Wider daily gene activity patterns and varied gene copy expression in Brassica rapa

Studies in Arabidopsis have demonstrated widespread circadian regulation of the transcriptome with distinct and overlapping genes responding to various entraining conditions (Covington et al., 2008; Michael et al., 2008). As noted above, genes contributing to circadian clock function have been retained in multiple copies following a WGD in B. rapa (Lou et al., 2012). We wondered whether there have been concomitant effects on the extent of circadian regulation of the B. rapa subsp. trilocularis (Yellow Sarson) R500 (henceforth called B. rapa R500) transcriptome and the expression patterns of circadian regulated paralogs. We conducted two RNA-seq experiments designed to capture the genes under circadian regulation entrained by light and temperature zeitgebers (German for ‘time givers’). We entrained plants to light-dark (LD) cycles at constant temperature or thermocycles (HC) in constant light before transfer to constant light at 20°C (LLHH; see Materials and methods). Following 24 hr in constant conditions leaf tissue from the youngest leaf was collected and flash-frozen in liquid nitrogen every 2 hr for 48 hr. All samples were harvested from biological replicates of plants from the LD and HC entrainment that had been transferred to constant conditions of LLHH.

To identify the circadian transcriptome, we analyzed the LD and HC datasets and ran the circadian analysis program RAIN (Thaben and Westermark, 2014), a nonparametric method for the detection of rhythms from a variety of waveforms that are typical of transcript abundance datasets. The 2 hr sampling regimen provided the resolution to capture more cycling genes than possible with typical 4 hr sampling (Hughes et al., 2017). Using a Benjamini-Hochberg corrected p-value of 0.01, we identified 16,447 high confidence circadian regulated genes from the two datasets. Of the 22,204 genes that were expressed in the RNA-seq datasets, 74% of them passed our cutoff for cycling in one or both conditions, indicating retention of circadian regulation of the transcriptome following WGD in B. rapa. To assign cycling genes to specific phase bins based on the timing of peak expression, we generated co-expression networks for each dataset using the weighted gene correlation network analysis (WGCNA) package in R (Langfelder and Horvath, 2008), which has proven to be an effective method for grouping similarly phased genes based on their expression pattern (Greenham et al., 2017). This resulted in 14 modules in the LD dataset and 10 modules in the HC dataset. To demonstrate the uniformity of the genes within each module, a heatmap was generated with the log₂ transformed expression data of each gene for all modules numbered based on their phase (Figure 1). Each module reflects a similarly phased set of genes that collectively are phased throughout the day with the LD_01 module showing peak expression at ZT24 (subjective dawn; ZT refers to Zeitgeber Time and indicates the number of hours since the last dark-to-light or cold-to-warm transition) and LD_09 showing peak expression at ZT36 (subjective dusk).

The similarity in patterns seen in the LD and HC heatmaps in terms of phasing and distribution of genes within those phase bins suggests that there may be considerable overlap in gene phasing after LD and HC entrainment. To quantify the overlap, we matched the genes across the two networks and compared the correlation of eigengenes (first principle component of the expression matrix for each module) between LD and HC modules. The circos graph in Figure 1 depicts the overlap between the two networks where the width of the ribbon represents the number of genes in common between the two modules and the color signifies the Pearson correlation coefficient between the eigengenes of the two datasets with dark orange being a correlation coefficient of 1. Because the modules are numbered based on their phase, similarly phased modules are arranged in the same order in the circos plot and the significant overlap and expression pattern between these modules is evident. Figure 1—figure supplement 1 shows the distributions of phase difference between LD and HC for all 16,447 genes that cycle in both conditions. 13,850 genes (~84%) have a phase difference of less than or equal to 4 hr. These comparisons demonstrate that most genes have the same or similar phasing when entrained by either photocycles or thermocycles. To further assess the similarity between these two datasets, GO ontologies were compared for each module from the LD and HC experiments (Supplementary file 1). This revealed similar biological processes enriched in the modules with a high correlation in expression patterns. For example, genes in LD_03 and HC_02 were both significantly enriched (FDR adjusted p-value<0.01) for photosynthetic processes and response to abiotic stress, consistent with their morning phased expression. Interestingly, genes in LD_07 and HC_07 were significantly enriched for protein phosphorylation suggesting a time-of-day regulation of this process, as in Arabidopsis (e.g. Choudhary et al., 2015).

The correlation between module membership is not a rigorous test of differential transcript abundance and many genes have low correlations with their module eigengene that are not reflected in the analysis in Figure 1. To the best of our knowledge, there was not a rigorous test available for identifying significantly different gene expression patterns that would classify the change based on phase, amplitude, or a combination of both. Rather than looking at differentially expressed genes at any given time point, we felt it was more important to classify a pattern change that encapsulated the entire time course. Therefore, we developed an R package, DiPALM, which takes advantage of the network analysis that assigns an eigengene to each module and thereby produces a minimal set of patterns representing the entire dataset. The expression correlation of a given gene to any module’s eigengene defines the module membership (kME) of that gene to the module. The combination of kMEs for a gene across all modules can be used to encode its expression pattern numerically and allows for quantitative comparisons between any two gene’s expression patterns. This allowed us to run a set of linear model contrasts (one for each eigengene) that is analogous to running a contrast of gene expression data between time points or treatments except in this case the kME value represents the entire expression profile across the time course. We first tested for differential expression patterns between the LD and HC datasets for the set of 15,101 genes that cycle after entrainment to either LD and HC. Both datasets were collected in constant conditions of light and temperature (LLHH) following different entrainment regimens as specified by their names. To generate a significance cutoff, we also ran the analysis on a permuted gene expression set where gene accessions were randomly re-assigned to expression patterns. P-values were then calculated using this permuted set. Using a p-value cutoff of 0.01, we identified just 1713 genes, or 11% of all cycling genes, that have altered patterns following LD versus HC entrainment. To quantify overall expression level variation, we ran a similar linear model analysis on the median expression level for all genes and identified 3465 (23%) of genes, only 448 of which overlapped with the pattern change list (Supplementary file 2). The 11% of cycling genes with entrainment-dependent cycling patterns are interesting but not within the scope of this manuscript. A functional enrichment analysis of these genes revealed translation initiation factors and ncRNA metabolic process among the significant (FDR adjusted p-value<0.01) functional categories (Supplementary file 1, ‘Differential_Pattern’ Tab). For the remainder of analyses with these datasets, we have combined LD and HC to increase our statistical power by having four replicates per time point rather than two. One significant advantage of a linear model-based framework is the ability to account for any identified effect. We make use of this feature by modeling any LD versus HC entrainment effects as a covariate. In other words, DiPALM gives us the ability to combine these datasets while still accounting for any differences between LD and HC entrainment. This combined dataset (referred to as LDHC) provides more statistical power for further analysis, particularly for the 89% of genes that show no significant change between LD and HC. For the other 11% that do have differences, these differences are taken into account and will not adversely affect the results.

The network analysis revealed that a large portion of the transcriptome exhibits rhythmic expression patterns. This implies that multi-copy paralogs have retained their rhythmic expression patterns and circadian regulation consistent with the preferential retention of circadian clock orthologs in B. rapa (Lou et al., 2012). Based on the gene dosage model, the balance in expression among different subunits of protein complexes must be maintained resulting in the proper adjustment of paralog expression level, in some cases resulting in one copy maintaining high expression while the other is repressed (Conant et al., 2014). To evaluate this model, we calculated the mean expression level for all cycling genes across the 48 hr time course. The mean expression level for the set of retained multi-copy paralogs was significantly higher (p-value=0.0 based on one-way ANOVA with Tukey’s test) than for genes retained in a single copy (Figure 2A). It is possible that one of the retained copies is expressed at a much higher level than the average single-copy gene as well as its paralog. To test whether this is the case, we separated all the cycling two- and three-copy paralogs into the highest and lowest expressed copies based on the average expression level across all time points. In the case of three-copy paralogs, we only included the highest and lowest expressed genes in the analysis. We compare this to randomly paired single-copy gene pairs that were also separated into high and low expression groups (Figure 2B). Surprisingly, the difference is observed in the low expressed paralog where these duplicated genes have significantly higher (p-value=0.0 based on one-way ANOVA with Tukey’s test) average expression levels compared to the genes retained in a single copy, suggesting that although the duplicate paralogs do appear to exhibit gene dosage, their overall expression is retained at a higher level than the expression of single-copy genes.

The retention of multi-copy genes that are under circadian regulation and maintained at a relatively high expression level led us to explore whether there is evidence for divergence in expression pattern that would support neo- or sub-functionalization among paralogs. To associate similar patterns, we applied the same WGCNA method to the combined LDHC dataset as was done for the individual analyses shown in Figure 1. This resulted in 12 modules with distinct phasing throughout the day that is visible when the eigengene expression for each module is presented as a heatmap (Figure 2C). Next, we wondered whether there was any association between the phase of expression and retained copies that may suggest certain biological processes that are phased to specific times of the day and may preferentially retain multi-copy genes. Based on the number of genes within each module, we ran a hypergeometric test to look for over- and under-enrichment of multi-copy genes within the modules. Surprisingly, we found that modules with phasing from morning to midday tend to be enriched for multi-copy genes. By contrast, evening and night phased modules were depleted for multi-copy genes (Figure 2D). These trends were not associated with the number of genes within the module as can be seen with two of the largest modules, LDHC_02 and LDHC_09, being over- and under-enriched, respectively. GO enrichment was carried out on a combined group of all multi-copy genes from the five morning modules with significant enrichment for multi-copy genes (p-value<0.05). The same was done for the group of all multi-copy genes from all four evening modules with significant depletion in multi-copy genes. Both of these sets appear to be representative of the whole modules from which they came with the morning-phase copied genes being significantly enriched (FDR adjusted p-value<0.01) for photosynthesis, translation, and response to abiotic stimulus genes. The evening-phase multi-copy genes were significantly enriched for protein phosphorylation and glycosinolate biosynthesis genes (Figure 2D, Supplementary file 3).

To look for signs of possible neo- or sub-functionalization among paralogs, we compared expression profiles to identify paralogs with significantly different expression patterns. For three-copy paralogs, where all three copies (a, b, and c) were expressed, these sets were converted into three, two-member pairs (ab, bc, and ac). We applied a similar linear modeling test using DiPALM that we ran on the LD and HC comparison but including the covariate in the model to account for differences following LD versus HC entrainment. We ran this analysis on 4433 pairs where both genes are expressed. We found 3743 (84%) pairs with differential median expression and 1883 (42%) pairs exhibiting differential expression patterns (p-value<0.01), the vast majority (1607/1883; 85%) of which showed both differential median expression and pattern (Supplementary file 4). However, this does not describe how the patterns differ. As with standard differential expression tests, it is critical to associate a direction of change in expression to know how a gene transcript is affected by treatment or condition. To isolate the type of pattern change for the paralogous pairs exhibiting significantly different patterns, we performed clustering on the vector of expression values across the combined LDHC data for each gene whose expression differed significantly from its paralog. This clustering had the effect of grouping genes based on their phase. Similar clustering was done for the significantly different median expression set. This clustering component is also part of the DiPALM package. A detailed description and example dataset of the analysis pipeline is provided with the package on CRAN (R Development Core Team, 2018).

To visualize the degree of pattern change, we generated a heatmap of each of the clustering methods with the paralogous gene pairs in the same row for comparison. As expected, the pattern clustering uncovered the changes in rhythmic patterns between pairs (Figure 3A) while the median expression clustering uncovered overall changes in transcript abundance (Figure 3—figure supplement 1). From the heatmap visualization, it is clear that the majority of the pattern change among paralogous pairs is the result of an altered phase of peak expression. In some cases (Figure 3A and B, clusters 4 and 12) the pairs are completely antiphase. This suggests a genome-wide expansion of phase domains among retained paralogs.

The divergence in expression pattern among retained paralogs led us to ask how diverged the retained pairs are with respect to their Arabidopsis ortholog (Figure 4A) and whether one copy exhibits an Arabidopsis-like expression pattern while the other copy exhibits a different expression pattern. One method of comparing the orthologs between B. rapa and Arabidopsis is to compare the phase of expression. However, assigning an accurate phase to circadian data from two cycles is challenging and often gene expression patterns show very broad peaks in abundance that can be difficult to classify, especially with the resolution of only 4 hr that is available for Arabidopsis. Also, because we are comparing two species, we have to consider the properties of the clock in each species. The B. rapa R500 clock has a slightly shorter period than Arabidopsis Col-0 that results in altered phasing among genes that is not indicative of divergence in function but arises merely from the different paces of the Arabidopsis and B. rapa oscillators. For example, if we plot the expression of single-copy circadian clock genes from B. rapa and their corresponding orthologs in Arabidopsis we see a leading phase in B. rapa (Figure 4—figure supplement 1). To avoid these phase complications, we chose a gene regulatory network (GRN) approach using GENIE3 (Huynh-Thu et al., 2010) that would provide additional statistical robustness by first predicting transcription factor (TF) targets based on expression dynamics within each species followed by a comparison of network connections between the species (Figure 4B).

We obtained previously published circadian microarray data from Arabidopsis that were generated under similar conditions with LD and HC entrainment (LL_LDHC, LL_LLHC, LL12_LDHH, and LL23_LDHH from Mockler et al., 2007). We selected Arabidopsis TFs from the Arabidopsis TF database (https://agris-knowledgebase.org/AtTFDB/↗) and B. rapa TFs from the Mapman annotation ‘RNA regulation of transcription’ in addition to known circadian clock TFs. This resulted in a list of 612 Arabidopsis and 2147 B. rapa TFs that were expressed in their respective datasets. For the target set, we included 9201 Arabidopsis expressed genes and the corresponding 14,541 B. rapa expressed orthologs. Separate GRNs were generated for Arabidopsis and B. rapa. To identify the significance of TF-target edges, we generated a permuted network by using the same TFs but shuffling the expression values for the target genes. These permuted network edges were used as a null distribution to select edges from the actual networks with a 5% FDR, resulting in an Arabidopsis GRN with 71,216 edges and a B. rapa GRN with 947,062 edges. A gene was said to be a target of a TF if one of these significant edges existed between them. We hypothesized that the paralogous TF in B. rapa that displayed more of the Arabidopsis orthologous function would have a greater overlap in targets in the network compared to the more divergent pair member. In other words, using TFs as features to describe the targets, how well can the expression of the target genes be explained by the expression of that TF (Figure 4B)? A set of 256 TFs exists where one Arabidopsis TF can be associated with two B. rapa paralogs and all three of these genes had target groups defined by their respective GRNs.

For each of these 256 sets, we examined the significance of the overlap between the Arabidopsis TF target group versus the corresponding B. rapa orthologous TF target groups. This resulted in two p-values for each group indicating how similar each B. rapa TF is to its orthologous Arabidopsis TF in terms of target gene overlap (Supplementary file 5). Next, we wanted to determine if the difference in these two p-values was significant; that is, does one of the B. rapa TFs show more conservation of target gene overlap with its Arabidopsis ortholog than the B. rapa paralogous TF? This was accomplished with another permutation-based test where genes were randomly sampled to create target groups of the same sizes. The two B. rapa versus Arabidopsis p-values were calculated and the difference was taken. This was repeated 10,000 times for each of the 256 TF sets. 49 TF pairs exhibited significant enrichment (p-value<0.05) for one B. rapa TF (assigned Br1) being more similar to Arabidopsis than its paralog (Supplementary file 5), suggesting possible divergence in function between these TFs (Figure 4C and D). It is worth noting that the size of the target group is not driving the enrichment as we see a broad distribution in target size and significance (Figure 4C). Among the list of 49 TFs, six are part of the core circadian clock (ELF3, ELF4, PRR9, PRR7, PRR5, and TOC1), and a seventh, RVE1, integrates the circadian clock and auxin pathways (Rawat et al., 2009; McClung, 2019). Based on the predicted targets of these TFs in the GENIE3 model, there are 11,559 B. rapa and 3387 Arabidopsis genes regulated by these 49 TFs, providing further support for an expansion of the circadian network in B. rapa. The divergence in TF target genes indicates several possible changes have occurred; these could include modifications to regulatory elements of the target genes, mutations that alter the TF protein binding efficiencies for motifs or interacting partners, or a combination of both. Alterations to regulatory elements associated with core TFs and/or target genes can lead to whole pathway-level restructuring. One possible mechanism for altered expression regulation is the distribution of conserved noncoding sequences (CNSs). A set of CNSs was identified across the Brassicaceae that show signs of selection (Haudry et al., 2013). To associate CNSs with the B. rapa R500 genome, we performed a BLAST analysis with a collection of ~63,000 CNSs against the B. rapa R500 genome. Provided the alignment met our BLAST filters, we allowed each CNS to have a maximum of three targets (see Materials and methods). We repeated the BLAST with the Arabidopsis genome but restricted each CNS to one target gene.

To test for altered regulatory element occurrences between target genes of the identified diverged TFs, we asked whether variation in CNS retention followed the same pattern as the observed gene expression changes. Do we see a similar divergence in B. rapa paralogous TF enrichment with Arabidopsis in the GENIE3 network if we replace the target set gene ortholog data with CNSs? With the list of genes and associated CNSs, we replaced the target genes in the GENIE3 networks with CNSs resulting in a network with TFs targeting a group of CNSs rather than genes. We performed the same permutation tests to assign significant enrichment to the groups to ask whether we could identify a B. rapa R500 TF ortholog that was more Arabidopsis-like than its paralog. We identified 68 significant TFs (p-value<0.05) in the CNS network (Figure 4E and F, Supplementary file 5), 35 of which overlapped with the 49 TFs identified based on target gene overlap (Figure 4G). The agreement between these two approaches is apparent when the corresponding p-value distributions from the overlap of targets are plotted (Figure 4D and F). In these boxplots, the Br1 enrichment for At target gene overlap is shown with the paralogous pairs connected by the red lines. Not only does the agreement between the target gene and CNS overlap further strengthen the support for those 35 TF pairs showing signs of divergence but suggests that the CNS distribution is associated with gene expression patterns and is a good predictor of expression variation. With this set of high confidence diverged TFs from the overlap group, we wondered whether changes in TF amino acid sequence contribute to the divergence between paralogous pairs in which case we would expect the Arabidopsis-like B. rapa TF to be more similar than its paralog. To test this, we ran a protein BLAST using the B. rapa TFs against the Arabidopsis genome and examined the distribution of blast scores for the more and less Arabidopsis-like TF. Results from the BLAST suggest very little association between amino acid sequence and TF divergence (Figure 4I) suggesting that changes to regulatory regions associated with target genes are likely to be a major driver of TF divergence. TF expression pattern changes are likely contributing to the divergence in regulation. To test this further, we conducted a similar analysis to the BLAST comparison where we looked at the expression correlation of B. rapa TFs versus the orthologous Arabidopsis TF. In general, the more Arabidopsis-like B. rapa TF was more likely to maintain a higher expression correlation to the Arabidopsis ortholog but the effect is not significant (Figure 4H, p-value 0.057) and several of the 35 TF sets tested do not show this result. However, due to the period difference between the Arabidopsis and B. rapa datasets (longer period confers delayed phase), it is difficult to compare expression patterns directly (Figure 4—figure supplement 1). While paralogous TFs likely bind to the same motifs, a divergence in expression may result in the loss of transcriptional coactivators or corepressors required for gene activation or repression due to temporal separation in expression pattern resulting from the shift in phase of that TF. Similarly, an altered phase of expression might allow interaction with new TF interacting factors to provide a new target affinity for that TF resulting in a new target set.

If the CNSs are driving the target gene expression differences we would expect them to be enriched for TF binding motifs (Haudry et al., 2013). To test for motif enrichment, we selected 12 of the TF groups where Br1 was significantly more Arabidopsis-like (see Supplementary file 5, third tab). For each TF (B. rapa paralogs and Arabidopsis ortholog), we took the collection of CNSs represented by their target genes in the GRNs and ran them against the HOMER motif analysis algorithm (Heinz et al., 2010). Because the CNSs are located throughout the gene (promoter, 5’UTR, introns, 3’UTR) we selected the sequence from 2 kb upstream of the start codon to the 3’UTR for each target gene for comparison. We also included just the 2 kb upstream sequence to compare to standard motif search parameters. For all 12 TFs tested, we found three– to fivefold greater enrichment for motifs in the CNS elements compared to the full length and 2 kb promoter background sets (Figure 4—figure supplement 2). This is consistent with the results from the GRN analysis showing that CNSs are as predictive as expression dynamics and contain important regulatory elements. Further studies are needed to look for associations between groups of CNSs with their corresponding binding motifs and specific gene expression patterns. Since the GENIE3 algorithm associates target genes based on a TF being an activator or repressor, the target genes typically have several major expression patterns (Figure 4B). Isolating distinct patterns and analyzing CNS variation between the target groups may reveal new motif groupings or novel motifs.

The gene balance hypothesis posits that multi-subunit complexes are sensitive to variations in stoichiometry resulting in dosage compensation to produce the same amount of product (Birchler and Veitia, 2014). Clustering based on paralog median expression levels did reveal a consistent trend with one paralog having significantly higher median expression levels compared to the other retained paralog of that pair (Figure 3—figure supplement 1). However, as previously demonstrated, the overall expression levels for multi-copy genes is higher than single-copy genes (Figure 2A and B). In addition, the rhythmicity in the paralogous pairs is still apparent, providing further support for focusing on the importance of the pattern of expression rather than simply the overall expression levels. This led us to wonder whether there is any indication that these pattern changes may contribute to new temporal responses to environmental stimuli such as abiotic stress. Gated stress response has been characterized in several plant species including Arabidopsis, poplar, rice, and B. rapa (Fowler et al., 2005; Wilkins et al., 2009; Wilkins et al., 2010; Greenham et al., 2017; Grinevich et al., 2019). If a time-of-day dependent stress-responsive gene in Arabidopsis is now present in two copies in B. rapa with altered expression patterns does this confer an expanded stress response window or does one copy exhibit stress response while the other does not?

To look for indications of divergence in the function, we used a mild drought time-course RNA-seq dataset (Greenham et al., 2017) to test for altered responses to drought among pairs of paralogs. We first used the well-watered control samples to identify the paralogous pairs with altered patterns. Consistent with the divergence in pattern change under circadian conditions, the same trend is apparent under the diel (LD) conditions of our drought time course. Out of 4664 total pairs where two copies show detectable expression levels, 3259 pairs had significantly different patterns under control conditions (p-value<0.01). In the circadian dataset, we observed just 42% of genes with altered pattern but these diel data reveal 70% of pairs with altered pattern. Similarly, 77% of pairs (3602) had significantly different median expression levels (Supplementary file 6). Of the total pairs, 50% had both genes identified as circadian-regulated and an additional 35% had one circadian-regulated gene, emphasizing the importance of the circadian clock in diel conditions. For the pairs with altered patterns under the well-watered conditions, 93% were tested (both genes were expressed) under circadian conditions and 52% showed significant pattern changes. These results point to even greater divergence of paralog expression patterns under real-world, diel conditions compared to circadian conditions.

To identify differential response to drought among paralogous pairs, we first ran DiPALM on all (23,248) expressed genes and tested each gene for pattern and expression changes in response to drought. We identified 3891 with a significant pattern change and only 327 with median expression changes (p-value<0.01; Supplementary file 7). Unlike the circadian dataset, the largest source of variation in expression is due to a phase change consistent with the importance of time-of-day gating of the stress response (Wilkins et al., 2010; Greenham et al., 2017). This provides further support for the dynamic nature of expression regulation and the limitations of simply quantifying transcript abundance differences at single time points. It should also be noted that this drought treatment captured the early signs of drought perception with very subtle expression changes during the first 24 hr and more evident changes in the subsequent 24 hr (Greenham et al., 2017). The ability to detect these distinct patterns using DiPALM highlights the effectiveness of the network pattern approach for capturing unique and unpredicted patterns. To test whether the paralogous pairs exhibiting differential expression patterns under well-watered conditions are enriched for drought-responsive genes, we performed a permutation test. We randomly sampled the same number of pairs from the full set of pairs (3259 out of 4664) for 10,000 permutations to identify the likelihood of selecting drought-responsive genes within the 3891 sampled set. As a result, the differentially patterned copies were enriched for genes with drought responsive patterns (p-value 0.0005; Figure 5A), whereas copies with differential median expression level were not (p-value 0.6457; Figure 5A). With enrichment of drought-responsive genes among these pairs exhibiting different patterns, we wondered whether these pairs are more or less likely to have one or both copies responding to drought. Using the same 10,000 randomly sampled sets of 3891 pairs, we estimated a null distribution of the expected number of pairs with one and two drought-responsive genes. Results from the permutations indicated significant enrichment for pairs in which one member is drought responsive (p-value<0.0001) and no enrichment for both copies being drought responsive (p-value 0.1852; Figure 5B and C). Thus, we observed enrichment for only one but not both paralogs responding to drought stress in B. rapa. This suggests that the genome-wide expansion of expression domains among paralogs is biologically meaningful, in this case for drought stress response. More broadly, these results have important implications for how we capture and characterize transcriptomic responses or ‘states’ when making predictions about paralog function. Temporal, spatial, and conditional regulation can reveal new expression dynamics.

An assessment of functional comparisons among paralog expression levels defines the gene with the highest expression in one or multiple tissue samples as ‘winning’ over the other (Woodhouse et al., 2014). This classification is often referred to when looking for signs of subgenome dominance within polyploid species. With our set of pairs with one drought-responsive paralog, we wondered whether the responsive member of a pair had a higher median expression level under control conditions. Of the 764 pairs with a drought-responsive paralog, the drought responsive gene was the lower expressing member in 420 pairs and the higher expressing member in 344 pairs in the control conditions. Thus, we conclude that transcript abundance, whether at a single time point or combined across a time series, is not a reliable predictor of a gene’s functional importance. As validation of this, we ran a standard linear model test at each time point comparing well-watered and drought treatments and did not detect significant transcript abundance differences for any genes. To detect the initial transcriptional response to drought perception, we had to incorporate the complete transcript profile into the differential expression test. What appears to be very subtle changes in the abundance of a subset of transcripts are contributing to the measured temporal physiological changes in Fv’/Fm’ and stomatal conductance also occurring at specific time points (Greenham et al., 2017). The ability to capture these early transcriptomic responses to the onset of drought offers new insight into how responsive the network is to slight adjustments in the temporal regulation of expression. The next challenge is to capture this fine-scale resolution across genotypes with diverse physiological responses to stress and identify the associated patterns.

Seeds of Brassica rapa subsp. trilocularis (Yellow Sarson) R500 were planted in (3.25’ x 3.625’) pots with a soil mixture of two parts Metro-Mix PX1 + one part Pro-Mix amended with 0.5 mL of Osmocote 18-6-12 fertilizer (Scotts, Marysville, OH). The first photocycle experiment (LD) involved entraining plants under 12 hr light/12 hr dark and constant 20°C (LDHH) for 15 days after sowing (DAS) before transfer to constant light at 20°C (LLHH). The second thermocycle experiment (HC) involved entraining B. rapa R500 plants under 24 hr light with 12 hr 20°C and 12 hr 10°C temperature cycles (LLHC) until 15 DAS before transfer to LLHH. Following 24 hr in constant conditions leaf tissue from the youngest leaf was collected and flash-frozen in liquid nitrogen every 2 hr for 48 hr. Lights in the chamber at plant height were ~130 µmol photons m⁻² s⁻¹. Plants were shifted to constant light and temperature (LLHH) for 24 hr before starting the leaf tissue sampling at ZT24. Leaf tissue (~100 mg) from the youngest fully developed leaf was harvested and frozen in liquid nitrogen every 2 hr for 48 hr (ZT24 – ZT72). At each time point, leaf tissue from 10 plants was collected.

Leaf tissue was ground to a fine powder using a Retsch Mixer Mill MM 400 (Vendor Scientific, Newtown, PA). The mRNA extraction was performed according to Greenham et al., 2017 and the strand-specific libraries according to Wang et al., 2011. For each leaf sample (~100 mg), 1 mL lysis binding buffer (LBB) was used to resuspend ground tissue. For each of two biological replicates, 200 µL aliquots of LBB lysate from each of five plants were pooled before mRNA isolation. Library size and quality was verified using a 2100-bioanalyzer (Agilent Technologies, Santa Clara, CA). Libraries were indexed and pooled into 12 sample sets and sequenced as 101 bp paired-end reads using Illumina HiSeq2500 (Illumina, San Diego, CA). Raw data have been submitted to GEO (http://ncbi.nlm.nih.gov/geo↗) under accession number GSE123654↗ (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE123654↗). The raw fasta reads were filtered using trimmomatic (Bolger et al., 2014) with mostly default settings (ILLUMINACLIP:./Tru-Seq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:25 MINLEN:50). Reads were aligned to the B. rapa R500 genome Brapa_R500_V1.2.fasta (https://genomevolution.org/CoGe/GenomeInfo.pl?gid=52010↗) using tophat2 (Kim et al., 2013; https://ccb.jhu.edu/software/tophat/index.shtml↗) with the following options: --library-type fr-firststrand -I 12000 G R500_v1.6.gff -M --max-segment-intron 12000 --max-coverage-intron 12000. Sample LD_ZT62_rep2 was identified as an outlier and removed, to avoid over-weighting rep1 the rep2 values were imputed by averaging the values of ZT60_rep1 and ZT64_rep1. Raw counts were generated in Subread version 1.6.3 (Liao et al., 2019; http://subread.sourceforge.net/↗) with the following options: -F SAF -M -T 6 --fraction -s 2 p -B -C. Count data was normalized using edgeR (Robinson et al., 2010; McCarthy et al., 2012; https://bioconductor.org/packages/release/bioc/html/edgeR.html↗) version 3.22.1 using 'calcNormFactors' and log₂ FPKM values were calculated using 'rpkm' with log = TRUE, prior.count = 0.1.

We created an R package that takes a raw count table of RNAseq data and runs differential pattern analysis from time series gene expression data. DiPALM is available through the Comprehensive R Archive Network (CRAN; https://cran.r-project.org/package=DiPALM↗) or via the Greenham Lab Github page (https://github.com/GreenhamLab/Brapa_R500_Circadian_Transcriptome↗; Greenham, 2020; copy archived at swh:1:rev:9d59bbc84659cbbdffa96413694e01298c9868bb↗). A sample dataset is provided with the package along with a detailed vignette and manual that describes the analysis pipeline.

A published set of simulated time-course expression datasets (Spies et al., 2019) was used for benchmarking. Data consists of eight separate time courses meant to simulate cell culture treatments with samples taken at various times after treatment along with untreated controls at each timepoint. In each dataset, 1200 genes were intentionally perturbed at various intensities using a set of predefined patterns of perturbation. These were taken to be the ‘differentially expressed’ genes or ‘positives’ in the test. This benchmarking test was run through DiPALM as well as various other methods tested by Spies et al. Table S1 shows the area under the receiver operating characteristic (AUROC) curve for all tests. The three top-performing methods found by Spies et al. are shown for comparison: splineTC (Michna et al., 2016), maSigPro (Nueda et al., 2014), and impulseDE2 (Fischer et al., 2018). Both the differential pattern (kME) and differential expression (median) detection parts of DiPALM were tested along with a combined score which simply takes the sum of the scores from both parts (combined). The method of running a traditional differential expression analysis on each timepoint separately and then taking the most significant timepoint for each gene was also tested (pairwise).

The entire analysis pipeline, starting with raw count data, was carried out using the R Statistical Programing Language (R Development Core Team, 2018) along with the Rstudio integrated development environment (R Studio Team, 2015). A comprehensive R markdown file is available through the Greenham Lab Github page (Greenham, 2020; https://github.com/GreenhamLab/Brapa_R500_Circadian_Transcriptome↗). This analysis script includes all data processing, statistical analysis and plotting that was used for this publication. Additional R packages were used in this analysis, including ‘edgeR’ (Robinson et al., 2010; McCarthy et al., 2012), ‘stringr’ (Wickham, 2019), ‘ggplot2’ (Wickham, 2016), ‘rain’ (Thaben and Westermark, 2014), ‘WGCNA’ (Langfelder and Horvath, 2008; Langfelder and Horvath, 2012), ‘circlize’ (Gu et al., 2014), and ‘pheatmap’ (Kolde, 2019).

For paralog expression comparisons we converted all three-copy paralogs into three two-way comparisons (copy 1 versus copy 2, copy 1 versus copy 3, copy 2 versus copy 3) in order to perform one analysis with the two-copy tests and enable consistency in the interpretation of the results (see line 606 of the markdown file). For the heatmap presented in Figure 3, Br1 and Br2 were assigned based on the sign of the summed t-statistics from the limma output (see line 709 of the markdown file).

For the drought RNAseq analysis we used our previous dataset (Greenham et al., 2017) and aligned the data to our new B. rapa R500 genome assembly (https://genomevolution.org/CoGe/OrganismView.pl?org_name=Brassica%20rapa↗) using the same pipeline described for the circadian datasets. These raw counts are available in a file called ‘DroughtTimeCourse_CountTable.csv’ on the Greenham Lab Github page (Greenham, 2020; https://github.com/GreenhamLab/Brapa_R500_Circadian_Transcriptome↗).

Using a list of canonical CNS sequences derived from Haudry et al., 2013, the B. rapa R500 and TAIR10 genomes were annotated for those CNSs using NCBI BLAST+. A local BLAST database for each reference genome was created using the command:

To get an initial set of CNS alignments, the following command was run for each reference genome:

Filtering was disabled in favor of a different scheme also used in Yocca et al., 2019. All alignments with a bitscore of 28.2 were dropped, and alignments with a smaller than 60% coverage of the CNS sequence (BLAST+’s ‘qcovs’ value) were also dropped. Finally, to ensure the CNS alignments are reasonably unique, all of the alignments for a particular CNS sequence were discarded if they appeared more than once in the TAIR10 reference, or more than three times in the B. rapa R500 reference. Since the B. rapa genome has undergone a genome triplication event relative to A. thaliana, three occurrences were seen as the maximum reasonable amount. Two BED files were generated for each genome containing the coordinates of each resulting alignment.

To associate the resulting CNS alignments with genes in the references, BEDtools was used to find the closest gene to each CNS location:

bedtools closest -s -t all -D a -a < cns.bed> -b < reference.bed>>CNS_prox_genes.txt

cns.bed is one of the two BED files generated in the previous section, and reference.bed is a gene annotation for the respective reference genome. The -s option constraints reported associations to be only on the same strand -- that is, CNS alignments and genes must appear on the same strand. -D tells BEDtools to report distances and ensures that the reported distances are signed (negative for occurring before the gene, positive for occurring after, and 0 for being intragenic).

Motif analysis was performed using HOMER -- specifically, findMotifsGenome.pl. This program requires that two sets of sequences be provided: a set of target sequences to be searched for motifs, and a set of background sequences for comparison to the target sequences to be compared to. Motif analyses were performed on three different target groups.

The first analysis consisted of searching the CNSs against an ‘extended’ promoter background. For each transcription factor group, the CNSs corresponding to all of the target genes in Ath, Br1, and Br2 separately were pulled and placed into three BED files containing the coordinates of the CNSs in TAIR10 and B. rapa R500, respectively. A background set of sequences was then generated for every gene in the TAIR10 and B. rapa R500 genomes using BEDtools:

bedtools slop -s -i < reference.bed> -g < reference.genome> -l 2000 r 0 > 2 kb_and_gene.bed

Reference.bed is a gene annotation for the reference genome, and reference.genome is a text file containing the lengths of each chromosome that BEDtools uses to ensure that the coordinates it outputs are valid. This outputs a BED file that annotates a background consisting of a 2 kb promoter region before each gene, as well as the gene itself, to the end of the 3’ UTR. This larger background sequence was selected rather than the 2 kb promoter alone since many CNSs occurred in the UTRs and introns, and so using only 2 kb promoters would leave out background sequence relevant to many of the CNSs, potentially skewing the results.

The second analysis searched the ‘extended’ promoter sequences against themselves. The target set consisted of ‘extended’ promoters corresponding to target genes in each transcription factor group, and the background consisted of a total list of sequences, including the target group, as per HOMER’s recommendations.

The third analysis consisted of a more traditional motif search of promoter regions against promoter-only background. In this case, neither the target nor background sequences contain CDS, introns, or UTRs as with the ‘extended’ regions defined in the previous two analyses. These promoters were pulled using BEDtools:

bedtools flank -s -i < reference.bed> -g < reference.genome> -l 2000 r 0 > 2 kb_promoters.bed

Everything is identical as above, except that bedtools flank does not include the genes themselves in the output, and only generates locations for promoter regions. As before, promoters corresponding to TF target groups were selected and then analyzed against the entire set of promoters. HOMER was run using its included plant motif database on all three datasets (–). Default parameters were used. Supplementary files 9 10

Motif analyses were performed separately for the Ath, Br1, and Br2 target groups. Given that three analyses were performed for each of these target groups, a total of nine motif analyses were performed for each TF group for a total of 108 motif analyses. Only the ‘knownMotifs’ output of HOMER was considered, which consists of a database search of target and background sequences against known plant motifs with a hypergeometric test to quantify significance. The de novo results were not used. For each of the 108 analyses, the outputs of the ‘knownMotifs’ analysis were simplified by grouping together found motifs that correspond to the same DNA-binding protein domain. Of each of these domain groups, the best p-value out of all the motifs found for that domain was selected as representative for the entire group. For each TF group, significant (p<0.01) domains were counted for the Ath, Br1, and Br2 target groups, for the CNS, ‘extended’ promoter, and promoter-only motif analyses.

The B. rapa R500 genome was used for all analyses in this study. It is available through CoGe under the Genome ID: 52010 (https://genomevolution.org/CoGe/GenomeInfo.pl?gid=52010↗).

Circadian time-course RNA-seq data for B. rapa entrained in light cycles (LDHH) and temperature cycle (LLHC) are available through NCBI GEO under accession number GSE123654↗ (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE123654↗).

Diel time-course RNA-seq under mild drought stress and well-watered conditions is from Greenham et al., 2017. Raw data are available through NCBI GEO under accession number GSE90841↗ (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90841↗). For this study, the data were re-mapped to the B. rapa R500 genome and raw count data are available through a Greenham Lab Github repository (Greenham, 2020; https://github.com/GreenhamLab/Brapa_R500_Circadian_Transcriptome↗). The file is named ‘DroughtTimeCourse_CountTable.csv’.

Full details of the data analysis including explanations, code and additional data files can be found in the above Github repository. The analysis is laid out in an R markdown file named ‘Brapa_CircadianTranscriptome_Markdown.Rmd’.