly pre-processed and normalized using RMA (Supplementary Data Fig. S1) to allow comparisons between the gene expression and translation datasets (Irizarry et al., 2003). For the transcriptome datasets, data from Birnbaum et al. (2005) and Brady et al. (2007) were used. Here, 19 cell- or tissue-type-specific root promoters driving GFP had been used in combination with cell sorting to obtain a transcriptome map of root cells. For the translatome datasets, data from Mustroph et al. (2009) were used. In this study, ten cell- or tissue-type-specific root promoters driving a FLAG-tagged RPL18 to achieve a translatome map of the root cells (the full list of promoters is available in Supplementary Data Table S1; see Material and Methods for further information). First, we considered the effect of separate RMA normalization of the datasets. We observed only slight differences in the distributions of probe log-intensities over all microarrays belonging either to the transcriptome or to the translatome datasets (Supplementary Data Fig. S2) indicating comparable average signal intensities between datasets and limiting the possibility of technical bias between the two datasets. We also affirmed high reproducibility of the biological replicates (correlation between replicates of 0·96 ± 0·03 in the transcriptome and 0·98 ± 0·01 in the translatome; Supplementary Data Fig. S3).

Four identical promoters had been used to obtain the transcriptome and translatome data (Birnbaum et al., 2003; Brady et al., 2007; Mustroph et al., 2009) and these therefore served as a first platform for our study (Table 1). Additionally, eight different promoters that target the same five cell types were also used in these two studies (Table 1). For example, WOL and SHR promoters are both indicated as vasculature-related; however, it is clear that the activity of these promoters may not exactly overlap. Nevertheless, these related promoters served as a second platform for our study. Hence, two scenarios were considered: (1) only data from the four identical promoter sets were used in comparisons (referred to as ‘identical’), and (2) combined data from the four identical promoters and the eight promoter sets that presumably target the same cell types were used in comparisons (referred to as ‘common’). Therefore, the ‘identical’ and ‘common’ datasets target four and five different cell types, respectively.

Table 1.

List of promoters and cell types common to the transcriptome and the translatome datasets

Cell type Transcriptome Translatome References 
Phloem companion cells  SUC2 (At1g22710), APL SUC2 (At1g22710), SULTR2 (Nawy et al., 2005; Lee et al., 2006; Brady et al., 2007; Mustroph et al., 2009
Root vasculature WOL (At2g01830) WOL (At2g01830), SHR (Brady et al., 2007; Mustroph et al., 2009
Quiescent centre AGL42, J0571, SCR (At3g54220) SCR (At3g54220) (Birnbaum et al., 2003; Brady et al., 2007; Mustroph et al., 2009
Cortex CORTEX CO2, PEP (based on whether it is meristamatic, elongation or maturation zones) (Lee et al., 2006; Brady et al., 2007; Mustroph et al., 2009
Non-hair cells/root atrichoblast epidermis GL2 (At1g79840) GL2 (At1g79840) (Brady et al., 2007; Mustroph et al., 2009
Cell type Transcriptome Translatome References 
Phloem companion cells  SUC2 (At1g22710), APL SUC2 (At1g22710), SULTR2 (Nawy et al., 2005; Lee et al., 2006; Brady et al., 2007; Mustroph et al., 2009
Root vasculature WOL (At2g01830) WOL (At2g01830), SHR (Brady et al., 2007; Mustroph et al., 2009
Quiescent centre AGL42, J0571, SCR (At3g54220) SCR (At3g54220) (Birnbaum et al., 2003; Brady et al., 2007; Mustroph et al., 2009
Cortex CORTEX CO2, PEP (based on whether it is meristamatic, elongation or maturation zones) (Lee et al., 2006; Brady et al., 2007; Mustroph et al., 2009
Non-hair cells/root atrichoblast epidermis GL2 (At1g79840) GL2 (At1g79840) (Brady et al., 2007; Mustroph et al., 2009

Identical promoters in both datasets are underlined. The promoters used in the analysis include SUC2 (Sucrose transporter 2), APL (Altered phloem development), SULTR2 (Sulfate transporter), WOL (Woodenleg), SHR (Shortroot), AGL42 (Agamous-like 42), JO571 (J0571), SCR (Scarecrow), CORTEX (Cortex), CO2 (Cortex specific transcript), PEP (Endopeptidase), and GL2 (Glabra2). In addition, the genomic coordinates of the identical promoters are also specified.

Transcription and translation of cell-wall-related genes are highly correlated

We investigated the variability in total or polysome-associated mRNA levels for any given gene to test how expression or translation patterns change across cell types. This is derived by employing the coefficient of variation (CV). Figure 3 shows the distribution of CVs for all genes (22 810 on the ATH1 platform) for the ‘identical’, and ‘common’, cell types of the translatome and transcriptome data. The transcriptome varies more (CV mean value: 0·066) than the translatome (CV mean value: 0·036) across the ‘identical’ promoter datasets. The ‘common’ promoter datasets revealed a similar scenario (CV mean value transcriptome: 0·072, translatome: 0·043).

Fig. 3.

Coefficient of variation (CV) of ribosome-associated (translatome) and total mRNA (transcriptome) for the identical (left) and common dataset (right). The distribution of obtained CV values for all 22 810 genes is visualized using kernel density estimates. In the identical dataset (n = 22 810, bandwidth = 0·002265), the mean CV value is 0·066 and 0·036 for the transcriptome and translatome, respectively. In the common dataset (n = 22 810, bandwidth = 0·002568), the mean CV value is 0·043 and 0·0072 for the transcriptome and translatome, respectively. In both comparisons, the translatome displays a smaller degree of variation in cell type expression levels.

Fig. 3.

Coefficient of variation (CV) of ribosome-associated (translatome) and total mRNA (transcriptome) for the identical (left) and common dataset (right). The distribution of obtained CV values for all 22 810 genes is visualized using kernel density estimates. In the identical dataset (n = 22 810, bandwidth = 0·002265), the mean CV value is 0·066 and 0·036 for the transcriptome and translatome, respectively. In the common dataset (n = 22 810, bandwidth = 0·002568), the mean CV value is 0·043 and 0·0072 for the transcriptome and translatome, respectively. In both comparisons, the translatome displays a smaller degree of variation in cell type expression levels.

To examine how similar a given gene's expression and translation patterns are across the different cell types we used PCC. Figure 4 shows the PCCs between translatome and transcriptome for all genes across the ‘identical’ and ‘common’ datasets, respectively. In the case of the ‘identical’ promoter dataset, the distribution of PCCs is best characterized by an almost uniform distribution, with a slightly higher frequency of positive PCC values (mean/median: 0·08/0·12; Fig. 4). When using the ‘common’ promoter dataset the distribution of observed gene-wise PCCs resembles a normal distribution (mean = median: 0·04) in which extreme absolute values of PCCs are less common (Fig. 4).

Fig. 4.

Pearson correlation coefficient (PCC) between ribosome-associated (translatome) and total mRNA (transcriptome) levels of the identical (red) and common promoter dataset (blue). The distribution of obtained PCC values for all 22 810 genes is visualized using kernel density estimates. In the identical dataset, the PCC distribution is characterized by an almost uniform shape and has a higher frequency of positive PCC values. In the common dataset, the PCC distribution resembles a normal distribution.

Fig. 4.

Pearson correlation coefficient (PCC) between ribosome-associated (translatome) and total mRNA (transcriptome) levels of the identical (red) and common promoter dataset (blue). The distribution of obtained PCC values for all 22 810 genes is visualized using kernel density estimates. In the identical dataset, the PCC distribution is characterized by an almost uniform shape and has a higher frequency of positive PCC values. In the common dataset, the PCC distribution resembles a normal distribution.

To estimate whether the observed PCC for a gene, i.e. correlation of its expression and translation, is higher or lower then what may be observed by chance, bootstrapping was employed. Here, we re-computed PCCs using 1000 randomized datasets. Next, the observed PCC values for each gene were compared with an empirical null distribution derived from the randomized bootstrapping analysis. This null distribution of PCCs was derived by performing a bootstrap procedure randomly selecting four (for the ‘identical’ analysis corresponding to four cell types) or eight (for the ‘common’ analysis corresponding to five cell types) promoters from the transcriptome and translatome dataset (in total 19 promoters and ten promoters, respectively, see Supplementary Data Table S1). By computing Z-scores, the strength of the observed PCC value can be compared to what is randomly expected. In theory, genes with high positive or negative PCC values should therefore display high absolute Z-scores. Finally, based on PCC and Z-score, each gene can be classified into one of two groups: genes with coupled total and polysome-associated mRNA levels (high PCC and Z-score of ≥1·96) or genes in which the mRNA levels are uncoupled (low PCC and Z-score of ≤–1·96).

A subsequent enrichment analysis of GO-BP terms allowed us to estimate if certain processes were enriched in either of the two groups of genes. Supplementary Data Tables S2 and S3 list enriched GO-BP terms found for genes with strong positive and negative PCC values, respectively. We found that 494 and 373 genes displayed uncoupled expression and translation for the ‘identical’ and ‘common’ promoters, respectively. These genes were enriched for GO-BP terms related to cell growth, root and meristem development, protein glycosylation and cytoskeletal organization (Supplementary Data Tables S2 and S3). However, it is important to remember that the two datasets, i.e. the transcript and translation datasets, were generated in two different labs with two different techniques, and it is therefore possible that some of the uncorrelated processes are due to these differences.

We found that 851 genes and 790 genes for the ‘identical’ and ‘common’ promoters displayed coupled expression and translation, respectively. GO-BP enrichment analyses showed that these genes are associated with regulation of transcription, post-translational modification (protein phosphorylation), and responses to various biotic and abiotic stimuli/stresses (Supplementary Data Tables S2 and S3). Moreover, we found that genes associated with cell-wall-related processes and root tissue formation processes were common. This comprises GO-BP terms such as cell wall modification, secondary cell wall biogenesis, xylan biosynthetic process, xylem and phloem pattern formation, and meristem initiation. These data are in agreement with various co-expression approaches that have been undertaken for secondary wall synthesis, i.e. many secondary wall genes are transcriptionally and functionally coordinated (Persson et al., 2005).

Co-expressed relationships at the transcriptional level are generally not preserved at the translational level

So far, our analysis has focused on quantifying the degree of similarity in expression and translation for individual genes across different cell types. However, one could also investigate whether larger contexts of genes are coordinated across the two levels. To assess whether genes that are transcriptionally coordinated, or co-expressed, are also coordinated on a translational level, we constructed co-expression and co-translation networks for the ‘identical’ and ‘common’ promoter datasets. Note that while a particular gene can exhibit changes between cell-specific transcription and translation, this does not exclude that the co-expression and co-translation neighbourhoods of genes are preserved, i.e. one could imagine that certain co-expressed genes change their translational patterns in a coordinated fashion.

For one particular gene, an EC score is derived by calculating the PCC between the adjacent edge-weights, i.e. the co-expression relationship, of the two networks thus capturing similarity of gene neighbourhoods (Fig. 2). Accordingly, genes displaying low EC scores show different patterns of co-expression relationships in the two respective networks, while high EC values indicate the presence of highly similar co-expression relationships on the translatome and transcriptome.  

The edges are weighted according to the similarity of expression/translation, which is defined as the PCC scores between the cell-specific expression/translation levels of the neighbouring genes. For each gene, the EC, i.e. the similarity of the gene's genome-wide co-expression and co-translation relationships, was calculated. For this, differences in the edge-weights of a gene's incident edges, i.e. its network neighbourhood, are compared between the co-expression and co-translation networks. The computation of EC score has been previously applied to elucidate the ‘expression context’ of orthologous genes in four Eukaryote species, successfully illustrating that co-expression neighbourhoods of orthologues are highly conserved (Dutilh et al., 2006). Figure 5 shows the distributions of EC scores using the ‘identical’ and ‘common’ promoters, respectively. For both the ‘identical’ and the ‘common’ datasets, the range of EC scores lies in the interval–0·4 to 0·5.

Fig. 5.

Expression conservation (EC) scores of co-expression relationships on the translatome and translatome within the identical (red) and common promoter dataset (blue). The distribution of obtained PCC values for all 22 810 genes is visualized using kernel density estimates. For both the identical and the common dataset, EC score values lie in the interval 0·4–0·5.

Fig. 5.

Expression conservation (EC) scores of co-expression relationships on the translatome and translatome within the identical (red) and common promoter dataset (blue). The distribution of obtained PCC values for all 22 810 genes is visualized using kernel density estimates. For both the identical and the common dataset, EC score values lie in the interval 0·4–0·5.

To validate the observed relationships on the level of co-expression and co-translation networks, we derived the correlation on a global scale by considering the entire networks. The observed value of the full-matrix correlation was 0·06 and 0·10 between the co-expression and co-translation networks for the ‘identical’ and ‘common’ promoters, respectively. To assess whether these values are different from what could be expected by chance, we again selected random cell type expression and translation data, and used 1000 bootstrap samples that then were compared against our observed similarities (Fig. 6). For both the ‘identical’ and the ‘common’ promoter sets we found the values to be statistically significantly lower than expected by chance (P < 0·01). These data suggest that globally, or genome-wide, co-expressed gene patterns are dissimilar from co-translational patterns in arabidopsis root cells.

Fig. 6.

Similarity of the co-expression and co-translation network for the identical (A) and common (B) dataset. The similarity of both networks is determined by the PCC of the adjacency representation of the networks, i.e. a full matrix correlation. Only 0·001 and 0·005 % of the 1000 pairs of networks derived from bootstrapping procedure exhibit lower correlations than the observed transcriptome and translatome networks, respectively. This indicates a low degree of coupling of co-expression relationships between the two system levels.

Fig. 6.

Similarity of the co-expression and co-translation network for the identical (A) and common (B) dataset. The similarity of both networks is determined by the PCC of the adjacency representation of the networks, i.e. a full matrix correlation. Only 0·001 and 0·005 % of the 1000 pairs of networks derived from bootstrapping procedure exhibit lower correlations than the observed transcriptome and translatome networks, respectively. This indicates a low degree of coupling of co-expression relationships between the two system levels.

Returning to a per-gene analysis, we investigated which genes either significantly deviated or correlated in co-expression and co-translation relationships (i.e. EC scores) by assessing statistical significance by Z-scores and bootstrapping. Hence, these genes either exhibited tight coupling (high EC score, and Z-scores ≥1·96) or uncoupling (low EC scores, and Z-scores≤–1·96; Fig. 2) of their co-expression and co-translation to other groups of genes, thus reflected in conserved or changed network neighbourhoods. Only 71 and 39 genes exhibited (statistically significant) high EC scores for the ‘identical’ and ‘common’ promoters, respectively. In contrast, 10 057 and 12 681 genes displayed (statistically significant) low EC scores for the two promoter sets, respectively. Subsequently, we tested if these sets of genes were enriched for certain GO-BP terms (Supplementary Data Tables S4 and S5). For both groups of genes (high/low EC scores) and promoter sets (‘identical’/‘common’), we found enrichment of the GO-BP terms DNA-dependent regulation of transcription, cell wall biogenesis and organization, transmembrane transport, cell wall organization and cell growth, and signal transduction. Due to the high number of genes with uncoupled co-expression and co-translation relationships, i.e. low EC scores, we found numerous GO-BP term enrichments, including a wide range of metabolic and catabolic, as well as transport processes.

Finally, to assess what types of genes both display a good correlation between expression and translation (high PCC score above) and retain a good correlation between co-expression and co-translation network neighbourhoods (high EC score, above) we identified such genes for the ‘identical’ and ‘common’ promoter sets. Figure 7 shows that ten genes (‘identical’ promoters) and two genes (‘common’ promoters) have these characteristics (Table 2). Furthermore, a more detailed network analysis was conducted to analyse whether additional network properties of those 12 genes deviate from the majority of genes and further explain the conservation/similarity of neighborhoods. Thus, un-weighted, classical gene-relevance networks were created, using a threshold, t = 0·9, for the PCC of two genes to decide whether an edge (≥t) or no edge (<t) is present. Based on this threshold, two networks were created dichotomously capturing co-expression and co-translation and properties of genes across the two system levels. The network properties considered here comprise: degree, edge betweenness, closeness, Eigen vector centrality, Alpha centrality and transitivity. Interestingly, for all of the tested properties, the 12 genes display network properties that are well distributed across the whole range of the corresponding properties as compared with all genes (Supplementary Data Fig. S4).

Table 2.

Genes displaying conserved expression levels (PCC) and co-expression relationships (EC scores) across root cell types in translatome and transcriptome

No. of cell types Array element Locus identifier Annotation 
260067_at AT1G73780 Protease inhibitor/seed storage/lipid transfer protein (LTP) family protein 
250108_at AT5G15150 ATHB-3 (Arabidopsis thaliana HOMEOBOX 3); DNA binding/transcription factor 
250322_at AT5G12870 AtMYB46/MYB46 (myb domain protein 46); DNA binding/transcription factor 
251009_at AT5G02640 Similar to unknown protein [Arabidopsis thaliana] (TAIR:AT3G46300.1); similar to hyp. protein [Vitis vinifera] (GB:CAN66779·1) 
260173_at AT1G71930 VND7 (VASCULAR RELATED NAC-DOMAIN PROTEIN 7); transcription factor 
253076_at AT4G36160 ANAC076/VND2 (VASCULAR-RELATED NAC-DOMAIN 2); transcription factor 
267613_at AT2G26700 Protein kinase family protein 
253120_at AT4G35790 ATPLDDELTA (Arabidopsis thaliana phospholipase D delta); phospholipase D 
266342_at AT2G01540 C2 domain-containing protein 
260468_at AT1G11100 SNF2 domain-containing protein/helicase domain-containing protein/zinc finger protein-related 
255637_at AT4G00750 Dehydration-responsive family protein 
260432_at AT1G68150 WRKY9 (WRKY DNA-binding protein 9); transcription factor 
No. of cell types Array element Locus identifier Annotation 
260067_at AT1G73780 Protease inhibitor/seed storage/lipid transfer protein (LTP) family protein 
250108_at AT5G15150 ATHB-3 (Arabidopsis thaliana HOMEOBOX 3); DNA binding/transcription factor 
250322_at AT5G12870 AtMYB46/MYB46 (myb domain protein 46); DNA binding/transcription factor 
251009_at AT5G02640 Similar to unknown protein [Arabidopsis thaliana] (TAIR:AT3G46300.1); similar to hyp. protein [Vitis vinifera] (GB:CAN66779·1) 
260173_at AT1G71930 VND7 (VASCULAR RELATED NAC-DOMAIN PROTEIN 7); transcription factor 
253076_at AT4G36160 ANAC076/VND2 (VASCULAR-RELATED NAC-DOMAIN 2); transcription factor 
267613_at AT2G26700 Protein kinase family protein 
253120_at AT4G35790 ATPLDDELTA (Arabidopsis thaliana phospholipase D delta); phospholipase D 
266342_at AT2G01540 C2 domain-containing protein 
260468_at AT1G11100 SNF2 domain-containing protein/helicase domain-containing protein/zinc finger protein-related 
255637_at AT4G00750 Dehydration-responsive family protein 
260432_at AT1G68150 WRKY9 (WRKY DNA-binding protein 9); transcription factor 

In total, ten genes could be identified using the scenario considering the identical promoters and two genes considering the common promoters.

Fig. 7.

Venn diagrams illustrating the overlap of genes displaying conserved expression levels (PCC) and co-expression relationships (EC scores) across root cell types in translatome and transcriptome. Ten genes could be identified using the scenario considering identical promoters (four promoters, left) and two genes considering common promoters (eight promoters, right).

Fig. 7.

Venn diagrams illustrating the overlap of genes displaying conserved expression levels (PCC) and co-expression relationships (EC scores) across root cell types in translatome and transcriptome. Ten genes could be identified using the scenario considering identical promoters (four promoters, left) and two genes considering common promoters (eight promoters, right).

Remarkably, the majority of the identified 12 genes are transcription factors, or contain predicted DNA binding protein domains, e.g. ATHB-3, MYB46, VND7 and WRKY9. Several of the genes are associated with key regulatory roles in roots, either for developmental or for response processes. For instance, WRKY9 is involved in mediating cell responses to nutrient deprivation (Shin and Schachtman, 2004; Shin et al., 2005), and the transcription factor MYB46 has a prominent role in the developmental programme of secondary wall biosynthesis (Zhong et al., 2007, 2013). In addition, VND7 has been characterized as transcriptional master switches for plant meta- and protoxylem formation in arabidopsis (Kubo et al., 2005). Thus, the enrichment analysis of these genes suggests that certain key genes for root development maintain a direct relationship between expression and translation (Table 3).

Table 3.

GSEA of genes displaying conserved expression/translation levels (PCC) and co-expression/co-translation relationships (EC scores) in the identical promoter dataset

GO term Term description No. of genes P 
GO:0006355 Regulation of transcription, DNA-dependent <0·01 
GO:0009741 Response to brassinosteroid stimulus <0·01 
GO:0010089 Xylem development <0·01 
GO:0010413 Glucuronoxylan metabolic process <0·01 
GO:0045492 Xylan biosynthetic process <0·01 
GO:0045893 Positive regulation of transcription, DNA-dependent <0·01 
GO term Term description No. of genes P 
GO:0006355 Regulation of transcription, DNA-dependent <0·01 
GO:0009741 Response to brassinosteroid stimulus <0·01 
GO:0010089 Xylem development <0·01 
GO:0010413 Glucuronoxylan metabolic process <0·01 
GO:0045492 Xylan biosynthetic process <0·01 
GO:0045893 Positive regulation of transcription, DNA-dependent <0·01 

Note that the corresponding two genes from the common promoter dataset did not result in a significant enrichment of GO-BP terms (cf. Table 2).

Root cell type similarity based on transcriptome and translatome

To complement the gene ‘centric’ analysis of (un-)coupled expression and translation, a cell type ‘centric’ analysis may reveal common themes among root cell types. Here, we attempted to elucidate whether transcriptional and/or translational patterns (termed themes) may be conserved across multiple cell types. To characterize a particular cell type, we first identified genes that showed differential expression and translation across the datasets. These estimates were derived for both the ‘identical’ and the ‘common’ promoter sets using ANOVA [FDR of 5 % by Benjamini–Hochberg (BH) multiple testing correction]. For the ‘identical’ promoters, a set of 890 genes displayed both differential expression and translation across the cell types (Fig. 8A). Moreover, for the five ‘common’ promoters, a set of 3923 genes showed differential expression and translation across the cell types (Fig. 8B). Considering expression and translation separately, we found that most genes exhibit differential expression across the cell types: ∼38 % (‘identical’ promoters) and 67 % (‘common’ promoters), while only ∼5 % (‘identical’ promoters) and ∼20 % (‘common’ promoters) of all genes display differential translation. Hierarchical clustering of the genes exhibiting differential expression on both levels, i.e. the 890 and 3923 genes above, revealed divergent patterns in cell-type-specificity (Supplementary Data Fig. S5).

Fig. 8.

Visualization of the set of differentially expressed (DE) genes across the transcriptome and translatome of the identical (A) and common (B) promoter datasets. Differential expression was assessed by ANOVA at an FDR rate of 5 %. The numbers in the Venn diagram correspond to the number of DE genes found in each system level and the intersection thereof.

Fig. 8.

Visualization of the set of differentially expressed (DE) genes across the transcriptome and translatome of the identical (A) and common (B) promoter datasets. Differential expression was assessed by ANOVA at an FDR rate of 5 %. The numbers in the Venn diagram correspond to the number of DE genes found in each system level and the intersection thereof.

We conducted a series of Tukey HSD tests (P < 0·05) for all genes displaying differential transcription and translation to further derive which cell-type-specific expression and translation levels differed significantly. Performed for each gene, the Tukey HSD post-hoc test allowed us to determine for which pair-wise cell type comparisons there is a significant difference in cell type expression/translation levels (Supplementary Data Fig. S6). When considering the four ‘identical’ promoters (representing cell types; Table 1) in the Tukey HSD test, we obtained a characteristic pattern of six pairwise cell type comparisons encoded for by a binary matrix (0 and 1). Then, for any given pair of cell types and system level, a significant difference in cell type expression or translation profile is assigned the value 1, and a similar expression profile is assigned a value of 0. We displayed the results as networks, here referred to as network motifs, to show patterns of similar gene levels. In these small networks, the four cell types (‘identical’ promoter dataset) correspond to nodes, and similarity between two cell types (=0) is indicated by an edge. From the 64 possible configurations of those networks (cf. Materials and Methods), we found that only five network motifs for the translatome and nine for the transcriptome occur more often than expected by chance (Supplementary Data Fig. S7). The significance of these motifs was tested empirically by permuting the results from the Tukey HSD test for each gene (i.e. shuffling the 0 and 1 values) and comparing the observed occurrence counts of genes for a particular motif with those obtained randomly over n = 1000 permutations (Supplementary Data Table S6). Furthermore, of all significantly occurring motifs only two are represented by a high number of genes [279 and 214 unique genes, motif 1 (Fig. 9A) and motif 2 (Fig. 9B), respectively] of the 890 differentially expressed genes. Both these motifs contain one isolated node, i.e. a cell type that is not connected by edges to any other cell types, indicating that this cell type is dissimilar based on the gene's expression or translation profile. Moreover, in both the motifs, the remaining three cell types are fully connected by edges, indicating similar behaviour of the relative levels of expression/translation of the genes included in the motif. The first motif (motif 1) shows that the majority of genes have dissimilar expression pattern in phloem companion cells (SUC2) as compared with the other three cell types (WOL, SCR and GL2). In the second motif (motif 2), the root quiescent centre (SCR) displays deviating expression patterns. Looking more closely at these differences shows that the major driving force behind the deviation in the phloem companion cells can be attributed to the transcriptomic datasets (214 differentially expressed genes), while the deviation of the quiescent root centre is largely due to differences in the translatome profiles (203 differentially expressed genes). An enrichment analysis using GO-BP terms associated with the genes in the first motif (214 genes) reveals mainly transport processes (general and transmembrane), as well as responses to sugar stimuli (glucose, sucrose and fructose) to be overrepresented (Supplementary Data Table S7). These data are in agreement with a major function of phloem companion cells in sugar transport (Stadler et al., 1995; Oparka and Turgeon, 1999; Williams et al., 2000). When we looked at the relative expression levels of the genes associated with this motif (again 214; Fig. 10) we found that the transcript levels were elevated. This is consistent with a role of the gene products in the function of these cells.

Fig. 9.

Two most commonly occurring network motifs for the transcriptome and translatome. A series of Tukey HSD tests were used on the common DE genes from the identical promoter dataset (890 genes) to detect significant pairwise cell type differences across the transcriptome and translatome. Network motifs were constructed using cell types as nodes and cell type similarity indicated by a red edge. Two network motifs, namely motif 1 and motif 2 in this figure, are represented by 279 and 214 unique genes, respectively.

Fig. 9.

Two most commonly occurring network motifs for the transcriptome and translatome. A series of Tukey HSD tests were used on the common DE genes from the identical promoter dataset (890 genes) to detect significant pairwise cell type differences across the transcriptome and translatome. Network motifs were constructed using cell types as nodes and cell type similarity indicated by a red edge. Two network motifs, namely motif 1 and motif 2 in this figure, are represented by 279 and 214 unique genes, respectively.

Fig. 10.

Boxplots of expression levels separated by promoters individually for translatome and transcriptome for network motif 1 (upper panel) and motif 2 (lower panel). In motif 1, 279 of 890 DE genes exhibit this characteristic cell-type-specific expression pattern. Furthermore, 214 genes correspond to motif 2.

Fig. 10.

Boxplots of expression levels separated by promoters individually for translatome and transcriptome for network motif 1 (upper panel) and motif 2 (lower panel). In motif 1, 279 of 890 DE genes exhibit this characteristic cell-type-specific expression pattern. Furthermore, 214 genes correspond to motif 2.

By contrast, genes associated with the second motif, i.e. where the root quiescent centre showed dissimilar expression patterns, were enriched for cell wall modification, xylan biosynthetic process and root hair cell differentiation/elongation. More importantly, the GO-BP terms oxidative stress, oxidation–reduction processes and auxin polar transport were also enriched. The quiescent centre cells typically accumulate high auxin levels that serve as a distal organizer (Sabatini et al., 1999). This is accompanied by the overproduction of reactive oxygen species. This is mediated by high levels of activity of ascorbate oxidase that cause reduction in the reduced form of ascorbic acid and glutathione and, simultaneously, an increase in the content of reactive oxygen species in the root quiescent centre cells (Ivanov, 2007; Tyburski et al., 2010). Oxidative stress represses proliferation of these cells, thus maintaining the cells in a quiescent state (Jiang et al., 2003). Expression levels of genes corresponding to this second motif are shown in Fig. 10, which indicates a relatively higher degree of translation in the root quiescent centre.

DISCUSSION

A long-standing question in cellular biology is how well the transcriptome is coupled to the proteome (Zanetti et al., 2005). Profiling of mRNAs associated with polysomes can give a rough estimate of a cell's or tissue's proteome. Hence, by comparing cell-type-specific levels of total and polysomal mRNA in a global context, one can derive to what extent expression and translation are coupled. Based on the efforts of Brady et al. (2007) and Mustroph et al. (2009) the arabidopsis root atlas allows us to analyse transcriptomic and translatomic data sets and to identify particular genes that show either a tight coupling, or an uncoupling, of expression and translation profiles over a collection of cell types. On the computational level, our study represents an extension to the analysis of Mustroph et al. (2009), who used their root and shoot data in combination with hypoxia conditions to identify DE genes at the single cell-, region- and organ-specific levels. Recently, Lin et al. (2014) have investigated the translatome of in vivo-grown pollen tubes from self-pollinated gynoecia of arabidopsis. By using a pollen-specific promoter, epitope-tagged polysomal–RNA complexes could be affinity purified to obtain mRNAs undergoing translation. The authors also employed joint (RMA-) normalization to compare the translatome data with publicly available transcriptomics datasets. Set theory and analysis of the differential behaviour of genes finally identified a group of genes important in in vivo pollen tube biology.

Furthermore, we examined the variance in expression and translation levels using CVs and tested the similarity of gene expression/translation patterns across the root cell types using PCC. The observed change in CVs (Fig. 3) and the presence of negative PCC values (Fig. 4) when globally comparing the translatome and transcriptome is similar to what was found by Tebaldi et al. (2012). Here, the authors concluded a general uncoupling of the translatome and transcriptome based on low correlations found using epidermal growth factor stimulation in mammalian HeLa cells. Uncoupling of transcriptome and translatome has also been documented in human and yeast cells in response to various stimuli and stresses (Mikulits et al., 2000; Grolleau et al., 2002). For example, yeast exposed to different stresses, such as amino acid depletion and fusel alcohol addition, show distinct translational profiles (Smirnova et al., 2005), suggesting there is a distinct role of translational regulation for rapid responses in cells to environmental stress. However, by further focusing the analysis to the level of individual genes, our results also revealed groups of genes displaying coupled transcription/translation involved in processes such as stress responses (e.g. wounding, bacteria, nitrogen starvation and osmotic stress). These findings are, on the other hand, in agreement with the study in yeast by Halbeisen and Gerber (2009), who found relatively high overall PCC values of 0·75–0·81 of the overall genomic (fold-) changes in expression upon different conditions of cellular stress, such as osmotic stress between transcriptome and translatome.

In our study, genes that show correlated transcription and translation are enriched in cell-wall-related processes, which is in agreement with co-expression approaches that have successfully been undertaken for secondary wall synthesis (Persson et al., 2005). Here, many secondary wall genes are transcriptionally and functionally coordinated, which implies that the translation also would be coordinated with the transcription (Mutwil et al., 2009; Ruprecht and Persson, 2012). While these processes appear to be coupled, most of the genes displayed uncoupled transcription and translation in the cell types considered in our analysis.

In addition, a high degree of uncoupling between transcription and translation was observed when investigating correlations in co-expression and co-translation relationships. Here, over 12 000 genes displayed altered co-expression patterns in the eight ‘common’ promoter datasets (Fig. 2). This may reflect a re-wiring of co-regulation of genes during translation compared with transcription. In addition, cell-type-specific mRNA abundance appeared different on the two levels with 11 453 genes differentially expressed exclusively in the transcriptome (Fig. 8B). Notably, large proportions of genes displaying conserved co-expression/co-translation neighbourhoods are transcription factors or are putatively involved in regulation of transcription.

Note that bootstrapping procedures were carried out to ensure the robustness of our analyses. The benefits of this approach are two-fold: the random PCC and EC scores account for, first, sample size and, second, differences in cell type promoter specificity and the presence of multiple promoters targeting the same cell type in the case of the ‘common’ promoter dataset. As a consequence, one can robustly classify genes whose total and polysomal-associated mRNA levels are coupled (high PCC and Z-score of ≥1·96) as well as genes that display an uncoupling of both mRNA levels (low PCC and Z-score of ≤–1·96). Accordingly, we employed the same statistical framework to confirm the similarity of co-expression and co-translation neighbourhoods in the network analysis to ensure robustness. Nevertheless, the observed effects must be carefully interpreted given that the datasets originate from different labs and, moreover, rely on different extraction procedures. Here, the identified coupled attributes of gene expression on the transcriptional and translational level are therefore remarkable. Moreover, many of our observations are in close agreement with well-established characteristics of root cell type function and development.

One of the limitations of our study is the available selection of promoters, i.e. cell types, for the datasets. Clearly, in the case of the correlation analyses of transcription and translation of the individual genes, a greater sample size would have been desirable. Also, in the case of the ‘common’ five cell types, artefacts may arise due to slight variation in promoter strength and specificity across the cell types. Therefore, it is impossible to rule out deviations in transcription and translation based on promoter patterns. Nevertheless, we found correlation between transcription and translation for genes that we anticipated, such as for the secondary wall genes discussed above. These results are reassuring, and may provide a foundation for future efforts in this area. We propose that using more cell-type-specific promoters and performing the transcript and translatome analyses in one lab using the same methods will generate a robust and interesting data series that may be used to improve our results. Such datasets would be of immense interest to understand coupled and un-coupled gene regulation in arabidopsis roots.

SUPPLEMENTARY DATA

Supplementary data are available online at www.aob.oxfordjournals.org and consist of the following. Gene List: details of all genes identified at any step of the analysis. Promoter Sequences: We have provided the promoter sequences of both the transcriptome and translatome dataset. Fig. S1: normalization of the CEL files. Fig. S2: histogram displaying the effects of joint and individual RMA normalization of the raw data (CEL files) from transcriptome and translatome. Fig. S3: correlation between the replicates. Fig. S4: degree of distribution and edge betweenness of the co-expression and co-translation network. Fig. S5: relative gene expression levels for the 890 genes for identical promoters displaying both differential expression and translation and 3922 genes with significant differences across common promoters in transcriptome and translatome. Fig. S6: conversion of pairwise differences in cell-type-specific expression levels derived by Tukey's HSD test, to cell type similarity networks. Fig. S7: all possible motif occurrences across the identical promoter data of the transcriptome and translatome. Table S1: list of available cell types and their corresponding promoters in the transcriptome and translatome dataset. Table S2: enriched GO terms of coupled/uncoupled gene expression patters on translatome and transcriptome for the identical promoter data. Table S3: enriched GO terms of coupled/uncoupled gene expression patterns on translatome and transcriptome for the common promoters. Table S4: GSEA of GO-BP terms for genes showing altered expression conservation for identical promoter data. Table S5: GSEA of GO-BP terms for altered expression conservation genes for the common promoters. Table S6: all possible motif occurrences across the identical promoters of transcriptome and translatome. The observed motifs are depicted in the order mentioned in the third column header. A characteristic pattern of the pairwise differences is represented by 1 (significant mean difference of expression values) and 0 (no significant mean difference of expression values). Table S7: enriched GO terms of genes with characteristic cell-type-specific gene expression patters found for motif 1 and motif 2.

ACKNOWLEDGEMENTS

This work was supported by the European Union Seventh Framework Programme (FP7 2007–2013) under the WallTraC project (grant agreement no. 263916). This paper reflects the authors' views only. The European Community is not liable for any use that may be made of the information contained herein.

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