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

De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca) Zoé Ribeyre, Claire Depardieu, Julien Prunier, Gervais Pelletier, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4365578/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction 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. Material and methods 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. Results 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. 2 A), 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. 2 B). 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. 2 C). 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. 3 B). 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. 3 D), while a 23% overlap was observed for down-regulated genes (Fig. 3 C). 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. 4 A). 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. 4 A-B, Fig. 5 A-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. 4 B, 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. 3 C-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. 5 A-B). The redox homeostasis process was well-represented with 126 DEGs (Fig. 5 C). 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. 6 A, 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. 6 B). 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. S3 B), and only 21% of up-regulated DEGs were common among all three clones (Fig. S3 A). Shared DEGs were involved in BP of defense mechanisms and macromolecule metabolism covering carbohydrates, lipids, and amino acids (Fig. S4 A), and were also associated with MF of catalytic activities including transferase, oxidoreductase, lyase, isomerase, and hydrolase activities (Fig. S4 C). 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 ). 4. Discussion 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. 4 B). 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 CO 2 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. 5 B), suggest an abrupt disruption of photosynthesis as the drought stress intensifies. Water availability significantly impacts photosynthesis, often causing a limitation in CO 2 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 CO 2 [ 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 CO 2 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. 4 A-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. 5 A). 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. 5 A). 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 ]. 5. Conclusions 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. Abbreviations ABA Abscisic acid AQP Aquaporin ATP Adenosine triphosphate BP Biological process CesA Cellulose synthase CSL Cellulose synthase-like CYT Cytochrome DEG Differentially expressed gene DET Differentially expressed transcript GO Gene ontology HSP Heat shock protein IDENT International Diversity Experiment Network with Trees LFC Log fold change LEA Late embryogenesis abundant protein MF 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 TF Transcription factor XTH Encoding xyloglucan endotransglucosylase/hydrolase Declarations 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. 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Additional Declarations No competing interests reported. Supplementary Files BMCAdditionalfilesRibeyreetal.docx TableS3.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS8.xlsx TableS9.xlsx TableS10.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4365578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312780895,"identity":"4bab8d6c-32df-496b-bc66-e2eb9cc622bb","order_by":0,"name":"Zoé Ribeyre","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACxjYwxQ8iEgpI0SLZANJiQJw1bGDS4ACYJEK9/OzDbQ8+tt2TMz6/OvHDAwMGeX6xAwSc1ZfYbjizrdjY7MbbzRJAhxnOnJ2AXwszD2ObNG9bQuK2G2c3gLQkGNwmoIUNpOVvW0L95hlnN/8gSgsPSAtjW0KCAX/vNuJskeBhbDfsOZdgOOMG7zaLBAMJwn6R72F/9uBHWYI8f//ZzTd/VNjI80sT0AIGjKCokQCrlCBCORj8AWL+A8SqHgWjYBSMgpEGAOw4QY3a83bKAAAAAElFTkSuQmCC","orcid":"","institution":"Département des Sciences Naturelles, Institut des Sciences de la Forêt Tempérée (ISFORT), Université du Québec en Outaouais (UQO)","correspondingAuthor":true,"prefix":"","firstName":"Zoé","middleName":"","lastName":"Ribeyre","suffix":""},{"id":312780896,"identity":"a2dbafff-e0d5-4573-8546-c0aab30d922a","order_by":1,"name":"Claire Depardieu","email":"","orcid":"","institution":"Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Depardieu","suffix":""},{"id":312780897,"identity":"b4cad44a-37f8-403e-99a1-5b27f4579da1","order_by":2,"name":"Julien Prunier","email":"","orcid":"","institution":"Plateforme de bioinformatique du Centre Hospitalier Universitaire de Québec","correspondingAuthor":false,"prefix":"","firstName":"Julien","middleName":"","lastName":"Prunier","suffix":""},{"id":312780898,"identity":"39bf2ee3-9906-48fc-a9fc-bbbf52c5ccbe","order_by":3,"name":"Gervais Pelletier","email":"","orcid":"","institution":"Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Center","correspondingAuthor":false,"prefix":"","firstName":"Gervais","middleName":"","lastName":"Pelletier","suffix":""},{"id":312780899,"identity":"5deac846-99cd-4a77-aef2-e5f8e7873507","order_by":4,"name":"Geneviève J. Parent","email":"","orcid":"","institution":"Laboratory of Genomics, Maurice- Lamontagne Institute, Fisheries and Oceans Canada","correspondingAuthor":false,"prefix":"","firstName":"Geneviève","middleName":"J.","lastName":"Parent","suffix":""},{"id":312780901,"identity":"796a8bd6-c6a0-4b09-8437-ce1691d006e0","order_by":5,"name":"John Mackay","email":"","orcid":"","institution":"Department of Plant Sciences, University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Mackay","suffix":""},{"id":312780906,"identity":"febf94d6-ce3d-4800-a6d6-1623adc5e209","order_by":6,"name":"Arnaud Droit","email":"","orcid":"","institution":"Plateforme de bioinformatique du Centre Hospitalier Universitaire de Québec","correspondingAuthor":false,"prefix":"","firstName":"Arnaud","middleName":"","lastName":"Droit","suffix":""},{"id":312780907,"identity":"5168cfc9-7865-4814-951e-ef3e41f68822","order_by":7,"name":"Jean Bousquet","email":"","orcid":"","institution":"Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"","lastName":"Bousquet","suffix":""},{"id":312780909,"identity":"a531f589-297a-493d-9a00-bce89b2c6fc2","order_by":8,"name":"Philippe Nolet","email":"","orcid":"","institution":"Département des Sciences Naturelles, Institut des Sciences de la Forêt Tempérée (ISFORT), Université du Québec en Outaouais (UQO)","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"Nolet","suffix":""},{"id":312780910,"identity":"d7bd4fa6-e302-43c9-8004-7a736bb230ca","order_by":9,"name":"Christian Messier","email":"","orcid":"","institution":"Département des Sciences Naturelles, Institut des Sciences de la Forêt Tempérée (ISFORT), Université du Québec en Outaouais (UQO)","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Messier","suffix":""}],"badges":[],"createdAt":"2024-05-03 18:39:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4365578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4365578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58302262,"identity":"c7329473-ddb9-450f-b0ce-12f3f322b477","added_by":"auto","created_at":"2024-06-13 16:43:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design and analysis pipeline used in this study. \u003c/strong\u003eThe analysis steps are presented in chronological order for the two boxes \u003cem\u003eTranscriptome assembly\u003c/em\u003e and\u003cem\u003e Transcriptomic analyses\u003c/em\u003e. The tool used for each type of analysis is reported.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/a8fc74b7defed1bf7573cba3.png"},{"id":58302002,"identity":"a484928b-18b0-484b-89bb-b1e70ad3510d","added_by":"auto","created_at":"2024-06-13 16:35:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacteristics and quality assessment of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ede novo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e assembly GCAT 4.0. \u003c/strong\u003e(A) Distribution of the number of transcript and unigene sequences as a function of the sequence length expressed in base pairs (bp). The two pie charts represent the results of the BUSCO analysis using (B) the \u003cem\u003eviridiplantae\u003c/em\u003e database (Viridiplantae_(odb10) and (C) the \u003cem\u003eembryophyta\u003c/em\u003edatabase (Embryophyta_(odb10)).\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/ee66b36c64c3d325f96a740c.png"},{"id":58302263,"identity":"b6d65b28-597f-49af-bab8-e12677a48248","added_by":"auto","created_at":"2024-06-13 16:43:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed unigenes\u003c/strong\u003e (\u003cstrong\u003eDEGs) in response to drought in white spruce. \u003c/strong\u003e(A) Volcano plot control versus water stressed white spruce trees on day 22. Down-regulated unigenes (FDR ⩽ 0.05 and a log2FC ⩽ -2) are shown in blue, while up-regulated unigenes (FDR ⩽0.05 and a log2FC ⩾ 2) are represented in red. Genes whose expression is not significantly altered by drought are identified by grey dots. (B) The number of differentially expressed unigenes (DEGs) is shown as a function of their regulation (in red, upwards, and in blue, downwards) for the four sampling days. The Venn Diagrams depict the overlaps of (C) downregulated and (D) upregulated differentially expressed genes across the days of sampling. The intensity of the color is positively correlated with the number of unigenes.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/38af67cefc5e35e56e0effc5.png"},{"id":58302004,"identity":"d4656a75-0d61-4748-9a86-446a8fa11e15","added_by":"auto","created_at":"2024-06-13 16:35:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":403750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of gene ontology enrichment analyses as a function of drought exposure time. \u003c/strong\u003e(A) The barplot represents the gene ontology (GO) annotation from OmicsBox of unique DEGs regulated on all time points. (B)\u003cstrong\u003e \u003c/strong\u003eThe scatterplots represent the enriched GO terms belonging to the biological process (BP) and molecular function (MF), as a function of exposure time to the water stress treatment (Days of sampling). Significantly enriched GO terms are shown with the transparency gradient based on -log10(FDR). The size of the dots indicates the ratio of the number of annotated sequences in the sample to the reference transcriptome GCAT 4.0. Enriched GO terms associated with up- and down-regulated sequences are shown in red and blue, respectively. Day 14 showed no significant enrichment and has been withdrawn from the graph for clarity.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/083d02cbbf1ceed11f733a1c.png"},{"id":58302015,"identity":"46aee8ee-ad89-4a1c-8735-d3eea3d13833","added_by":"auto","created_at":"2024-06-13 16:35:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":634036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathways based on MapMan classification of differentially expressed genes (DEGs) involved in drought stress responses after 22 days in white spruce seedlings.\u003c/strong\u003e Expression profiles of DEGs involved in metabolism overview (A), photosynthesis (B), abiotic stresses and redox homeostasis (C) are presented. The schematic representation of panel (C) was obtained after modifying MapMan's original pathways (biotic stress and cellular response overview pathways) to improve and more concisely synthesize the results obtained in the context of our specific short-term drought experiment. The scale bar represents the up- (red) and down- (blue) regulation of gene expression based on log2FC scores.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/ebfb9ad11636252432c12163.png"},{"id":58302011,"identity":"44b573ac-5b03-406e-82ce-a1e524562df4","added_by":"auto","created_at":"2024-06-13 16:35:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMain classes of transcription factors (TFs) unigenes significantly regulated in response to drought.\u003c/strong\u003e (A) Histogram showing the number of up (red) or down (blue) regulated genes for the most represented classes of drought-responsive TFs. (B) Heatmap showing the expression of TFs belonging to key TF classes in the response to drought conditions. To the right of the heatmap is the log2 fold change (log2Foldchange), which corresponds to the level of regulation of transcription factor expression when it was detected significantly regulated for a given time point. In cases where a TF was up-regulated at more than one time point, the log2foldchange was averaged over multiple time points and plotted in the heatmap. The complete list of drought-responsive TFs is presented in Supplementary Table S5.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/e580cfeee843019afb4be4dd.png"},{"id":59513185,"identity":"da4d7c54-0d6f-40b1-8f85-7932fcd23930","added_by":"auto","created_at":"2024-07-02 17:03:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3654004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/a2a5021d-adb0-43d6-a65f-421f4d01ca53.pdf"},{"id":58302005,"identity":"d836ba10-bde2-4afd-9822-bc131498f3d0","added_by":"auto","created_at":"2024-06-13 16:35:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1119226,"visible":true,"origin":"","legend":"","description":"","filename":"BMCAdditionalfilesRibeyreetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/410ad737fdf111912cce75ef.docx"},{"id":58302008,"identity":"d8236ac5-1dda-415d-93e1-0deb66d0b600","added_by":"auto","created_at":"2024-06-13 16:35:07","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8820114,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/4d5b54dcb2ba6ef9bad63e50.xlsx"},{"id":58302007,"identity":"718e6b6a-69f7-4ac5-8e7a-ca85453f81f8","added_by":"auto","created_at":"2024-06-13 16:35:07","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":179612,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/25dad628565a25166b7e8876.xlsx"},{"id":58302267,"identity":"02a767e9-6ce8-4486-883a-72810c760cca","added_by":"auto","created_at":"2024-06-13 16:43:07","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1319664,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/86ac77f72969e400798da4bc.xlsx"},{"id":58302264,"identity":"751c889b-5571-4446-886b-b56d516ad506","added_by":"auto","created_at":"2024-06-13 16:43:07","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":497815,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/55b3f8b63002179bfcef9bb7.xlsx"},{"id":58302545,"identity":"a18aea47-8722-4f77-8c31-cedd8b452ea0","added_by":"auto","created_at":"2024-06-13 16:51:07","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":291102,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/e3c3e6e293df148c00ab856d.xlsx"},{"id":58302013,"identity":"40e56cb3-6e15-44f3-8970-da3ea150516e","added_by":"auto","created_at":"2024-06-13 16:35:07","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":378486,"visible":true,"origin":"","legend":"","description":"","filename":"TableS9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/46fc8c647c988c1ea802abcf.xlsx"},{"id":58302544,"identity":"4673c12f-3932-462a-a0d7-a1c424d26746","added_by":"auto","created_at":"2024-06-13 16:51:07","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":57437,"visible":true,"origin":"","legend":"","description":"","filename":"TableS10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365578/v1/cfb4ca4cb4f8cafa5dcdddf9.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change projections raise concerns about trees having to cope with intensified and frequent extreme events [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Drought is currently causing heightened disruptions in forests, diminishing resilience and increasing mortality rates [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; Laverdi\u0026egrave;re et al., 2022 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; Soro et al., 2023 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]). However, in spite of recent progress [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], there are still large gaps in our understanding of the complex molecular response of trees to drought at the transcriptome-wide level.\u003c/p\u003e \u003cp\u003eDrought 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 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. More specifically, the pivotal role of the phytohormone ABA as a precursor molecule in drought stress signaling is widely acknowledged in coniferous species [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous studies have revealed modifications in the regulation of genes responsible for synthesizing and transporting defense molecules such as flavonoids and terpenoids [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], antioxidants involved in ROS scavenging [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], osmoprotectants such as carbohydrates [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and proline [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Heat shock proteins (HSPs) play a pivotal role in safeguarding and stabilizing proteins under drought conditions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, chaperone proteins like dehydrins, a subset of late embryogenesis abundant (LEA) proteins, maintain protein and cell membrane stability throughout the hydric constraint [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Finally, genes involved in transcriptional regulation networks, such as AP2/ERF, bZIP, TCP, WRKY, and MYB transcription factors, coordinate molecular responses to drought [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhite spruce (\u003cem\u003ePicea glauca\u003c/em\u003e (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 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] 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 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is also considered to be a model conifer species for genetic and genomic investigations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; several studies have shown the susceptibility of white spruce to drought, as demonstrated by a marked reduction in growth [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], increased mortality [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and changes in population abundance and distribution [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly to numerous conifers of the \u003cem\u003ePinophyta\u003c/em\u003e group, white spruce swiftly initiates the ABA pathway under drought conditions, leading to early stomatal closure [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This process reduces water loss while also concurrently decreasing photosynthetic uptake, thereby posing a risk of carbohydrate depletion if the drought persists [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The importance of intraspecific genetic variation for drought response has also been recently highlighted in white spruce [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent 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 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Despite the rapid proliferation of genomic resources, including nuclear [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], mitochondrial, and chloroplast genomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], gene catalogs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], SNP catalogs [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and quantitative trait loci (QTL) analyses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] brought about by advancements in Next Generation Sequencing (NGS) and high-throughput genotyping technologies, these resources still remain incomplete and fragmented [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Considering the extensive gene flow linking natural populations of white spruce [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], the relatively recent nature of local genetic adaptation to climate following Holocene recolonization [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and the highly multigenic nature of local adaptation to climate in spruces [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], 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.\u003c/p\u003e \u003cp\u003eThis study was carried out in white spruce and had two main objectives. First, we developed a \u003cem\u003ede novo\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Generation and annotation of the white spruce transcriptome assembly, GCAT 4.0\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Plant material\u003c/h2\u003e \u003cp\u003eA \u003cem\u003ede novo\u003c/em\u003e transcriptome was assembled from RNA-Seq data obtained from three distinct experiments involving the collection of \u003cem\u003ePicea glauca\u003c/em\u003e 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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e 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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e 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 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. 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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Additional File 1), as previously described in Stival Sena et al. (2018) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Strategy and quality assessment of the assembly\u003c/h2\u003e \u003cp\u003eQuality of RNA-seq raw sequence data was first checked using FASTQC v0.11.9 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Raw reads were cleaned using Trimmomatics.0.39 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] 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 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] and IDBA-UD assemblers [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] integrated within the a5 pipeline [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Transcriptome assemblies were then scaffolded with one another using LINKS 1.8.6 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The resulting consensus assembly was then scaffolded again with a previously published \u003cem\u003ePicea glauca\u003c/em\u003e transcriptome assembly [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] 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 \u003cem\u003eEmbryophyta\u003c/em\u003e and \u003cem\u003eViridiplantae\u003c/em\u003e reference databases (odb10) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] is available in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Functional annotation of transcripts\u003c/h2\u003e \u003cp\u003eFunctional annotation for the new transcriptome assembly was retrieved by sequence similarity searches using BLASTx of OmicsBox [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] (cut-off E-value of \u0026le;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) 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 \u003cem\u003ede novo\u003c/em\u003e assembly GCAT 4.0, BLASTx analyses were performed against several public databases such as PlantTFDB and \u003cem\u003eViridiplantae\u003c/em\u003e, using DIAMOND-aligner v.2.0.14 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Analysis parameters were set to \"sensitive\" mode, k-1, b1.2 and an E-value of \u0026le;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. The OmicsBox assembly annotation has been deposited and is publicly available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ZoeRibeyre/BMC_Article_Ribeyre_et_al.git\u003c/span\u003e\u003cspan address=\"https://github.com/ZoeRibeyre/BMC_Article_Ribeyre_et_al.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). 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 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Results of BLASTx and BLASTn analyses performed to annotate GCAT 4.0 are detailed in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e and the summary of results are available in Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe presence of transcription factors (TFs) was additionally corroborated by analyzing BLASTx results against the plant transcription factor database PlantRegMap/PlantTFDB v5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://planttfdb.gao-lab.org/\u003c/span\u003e\u003cspan address=\"http://planttfdb.gao-lab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] 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 \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e. The number of putative unique genes contained in the \u003cem\u003ede novo\u003c/em\u003e 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 \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Drought stress experiment and transcriptome analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Plant material, water treatment, and RNA sequencing\u003c/h2\u003e \u003cp\u003eThe transcriptomic analyses were carried out from RNA-seq data from the greenhouse drought experiment described by Stival Sena et al. (2018) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] to characterize the response of \u003cem\u003ePicea glauca\u003c/em\u003e seedlings (\"Experiment 3\"; Methods S1 and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Differential expression and enrichment analyses\u003c/h2\u003e \u003cp\u003eDifferential 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to identify the transcripts significantly up- or down-regulated throughout drought intensification (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). Differential expression analyses were conducted by pseudo-aligning high-quality reads against the assembly GCAT 4.0 using Kallisto v0.48.0 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Read counts were normalized using DESeq2 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] 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 \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e), between drought-treated and control samples were then identified using the R package DESeq2 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] with an absolute threshold of 2 for LFC and an adjusted p-value of 0.05.\u003c/p\u003e \u003cp\u003eGene ontology (GO) enrichment analyses were performed on significant DETs using the OmicsBox and a Fisher's exact test [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. GO enrichment analyses were based on lists of DETs whose expression was significantly regulated at each time point (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). Venn diagrams were generated to highlight unique and shared DEGs between time points and clones using Venn diagrams (ggVennDiagram v1.2.2 R package, [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]; Venndetail v1.16.0 R package, [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe functions and the regulation of DEGs were visualized using metabolic pathway diagrams from MapMan v3.6.0RC1 [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] (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 \u003cem\u003ede novo\u003c/em\u003e assembly transcriptome GCAT 4.0) by assigning sequences to the corresponding Bin terms [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. 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.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Statistics and quality assessment of the new white spruce \u003cem\u003ede novo\u003c/em\u003e assembly, GCAT 4.0\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003ede novo\u003c/em\u003e 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 \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). 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 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The contig N50 stands at 1,816 bp, and the contig N90 is 746 bp (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and a median length of 1,173 bp and a mean length of 1,488 bp (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To assess the assembly's completeness, a BUSCO analysis was performed using the \u003cem\u003eViridiplantae\u003c/em\u003e odb10 and \u003cem\u003eEmbryophyta\u003c/em\u003e odb10 databases. Within the 425 \u003cem\u003eViridiplantae\u003c/em\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Additionally, among the 1,614 \u003cem\u003eEmbryophyta\u003c/em\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Temporal dynamics in gene expression in response to drought\u003c/h2\u003e \u003cp\u003eAnalysis 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), while a 23% overlap was observed for down-regulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Identification of key functions involved in short-term drought response\u003c/h2\u003e \u003cp\u003eThe 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, MF panel).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Gene expression changes under intense water stress\u003c/h2\u003e \u003cp\u003eA 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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S9), and included 68% of the total down-regulated DEGs and 52% of the total up-regulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). The redox homeostasis process was well-represented with 126 DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Furthermore, the identification of InterPro domains indicated many DEGs encoding Leucine-rich repeat and kinases proteins, alpha-beta hydrolases, AAA\u0026thinsp;+\u0026thinsp;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. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Identification of drought-responsive transcription factors\u003c/h2\u003e \u003cp\u003eA 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). From day 18 to day 22, a notable increase in the magnitude of the change was observed for most of the transcription factors (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). 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).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Intraspecific genetic variation of drought-responsive genes\u003c/h2\u003e \u003cp\u003eThe 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. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Overall time points, no down-regulated DEGs were shared among clones (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB), and only 21% of up-regulated DEGs were common among all three clones (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). Shared DEGs were involved in BP of defense mechanisms and macromolecule metabolism covering carbohydrates, lipids, and amino acids (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA), and were also associated with MF of catalytic activities including transferase, oxidoreductase, lyase, isomerase, and hydrolase activities (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC). 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. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis 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.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Quality of the new transcriptome assembly\u003c/h2\u003e \u003cp\u003eA 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The completeness achieved in the GCAT 4.0 assembly is consistent with similar investigations conducted on various conifer species [\u003cspan additionalcitationids=\"CR74 CR75\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e–\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. 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 \u003cem\u003eViridiplantae\u003c/em\u003e database (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The genome size of white spruce is 20 Gb [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and the number of functional genes is estimated at 30,410 [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Signaling and hormonal response to drought\u003c/h2\u003e \u003cp\u003eIn 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 [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. 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 \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Isohydric species, like white spruce, activate early stomatal closure in response to drought [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with ABA playing a key role in reducing water loss [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. In our study, biological processes (BP) related to ABA were enriched on days 18 and 22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). 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 \u003cem\u003ePicea abies\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and \u003cem\u003ePinus massoniana\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eOn 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 \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e) were observed. We identified five putative AUX/IAA sequences, known to be involved in early auxin signaling and regulated in response to drought [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. 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 \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e; [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. 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 CO\u003csub\u003e2\u003c/sub\u003e uptake in white spruce.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Negative impact of drought on photosynthesis, growth, and water transport\u003c/h2\u003e \u003cp\u003eThe numerous down-regulated genes linked to photosynthesis, particularly showing a more pronounced decline after 18 and 22 days of drought treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), suggest an abrupt disruption of photosynthesis as the drought stress intensifies. Water availability significantly impacts photosynthesis, often causing a limitation in CO\u003csub\u003e2\u003c/sub\u003e uptake due to reduced stomatal and mesophyll conductance [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Here, three DEGs were associated with rubisco activity, including two encoding Ribulose-1,5-bisphosphate carboxylase/oxygenase and one related to rubisco activase (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e), which plays a pivotal role in the assimilation and fixation of CO\u003csub\u003e2\u003c/sub\u003e [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. In plants, PSI and PSII play pivotal roles in capturing light energy and facilitating the transfer of electrons within the electron transport chain [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. In line with previous studies, our findings suggest that prolonged periods of water stress can adversely affect both PSI and PSII [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. We also observed a decrease in the expression of 13 DEGs associated with photosynthetic pigments such as chlorophyll a, chlorophyll b, and carotenoids (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e), which is in line with previous research conducted on conifers [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Our study highlights a significant disruption of photosynthesis in white spruce under drought conditions, which may be due to both reduced CO\u003csub\u003e2\u003c/sub\u003e uptake and damage to numerous components within the photosynthetic chain.\u003c/p\u003e \u003cp\u003eWater stress in trees leads to reduced growth, even before a decline in photosynthesis occurs [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], 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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e] 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consistent with previous findings in water-stressed \u003cem\u003eAbies alba\u003c/em\u003e seedlings, we observed a decreased expression of genes encoding xyloglucan endotransglucosylase/hydrolase (XTH), a crucial enzyme involved in plant cell wall reconstruction [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. These findings emphasize the disruption of several crucial growth-related processes in white spruce induced by drought.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Regulation of the carbohydrate and lipid metabolisms\u003c/h2\u003e \u003cp\u003eWe reported an enrichment of carbohydrate metabolism under drought conditions on days 18 and 22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). 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 [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]; the compounds may serve as carbon precursors for the synthesis of defense compounds and act as signaling molecules [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. In our study, six differentially expressed genes (DEGs) were associated with sucrose synthase and eight with the sucrose and hexose transporters SWEETs (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e), 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 [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], recent findings in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e suggest that the incompatibility between growth and defense may also be due to the antagonistic nature of the molecular pathways regulating these two processes [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLipids play essential roles in cell membrane structure, energy storage, and signaling [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. Various conifer species, such as those found in the \u003cem\u003eLarix\u003c/em\u003e, \u003cem\u003ePinus\u003c/em\u003e, and \u003cem\u003ePicea\u003c/em\u003e genera, possess substantial lipid reserves [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e], 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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). 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 [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. 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 \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). These enzymes play a crucial role in the glyoxylate cycle, providing essential precursors for gluconeogenesis, the process of converting non-carbohydrate precursors into carbohydrates [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Drought-responsive genes coding for protective defense and stress resistance and resilience\u003c/h2\u003e \u003cp\u003eA 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 \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). 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 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Oxidative stress damages various structures and molecules, such as membrane lipids, proteins, photosynthetic pigments, and nucleic acids [\u003cspan additionalcitationids=\"CR111\" citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e–\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e], consistent with a role in drought response in conifers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Our study also identified numerous cytochrome P450 genes (CYTs) that were down-regulated (42 DEGs) and up-regulated (7 DEGs) under stress conditions (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). In contrast, previous observations in \u003cem\u003ePinus elliottii\u003c/em\u003e showed only up-regulation [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. CYTs play a crucial role in drought response by contributing to antioxidant activities and defense response in plants [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. \u003cem\u003eCsCYT75B1\u003c/em\u003e, a gene of \u003cem\u003eCitrus sinensis\u003c/em\u003e, was associated with flavonoid metabolism and was highly expressed after drought stress, contributing to drought tolerance by elevating ROS scavenging activities [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]. Due to the interaction of CYTs whose expression is induced with other key genes in response to water stress [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e], the pivotal role of CYTs will require further investigation in white spruce and coniferous species.\u003c/p\u003e \u003cp\u003eHSPs and LEA proteins are chaperone proteins that protect cells from abiotic stress by stabilizing proteins and membranes under stress [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e], and overexpression of the \u003cem\u003eVaDJI\u003c/em\u003e gene coding for a DnaJ protein conferred ABA insensitivity and drought tolerance in transgenic tobacco [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. In addition, the expression of 33 LEA genes (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e), including 13 up-regulated putative dehydrins (Table S10, GCAT3.3 genes) belonging to the LEA sub-group II [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e] was noted. As previously observed in white spruce, we found that the expression of \u003cem\u003ePgDhn33, PgDhn35\u003c/em\u003e and \u003cem\u003ePgDhn16\u003c/em\u003e was strongly induced, while the expression of \u003cem\u003ePgDhn37\u003c/em\u003e was repressed (Table S10). \u003cem\u003ePinaceae\u003c/em\u003e dehydrin induction appears to occur after a certain period of drought [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e] 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 [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e], were induced under drought conditions (GQ03714_K21, GQ03512_J05), as previously reported in white spruce (Table S10; [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Key transcription factors involved in the transcriptional control of drought-responsive genes\u003c/h2\u003e \u003cp\u003eSeveral 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 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. A majority of NAC TFs were up-regulated in response to drought as reported in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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 \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e), suggesting their potential importance for drought tolerance in white spruce. The two MYB sequences identified in this study had homologies with putative \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteins known to enhance protection against oxidative damage or to be involved in growth, phenylpropanoid biosynthesis, and the ABA signaling pathway [\u003cspan additionalcitationids=\"CR128\" citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e–\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. Drought-induced AP2/ERF genes in our study were close homologs to ethylene responsive elements in other species [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e]. Recent findings have shown that certain AP2/ERF genes can improve drought tolerance in conifers [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. 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 \u003cem\u003eArabidopsis thaliana\u003c/em\u003e [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e]. In our study, we observed contrasting expression patterns among WRKY members under drought conditions. Similar findings were reported in \u003cem\u003ePinus massoniana\u003c/em\u003e, where some WRKY genes responded to drought stress induced by exogenous ABA, resulting in improved drought tolerance in transgenic tobacco plants [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e]. Numerous zinc finger TFs were identified in white spruce (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e), and homologs found in the PlantTFDB database indicate a potential role in stomatal aperture, ROS production and drought tolerance [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Intraspecific genetic variation in gene regulation under drought stress: findings and future avenues\u003c/h2\u003e \u003cp\u003eIntraspecific genetic variation in drought response is crucial for selection and adaptation in tree populations faced with environmental change [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e]. Recent studies in white spruce have highlighted the role of genetic variation among populations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], as well as the genomic and transcriptomic basis for drought response and resilience [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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 \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). 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 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 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 \u003cem\u003eAbies pinsapo\u003c/em\u003e with contrasting gene expression patterns among post-drought phenotypes [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbscisic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAQP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAquaporin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdenosine triphosphate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCesA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCellulose synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCellulose synthase-like\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCYT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCytochrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed transcript\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeat shock protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDENT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Diversity Experiment Network with Trees\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLog fold change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLate embryogenesis abundant protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-structural carbohydrate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNext generation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOpen reading frame\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOsmotin/thaumatin-like\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlasma membrane intrinsic protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhotosystem I\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhotosystem II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRNA-sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive oxygen species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscription factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eXTH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEncoding xyloglucan endotransglucosylase/hydrolase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank Juliana Stival Sena (Natural Resources Canada), Justine Laou\u0026eacute; (IMBE. Aix-Marseille, France) and Armand S\u0026eacute;guin (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.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis 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 \u0026nbsp;PSR-SIIRI-836 co-led by J. Mackay and J. Bousquet.\u003c/p\u003e\n\u003ch2\u003eAuthor\u0026rsquo;s contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eZ. 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. \u0026nbsp;and G. P. performed the analyses and discussed the results. Z. R. \u0026nbsp;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.\u003c/p\u003e\n\u003ch2\u003eEthics declarations\u003c/h2\u003e\n\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eWhite spruce samples used for \u003cem\u003ede novo\u003c/em\u003e 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.\u003c/p\u003e\n\u003ch3\u003eEthical consideration\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eForzieri G, Dakos V, McDowell NG, Ramdane A, Cescatti A. 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Front Plant Sci. 2021;12:648312.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transcriptomics, drought tolerance, conifer, water stress, global change, transcription factor, lipid metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4365578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4365578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eForests 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 (\u003cem\u003ePicea glauca\u003c/em\u003e (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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e \u003cem\u003eDe novo\u003c/em\u003e 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.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur 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.\u003c/p\u003e","manuscriptTitle":"De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 16:35:02","doi":"10.21203/rs.3.rs-4365578/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76f228dd-8e09-4fa4-9ca9-61c6376fb78e","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T16:55:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 16:35:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4365578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4365578","identity":"rs-4365578","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}