De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca)

doi:10.21203/rs.3.rs-4365578/v1

Download PDF

Research Article

De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca)

https://doi.org/10.21203/rs.3.rs-4365578/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Forests face an escalating threat from the increasing frequency of extreme drought events driven by climate change. To address this challenge, it is crucial to understand how widely distributed species of economic or ecological importance may respond to drought stress. Here, we used RNA-sequencing to investigate transcriptome responses at increasing levels of water stress in white spruce (Picea glauca (Moench) Voss), distributed across North America. We began by generating a transcriptome assembly emphasizing short-term drought stress at different developmental stages. We also analyzed differential gene expression at four time points over 22 days in a controlled drought stress experiment involving 2-year-old plants and three genetically unrelated clones.

Results

De novo transcriptome assembly and gene expression analysis revealed a total of 33,287 transcripts (18,934 annotated unique genes), with 4,425 unique drought-responsive genes. Many transcripts that had predicted functions associated with photosynthesis, cell wall organization, and water transport were down-regulated under drought conditions, while transcripts linked to abscisic acid response and defense response were up-regulated. Our study highlights a previously uncharacterized effect of drought stress on lipid metabolism genes in conifers and significant changes in the expression of several transcription factors, suggesting a regulatory response potentially linked to drought response or acclimation.

Conclusion

Our research represents a fundamental step in unraveling the molecular mechanisms underlying short-term drought responses in white spruce seedlings. In addition, it provides a valuable source of new genetic data that could contribute to genetic selection strategies aimed at enhancing the drought resistance and resilience of white spruce to changing climates.

Transcriptomics

drought tolerance

conifer

water stress

global change

transcription factor

lipid metabolism

Climate change projections raise concerns about trees having to cope with intensified and frequent extreme events [1]. Drought is currently causing heightened disruptions in forests, diminishing resilience and increasing mortality rates [2]. Future climates may reduce the productivity of essential conifer species in forests, underscoring the importance of prioritizing resilient and productive species for warmer, drier conditions. In this regard, recent research efforts started to look at methods and approaches for the selection and breeding of more resilient conifers (e.g., Depardieu et al., 2020 [3]; Laverdière et al., 2022 [4]; Soro et al., 2023 [5]). However, in spite of recent progress [6–9], there are still large gaps in our understanding of the complex molecular response of trees to drought at the transcriptome-wide level.

Drought response in long-lived woody plants such as conifers involves an extensive network of genes and complex molecular mechanisms. Indeed, several major gene classes or families are involved in short-term physiological responses to drought, including reduced growth, alterations in photosynthesis, regulation of water transport and hormones within the tree, stomatal regulation, and the maintenance of osmotic balance [10]. More specifically, the pivotal role of the phytohormone ABA as a precursor molecule in drought stress signaling is widely acknowledged in coniferous species [8]. In addition, aquaporins (AQPs) and ion channels facilitate the transport of water and ions across cell membranes, playing a critical role in regulating water balance in trees [11]. Previous studies have revealed modifications in the regulation of genes responsible for synthesizing and transporting defense molecules such as flavonoids and terpenoids [7, 12], antioxidants involved in ROS scavenging [13, 14], osmoprotectants such as carbohydrates [15, 16], and proline [17, 18]. Heat shock proteins (HSPs) play a pivotal role in safeguarding and stabilizing proteins under drought conditions [16]. Similarly, chaperone proteins like dehydrins, a subset of late embryogenesis abundant (LEA) proteins, maintain protein and cell membrane stability throughout the hydric constraint [6, 19]. In such conditions, alterations in the composition and structure of the cell wall are directed by the control of genes linked to the synthesis of cell wall polysaccharides and membrane [20, 21]. Finally, genes involved in transcriptional regulation networks, such as AP2/ERF, bZIP, TCP, WRKY, and MYB transcription factors, coordinate molecular responses to drought [14, 22–24].

White spruce (Picea glauca (Moench) Voss) is a conifer species widely distributed across Canada and the northern USA, known for its straight grained and strong wood, making it valuable for lumber and pulp production [25, 26] in addition to its ecological importance. Its rapid growth in various environments makes white spruce an important species in forestry and reforestation efforts, representing a significant portion of Canada's forest inventory and being one of the most widely planted tree species [25, 27]. It is also considered to be a model conifer species for genetic and genomic investigations [28]; several studies have shown the susceptibility of white spruce to drought, as demonstrated by a marked reduction in growth [3, 5, 29, 30], increased mortality [31, 32], and changes in population abundance and distribution [33]. Similarly to numerous conifers of the Pinophyta group, white spruce swiftly initiates the ABA pathway under drought conditions, leading to early stomatal closure [34]. This process reduces water loss while also concurrently decreasing photosynthetic uptake, thereby posing a risk of carbohydrate depletion if the drought persists [35]. The importance of intraspecific genetic variation for drought response has also been recently highlighted in white spruce [3, 36]. Thus, characterization of its intraspecific variability at the molecular level appears essential to better delineate the tolerance threshold of stress and identify potential genetic traits governing a tree species' drought response and resilience [37]. Understanding white spruce's molecular response to drought is crucial for elucidating acclimatization and adaptation mechanisms, informing sustainable forest management, and enhancing resilience to changing climates [28].

Recent studies that have identified genes associated with drought adaptation in white spruce are often based on the testing of more of less extensive lists of candidate genes rather than the entire transcriptome [7, 38]. Despite the rapid proliferation of genomic resources, including nuclear [39, 40], mitochondrial, and chloroplast genomes [41], gene catalogs [42], SNP catalogs [43–45], and quantitative trait loci (QTL) analyses [12, 46, 47] brought about by advancements in Next Generation Sequencing (NGS) and high-throughput genotyping technologies, these resources still remain incomplete and fragmented [48, 49]. Considering the extensive gene flow linking natural populations of white spruce [50], the relatively recent nature of local genetic adaptation to climate following Holocene recolonization [51], and the highly multigenic nature of local adaptation to climate in spruces [38, 51], it is anticipated that there may be dozens to hundreds of genes potentially involved in drought response and resilience. Consequently, it is imperative that genome-wide and/or transcriptome-wide studies are conducted to elucidate the molecular bases of these polygenic traits more comprehensively.

This study was carried out in white spruce and had two main objectives. First, we developed a de novo transcriptome assembly based on RNA sequencing (RNA-Seq) of samples sourced from trees across distinct developmental stages and subjected to diverse stress factors. This approach aimed to capture a broad sampling of expressed genes, specifically emphasizing the response to drought conditions. Second, we investigated changes in gene expression in foliage tissues aiming to identify the molecular mechanisms underlying response to short-term water stress. We sought to characterize changes in gene expression and identify key genes, metabolic pathways, and regulators that contribute to response to drought in white spruce, while also exploring the intraspecific variation in drought-responsive genes.

2.1. Generation and annotation of the white spruce transcriptome assembly, GCAT 4.0

2.1.1. Plant material

A de novo transcriptome was assembled from RNA-Seq data obtained from three distinct experiments involving the collection of Picea glauca foliage. Within these experiments, sample types were selected to cover a wide range of conditions, with a particular focus on drought conditions, and represent diverse genes that are regulated in response to stress. A total of 16 samples came from a common garden experiment belonging to the International Diversity Experiment Network with Trees (IDENT) network, where eight trees had been subjected to water exclusion and eight others to summer irrigation since 2014 ("Experiment 1", see Methods S1 and Table S1 in Additional File 1 for details). Six other samples came from a greenhouse experiment with a budworm-induced biotic stress treatment ("Experiment 2"; Methods S1 and Table S1 in Additional File 1). The inclusion of data from Experiment 2 was motivated by the reported points of convergence in the signalling networks involved in responses to abiotic and biotic stresses in plants [52, 53]. Six samples were from a greenhouse drought stress experiment on young clonal seedlings including three water-stressed and three well-watered seedlings (control seedlings in "Experiment 3"; Methods S1 and Table S1 in Additional File 1), as previously described in Stival Sena et al. (2018) [6].

2.1.2. Strategy and quality assessment of the assembly

Quality of RNA-seq raw sequence data was first checked using FASTQC v0.11.9 [54]. Raw reads were cleaned using Trimmomatics.0.39 [55] to remove poorly sequenced nucleotides and remaining adaptor sequences. Clean reads were further filtered for length longer than 30 bp. For each sample, filtered reads were used to produce a transcriptome assembly using the SGA [56] and IDBA-UD assemblers [57] integrated within the a5 pipeline [58]. Transcriptome assemblies were then scaffolded with one another using LINKS 1.8.6 [40]. The resulting consensus assembly was then scaffolded again with a previously published Picea glauca transcriptome assembly [42] using LINKS 1.8.6 and sequences shorter than 500 bp were removed as they were not likely to code for functional proteins. The completeness of this new assembly, hereafter named GCAT 4.0, was then evaluated using BUSCO (Benchmarking Universal Single-Copy Orthologs) v5.4.3 with -m transcriptome option and sequence comparison with the Embryophyta and Viridiplantae reference databases (odb10) [59]. The number of open reading frames (ORFs) and other complementary statistics were performed using the TRAPID web server and the PLAZA version 4.5 database [60] is available in Table S2.

2.1.3. Functional annotation of transcripts

Functional annotation for the new transcriptome assembly was retrieved by sequence similarity searches using BLASTx of OmicsBox [61] (cut-off E-value of ≤ 10^− 5) against the Refseq database from the NCBI (Accessed September 29th, 2022). The description of protein signatures was obtained after detection of homologous protein domains of translated sequences following a search of the Interpro database using the OmicsBox. Gene Ontology (GO) annotations including GO molecular function, GO biological process and GO cellular component terms were also obtained for each individual transcript using OmicsBox. To obtain a complete annotation of the de novo assembly GCAT 4.0, BLASTx analyses were performed against several public databases such as PlantTFDB and Viridiplantae, using DIAMOND-aligner v.2.0.14 [62]. Analysis parameters were set to "sensitive" mode, k-1, b1.2 and an E-value of ≤ 10^− 5. The OmicsBox assembly annotation has been deposited and is publicly available (https://github.com/ZoeRibeyre/BMC_Article_Ribeyre_et_al.git). BLASTx were also performed against the transcriptomes of six tree and herbaceous species (data downloaded from PLAZA 5.0, sub-sections Locus FASTA Data - Protein files - Selected transcript [63]. Results of BLASTx and BLASTn analyses performed to annotate GCAT 4.0 are detailed in Table S3 and the summary of results are available in Table S4.

The presence of transcription factors (TFs) was additionally corroborated by analyzing BLASTx results against the plant transcription factor database PlantRegMap/PlantTFDB v5.0 (http://planttfdb.gao-lab.org/; [64, 65] and the Refseq database, and based on protein signatures detected using OmicsBox. The complete list of transcription factors and their annotation is reported in Table S5. The number of putative unique genes contained in the de novo transcriptome assembly GCAT 4.0 was determined by BLASTn analysis against the latest white spruce reference genome publicly available on NCBI (WS77111v2, Accessed on July 2022; Table S3).

2.2. Drought stress experiment and transcriptome analysis

2.2.1. Plant material, water treatment, and RNA sequencing

The transcriptomic analyses were carried out from RNA-seq data from the greenhouse drought experiment described by Stival Sena et al. (2018) [6] to characterize the response of Picea glauca seedlings ("Experiment 3"; Methods S1 and Table S1 in Additional File 1). Briefly, data were obtained from foliage of three genetically unrelated 2-year-old clones (C8, C11 and C95). A sampling of six plants per condition (well-watered control and drought-stressed without watering) comprising two replicates per clone was taken at 0, 14, 18 and 22 days (Fig. 1, Table S1).

2.2.2. Differential expression and enrichment analyses

Differential expression analyses between drought-stressed and control seedlings were carried out to identify transcripts involved in conifer drought response. A first set of analyses was carried out at each time point (Analysis 1, Fig. 1) to identify the transcripts significantly up- or down-regulated throughout drought intensification (Table S6). Considering the insufficient number of replicates to perform a clone-by-clone analysis for each time point, the transcripts differentially expressed for each clone were determined by comparing water stress versus control conditions for all time points (Analysis 2, Fig. 1; Table S7). Differential expression analyses were conducted by pseudo-aligning high-quality reads against the assembly GCAT 4.0 using Kallisto v0.48.0 [66]. Read counts were normalized using DESeq2 [67] and DESeq-normalized expression values were then used to calculate the fold change for a given transcript expressed as a log2-fold change (LFC). Differentially expressed transcripts (DETs), and corresponding genes (DEGs; Table S6), between drought-treated and control samples were then identified using the R package DESeq2 [67] with an absolute threshold of 2 for LFC and an adjusted p-value of 0.05.

Gene ontology (GO) enrichment analyses were performed on significant DETs using the OmicsBox and a Fisher's exact test [61]. GO enrichment analyses were based on lists of DETs whose expression was significantly regulated at each time point (Table S8). Venn diagrams were generated to highlight unique and shared DEGs between time points and clones using Venn diagrams (ggVennDiagram v1.2.2 R package, [68]; Venndetail v1.16.0 R package, [69].

The functions and the regulation of DEGs were visualized using metabolic pathway diagrams from MapMan v3.6.0RC1 [70, 71] (Table S9). MapMan manages a hierarchical tree structure that describes different functional categories or "Bins" according to the MapMan nomenclature. The Mercator4 online tool was used to create the mapping file required to run MapMan from a FASTA file (the de novo assembly transcriptome GCAT 4.0) by assigning sequences to the corresponding Bin terms [72]. This analysis was conducted at the gene level; therefore, the most representative transcript for each gene was selected for these analyses. All available metabolic pathway diagrams were downloaded from the MapMan interface and visualized following the analyses. We selected the pathway diagrams for metabolism (X4.5 Metabolism Overview R5.0) and photosynthesis (X4.5 Photosynthesis R5.0) as those had the most DEGs identified in our study. To improve understanding of the results in the context of drought-related response, we graphically synthesized parts from the Cellular_response_overview pathway and abiotic results from the Biotic Stress pathway by removing sections with very few or no DEGs.

3.1. Statistics and quality assessment of the new white spruce de novo assembly, GCAT 4.0

The de novo transcriptome assembly conducted in this study encompasses a total of 33,287 unique transcripts, corresponding to 18,934 unique genes, as determined through BLASTn analysis against the reference genome of white spruce (Table S3). A total of 33,283 potential open reading frames (ORFs) with an average length of 852 base pairs (bp) were identified using the TRAPID pipeline [60]. The contig N50 stands at 1,816 bp, and the contig N90 is 746 bp (Table S2). Sequence length distribution showed that the transcriptome assembly encompassed a wide range of transcript sizes: 56.2% spanning from 1,000 bp to 4,000 bp, 40.7% ranging from 500 bp to 1,000 bp, and 3.1% exceeding 4,000 bp (Fig. 2A), and a median length of 1,173 bp and a mean length of 1,488 bp (Table S2). To assess the assembly's completeness, a BUSCO analysis was performed using the Viridiplantae odb10 and Embryophyta odb10 databases. Within the 425 Viridiplantae odb10 BUSCO groups, 94.1% were identified as complete and single-copy, 2.6% as complete and duplicated, 3.1% as fragmented, and only 0.2% were absent (Fig. 2B). Additionally, among the 1,614 Embryophyta odb10 BUSCO groups, 82.9% were categorized as complete and single-copy, 4.5% as complete and duplicated, 4.0% as fragmented, and 8.6% were found to be missing (Fig. 2C).

3.2. Temporal dynamics in gene expression in response to drought

Analysis of differentially expressed genes (DEGs) identified 4,425 out of the 18,934 detected unigenes in response to drought, with 1,370 up-regulated unigenes and 3,055 down-regulated unigenes (Fig. 3). As the water stress intensifies over time, an increasing number of both up-regulated and down-regulated genes were observed, showing an initial response affecting a few genes followed by changes in a very large number of genes expressed; 16 DEGs were identified on day 0, followed by 88 genes on day 14. Subsequently, the number of regulated genes escalated to 1,620 on day 18, reaching a substantial peak of 4,186 on day 22 (Fig. 3B). The DEGs were not the same from the beginning to the end of the treatment. Specifically, an overlap of 37% was observed exclusively for up-regulated genes between days 18 and 22 (Fig. 3D), while a 23% overlap was observed for down-regulated genes (Fig. 3C).

3.3. Identification of key functions involved in short-term drought response

The MapMan analysis illustrated the key metabolic pathways involved in the water stress response of white spruce seedlings. It showed that 32.4% of the 4,425 drought-responsive genes were assigned to Bins belonging to 32 major functional groups (Table S9). The most represented functions encompassed enzymatic classification (35.46%), solute transport (10.08%), RNA biosynthesis (9.19%), protein modification (5.95%), cell wall organization (4.65%), protein homeostasis (4.44%), phytohormone action (3.55%), photosynthesis (2.82%), carbohydrate metabolism (2.77%), and lipid metabolism (2.56%) (Table S9). The level 3 Gene Ontology (GO) annotation identified highly represented biological processes (BP) such as transmembrane transport (204 DEGs), signaling (107 DEGs), carbohydrate metabolism (168 DEGs), and lipid metabolism (107 DEGs). Notably, both photosynthesis and cell wall biosynthesis/organization processes had a substantial proportion of down-regulated DEGs (95.6 and 78.1%, respectively). The GO annotation also revealed a substantial presence of molecular functions (MF), such as oxidoreductase activity (385 DEGs), hydrolase activity (344 DEGs), and catalytic activity (260 DEGs) (Fig. 4A).

The GO enrichment analysis performed on differentially expressed transcripts (DETs) indicated that BP and MF changed early and late in the experiment. Before day 18, MFs associated with the regulation of molecular function, cellular process, and catalytic activity were enriched. In contrast, up-regulated DETs on day 18 and day 22 were associated with responses with osmotic stress, hormone stimuli including abscisic acid (ABA), reactions to external stimuli, defense mechanisms, and carbohydrate and lipid metabolism. The photosynthesis process was among the enriched BPs linked to down-regulated DETs at day 22 (Fig. 4A-B, Fig. 5A-B). Several molecular functions were also enriched in both up-regulated and down-regulated DETs, particularly involving catalytic activity and oxidoreductase activity. However, the observed enrichment of lyase and antioxidant activities was unique to up-regulated DETs (Fig. 4B, MF panel).

3.4. Gene expression changes under intense water stress

A distinct MapMan analysis was conducted only on DEGs at day 22, which had by far the most water stress responsive DEGs (4,186 DEGs; Fig. 5, Table S9), and included 68% of the total down-regulated DEGs and 52% of the total up-regulated DEGs (Fig. 3C-D). The data included 268 DEGs associated with cell wall organization (62 unigenes), with a prevalent down-regulation observed in photosynthesis metabolism (45 unigenes), lipid metabolism (43 unigenes), carbohydrate metabolism (39 unigenes), and secondary metabolism (34 unigenes), primarily connected to terpenoids and phenolic compounds (Fig. 5A-B). The redox homeostasis process was well-represented with 126 DEGs (Fig. 5C). Furthermore, the identification of InterPro domains indicated many DEGs encoding Leucine-rich repeat and kinases proteins, alpha-beta hydrolases, AAA + ATPases, and cytochrome P450 specifically on day 22. Moreover, members of heat shock proteins (HSPs), dehydrins, major intrinsic proteins, and late embryogenesis abundant proteins (LEA) were also detected (Fig. S2).

3.5. Identification of drought-responsive transcription factors

A total of 104 transcription factors (TFs) were differentially expressed in the present study, with 63 up-regulated and 41 down-regulated unigenes classified into 15 well-represented classes (Fig. 6A, Table S5). The most represented classes were the RING type zinc fingers (26 DEGs), followed by NAC (15 DEGs) and AP2/ERF (14 DEGs). Notably, the RING and C2H2 type zinc finger genes, as well as the WRKY genes, had a predominantly up-regulated expression under drought. The AP2/ERF and AUX/IAA classes contained an equal number of genes with both up and down regulation, while the CBF/NF, PLATZ, and PHD type zinc finger subfamilies exclusively had up-regulated genes. The data showed that most changes in the expression of these TFs occur after 18 days of drought (Fig. 6B). From day 18 to day 22, a notable increase in the magnitude of the change was observed for most of the transcription factors (Table S5). The expression of several genes occurred after 22 days of drought stress (LFC higher than 5) including sequences for two NACs (JZKD02S0798697.1, JZKD02S0134190.1), one zinc finger (JZKD02S0142429.1) and one AP2/ERF (JZKD02S0821393.1).

3.6. Intraspecific genetic variation of drought-responsive genes

The present study used three genetically unrelated clones and a clone-to-clone analysis identified 638 up-regulated and 63 down-regulated differentially expressed genes, indicating intraspecific gene expression differences under drought (Fig. S3). Overall time points, no down-regulated DEGs were shared among clones (Fig. S3B), and only 21% of up-regulated DEGs were common among all three clones (Fig. S3A). Shared DEGs were involved in BP of defense mechanisms and macromolecule metabolism covering carbohydrates, lipids, and amino acids (Fig. S4A), and were also associated with MF of catalytic activities including transferase, oxidoreductase, lyase, isomerase, and hydrolase activities (Fig. S4C). GO enrichment analysis showed that the most enriched BP or MF was similar across all three clones, including catalytic and antioxidant activities, as well as defense response processes (Fig. S4).

This study presents an expanded white spruce transcriptome under water stress, enhancing transcriptomic resources, and characterizing key regulated genes in this conifer model species. The new transcriptome assembly allowed for a great characterization of key genes that are regulated under drought conditions. Our transcriptomic analysis describes the regulation of genes in white spruce after 22 days of water stress, revealing a significant increase in differentially regulated genes (DEGs) compared to controls, with over 4,000 DEGs by day 22. This robust regulation underscores the intensity of the treatment and the strong response in white spruce. The gene expression data suggests that the treatment disrupted numerous physiological processes, as expected for this drought-sensitive species. We identified several drought-responsive genes associated with photosynthesis, growth, water transport, sugar and lipid metabolism, and defense mechanisms.

4.1. Quality of the new transcriptome assembly

A new transcriptome assembly of white spruce has been generated based on needles representing different developmental stages (seedlings and saplings) and exposed to various conditions, notably water stress to complement the previously published and 2011 dated representation of genes expressed under such environmental conditions (Fig. 1). The completeness achieved in the GCAT 4.0 assembly is consistent with similar investigations conducted on various conifer species [73–76]. Our assembly approach yielded a high proportion of complete and single-copy genes, with minimal redundancy of complete genes at 2.6% when compared to the Viridiplantae database (Fig. 2). The genome size of white spruce is 20 Gb [40] and the number of functional genes is estimated at 30,410 [77]. The annotation analysis of the GCAT 4.0 assembly revealed 18,934 unigenes, representing roughly 62.26% of the total estimated genomic gene count, highlighting the substantial representation of genes, especially considering that the assembly exclusively originated from needle tissue. The GCAT 4.0 transcriptome assembly represents a robust foundation that complements the previously published assembly to investigate the molecular pathways involved in the response of white spruce needles to drought stress.

4.2. Signaling and hormonal response to drought

In response to water deficit, plants initiate a cascade of hormonal and signaling pathways that orchestrate both molecular and physiological responses toward drought tolerance. These processes involve the activation of genes responsible for the synthesis and signaling pathway of the stress hormone abscisic acid (ABA), which is facilitated by a variety of protein kinases and tyrosine phosphatases [78]. Our transcriptomic analysis identified 136 putative protein kinases with drought-responsive expression with approximately two-thirds being down-regulated. We also observed an up-regulation of three tyrosine phosphatases (Table S6). Isohydric species, like white spruce, activate early stomatal closure in response to drought [34], with ABA playing a key role in reducing water loss [79, 80]. While it has been traditionally suggested that ABA biosynthesis and signaling occur in the roots before being transported to the leaves to initiate stomatal closure in drought-stressed plants, recent research indicates that these mechanisms may start directly in the leaves of pine and spruce species [81, 82]. In our study, biological processes (BP) related to ABA were enriched on days 18 and 22 (Fig. 4B). The differentially expressed genes (DEGs) associated with ABA biosynthesis and signal transduction were primarily identified at days 18 and 22, but some were also detected within the first 14 days of treatment. Specifically, we identified one up-regulated gene related to NCED3 (9-cis-epoxycarotenoid dioxygenase 3), a key enzyme involved in ABA synthesis and previously observed in the drought stress response of Picea abies [8] and Pinus massoniana [24]. Additionally, we found one up-regulated gene associated with the ABA receptor PYL, which plays a role in inhibiting PP2C (2C-type protein phosphatases), known as a negative regulator of the ABA-signaling enhancer SNF1-related protein kinase (SnRK2) [8, 24]. Our findings support the significance of ABA-related genes in the response of white spruce and suggest that the intensity and duration of the stress amplify this signaling pathway.

On days 18 and 22, several up-regulated DEGs related to hormones other than ABA, particularly auxin (22 DEGs), as well as ethylene (15 DEGs) and jasmonate (1 DEG) (Table S6) were observed. We identified five putative AUX/IAA sequences, known to be involved in early auxin signaling and regulated in response to drought [83]. We observed five up-regulated and four down-regulated genes belonging to the SAUR (small auxin upregulated RNA) -like auxin-responsive protein family, which may influence tree drought tolerance by establishing leaf auxin concentration gradients and regulating stomatal closure [84]. The expression of six putative Dormancy/auxin-associated proteins, which play pivotal roles in responding to stress and impacting plant growth and development, was also detected (Table S6; [85]. Thus, our findings suggest that drought stress initiated hormonal signal transduction, particularly in the case of auxin, which exerts an influence on growth and photosynthesis by modulating CO₂ uptake in white spruce.

4.3. Negative impact of drought on photosynthesis, growth, and water transport

The numerous down-regulated genes linked to photosynthesis, particularly showing a more pronounced decline after 18 and 22 days of drought treatment (Fig. 4, Fig. 5B), suggest an abrupt disruption of photosynthesis as the drought stress intensifies. Water availability significantly impacts photosynthesis, often causing a limitation in CO₂ uptake due to reduced stomatal and mesophyll conductance [86]. Alterations in photosynthesis can also be attributed to metabolic disruptions induced by oxidative stress, leading to the degradation of cellular membranes, components of the electron transport chain, and photosynthetic pigments, among others [87, 88]. Here, three DEGs were associated with rubisco activity, including two encoding Ribulose-1,5-bisphosphate carboxylase/oxygenase and one related to rubisco activase (Table S6), which plays a pivotal role in the assimilation and fixation of CO₂ [86]. We observed a down-regulation of genes associated with critical components of the electron transport chain, including one DEG related to the cytochrome b6f complex, seven DEGs associated with Photosystem I (PSI), and four DEGs linked to Photosystem II (PSII). The cytochrome b6f complex expedites the movement of electrons between these two photosystems, resulting in the formation of a proton gradient that drives the synthesis of adenosine triphosphate (ATP) [89]. In plants, PSI and PSII play pivotal roles in capturing light energy and facilitating the transfer of electrons within the electron transport chain [90]. In line with previous studies, our findings suggest that prolonged periods of water stress can adversely affect both PSI and PSII [91, 92]. We also observed a decrease in the expression of 13 DEGs associated with photosynthetic pigments such as chlorophyll a, chlorophyll b, and carotenoids (Table S6), which is in line with previous research conducted on conifers [88, 92, 93]. Our study highlights a significant disruption of photosynthesis in white spruce under drought conditions, which may be due to both reduced CO₂ uptake and damage to numerous components within the photosynthetic chain.

Water stress in trees leads to reduced growth, even before a decline in photosynthesis occurs [94, 95]. This growth reduction involves decreased cell wall expansion due to turgor loss and osmotic imbalances, as well as a decline in cell division and wall construction [96]. In our study, two potential osmotin/thaumatin-like (OTL) proteins had decreased expression but a further nine OTL genes were induced. These genes play a role in maintaining cellular osmolarity during stress, as indicated by [97], suggesting a probable osmotic adjustment in white spruce under drought conditions. Water relations in trees are profoundly impacted by water stress, and the regulation of water transport and cell turgor pressure relies on specialized water channels called aquaporins (AQPs). Consistent with the substantial decrease in water potential measured in the same white spruce seedlings subjected to the same drought experiment [6], the down-regulation of ten aquaporins, specifically plasma membrane intrinsic proteins (PIPs), indicated a reduction of water transport in needles. These observations align with previous research in spruces and pines [22, 24, 98] and support a water conservation mechanism by the reduction in AQPs expression during water stress in conifers. The enrichment of down-regulated transcripts related to cell wall organization or biosynthesis (Fig. 4A-B) highlights the reduction in cell division and wall construction under drought conditions. We identified 17 DEGs linked to both the cellulose synthase (CesAs) involved in cellulose synthesis within primary cell walls, and cellulose synthase-like (CSLs) families recognized for their contribution to secondary cell wall synthesis [20]. Consistent with previous findings in water-stressed Abies alba seedlings, we observed a decreased expression of genes encoding xyloglucan endotransglucosylase/hydrolase (XTH), a crucial enzyme involved in plant cell wall reconstruction [23, 99]. These findings emphasize the disruption of several crucial growth-related processes in white spruce induced by drought.

4.4. Regulation of the carbohydrate and lipid metabolisms

We reported an enrichment of carbohydrate metabolism under drought conditions on days 18 and 22 (Fig. 5A). A common defense mechanism in drought-affected trees is to reallocate carbon resources away from growth and toward storage of non-structural carbohydrates (NSCs), such as starch and soluble sugars, e.g., sucrose. The concentration of these compounds increases in root and woody tissues and contributes to maintaining osmotic balance [95, 100]; the compounds may serve as carbon precursors for the synthesis of defense compounds and act as signaling molecules [101]. In our study, six differentially expressed genes (DEGs) were associated with sucrose synthase and eight with the sucrose and hexose transporters SWEETs (Table S6), suggesting that sucrose levels increased in white spruce seedlings after several days of water stress. While competition for a limited pool of available resources has long been considered the driving force behind the trade-off between growth and defense [102], recent findings in Arabidopsis thaliana suggest that the incompatibility between growth and defense may also be due to the antagonistic nature of the molecular pathways regulating these two processes [103].

Lipids play essential roles in cell membrane structure, energy storage, and signaling [104]. Various conifer species, such as those found in the Larix, Pinus, and Picea genera, possess substantial lipid reserves [105, 106], but our understanding of lipid metabolism in conifers under water deficit conditions remains limited. Lipid metabolism was altered in response to drought stress in our experiment, primarily affecting glycerolipid metabolism (Fig. 5A). Glycerolipids are crucial for thylakoid lipid bilayer formation and efficient photosynthesis, and decreased levels of these molecules have been linked to reduced photosynthesis in higher plants [107]. Drought induced the regulation of genes associated with fatty acid metabolism, leading to the up-regulation of putative malate synthases (3 DEGs), citrate synthases (2 DEGs), and isocitrate lyase (1 DEG) (Table S6). These enzymes play a crucial role in the glyoxylate cycle, providing essential precursors for gluconeogenesis, the process of converting non-carbohydrate precursors into carbohydrates [108]. While most research on conifers under water stress has traditionally focused on sugar metabolism, lipid metabolism has frequently been underemphasized. Nonetheless, our findings highlight a shift in the regulation of genes associated with lipid metabolism, underscoring its active role in drought responses in white spruce. This aspect merits deeper exploration in coniferous species.

4.5. Drought-responsive genes coding for protective defense and stress resistance and resilience

A strong representation of antioxidant activity was observed among the DEGs in our study (Fig.s 4A-B, 5A-C), with increased expression of putative glutathione peroxidases (3 DEGs), glutathione S-transferases (10 DEGs), peroxidases (11 DEGs) and catalases (3 DEGs) (Table S6). Many protective molecules such as antioxidants proteins, late embryogenesis abundant proteins (LEA), heat shock proteins (HSPs), and other types of molecules are involved in drought responses of coniferous species [9]. Reactive oxygen species (ROS) can act as signaling molecules initially during stress, but prolonged or intensified stress increases ROS production, disrupting redox balance and causing oxidative stress [109]. Oxidative stress damages various structures and molecules, such as membrane lipids, proteins, photosynthetic pigments, and nucleic acids [110–112]. This damage seems to be avoided by trees through the production of protective enzymes and molecules to maintain homeostasis and counteract oxidative stress. The balance of antioxidant enzymes plays a major role in ROS scavenging mechanism in plants [113, 114], consistent with a role in drought response in conifers [13, 24, 88]. Our study also identified numerous cytochrome P450 genes (CYTs) that were down-regulated (42 DEGs) and up-regulated (7 DEGs) under stress conditions (Table S6). In contrast, previous observations in Pinus elliottii showed only up-regulation [115]. CYTs play a crucial role in drought response by contributing to antioxidant activities and defense response in plants [116, 117]. CsCYT75B1, a gene of Citrus sinensis, was associated with flavonoid metabolism and was highly expressed after drought stress, contributing to drought tolerance by elevating ROS scavenging activities [118]. Due to the interaction of CYTs whose expression is induced with other key genes in response to water stress [117], the pivotal role of CYTs will require further investigation in white spruce and coniferous species.

HSPs and LEA proteins are chaperone proteins that protect cells from abiotic stress by stabilizing proteins and membranes under stress [9, 10]. Drought-responsive genes coding for Hsp90, Hsp70 and Hsp20 proteins (11 DEGs), Chaperone DnaJ-domain proteins or Hsp40 (9 DEGs) were identified in our study. DnaJ proteins are the main co-chaperones modulating the Hsp70 functions [119], and overexpression of the VaDJI gene coding for a DnaJ protein conferred ABA insensitivity and drought tolerance in transgenic tobacco [120]. In addition, the expression of 33 LEA genes (Table S6), including 13 up-regulated putative dehydrins (Table S10, GCAT3.3 genes) belonging to the LEA sub-group II [121] was noted. As previously observed in white spruce, we found that the expression of PgDhn33, PgDhn35 and PgDhn16 was strongly induced, while the expression of PgDhn37 was repressed (Table S10). Pinaceae dehydrin induction appears to occur after a certain period of drought [10, 122] which could indicate an increasing role of these genes in stress protection as the stress intensity rises. Interestingly, the expression of two key NLRs or NBS-LRRs (nucleotide-binding, leucine-rich-repeat) genes, known to play a central role in plant resilience to stress and linked to resistance pathogens in conifers [123], were induced under drought conditions (GQ03714_K21, GQ03512_J05), as previously reported in white spruce (Table S10; [124].

4.6. Key transcription factors involved in the transcriptional control of drought-responsive genes

Several classes of transcription factors (TFs) including AP2/ERF, NAC, WRKY, MYB, and zinc finger homeodomain TFs were drought-responsive in our study, consistent with other reports in conifer species [13, 14, 24, 125]. A majority of NAC TFs were up-regulated in response to drought as reported in Arabidopsis thaliana [126]. Interestingly, a drought-responsive gene annotated as CCCH-type zinc finger (GQ03707_G19) and a WRKY (GQ04107_D16) in our study were also reported as key genes involved in drought adaptation in white spruce [7]. The expression of 25 TFs including five AP2/ERF, five NAC, eight RING-type zinc fingers were induced after 18 days of drought treatment (Table S5), suggesting their potential importance for drought tolerance in white spruce. The two MYB sequences identified in this study had homologies with putative Arabidopsis thaliana proteins known to enhance protection against oxidative damage or to be involved in growth, phenylpropanoid biosynthesis, and the ABA signaling pathway [127–129]. Drought-induced AP2/ERF genes in our study were close homologs to ethylene responsive elements in other species [130]. Recent findings have shown that certain AP2/ERF genes can improve drought tolerance in conifers [131]. DREB subfamily genes within the AP2/ERF group, induced in response to drought stress, are known to activate downstream stress resistance genes and enhance plant drought resistance independently of the ABA signaling pathway, as observed in Arabidopsis thaliana [132]. In our study, we observed contrasting expression patterns among WRKY members under drought conditions. Similar findings were reported in Pinus massoniana, where some WRKY genes responded to drought stress induced by exogenous ABA, resulting in improved drought tolerance in transgenic tobacco plants [133]. Numerous zinc finger TFs were identified in white spruce (Table S5), and homologs found in the PlantTFDB database indicate a potential role in stomatal aperture, ROS production and drought tolerance [134, 135].

4.7. Intraspecific genetic variation in gene regulation under drought stress: findings and future avenues

Intraspecific genetic variation in drought response is crucial for selection and adaptation in tree populations faced with environmental change [136]. Recent studies in white spruce have highlighted the role of genetic variation among populations [3], as well as the genomic and transcriptomic basis for drought response and resilience [6, 7]. In our study, inter-clonal differences in genes expressed underlies most of the variance in the drought response. Only 21% of the up-regulated and none of the down-regulated genes were common to the three clones, suggesting that the gene network involved in drought response varies widely between genotypes. Alternatively, biological processes and metabolic functions of DEGs were highly similar between genotypes (Table S7). Dissimilar genetic networks and similar metabolic responses involved in water-stress response among genotypes were also observed for two clones of loblolly pine with opposite phenotypes for drought tolerance [14]. In our study, we did not compare inter-clonal drought tolerance, but major phenotypic differences between genetically unrelated clones in drought responses were not observed. Our results are also congruent with those of the fir Abies pinsapo with contrasting gene expression patterns among post-drought phenotypes [125]. Future studies contrasting drought-induced responses between genotypes of various species of conifers and gymnosperms will likely help to appreciate the variance in gene networks underlying conifer drought responses and improve selection strategies to cope with climate changes. From a prospective standpoint, exploring transcriptome-wide expression within conifer species with diverse ecological preferences holds promise for unraveling the nuanced modulation of gene expression in response to drought. Also, it appears important to investigate responses of epigenetic nature, which are likely to bear an important adaptive role in addition to modulation of transcriptome-wide expression [10].

Our study has provided new and valuable transcriptomic data for understanding how white spruce responds to water stress conditions. By conducting a transcriptomic analysis and monitoring white spruce over time during water stress, we have identified specific gene sets at different time points that shed light on the response to drought. The genes we identified are involved in major known biological processes, including hormonal responses, photosynthesis, growth, cell wall organization, water transport, carbohydrate metabolism, and defense mechanisms, all of which are essential for drought tolerance and adaptation. One particularly interesting finding is the significant regulation of lipid metabolism, a process that has not been extensively studied in conifers and requires further investigation. We believe that future research should prioritize the identification and the comparison of key genes and mechanisms involved in drought response and post-drought recovery of plants. This information could be instrumental in shaping effective genetic selection strategies to enhance white spruce's resistance and resilience in the face of drought induced by climate change. Ultimately, this knowledge can inform forest management practices aimed at supporting conifer regeneration and growth in increasingly challenging dry conditions.

ABA

Abscisic acid

AQP

Aquaporin

ATP

Adenosine triphosphate

Biological process

CesA

Cellulose synthase

CSL

Cellulose synthase-like

CYT

Cytochrome

DEG

Differentially expressed gene

DET

Differentially expressed transcript

Gene ontology

HSP

Heat shock protein

IDENT

International Diversity Experiment Network with Trees

LFC

Log fold change

LEA

Late embryogenesis abundant protein

Molecular function

NCS

Non-structural carbohydrate

NGS

Next generation sequencing

ORF

Open reading frame

OTL

Osmotin/thaumatin-like

PIP

Plasma membrane intrinsic protein

PSI

Photosystem I

PSII

Photosystem II

QTL

Quantitative trait loci

RNA-seq

RNA-sequencing

ROS

Reactive oxygen species

Transcription factor

XTH

Encoding xyloglucan endotransglucosylase/hydrolase

Acknowledgments

The authors would like to thank Juliana Stival Sena (Natural Resources Canada), Justine Laoué (IMBE. Aix-Marseille, France) and Armand Séguin (Natural Ressources Canada) for constructive discussions. We are also grateful to Bill Parker (Ontario Ministry of Natural Resources) for facilitating access to sampling at the IDENT plantation in Sault Ste. Marie, Ontario. Thanks are extended to Laurence Danvoye (ISFORT, UQO) and Yann Surget-Groba (ISFORT, UQO) for their help with RNA extraction, and Lana Ruddick for language editing of the manuscript.

Funding

This study was funded by the Canada Chair on the Resilience of Forests to Global Changes awarded to C. Messier; the National Sciences and Engineering Research Council of Canada Discovery Grant to J. Bousquet; the Spruce-Up LSARP project co-led by J. Bousquet and J. Bohlmann with funding from Genome Canada, Genome Quebec and Genome BC (234FOR) and MDev, Quebec Ministry of economic development, innovations and export, project number PSR-SIIRI-836 co-led by J. Mackay and J. Bousquet.

Author’s contributions

Z. R., C. D., J. M. and G. J. P. designed the study and C. M. contributed to the development of the IDENT plantation. Z. R., C. D., J. P., G. J. P. and J. M. designed methods and carried out the experiments. Z. R., J. P., C. D. and G. P. performed the analyses and discussed the results. Z. R. and C. D. wrote the manuscript with contributions and feedback from J. P., J. M., J. B., G. J. P., C. M., P. N., G. P. and A. D. All authors contributed to the article and approved the submitted version.

Ethics declarations

Ethics approval and consent to participate

White spruce samples used for de novo transcriptome assembly were collected from the IDENT experimental plot, where access and sampling permission was provided by the Forest Research and Monitoring Section of the Ontario Forest Research Institute.

Ethical consideration

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Forzieri G, Dakos V, McDowell NG, Ramdane A, Cescatti A. Emerging signals of declining forest resilience under climate change. Nature. 2022;608:534–9.
Hartmann H, Bastos A, Das AJ, Esquivel-Muelbert A, Hammond WM, Martínez-Vilalta J, et al. Climate change risks to global forest health: emergence of unexpected events of elevated tree mortality worldwide. Annu Rev Plant Biol. 2022;73:673–702.
Depardieu C, Girardin MP, Nadeau S, Lenz P, Bousquet J, Isabel N. Adaptive genetic variation to drought in a widely distributed conifer suggests a potential for increasing forest resilience in a drying climate. New Phytol. 2020;227:427–39.
Laverdière J, Lenz P, Nadeau S, Depardieu C, Isabel N, Perron M, et al. Breeding for adaptation to climate change: genomic selection for drought response in a white spruce multi-site polycross test. Evol Appl. 2022;15:383–402.
Soro A, Lenz P, Roussel J-R, Larochelle F, Bousquet J, Achim A. The phenotypic and genetic effects of drought-induced stress on apical growth, ring width, wood density and biomass in white spruce seedlings. New For. 2023;54:789–811.
Stival Sena J, Giguère I, Rigault P, Bousquet J, Mackay J. Expansion of the dehydrin gene family in the Pinaceae is associated with considerable structural diversity and drought-responsive expression. Tree Physiol. 2018;38:442–56.
Depardieu C, Gérardi S, Nadeau S, Parent GJ, Mackay J, Lenz P, et al. Connecting tree-ring phenotypes, genetic associations and transcriptomics to decipher the genomic architecture of drought adaptation in a widespread conifer. Mol Ecol. 2021;30:3898–917.
Haas JC, Vergara A, Serrano AR, Mishra S, Hurry V, Street NR. Candidate regulators and target genes of drought stress in needles and roots of Norway spruce. Tree Physiol. 2021;41:1230–46.
Baldi P, La Porta N. Toward the genetic improvement of drought tolerance in conifers: an integrated approach. Forests. 2022;13:2016.
Moran E, Lauder J, Musser C, Stathos A, Shu M. The genetics of drought tolerance in conifers. New Phytol. 2017;216:1034–48.
Wang Y, Zhao Z, Liu F, Sun L, Hao F. Versatile roles of aquaporins in plant growth and development. Int J Mol Sci. 2020;21:9485.
Laoué J, Depardieu C, Gérardi S, Lamothe M, Bomal C, Azaiez A, et al. Combining QTL mapping and transcriptomics to decipher the genetic architecture of phenolic compounds metabolism in the conifer white spruce. Front Plant Sci. 2021;12:675108.
Fox H, Doron-Faigenboim A, Kelly G, Bourstein R, Attia Z, Zhou J, et al. Transcriptome analysis of Pinus halepensis under drought stress and during recovery. Tree Physiol. 2018;38:423–41.
Li W, Lee J, Yu S, Wang F, Lv W, Zhang X, et al. Characterization and analysis of the transcriptome response to drought in Larix kaempferi using PacBio full-length cDNA sequencing integrated with de novo RNA-seq reads. Planta. 2021;253:28.
He W, Liu H, Qi Y, Liu F, Zhu X. Patterns in nonstructural carbohydrate contents at the tree organ level in response to drought duration. Glob Change Biol. 2020;26:3627–38.
Zhang L, Yan S, Zhang S, Yan P, Wang J, Zhang H. Glutathione, carbohydrate and other metabolites of Larix olgensis A. Henry reponse to polyethylene glycol-simulated drought stress. PLoS ONE. 2021;16:e0253780.
Sancho-Knapik D, Sanz MÁ, Peguero-Pina JJ, Niinemets Ü, Gil-Pelegrín E. Changes of secondary metabolites in Pinus sylvestris L. needles under increasing soil water deficit. Ann Sci. 2017;74:24.
Xiao F, Zhao Y, Wang X-R, Liu Q, Ran J. Transcriptome analysis of needle and root of Pinus massoniana in response to continuous drought stress. Plants. 2021;10:769.
Velasco-Conde T, Yakovlev I, Majada J, Aranda I, Johnsen Ø. Dehydrins in maritime pine (Pinus pinaster) and their expression related to drought stress response. Tree Genet Genomes. 2012;8:957–73.
Wang T, McFarlane HE, Persson S. The impact of abiotic factors on cellulose synthesis. J Exp Bot. 2016;67:543–52.
Coleman HD, Brunner AM, Tsai C-J. Synergies and entanglement in secondary cell wall development and abiotic stress response in trees. Front Plant Sci. 2021;12.
Lorenz WW, Alba R, Yu Y-S, Bordeaux JM, Simões M, Dean JF. Microarray analysis and scale-free gene networks identify candidate regulators in drought-stressed roots of loblolly pine (P. taeda L). BMC Genomics. 2011;12:264.
Behringer D, Zimmermann H, Ziegenhagen B, Liepelt S. Differential gene expression reveals candidate genes for drought stress response in Abies alba (Pinaceae). PLoS ONE. 2015;10:e0124564.
Du M, Ding G, Cai Q. The transcriptomic responses of Pinus massoniana to drought stress. Forests. 2018;9:326.
Zhang S, Koubaa A. Softwoods of eastern Canada: Their silvics, characteristics, manufacturing and end-uses. Forintek Canada Corporation; 2008.
Hassegawa M, Savard M, Lenz PRN, Duchateau E, Gélinas N, Bousquet J, et al. White spruce wood quality for lumber products: priority traits and their enhancement through tree improvement. Int J Res. 2020;93:16–37.
Mullin T, Andersson Gull B, Bastien J-C, Beaulieu J, Burdon R, Dvorak W, Bousquet J et al. CRC Press and Science, New York; 2011. 40–127.
Bousquet J, Gérardi S, De Lafontaine G, Jaramillo-Correa JP, Pavy N, Prunier J, Rajora OP et al. Springer Nature, Switzerland; 2021. 1–64.
Hogg EH, Michaelian M, Hook TI, Undershultz ME. Recent climatic drying leads to age-independent growth reductions of white spruce stands in western Canada. Glob Change Biol. 2017;23:5297–308.
Sullivan PF, Brownlee AH, Ellison SBZ, Cahoon SMP. Comparative drought sensitivity of co-occurring white spruce and paper birch in interior Alaska. J Ecol. 2021;109:2448–60.
Peng C, Ma Z, Lei X, Zhu Q, Chen H, Wang W, et al. A drought-induced pervasive increase in tree mortality across Canada’s boreal forests. Nat Clim Change. 2011;1:467–71.
Lu P, Parker WC, Colombo SJ, Skeates DA. Temperature-induced growing season drought threatens survival and height growth of white spruce in southern Ontario, Canada. Ecol Manag. 2019;448:355–63.
D’Orangeville L, Houle D, Duchesne L, Phillips RP, Bergeron Y, Kneeshaw D. Beneficial effects of climate warming on boreal tree growth may be transitory. Nat Commun. 2018;9:3213.
Brodribb TJ, McAdam SAM, Jordan GJ, Martins SCV. Conifer species adapt to low-rainfall climates by following one of two divergent pathways. Proc Natl Acad Sci U S A. 2014;111:14489–93.
McDowell N, Pockman WT, Allen CD, Breshears DD, Cobb N, Kolb T, et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 2008;178:719–39.
Depardieu C, Lenz P, Marion J, Nadeau S, Girardin MP, Marchand W et al. Contrasting physiological strategies explain heterogeneous responses to severe drought conditions within local populations of a widespread conifer. Sci Total Environ. 2024;171174.
Gazol A, Fajardo A, Camarero JJ. Contributions of intraspecific variation to drought tolerance in trees. Curr Rep. 2023. https://doi.org/10.1007/s40725-023-00199-w.
Hornoy B, Pavy N, Gérardi S, Beaulieu J, Bousquet J. Genetic adaptation to climate in white spruce involves small to moderate allele frequency shifts in functionally diverse genes. Genome Biol Evol. 2015;7:3269–85.
Birol I, Raymond A, Jackman SD, Pleasance S, Coope R, Taylor GA, et al. Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data. Bioinformatics. 2013;29:1492–7.
Warren RL, Keeling CI, Yuen MMS, Raymond A, Taylor GA, Vandervalk BP, et al. Improved white spruce (Picea glauca) genome assemblies and annotation of large gene families of conifer terpenoid and phenolic defense metabolism. Plant J. 2015;83:189–212.
Jackman SD, Warren RL, Gibb EA, Vandervalk BP, Mohamadi H, Chu J, et al. Organellar genomes of white spruce (Picea glauca): assembly and annotation. Genome Biol Evol. 2016;8:29–41.
Rigault P, Boyle B, Lepage P, Cooke JEK, Bousquet J, MacKay JJ. A white spruce gene catalog for conifer genome analyses. Plant Physiol. 2011;157:14–28.
Pavy N, Pelgas B, Beauseigle S, Blais S, Gagnon F, Gosselin I, et al. Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce. BMC Genomics. 2008;9:21.
Pavy N, Gagnon F, Rigault P, Blais S, Deschênes A, Boyle B, et al. Development of high-density SNP genotyping arrays for white spruce (Picea glauca) and transferability to subtropical and nordic congeners. Mol Ecol Resour. 2013;13:324–36.
Pavy N, Gagnon F, Deschênes A, Boyle B, Beaulieu J, Bousquet J. Development of highly reliable in silico SNP resource and genotyping assay from exome capture and sequencing: an example from black spruce (Picea mariana). Mol Ecol Resour. 2016;16:588–98.
Pelgas B, Bousquet J, Meirmans PG, Ritland K, Isabel N. QTL mapping in white spruce: gene maps and genomic regions underlying adaptive traits across pedigrees, years and environments. BMC Genomics. 2011;12:145.
Pavy N, Lamothe M, Pelgas B, Gagnon F, Birol I, Bohlmann J, et al. A high-resolution reference genetic map positioning 8.8 K genes for the conifer white spruce: structural genomics implications and correspondence with physical distance. Plant J. 2017;90:189–203.
De La Torre AR, Birol I, Bousquet J, Ingvarsson P, Jansson S, Jones S et al. Insights into conifer giga-genomes. Plant Physiol. 2014;166.
Neale DB, Wheeler NC. Gene expression and the transcriptome. The conifers: genomes, variation and evolution. Cham: Springer International Publishing; 2019. pp. 91–117.
Jaramillo-Correa JP, Beaulieu J, Bousquet J. Contrasting evolutionary forces driving population structure at expressed sequence tag polymorphisms, allozymes and quantitative traits in white spruce. Mol Ecol. 2001;10:2729–40.
Prunier J, Laroche J, Beaulieu J, Bousquet J. Scanning the genome for gene SNPs related to climate adaptation and estimating selection at the molecular level in boreal black spruce. Mol Ecol. 2011;20:1702–16.
Fujita M, Fujita Y, Noutoshi Y, Takahashi F, Narusaka Y, Yamaguchi-Shinozaki K, et al. Crosstalk between abiotic and biotic stress responses: a current view from the points of convergence in the stress signaling networks. Curr Opin Plant Biol. 2006;9:436–42.
Leisner CP, Potnis N, Sanz-Saez A. Crosstalk and trade-offs: plant responses to climate change-associated abiotic and biotic stresses. Plant Cell Environ. 2023;46:2946–63.
Andrews S. FastQC: a quality control tool for high throughput sequence data. 2017.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
Simpson JT, Durbin R. Efficient de novo assembly of large genomes using compressed data structures. Genome Res. 2012;22:549–56.
Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–8.
Coil D, Jospin G, Darling AE. A5-miseq: an updated pipeline to assemble microbial genomes from Illumina MiSeq data. Bioinforma Oxf Engl. 2015;31:587–9.
Manni M, Berkeley MR, Seppey M, Zdobnov EM. Busco: assessing genomic data quality and beyond. Curr Protoc. 2021;1:e323.
Bucchini F, Del Cortona A, Kreft Ł, Botzki A, Van Bel M, Vandepoele K. TRAPID 2.0: a web application for taxonomic and functional analysis of de novo transcriptomes. Nucleic Acids Res. 2021;49:e101–101.
Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36:3420–35.
Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18:366–8.
Van Bel M, Silvestri F, Weitz EM, Kreft L, Botzki A, Coppens F, et al. PLAZA 5.0: extending the scope and power of comparative and functional genomics in plants. Nucleic Acids Res. 2022;50:D1468–74.
Jin J, Tian F, Yang D-C, Meng Y-Q, Kong L, Luo J, et al. PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants. Nucleic Acids Res. 2017;45:D1040–5.
Tian F, Yang D-C, Meng Y-Q, Jin J, Gao G. PlantRegMap: charting functional regulatory maps in plants. Nucleic Acids Res. 2020;48:D1104–13.
Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–7.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
Gao C-H, Yu G, Cai P. ggVennDiagram: an intuitive, easy-to-use, and highly customizable R package to generate Venn diagram. Front Genet. 2021;12:706907.
Guo K. VennDetail. 2019.
Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, et al. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J Cell Mol Biol. 2004;37:914–39.
Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B, Hallab A, et al. Mapman4: a refined protein classification and annotation framework applicable to multi-omics data analysis. Mol Plant. 2019;12:879–92.
Bolger M, Schwacke R, Usadel B. Mapman visualization of RNA-seq data using mercator4 functional annotations. Methods Mol Biol Clifton NJ. 2021;2354:195–212.
Lee IH, Han H, Koh YH, Kim IS, Lee S-W, Shim D. Comparative transcriptome analysis of Pinus densiflora following inoculation with pathogenic (Bursaphelenchus xylophilus) or non-pathogenic nematodes (B. thailandae). Sci Rep. 2019;9:12180.
Ojeda DI, Mattila TM, Ruttink T, Kujala ST, Kärkkäinen K, Verta J-P et al. Utilization of tissue ploidy level variation in de novo transcriptome assembly of Pinus sylvestris. G3 GenesGenomesGenetics. 2019;9:3409–21.
Breidenbach N, Sharov VV, Gailing O, Krutovsky KV. De novo transcriptome assembly of cold stressed clones of the hexaploid Sequoia sempervirens (D. Don) Endl. Sci Data. 2020;7:239.
Visser EA, Kampmann TP, Wegrzyn JL, Naidoo S. Multispecies comparison of host responses to Fusarium circinatum challenge in tropical pines show consistency in resistance mechanisms. Plant Cell Environ. 2023;46:1705–25.
Gagalova KK, Warren RL, Coombe L, Wong J, Nip KM, Yuen MMS, et al. Spruce giga-genomes: structurally similar yet distinctive with differentially expanding gene families and rapidly evolving genes. Plant J. 2022;111:1469–85.
Klápště J, Dungey HS, Telfer EJ, Suontama M, Graham NJ, Li Y et al. Marker selection in multivariate genomic prediction improves accuracy of low heritability traits. Front Genet. 2020;11.
Brodribb TJ, McAdam SAM. Abscisic acid mediates a divergence in the drought response of two conifers. Plant Physiol. 2013;162:1370–7.
Brunner I, Herzog C, Dawes MA, Arend M, Sperisen C. How tree roots respond to drought. Front Plant Sci. 2015;6.
Mitchell PJ, McAdam SAM, Pinkard EA, Brodribb TJ. Significant contribution from foliage-derived ABA in regulating gas exchange in Pinus radiata. Tree Physiol. 2017;37:236–45.
Pashkovskiy PP, Vankova R, Zlobin IE, Dobrev P, Ivanov YV, Kartashov AV, et al. Comparative analysis of abscisic acid levels and expression of abscisic acid-related genes in Scots pine and Norway spruce seedlings under water deficit. Plant Physiol Biochem. 2019;140:105–12.
Luo J, Zhou J-J, Zhang J-Z. AUX/IAA gene family in plants: molecular structure, regulation, and function. Int J Mol Sci. 2018;19:259.
Li S, Yan X, Huang X, Addo-Danso S, Lin S, Zhou L. Physiological differences and transcriptome analysis reveal that high enzyme activity significantly enhances drought tolerance in chinese fir (Cunninghamia lanceolata). Forests. 2023;14:967.
de Souza GB, Mendes TA, de Fontes O, Barros PP, de Gonçalves V, de Ferreira AB. Genome-wide identification and expression analysis of dormancy-associated gene 1/auxin repressed protein (DRM1/ARP) gene family in Glycine max. Prog Biophys Mol Biol. 2019;146:134–41.
Perdomo JA, Capó-Bauçà S, Carmo-Silva E, Galmés J. Rubisco and rubisco activase play an important role in the biochemical limitations of photosynthesis in rice, wheat, and maize under high temperature and water deficit. Front Plant Sci. 2017;8:490.
Drake JE, Power SA, Duursma RA, Medlyn BE, Aspinwall MJ, Choat B, et al. Stomatal and non-stomatal limitations of photosynthesis for four tree species under drought: A comparison of model formulations. Agric Meteorol. 2017;247:454–66.
Lei P, Liu Z, Li J, Jin G, Xu L, Ji X, et al. Integration of the physiology, transcriptome and proteome reveals the molecular mechanism of drought tolerance in Cupressus gigantea. Forests. 2022;13:401.
Foyer CH, Neukermans J, Queval G, Noctor G, Harbinson J. Photosynthetic control of electron transport and the regulation of gene expression. J Exp Bot. 2012;63:1637–61.
Johnson JE, Berry JA. The role of Cytochrome b6f in the control of steady-state photosynthesis: a conceptual and quantitative model. Photosynth Res. 2021;148:101–36.
Shimakawa G, Miyake C. Oxidation of P700 ensures robust photosynthesis. Front Plant Sci. 2018;9.
Zlobin IE, Kartashov AV, Pashkovskiy PP, Ivanov YV, Kreslavski VD, Kuznetsov VV. Comparative photosynthetic responses of Norway spruce and Scots pine seedlings to prolonged water deficiency. J Photochem Photobiol B. 2019;201:111659.
Schiop ST, Al Hassan M, Sestras AF, Boscaiu M, Sestras RE, Vicente O. Biochemical responses to drought, at the seedling stage, of several Romanian Carpathian populations of Norway spruce (Picea abies L. Karst). Trees. 2017;31:1479–90.
Granda E, Camarero JJ. Drought reduces growth and stimulates sugar accumulation: new evidence of environmentally driven non-structural carbohydrate use. Tree Physiol. 2017;37:997–1000.
Piper FI, Fajardo A, Hoch G. Single-provenance mature conifers show higher non-structural carbohydrate storage and reduced growth in a drier location. Tree Physiol. 2017;37:1001–10.
Gall HL, Philippe F, Domon J-M, Gillet F, Pelloux J, Rayon C. Cell wall metabolism in response to abiotic stress. Plants. 2015;4:112–66.
de Jesús-Pires C, Ferreira-Neto JRC, Pacifico Bezerra-Neto J, Kido EA, de Oliveira Silva RL, Pandolfi V, et al. Plant thaumatin-like proteins: function, evolution and biotechnological applications. Curr Protein Pept Sci. 2020;21:36–51.
Laur J, Hacke UG. Exploring Picea glauca aquaporins in the context of needle water uptake and xylem refilling. New Phytol. 2014;203:388–400.
Cheng Z, Zhang X, Yao W, Gao Y, Zhao K, Guo Q, et al. Genome-wide identification and expression analysis of the xyloglucan endotransglucosylase/hydrolase gene family in poplar. BMC Genomics. 2021;22:804.
Hartmann H, Trumbore S. Understanding the roles of nonstructural carbohydrates in forest trees - from what we can measure to what we want to know. New Phytol. 2016;211:386–403.
Jeandet P, Formela-Luboińska M, Labudda M, Morkunas I. The role of sugars in plant responses to stress and their regulatory function during development. Int J Mol Sci. 2022;23:5161.
Figueroa-Macías JP, García YC, Núñez M, Díaz K, Olea AF, Espinoza L. Plant growth-defense trade-offs: molecular processes leading to physiological changes. Int J Mol Sci. 2021;22:693.
Neuser J, Metzen CC, Dreyer BH, Feulner C, van Dongen JT, Schmidt RR, et al. HBI1 mediates the trade-off between growth and immunity through its impact on apoplastic ROS homeostasis. Cell Rep. 2019;28:1670–e16783.
Kim HU. Lipid metabolism in plants. Plants. 2020;9:871.
Hoch G, Richter A, Körner C. Non-structural compounds in temperate forest trees. Plant Cell Environ. 2003;26:1067–81.
Tomasella M, Petrussa E, Petruzzellis F, Nardini A, Casolo V. The possible role of non-structural carbohydrates in the regulation of tree hydraulics. Int J Mol Sci. 2019;21:144.
Kobayashi K, Endo K, Wada H. Roles of lipids in photosynthesis. In: Nakamura Y, Li-Beisson Y, editors. Lipids in plant and algae development. Cham: Springer International Publishing; 2016. pp. 21–49.
Walker RP, Chen Z-H, Famiani F. Gluconeogenesis in plants: a key interface between organic acid/amino acid/lipid and sugar metabolism. Molecules. 2021;26:5129.
Mukarram M, Choudhary S, Kurjak D, Petek A, Khan MMA. Drought: sensing, signalling, effects and tolerance in higher plants. Physiol Plant. 2021;172:1291–300.
Chan Z, Yokawa K, Kim W-Y, Song C-P. Editorial: ROS regulation during plant abiotic stress responses. Front Plant Sci. 2016;7.
Bilska K, Wojciechowska N, Alipour S, Kalemba EM. Ascorbic acid-the little-known antioxidant in woody plants. Antioxid Basel Switz. 2019;8:645.
Corpas FJ, González-Gordo S, Palma JM. Plant peroxisomes: a factory of reactive species. Front Plant Sci. 2020;11.
Sofo A, Scopa A, Nuzzaci M, Vitti A. Ascorbate peroxidase and catalase activities and their genetic regulation in plants subjected to drought and salinity stresses. Int J Mol Sci. 2015;16:13561–78.
Vaish S, Gupta D, Mehrotra R, Mehrotra S, Basantani MK. Glutathione S-transferase: a versatile protein family. 3 Biotech. 2020;10:321.
Zhang Y, Diao S, Ding X, Sun J, Luan Q, Jiang J. Transcriptional regulation modulates terpenoid biosynthesis of Pinus elliottii under drought stress. Ind Crops Prod. 2023;202:116975.
Pandian BA, Sathishraj R, Djanaguiraman M, Prasad PVV, Jugulam M. Role of cytochrome P450 enzymes in plant stress response. Antioxidants. 2020;9:454.
Tahmasebi A, Niazi A, Akrami S. Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species. Sci Rep. 2023;13:847.
Rao MJ, Xu Y, Tang X, Huang Y, Liu J, Deng X, et al. CsCYT75B1, a citrus cytochrome P450 gene, is involved in accumulation of antioxidant flavonoids and induces drought tolerance in transgenic Arabidopsis. Antioxid Basel Switz. 2020;9:161.
Kampinga HH, Craig EA. The HSP70 chaperone machinery: J proteins as drivers of functional specificity. Nat Rev Mol Cell Biol. 2010;11:579–92.
Gautam R, Meena RK, Rampuria S, Shukla P, Kirti PB. Ectopic expression of DnaJ type-I protein homolog of Vigna aconitifolia (VaDJI) confers ABA insensitivity and multiple stress tolerance in transgenic tobacco plants. Front Plant Sci. 2023;14:1135552.
Liu Y, Song Q, Li D, Yang X, Li D. Multifunctional roles of plant dehydrins in response to environmental stresses. Front Plant Sci. 2017;8.
Perdiguero P, Barbero MC, Cervera MT, Soto A, Collada C. Novel conserved segments are associated with differential expression patterns for Pinaceae dehydrins. Planta. 2012;236:1863–74.
Liu J-J, Ekramoddoullah AKM. The CC-NBS-LRR subfamily in Pinus monticola: targeted identification, gene expression, and genetic linkage with resistance to Cronartium ribicola. Phytopathology. 2007;97:728–36.
Van Ghelder C, Parent GJ, Rigault P, Prunier J, Giguère I, Caron S, et al. The large repertoire of conifer NLR resistance genes includes drought responsive and highly diversified RNLs. Sci Rep. 2019;9:11614.
Cobo-Simón I, Maloof JN, Li R, Amini H, Méndez-Cea B, García-García I, et al. Contrasting transcriptomic patterns reveal a genomic basis for drought resilience in the relict fir Abies pinsapo Boiss. Tree Physiol. 2023;43:315–34.
Wang M, Ren L-T, Wei X-Y, Ling Y-M, Gu H-T, Wang S-S et al. NAC transcription factor TwNAC01 positively regulates drought stress responses in Arabidopsis and Triticale. Front Plant Sci. 2022;13.
Lu D, Wang T, Persson S, Mueller-Roeber B, Schippers JHM. Transcriptional control of ROS homeostasis by KUODA1 regulates cell expansion during leaf development. Nat Commun. 2014;5:3767.
Agarwal P, Mitra M, Banerjee S, Roy S. MYB4 transcription factor, a member of R2R3-subfamily of MYB domain protein, regulates cadmium tolerance via enhanced protection against oxidative damage and increases expression of PCS1 and MT1C in Arabidopsis. Plant Sci. 2020;297:110501.
Wyrzykowska A, Bielewicz D, Plewka P, Sołtys-Kalina D, Wasilewicz-Flis I, Marczewski W, et al. The MYB33, MYB65, and MYB101 transcription factors affect Arabidopsis and potato responses to drought by regulating the ABA signaling pathway. Physiol Plant. 2022;174:e13775.
Kitajima S, Koyama T, Ohme-Takagi M, Shinshi H, Sato F. Characterization of gene expression of NsERFs, transcription factors of basic PR genes from Nicotiana sylvestris. Plant Cell Physiol. 2000;41:817–24.
Zhang J, Wang D, Chen P, Zhang C, Yao S, Hao Q, et al. The transcriptomic analysis of the response of Pinus massoniana to drought stress and a functional study on the ERF1 transcription factor. Int J Mol Sci. 2023;24:11103.
Rehman S, Mahmood T. Functional role of DREB and ERF transcription factors: regulating stress-responsive network in plants. Acta Physiol Plant. 2015;37:178.
Sun Y, Oh D-H, Duan L, Ramachandran P, Ramirez A, Bartlett A, et al. Divergence in the ABA gene regulatory network underlies differential growth control. Nat Plants. 2022;8:549–60.
Hsu K-H, Liu C-C, Wu S-J, Kuo Y-Y, Lu C-A, Wu C-R, et al. Expression of a gene encoding a rice RING zinc-finger protein, OsRZFP34, enhances stomata opening. Plant Mol Biol. 2014;86:125–37.
Ding S, Zhang B, Qin F. Arabidopsis RZFP34/CHYR1, a ubiquitin E3 ligase, regulates stomatal movement and drought tolerance via SnRK2.6-mediated phosphorylation. Plant Cell. 2015;27:3228–44.
Schueler S, George J-P, Karanitsch-Ackerl S, Mayer K, Klumpp RT, Grabner M. Evolvability of drought response in four native and non-native conifers: opportunities for forest and genetic resource management in Europe. Front Plant Sci. 2021;12:648312.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca)

Status:

Version 1

Abstract

Background

Results

Conclusion

Figures

1. Introduction

2. Material and methods

2.1. Generation and annotation of the white spruce transcriptome assembly, GCAT 4.0

2.1.1. Plant material

2.1.2. Strategy and quality assessment of the assembly

2.1.3. Functional annotation of transcripts

2.2. Drought stress experiment and transcriptome analysis

2.2.1. Plant material, water treatment, and RNA sequencing

2.2.2. Differential expression and enrichment analyses

3. Results

3.1. Statistics and quality assessment of the new white spruce de novo assembly, GCAT 4.0

3.2. Temporal dynamics in gene expression in response to drought

3.3. Identification of key functions involved in short-term drought response

3.4. Gene expression changes under intense water stress

3.5. Identification of drought-responsive transcription factors

3.6. Intraspecific genetic variation of drought-responsive genes

4. Discussion

4.1. Quality of the new transcriptome assembly

4.2. Signaling and hormonal response to drought

4.3. Negative impact of drought on photosynthesis, growth, and water transport

4.4. Regulation of the carbohydrate and lipid metabolisms

4.5. Drought-responsive genes coding for protective defense and stress resistance and resilience

4.6. Key transcription factors involved in the transcriptional control of drought-responsive genes

4.7. Intraspecific genetic variation in gene regulation under drought stress: findings and future avenues

5. Conclusions

Abbreviations

Declarations

Acknowledgments

Funding

Author’s contributions

Ethics declarations

Ethics approval and consent to participate

Ethical consideration

Consent for publication

Competing interests

References

Additional Declarations

Supplementary Files

Status:

Version 1