Identification of drought stress-responsive genes in Korean fir (Abies koreana) through comparative RNA-seq analysis

Identification of drought stress-responsive genes in Korean fir (Abies koreana) through comparative RNA-seq analysis | 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 Identification of drought stress-responsive genes in Korean fir (Abies koreana) through comparative RNA-seq analysis Hyeong Cheol Park, Jung Eun Hwang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4690654/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 Abies koreana , an endangered species sensitive to drought, faces severe decline due to climate change-induced water deficit. Results In this study, we performed a comprehensive transcriptomic analysis of A. koreana under drought stress. RNA-seq data analysis revealed 85,403 contigs of extended lengths, indicative of a high-quality assembly. Gene Ontology classification revealed strong drought stress-responsive regulation of unigenes associated with various biological processes, cellular components, and molecular functions. Gene expression levels showed variation at different time points, indicating a nuanced plant response. Numerous transcription factors, notably those belonging to ERF, MYB, LBD, and NAC families, were identified, underscoring their critical roles in drought tolerance. Finally, qRT-PCR validation confirmed the reliability of RNA-seq data. Conclusion Overall, this study reveals candidate genes required for drought tolerance in A. koreana , thus supporting future tree breeding programs for effective forest management. Abies koreana transcriptomic analysis drought stress transcription factors Gene Ontology classification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Abies species, commonly known as firs, thrive in cool, moist climates with well-distributed rainfall, being adapted to specific moisture and environmental conditions [ 1 , 2 ]. Global warming is a major threat to Abies species, including Abies koreana (Korean fir), worldwide, because of the decrease in soil moisture content [ 3 , 4 ]. Abies species are sensitive to drought stress and exhibit signs such as reduced growth, wilting, and needle browning under drought conditions [ 2 , 5 ]. A. koreana , prevalent in the southern region of the Korean peninsula, serves as an essential ornamental tree, holding considerable importance within forest ecosystems and human societies [ 6 , 7 ]. However, since the early 2000s, a severe decline has been observed in the distribution of A. koreana , primarily because of global warming-induced drought [ 8 , 9 , 10 ]. Therefore, A. koreana has been recognized as a climate-sensitive biological indicator species in Korea [ 11 ] and has been categorized as an endangered species by the International Union for Conservation of Nature (IUCN) [ 12 ]. The conservation of A. koreana to ensure its sustained presence and positive contribution to forest ecosystems is imperative. Thus, it is essential to uncover the adaptive molecular mechanisms of A. koreana in response to drought stress. This understanding is pivotal for enhancing forest management in the face of climate change-induced drought challenges Transcriptomic analysis has been used to determine gene-specific expression and facilitate variant detection, genome-wide transcript characterization, and differential gene expression analysis in A. koreana [ 13 , 14 ]. These studies have advanced our understanding of the molecular response and stress response pathways of A. koreana under conditions of elevated CO 2 and heat [ 13 , 14 ]. Je et al. (2018) focused on the photosynthetic responses of A. koreana to drought under varying light conditions [ 15 ]. However, comparative transcriptomic analyses in A. koreana under drought conditions remain unexplored. Transcriptomic analysis aids in identifying key genes crucial for drought tolerance and imperative for the development of drought-resistant varieties in tree breeding programs. In this study, we performed transcriptomic analysis of A. koreana trees exposed to drought stress for different time periods. Our results reveal candidate genes associated with drought tolerance, which could be used for the development of drought-resistant A. koreana cultivars. Results Determination of soil water content (SWC) under drought stress Three-year-old A. koreana plants were exposed to drought conditions for different durations (0, 7, 10, and 14 d). Drought-treated plants, particularly those subjected to drought for 14 d, exhibited delayed growth compared with non-treated plants (control; 0 d) (Fig. 1 a). SWC decreased throughout the drought treatment, starting at 7 d and reaching less than 20% at 14 d (Fig. 1 b), approximately a two-fold decline compared with SWC at 0 d (control). RNA-Seq, read assembly and annotation Twenty-four RNA-seq libraries produced 20.7–31.9 million raw reads, with a clean reads rate of 84.82–95.83% per read. In addition, the percentage of trimmed reads per sample ranged from 67.53–88.78%, indicating the reliability, accuracy, and interpretability of the RNA-seq data (Table 1 ). High-quality trimmed (clean) reads were deposited in the NCBI Sequence Read Archive database (accession numbers: SAMN40219202, SAMN40219203, SAMN40219204, SAMN40219205, SAMN40219206, SAMN40219207, SAMN40219208, SAMN40219209, SAMN40219210, SAMN40219211, SAMN40219212, and SAMN40219213), and subsequently assembled into 85,403 contigs (N50 value = 2,087 bp; average length = 1,364 bp; minimum length = 200 bp; and maximum length = 63,417 bp) using the Trinity software (Table 2 ). Finally, the contigs were assembled into 42,839 unigenes (N50 value = 1,901 bp; average length = 1,130 bp; minimum length = 200 bp; and maximum length = 63,417 bp) (Table 2 ). Table 1 Summary of the RNA-seq data of Korean fir ( Abies koreana ) Sample ID Raw Reads Number Clean Reads Number Clean Reads Rate (%) Trimmed/Raw Rate (%) Control-1 27,442,331 2,629,788,396 95.83 88.78 27,442,331 2,482,550,880 90.46 83.81 Control-2 31,954,330 3,029,957,440 94.82 85.81 31,954,330 2,917,332,071 91.30 82.62 Control-3 21,319,833 2,029,347,938 95.19 86.16 21,319,833 1,947,939,322 91.37 82.70 7d-1 24,452,113 2,333,517,445 95.43 85.89 24,452,113 2,225,066,333 91.00 81.90 7d-2 21,759,297 2,062,197,925 94.77 81.96 21,759,297 1,900,201,896 87.33 75.52 7d-3 25,493,814 2,450,449,606 96.12 87.13 25,493,814 2,222,188,219 87.17 79.02 10d-1 23,246,102 2,224,376,086 95.69 86.61 23,246,102 2,128,785,999 91.58 82.89 10d-2 27,080,678 2,566,325,074 94.77 81.98 27,080,678 2,368,793,197 87.47 75.67 10d-3 24,286,984 2,292,459,622 94.39 79.99 24,286,984 2,115,170,972 87.09 73.80 14d-1 24,228,757 2,320,740,509 95.78 88.41 24,228,757 2,174,459,262 89.75 82.83 14d-2 20,704,358 1,939,475,753 93.67 74.58 20,704,358 1,756,208,583 84.82 67.53 14d-3 21,678,530 2,076,064,545 95.77 88.35 21,678,530 1,940,549,440 89.51 82.58 Table 2 Summary of the assembled A. koreana transcripts Parameter Contigs Unigenes Total trinity transcripts 85,403 42,839 Minimum length (bp) 200 200 Maximum length (bp) 63,417 63,417 Average length (bp) 1,364 1,130 N50 (bp) 2,087 1,901 Total length (bp) 116,506,792 48,414,644 Functional annotation and classification of unigenes All assembled unigenes (42,839) were subjected to GO enrichment analysis using the GO database and WEGO tool. Of the 42,839 unigenes, 29,739 (69.42%) were successfully annotated using the GO database. The annotated A. koreana genes were functionally categorized into three major functional domains (biological process, cellular component, and molecular function), based on the GO classification system (Fig. 2 ). In the biological process domain, the most prominent subgroups were 'cellular process' (13,805 unigenes), 'metabolic process' (13,051 unigenes), and 'response to stimulus' (7,360 unigenes). In the cellular component domain, the prevailing groups included 'cell' (19,410 unigenes), 'cell part' (19,326 unigenes), and 'organelle' (15,230 unigenes) (Fig. 2 and Additional file 2: Table S2). Within the molecular function domain, the most abundant groups were 'binding' (10,158 unigenes), 'catalytic activity' (9,334 unigenes), and 'transporter activity' (1,350 unigenes) (Fig. 2 and Additional file 2: Table S2). Of the 42,839 assembled unigenes, 23,947 (55.9%) were categorized in 26 KOG/COG clusters. Of these 23,947 unigenes, 6,533 unigenes, which accounted for the highest proportion, were predicted to perform general function, while 2,369 unigenes were predicted to be involved in signal transduction mechanisms (Fig. 3 ). Expression analysis and functional annotation of DEGs To identify A. koreana genes are potentially involved in the drought stress response, their expression profiles were analyzed. Genes exhibiting more than a two-fold change in expression were compared across the 7-, 10-, and 14-d drought treatments, with 0-d treatment serving as the control. The analysis revealed distinctive increases or decreases in gene expression, particularly in the 10-d treatment compared with the 7-d and 14-d treatments. Specifically, at 7 d, 313 genes showed more than a two-fold increase in expression, while 547 genes exhibited decreased expression. At 10 d, 566 genes showed more than a two-fold increase in expression, and 627 genes showed decreased expression. At 14 d, 449 genes exhibited more than a two-fold increase in expression, and 347 genes showed decreased expression. These findings provide valuable insights into the dynamic changes in gene expression in response to drought stress in A. koreana (Fig. 4 ). Additionally, 218 genes were commonly upregulated and 448 genes were commonly downregulated in the 7- and 10-d treatments compared with the 14-d treatment (Fig. 5 ). Compared with the 0-d (control) treatment, 50 genes were commonly upregulated and 77 genes were commonly downregulated across all three drought treatments (Fig. 5 a and b). Based on these results, we speculated that the response of A. koreana plants to drought stress is more similar at 7 and 10 d than at 14 d. Functional analysis of DEGs revealed that the majority of genes expressed at 7 and 10 d were related to GO terms ‘metabolic process’ and ‘catalytic activity’, indicating that genes related to metabolic process and catalytic activity were more active at 7 and 10 d (Fig. 6 ). A high number of genes was related to the ‘response to stimulus’ at 10 d, suggesting that stress response-related genes are most active at 10 d in A. koreana . Identification of TFs involved in drought stress TFs serve as pivotal regulators in plants, playing a crucial role in sensing and orchestrating plant responses to drought stress. Our study identified several drought stress-responsive TF genes in A. koreana , particularly those belonging to bHLH , ERF , MYB , WRKY , LBD , and NAC families (Fig. 7 ). A high number of genes encoding ethylene-responsive element-binding factors (ERFs) displayed upregulation in response to drought stress, while a significant number of genes encoding basic helix-loop-helix (bHLH) TFs exhibited downregulation. Furthermore, a considerable number of the MYB TF genes showed both increased and decreased expression patterns (Fig. 7 , and Additional file 3: Table S3). MYB TFs not only participate in various phytohormone signal transduction pathways triggered by external stimuli, like drought, but also potentially influence the interplay between different plant hormones. Collectively, the diverse expression patterns of different TF family genes observed in this study suggest that A. koreana employs a complex and extensive regulatory network to respond to drought stress, and that these various TFs play pivotal roles in mediating the drought stress response. Validation of differentially expressed TF genes using qRT-PCR Six TF-encoding DEGs were randomly selected to validate the RNA-seq data. The expression profiles of these six DEGs were compared between the control (0 d) and drought stress (7, 10, and 14 d) treatments by qRT-PCR. The selected DEGs included genes encoding two ERF TFs (Akoreana1SL019838t0001, Akoreana1SL006588t000), one BES1 TF (Akoreana1SL007270t0001), one HD-ZIP TF (Akoreana1SL003569t0001), one LBD1 TF (Akoreana1SL011234t0001), and one MYB TF (Akoreana1SL005911t0001) (Additional file 1: Table S1 ). The expression levels of all six TF genes were found to be higher in drought-treated plants than in control plants, indicating the drought-induced upregulation of these genes (Fig. 8 ). Furthermore, the expression profiles of genes determined by qRT-PCR across the drought treatments were similar to those determined by RNA-seq, strongly confirming the reliability of our RNA-seq data. Discussion Abies species, including A. koreana , are sensitive to drought stress [ 2 , 3 , 4 , 5 ]. A. koreana has been categorized as an endangered species owing to its severe decline since the early 2000s, primarily due to the drought induced by global warming [ 8 , 9 , 10 ]. Thus, to ensure the sustained presence and positive contribution of Abies species to forest ecosystems, conservation efforts are imperative. However, the drought stress response of A. koreana at the molecular level remains unknown. Transcriptomic analysis has been used to understand the molecular response of A. koreana to elevated CO 2 and temperature [ 13 , 14 ] as well as to identify key genes crucial for drought tolerance in other tree species [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Therefore, in this study, we analyzed the transcriptome of A. koreana plants exposed to drought stress. Our results reveal key A. koreana genes associated with drought tolerance, which could be used to aid tree breeding programs for the enhancement of forest management in the face of climate change-induced drought challenges. Three-year-old A. koreana plants were initially planted in pots containing soil with 50% moisture content. Subsequently, watering was withheld for 14 d to induce water deficit. A reduction in SWC was observed from the 7th day of drought treatment compared with the control (0 d). Additionally, the drought-treated plants exhibited delayed growth, a sign of water deficit in plants (Fig. 1 ). To investigate the transcriptomic changes in A. koreana plants under drought conditions, RNA was extracted from the leaves of plants treated with drought stress for 0 (control), 7, 10, and 14 d. The transcriptomic analysis yielded 85,403 contigs, with an average length of 1,364 bp and an N50 value of 2,087 bp (Table 2 ). Notably, the average length and N50 value of A. koreana contigs obtained under drought conditions surpassed those of contigs assembled in other tree species facing similar conditions. The contigs of A. koreana were longer than those of Tree peony (average length: 825 bp, N50: 1,368 bp), Aleppo pine (average length: 906.47 bp, N50: 1,269 bp), Masson pine (average length: 695 bp, N50: 1,227 bp), Ammopiptanthus mongolicus (average length: 304 bp, N50: 471 bp), and Prosopis juliflora (average length: 428 bp, N50: 714 bp) [ 17 , 18 , 19 , 20 , 21 ]. The longer average length and N50 value of A. koreana contigs obtained in this study strongly suggest that the RNA-seq data represent a complete set of transcripts and a high-quality transcriptome assembly. Based on the GO classification, unigenes involved in cellular processes, metabolic processes, response to stimuli, cell, cell parts, organelles, binding, and catalytic activities were found to be strongly regulated in response to drought stress (Fig. 2 ). This suggests the significant regulation of unigenes associated with various biological processes, cellular components, and molecular functions in response to drought stress, indicating their substantial involvement in the plant response to drought. Similar observations were reported in other tree species exposed to drought conditions [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ], indicating a commonality in the regulatory responses to drought stress across different tree species. In this study, the number of upregulated genes was higher than that of downregulated genes on day 14 but than that of downregulated genes on days 7 and 10 (Fig. 4 a). The observed changes in the number of upregulated and downregulated genes at different time points during the drought treatment can be explained by the dynamic nature of the plant response to drought stress [ 23 , 24 ]. This dynamic response involves the activation and repression of different sets of genes at different stages of stress. The higher number of downregulated genes on days 7 and 10 could reflect an early response mechanism where the plant initially downregulates certain processes to cope with the immediate impact of drought stress. With the continuation of stress, the plant may switch to upregulating specific genes to initiate more advanced adaptive responses, leading to a greater impact on its gene expression profiles. Therefore, the observed increase in the number of upregulated genes on the 14th day of drought may reflect the activation of specific regulatory pathways that mediate the acclimation of plants to prolonged water deficit [ 23 , 24 ]. Several studies have shown that the response to drought stress is a complex process involving the regulation of a large number of genes, including those encoding TFs that play critical roles in the adaptation to abiotic stresses [ 25 , 26 ]. For example, TFs belonging to the ERF, MYB, WRKY, NAC, and bHLH families have been reported to be involved in the regulation of gene expression in response to drought stress in various plant species, including A. koreana [ 13 , 14 , 25 , 26 ]. These TFs are known to directly regulate the expression of stress-responsive genes, and their upregulation is associated with enhanced tolerance to drought stress in tress species [ 27 , 28 , 29 , 30 , 31 , 32 ]. Therefore, a substantial number of TF genes were identified in this study, including those encoding bZIP, HD-ZIP, bHLH, ERF, MYB, WRKY, LBD, and NAC TFs, which have been implicated in various physiological processes, such as stomatal movement and hormone signal transduction. Notably, the number of ERF TFs was found to be greater than that of other TFs, and a large number of ERF TFs were upregulated under drought stress (Fig. 7 ). A similarly high number of upregulated ERF genes was observed in A. koreana under other stresses such as heat and CO 2 stress [ 13 , 14 ], strongly indicating the significant role of ERF TFs in the adaptation of A. koreana to different abiotic stresses. MYB, ERF, NAC, and bHLH TF families are known to be large and functionally diverse. Different members of each TF family may play distinct roles and functions in response to stress [ 25 , 26 ]. The observed mix of upregulated and downregulated TF genes across the different drought treatments (7, 10, and 14 d) suggests that A. koreana employs a complex and dynamic regulatory network to respond to drought stress. In addition, the observed upregulation or downregulation of specific MYB , ERF , NAC , and bHLH TF genes could be related to their functions in distinct signaling pathways or cellular processes activated at different stages of drought stress [ 25 , 26 ]. The functions of TFs in conferring tolerance to drought stress in woody crops have been extensively analyzed. Overexpression of ERF genes has been shown to enhance tolerance to drought stress in woody crops [ 33 , 34 ]. Additionally, MdHB-7 , which encodes an HD-Zip TF, was found to promote drought tolerance in apple [ 35 ]. Furthermore, recent studies show that the PtrbZIP3 (basic leucine zipper) TF as well as NAC, WRKY, bHLH, LBD, and MYB TFs play essential roles in the response to drought stress, and overexpression of genes encoding these TFs increases drought tolerance in woody crops [ 27 , 28 , 29 , 30 , 31 ]. In this study, we confirmed the reliability of our RNA-seq data by analyzing the expression patterns of six genes encoding TFs (BES1, HD-ZIP, bHLH, ERF, MYB, and LBD) at different time points using qRT-PCR (Fig. 8 ). Overall, the expression of stress-responsive genes, including those encoding TFs, was induced in a time-dependent manner in response to drought stress. Taken together, this study revealed several candidate genes, including those encoding ERF, MYB, LBD, and NAC TFs, associated with drought tolerance in A. koreana . Although further investigation is needed to gain valuable insights into the role of these genes in the adaptation of A. koreana plants to drought stress, we predict that the results of this study will aid tree breeding programs and facilitate the enhancement of forest management in the face of climate change-induced drought challenges. Conclusions Our transcriptomic analysis of A. koreana plants subjected to drought stress unveiled a robust molecular response. This study sheds light on the dynamic regulation of genes, emphasizing the intricate roles of ERF, MYB, LBD, and NAC TFs in drought adaptation. The observed temporal gene expression patterns offer valuable insights into early and late stress responses. These findings enhance our understanding of the molecular mechanisms employed by A. koreana to survive drought. We anticipate that the identified candidate genes will inform tree breeding efforts, enhancing forest management strategies to mitigate the damaging effects of climate change-induced drought on A. koreana and safeguard the future of this endangered species. Methods Plant materials Seeds of Korean fir ( Abies koreana Wilson), collected from Mount Halla on Jeju Island, Korea, were subjected to a low temperature (4°C) for 3 months to break seed dormancy, and then sown in seedling trays containing soil. One year after sowing, the seedlings were transplanted into individual soil-filled pots and grown for 3 years in a greenhouse under natural sunlight. Three-year-old A. koreana obtained from Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science, Jeju, Korea were used in this study as the plant material for drought treatment. Drought treatment and soil water content (SWC) determination To induce drought stress, the watering of 3-year-old A. koreana plants was withheld for 7, 10, and 14 d. SWC was measured by using a handheld soil water content measuring device (DM-18, Takemura Electric, Nara, Japan), according to the manufacturer’s instructions. The status of the plants just prior to the drought treatment was recorded as control (0 d), and SWC was measured at 0, 7, 10, and 14 d. RNA extraction and RNA-seq library construction The needles (leaves) of A. koreana plants were collected for RNA extraction after 0, 7, 10, and 14 d of drought stress treatment. RNA was extracted using TRIzol reagent, according to the manufacturer’s instructions (GibcoBRL, Cleveland, OH, USA). The quality and concentration of RNA samples were assessed using 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). Only the RNA samples of high quality were employed for the generation of RNA-seq libraries, which were prepared using the Illumina® TruSeq™ RNA Sample Preparation Kit (Illumina, Inc., San Diego, CA, USA), in accordance with the manufacturer’s recommended protocols. Subsequently, the RNA-seq libraries were amplified via polymerase chain reaction (PCR), following Illumina guidelines. The resulting libraries, characterized by insert sizes of 200 bp, were sequenced on the Illumina HiSeq 2000 platform to yield 101-bp paired-end reads. Three distinct biological samples (i.e., three different plants) were utilized for RNA extraction and library construction. RNA-seq data analysis The raw RNA-seq reads were subjected to initial processing, including the removal of adaptor sequences and low-quantity reads, using Trimmomatic (version 0.32). Subsequently, the trimmed reads from all experimental conditions were integrated and de novo assembled using the Trinity software (version R20140717) [ 36 ], with default settings, to generate a suitable set of reference contigs, denoted as unigenes. These unigenes were functionally annotated using BLASTX (BLAST 2.6.0+), based on the NCBI non-redundant protein sequences and Kyoto Encyclopedia for Genes and Genomes (KEGG) database. Furthermore, a functional enrichment analysis of unigenes within the Gene Ontology (GO) categories of "molecular function", "biological process", and "cellular component" was conducted using the Blast2GO program (version 2.5.0) [ 37 ]. The quantification of genes assigned to each GO term was performed using the Web Gene Ontology Annotation Plot (WEGO) tool ( http://wego.genomics.org.cn/ ). The unigenes were also aligned to the Clusters of Orthologous Groups (COG) database to predict and classify gene functions. Identification of differentially expressed genes (DEGs) Expression levels of unigenes were calculated using the fragments per kb per million fragments method [ 38 ]. Subsequently, gene expression profiles obtained from the RNA-seq data were analyzed using the RNA-Seq by Expectation-Maximization (RSEM) software [ 39 ], integrated with the Trinity package. Genes showing significant differences in expression between treatment and control groups were identified as DEGs, based on two criteria: fold change (FC) ≥ 2 and P-value < 0.05. Additionally, Venn diagrams generated in Venny v2.1.0 ( https://bioinfogp.cnb.csic.es/tools/venny/ ) were utilized to examine DEGs across the different drought treatments. Identification of drought stress-responsive transcription factors (TFs) To determine the regulation of TFs in response to drought stress, plant-specific TFs were obtained from the Plant Transcription Factor Database ( http://plntfdb.bio.uni-potsdam.de/v3.0/ ). A BLASTX algorithm-based search was conducted using DEG sequences with an E-value cut-off of ≤ 1e − 10 , and the unigenes were classified according to gene family-related information. Validation of DEGs using quantitative real timePCR (qRTPCR) To assess the reliability of the RNA-seq data, six DEGs ( Akoreana1SL003569t0001 [ HD-ZIP ], Akoreana1SL011234t0001 [ LBD ], Akoreana1SL007270t0001 [ BES1 ], Akoreana1SL019838t0001 [ ERF ], Akoreana1SL006588t0001 [ ERF ], and Akoreana1SL005911t0001 [MYB]) were randomly selected, and their expression levels were quantified using qRT-PCR. Total RNA was extracted from the needles of A. koreana plants subjected to drought stress for 0, 7, 10, and 14 d. RNA extraction, cDNA synthesis, and qRT-PCR analysis were conducted as described previously [ 13 ]. Primer sequences and PCR conditions utilized for examining gene expression are provided in Additional file 1: Table S1 . Relative expression levels were determined using the 2 –∆∆Ct method, and error bars represent the standard deviation values for triplicates. Abbreviations bHLH: basic Helix-Loop-Helix; COG database: Clusters of Orthologous Groups database; DEG: Differentially Expressed Gene; ERF: Ethylene-Responsive element-binding Factor; GO: Gene Ontology; IUCN: International Union for Conservation of Nature; KEGG database: Kyoto Encyclopedia for Genes and Genomes database; NCBI: National Center for Biotechnology Information; qRT-PCR: quantitative Real Time-Polymerase Chain Reaction; RNA seq: RNA sequencing; RSEM software: RNA-Seq by Expectation-Maximization software; SWC: Soil Water Content; TF: Transcription Factor; WEGO tool: WEb Gene Ontology annotation plot tool Declarations Supplementary information Supplementary information for this paper are available at BMC Plant Biology Online. Acknowledgments We thank the Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science, Jeju, Korea, for providing the 3-year-old Korean fir ( Abies koreana ) plants used in this study. The authors deeply appreciate Jong-won Park for providing assistance in the qRT-PCR experiment. We particularly thank Yunjeong Kim, Da Young Lee, and Da Young Park for their assistance in various experiments. This work was supported by a grant from the National Institute of Ecology (NIE), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIE-B-2024-15). Authors ’ contributions H.C.P. designed the experiments; H.C.P. and J.E.H. conducted the experiments and analyzed the transcriptome data; H.C.P. drafted the manuscript and maintained the plant materials; H.C.P and J.E.H discussed the results and finalized the manuscript. Funding This work was supported by a grant from the National Institute of Ecology (NIE), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIE-B-2024-15). Availability of data and materials The high-quality trimmed (clean) read data from RNA-seq are available in the NCBI Sequence Read Archive database (accession numbers: SAMN40219202, SAMN40219203, SAMN40219204, SAMN40219205, SAMN40219206, SAMN40219207, SAMN40219208, SAMN40219209, SAMN40219210, SAMN40219211, SAMN40219212, and SAMN40219213). Ethics approval and consent to participate Three-year-old A. koreana used in this study were provided from Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science, Jeju, Korea. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. 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Transcriptome analysis of Pinus halepensis under drought stress and during recovery. Tree Physiol. 2018;38:423-441. Zhao D, Zhang X, Fang Z, Wu Y, Tao J. Physiological and transcriptomic analysis of tree peony ( Paeonia section Moutan DC.) in response to drought stress. Forests. 2019;10:135. Kim T-L, Lim H, Denison MIJ, Oh C. Transcriptomic and physiological analysis reveals genes associated with drought stress responses in Populus alba × Populus glandulosa . Plants. 2023;12:3238. Seleiman MF, Al-Suhaibani N, Ali N, Akmal M, Alotaibi M, Refay Y, Dindaroglu T, Abdul-Wajid HH, Battaglia ML. Drought stress impacts on plants and different approaches to alleviate its adverse effects. Plants (Basel). 2021;10:259. Lozano-Elena F, Fàbregas N, Coleto-Alcudia V, Caño-Delgado AI. Analysis of metabolic dynamics during drought stress in Arabidopsis plants. Sci Data. 2022;9:90. Joshi R, Wani SH, Singh B, Bohra A, Dar ZA, Lone AA, Pareek A, Singla-Pareek SL. Transcription factors and plants response to drought stress: current understanding and future directions. Front Plant Sci. 2016;7:1029. Hu Y, Chen X, Shen X. Regulatory network established by transcription factors transmits drought stress signals in plant. Stress Biol. 2022;2:26. Jia D, Jiang Q, van Nocker S, Gon, X, Ma F. An apple (Malus domestica) NAC transcription factor enhances drought tolerance in transgenic apple plants. Plant Physiol Biochem. 2019;139:504-512. Liu Y, Yang T, Lin Z, Gu B, Xing C, Zhao L, Dong H, Gao J, Xie Z, Zhang S, Huang X. A WRKY transcription factor PbrWRKY53 from Pyrus betulaefolia is involved in drought tolerance and AsA accumulation. Plant Biotechnol J. 2019;17:1770-1787. Gao Y, Wang K, Wang R, Wang L, Liu H, Wu M, Xiang Y. Identification and expression analysis of LBD genes in moso bamboo ( Phyllostachys edulis ). J Plant Growth Regul. 2022;41:2798-2817. Liang B, Wan S, Ma Q, Yang L, Hu W, Kuang L, Xie J, Huang Y, Liu D, Liu Y. A novel bHLH transcription factor PtrbHLH66 from trifoliate orange positively regulates plant drought tolerance by mediating root growth and ROS scavenging. Int J Mol Sci. 2022;23:15053. Song Q, Kong L, Yang J, Lin M, Zhang Y, Yang X, Wang X, Zhao Z, Zhang M, Pan J, Zhu S, Jiao B, Xu C, Luo K. The transcription factor PtoMYB142 enhances drought tolerance in Populus tomentosa by regulating gibberellin catabolism. Plant J. 2024;118:42-57. Zhou M, Cheng H, Chiang VL, Li W, Yang C, Wang C. PtrbZIP3 transcription factor regulates drought tolerance of Populus trichocarpa . Environ Exp Botany. 2023;208:105231. Huan X, Wang X, Zou S, Zhao K, Han Y, Wang S. Transcription factor ERF194 modulates the stress-related physiology to enhance drought tolerance of poplar. Int J Mol Sci. 2023;24:788. Kong L, Song Q, Wei H, Wang Y, Lin M, Sun K, Zhang Y, Yang J, Li C, Luo K. The AP2/ERF transcription factor PtoERF15 confers drought tolerance via JA‐mediated signaling in Populus . New Phytol. 2023;240:1848-1867. Zhao S, Gao H, Jia X, Wang H, Ke M, Ma F. The HD-Zip I transcription factor MdHB-7 regulates drought tolerance in transgenic apple ( Malus domestica ). Environ Exp Botany. 2020;180:104246. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644-652. Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M, Dopazo J, Conesa A. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36:3420-3435. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;7:621-628. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863-864. Additional Declarations No competing interests reported. Supplementary Files SupplementaryinformationParkandHwang.docx 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-4690654","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335485492,"identity":"107d4635-49c3-47e2-8675-838c20e576f8","order_by":0,"name":"Hyeong Cheol Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACAzB5wIaBDcJPIFpLGulaDsP4RGgxZz978HPFmfN5fOwNbB8+MKTlE9Ri2ZOXLHnmxu1iNp4DzDNnMORYNhB02A0eA8mGD7cT2yQSmJl5GCoMCNoC1GL8s+HDucQ2+QfMzH+I1GIm2XDjANAWBmZmBoYcIrScyUuzbDiTnNjGk9jM2GOQRoSW42cP32w4Zpc4v/3wYYYfFcmEtTAw8MAYjA2waCJayygYBaNgFIwCHAAAFgY6SGSzRQoAAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Ecology","correspondingAuthor":true,"prefix":"","firstName":"Hyeong","middleName":"Cheol","lastName":"Park","suffix":""},{"id":335485493,"identity":"01d3260b-73ea-4772-88be-47d8fb927068","order_by":1,"name":"Jung Eun Hwang","email":"","orcid":"","institution":"National Institute of Ecology","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Eun","lastName":"Hwang","suffix":""}],"badges":[],"createdAt":"2024-07-05 08:10:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4690654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4690654/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61940092,"identity":"56e5ee14-f6b1-4f63-b525-7566babfa9f4","added_by":"auto","created_at":"2024-08-07 10:07:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":821367,"visible":true,"origin":"","legend":"\u003cp\u003eVisual assessment of 3-year-old \u003cem\u003eAbies koreana\u003c/em\u003e plants and soil under drought conditions. \u003cstrong\u003ea\u003c/strong\u003e Plant growth; and \u003cstrong\u003eb\u003c/strong\u003e soil water content.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/77bf67c89c23ec6a6b96d423.png"},{"id":61940503,"identity":"d0179b3c-c03a-492f-b4ec-2b8110898b87","added_by":"auto","created_at":"2024-08-07 10:15:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":758228,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) classification of unigenes assembled from the RNA-seq data of \u003cem\u003eA. koreana\u003c/em\u003e. The histogram shows unigenes belonging to GO terms in three main functional categories: biological process, cellular component, and molecular function. The x-axis lists the specific GO terms within each category; y-axis shows the number of unigenes associated with each term.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/c0c4d295fdacd69360235a03.png"},{"id":61940502,"identity":"15ce5e75-f7bc-457a-81df-0cd51989fc7f","added_by":"auto","created_at":"2024-08-07 10:15:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":623543,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional classification of assembled \u003cem\u003eA. koreana\u003c/em\u003e unigenes into 26 Clusters of Orthologous Groups (COG) categories. The x-axis is labeled with uppercase letters indicating the different COG categories, which are detailed in an accompanying table on the right side; y-axis represents the number of unigenes in each category.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/c6f063fe36507afe1b053659.png"},{"id":61939555,"identity":"9169c2ca-08ea-49dc-a3cd-c50732456cae","added_by":"auto","created_at":"2024-08-07 09:59:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":658245,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of differentially expressed \u003cem\u003eA. koreana\u003c/em\u003e unigenes under different drought conditions. \u003cstrong\u003ea\u003c/strong\u003e Distribution of upregulated and downregulated unigenes at 7, 10, and 14 d compared with the control condition (0 d). \u003cstrong\u003eb\u003c/strong\u003e Volcano plot illustrating gene expression-level changes at 7 d, with significantly upregulated genes shown in red and downregulated genes shown in green. \u003cstrong\u003ec\u003c/strong\u003e Volcano plot showing gene expression-level changes at 10 d, with significantly upregulated genes in red and downregulated genes in green. \u003cstrong\u003ed\u003c/strong\u003e Volcano plot depicting gene expression-level changes at 14 d, with significantly upregulated genes in red and downregulated genes in green. Upregulated and downregulated unigenes were identified based on two criteria: fold change (FC) ≥ 2 and P-value \u0026lt; 0.05, compared with the control.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/f7a48a0a944e11b36591be64.png"},{"id":61939553,"identity":"8c319d9a-baf6-452f-8a7e-ef53e3fdbf9f","added_by":"auto","created_at":"2024-08-07 09:59:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":253642,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagrams representing the overlap of differentially expressed genes in \u003cem\u003eA. koreana\u003c/em\u003e under drought stress. \u003cstrong\u003ea,\u003c/strong\u003e \u003cstrong\u003eb\u003c/strong\u003e Numbers of upregulated genes (\u003cstrong\u003ea\u003c/strong\u003e) and downregulated genes (\u003cstrong\u003eb\u003c/strong\u003e) across different periods of drought stress (7, 10, and 14 d). Each colored circle corresponds to a specific time point, and the numbers correspond to the number of genes uniquely or commonly upregulated (\u003cstrong\u003ea\u003c/strong\u003e) or downregulated (\u003cstrong\u003eb\u003c/strong\u003e) compared with the control condition.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/5cbab86801e9025ffe4fffc4.png"},{"id":61939554,"identity":"72c98f68-b435-4169-aaa2-7321747ce229","added_by":"auto","created_at":"2024-08-07 09:59:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":216302,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of differentially expressed genes identified in \u003cem\u003eA. koreana\u003c/em\u003e plants subjected to drought stress for 7, 10, and 14 d. The histogram illustrates the number of genes associated with selected GO terms within three categories (biological process, molecular function, and cellular component) at three time points: 7 d (red), 10 d (blue), and 14 d (green).\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/374098896098349e790c7eb9.png"},{"id":61939556,"identity":"1aeef67f-24ec-4ae8-a4a2-82dde05d77c5","added_by":"auto","created_at":"2024-08-07 09:59:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":328571,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of transcription factor (TF) genes in \u003cem\u003eA. koreana\u003c/em\u003e under drought stress conditions. The bar chart represents the number of TF genes upregulated (warm colors) and downregulated (cool colors) after 7 d (dark orange and dark blue), 10 d (medium orange and medium blue), and 14 d (light orange and light blue) of drought treatment. Each bar corresponds to a specific TF family.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/4a2e3ed5f04782d4213823d3.png"},{"id":61939557,"identity":"4ceaa6fa-33e0-45b1-9eed-7b10c25a9d37","added_by":"auto","created_at":"2024-08-07 09:59:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":858565,"visible":true,"origin":"","legend":"\u003cp\u003eExpression validation of six differentially expressed TF genes in \u003cem\u003eA. koreana\u003c/em\u003e under drought stress conditions (0, 7, 10, and 14 d) by qRT-PCR. Data represent mean ± SE of three replicates.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/cf5bfae679b2a67cf717c4b5.png"},{"id":62255078,"identity":"d76043ca-f58c-4186-90a7-5ea604a47e48","added_by":"auto","created_at":"2024-08-12 07:08:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5518057,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/48f6110b-ecb1-4dba-a2f0-88717093eb0b.pdf"},{"id":61939550,"identity":"642276a4-9d4e-410b-820b-aa74127b14c2","added_by":"auto","created_at":"2024-08-07 09:59:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41423,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryinformationParkandHwang.docx","url":"https://assets-eu.researchsquare.com/files/rs-4690654/v1/e167315b158ef24442f63895.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of drought stress-responsive genes in Korean fir (Abies koreana) through comparative RNA-seq analysis","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cem\u003eAbies\u003c/em\u003e species, commonly known as firs, thrive in cool, moist climates with well-distributed rainfall, being adapted to specific moisture and environmental conditions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Global warming is a major threat to \u003cem\u003eAbies\u003c/em\u003e species, including \u003cem\u003eAbies koreana\u003c/em\u003e (Korean fir), worldwide, because of the decrease in soil moisture content [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cem\u003eAbies\u003c/em\u003e species are sensitive to drought stress and exhibit signs such as reduced growth, wilting, and needle browning under drought conditions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. \u003cem\u003eA. koreana\u003c/em\u003e, prevalent in the southern region of the Korean peninsula, serves as an essential ornamental tree, holding considerable importance within forest ecosystems and human societies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, since the early 2000s, a severe decline has been observed in the distribution of \u003cem\u003eA. koreana\u003c/em\u003e, primarily because of global warming-induced drought [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, \u003cem\u003eA. koreana\u003c/em\u003e has been recognized as a climate-sensitive biological indicator species in Korea [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and has been categorized as an endangered species by the International Union for Conservation of Nature (IUCN) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The conservation of \u003cem\u003eA. koreana\u003c/em\u003e to ensure its sustained presence and positive contribution to forest ecosystems is imperative. Thus, it is essential to uncover the adaptive molecular mechanisms of \u003cem\u003eA. koreana\u003c/em\u003e in response to drought stress. This understanding is pivotal for enhancing forest management in the face of climate change-induced drought challenges\u003c/p\u003e \u003cp\u003eTranscriptomic analysis has been used to determine gene-specific expression and facilitate variant detection, genome-wide transcript characterization, and differential gene expression analysis in \u003cem\u003eA. koreana\u003c/em\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These studies have advanced our understanding of the molecular response and stress response pathways of \u003cem\u003eA. koreana\u003c/em\u003e under conditions of elevated CO\u003csub\u003e2\u003c/sub\u003e and heat [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Je et al. (2018) focused on the photosynthetic responses of \u003cem\u003eA. koreana\u003c/em\u003e to drought under varying light conditions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, comparative transcriptomic analyses in \u003cem\u003eA. koreana\u003c/em\u003e under drought conditions remain unexplored. Transcriptomic analysis aids in identifying key genes crucial for drought tolerance and imperative for the development of drought-resistant varieties in tree breeding programs.\u003c/p\u003e \u003cp\u003eIn this study, we performed transcriptomic analysis of \u003cem\u003eA. koreana\u003c/em\u003e trees exposed to drought stress for different time periods. Our results reveal candidate genes associated with drought tolerance, which could be used for the development of drought-resistant \u003cem\u003eA. koreana\u003c/em\u003e cultivars.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of soil water content (SWC) under drought stress\u003c/h2\u003e \u003cp\u003eThree-year-old \u003cem\u003eA. koreana\u003c/em\u003e plants were exposed to drought conditions for different durations (0, 7, 10, and 14 d). Drought-treated plants, particularly those subjected to drought for 14 d, exhibited delayed growth compared with non-treated plants (control; 0 d) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). SWC decreased throughout the drought treatment, starting at 7 d and reaching less than 20% at 14 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), approximately a two-fold decline compared with SWC at 0 d (control).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRNA-Seq, read assembly and annotation\u003c/h2\u003e \u003cp\u003eTwenty-four RNA-seq libraries produced 20.7\u0026ndash;31.9\u0026nbsp;million raw reads, with a clean reads rate of 84.82\u0026ndash;95.83% per read. In addition, the percentage of trimmed reads per sample ranged from 67.53\u0026ndash;88.78%, indicating the reliability, accuracy, and interpretability of the RNA-seq data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). High-quality trimmed (clean) reads were deposited in the NCBI Sequence Read Archive database (accession numbers: SAMN40219202, SAMN40219203, SAMN40219204, SAMN40219205, SAMN40219206, SAMN40219207, SAMN40219208, SAMN40219209, SAMN40219210, SAMN40219211, SAMN40219212, and SAMN40219213), and subsequently assembled into 85,403 contigs (N50 value\u0026thinsp;=\u0026thinsp;2,087 bp; average length\u0026thinsp;=\u0026thinsp;1,364 bp; minimum length\u0026thinsp;=\u0026thinsp;200 bp; and maximum length\u0026thinsp;=\u0026thinsp;63,417 bp) using the Trinity software (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, the contigs were assembled into 42,839 unigenes (N50 value\u0026thinsp;=\u0026thinsp;1,901 bp; average length\u0026thinsp;=\u0026thinsp;1,130 bp; minimum length\u0026thinsp;=\u0026thinsp;200 bp; and maximum length\u0026thinsp;=\u0026thinsp;63,417 bp) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the RNA-seq data of Korean fir (\u003cem\u003eAbies koreana\u003c/em\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw Reads Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClean Reads Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClean Reads Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrimmed/Raw Rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eControl-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27,442,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,629,788,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27,442,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,482,550,880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eControl-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31,954,330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,029,957,440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31,954,330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,917,332,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eControl-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,319,833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,029,347,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,319,833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,947,939,322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7d-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,452,113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,333,517,445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,452,113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,225,066,333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7d-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,759,297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,062,197,925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,759,297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,900,201,896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7d-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25,493,814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,450,449,606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25,493,814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,222,188,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10d-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23,246,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,224,376,086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23,246,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,128,785,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10d-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27,080,678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,566,325,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27,080,678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,368,793,197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10d-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,286,984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,292,459,622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,286,984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,115,170,972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14d-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,228,757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,320,740,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,228,757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,174,459,262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14d-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,704,358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,939,475,753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,704,358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,756,208,583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14d-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,678,530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,076,064,545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,678,530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,940,549,440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the assembled \u003cem\u003eA. koreana\u003c/em\u003e transcripts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnigenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal trinity transcripts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42,839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63,417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN50 (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116,506,792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48,414,644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation and classification of unigenes\u003c/h2\u003e \u003cp\u003eAll assembled unigenes (42,839) were subjected to GO enrichment analysis using the GO database and WEGO tool. Of the 42,839 unigenes, 29,739 (69.42%) were successfully annotated using the GO database. The annotated \u003cem\u003eA. koreana\u003c/em\u003e genes were functionally categorized into three major functional domains (biological process, cellular component, and molecular function), based on the GO classification system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the biological process domain, the most prominent subgroups were 'cellular process' (13,805 unigenes), 'metabolic process' (13,051 unigenes), and 'response to stimulus' (7,360 unigenes). In the cellular component domain, the prevailing groups included 'cell' (19,410 unigenes), 'cell part' (19,326 unigenes), and 'organelle' (15,230 unigenes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Additional file 2: Table S2). Within the molecular function domain, the most abundant groups were 'binding' (10,158 unigenes), 'catalytic activity' (9,334 unigenes), and 'transporter activity' (1,350 unigenes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Additional file 2: Table S2). Of the 42,839 assembled unigenes, 23,947 (55.9%) were categorized in 26 KOG/COG clusters. Of these 23,947 unigenes, 6,533 unigenes, which accounted for the highest proportion, were predicted to perform general function, while 2,369 unigenes were predicted to be involved in signal transduction mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExpression analysis and functional annotation of DEGs\u003c/h2\u003e \u003cp\u003eTo identify \u003cem\u003eA. koreana\u003c/em\u003e genes are potentially involved in the drought stress response, their expression profiles were analyzed. Genes exhibiting more than a two-fold change in expression were compared across the 7-, 10-, and 14-d drought treatments, with 0-d treatment serving as the control. The analysis revealed distinctive increases or decreases in gene expression, particularly in the 10-d treatment compared with the 7-d and 14-d treatments. Specifically, at 7 d, 313 genes showed more than a two-fold increase in expression, while 547 genes exhibited decreased expression. At 10 d, 566 genes showed more than a two-fold increase in expression, and 627 genes showed decreased expression. At 14 d, 449 genes exhibited more than a two-fold increase in expression, and 347 genes showed decreased expression. These findings provide valuable insights into the dynamic changes in gene expression in response to drought stress in \u003cem\u003eA. koreana\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, 218 genes were commonly upregulated and 448 genes were commonly downregulated in the 7- and 10-d treatments compared with the 14-d treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Compared with the 0-d (control) treatment, 50 genes were commonly upregulated and 77 genes were commonly downregulated across all three drought treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and b). Based on these results, we speculated that the response of \u003cem\u003eA. koreana\u003c/em\u003e plants to drought stress is more similar at 7 and 10 d than at 14 d. Functional analysis of DEGs revealed that the majority of genes expressed at 7 and 10 d were related to GO terms \u0026lsquo;metabolic process\u0026rsquo; and \u0026lsquo;catalytic activity\u0026rsquo;, indicating that genes related to metabolic process and catalytic activity were more active at 7 and 10 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A high number of genes was related to the \u0026lsquo;response to stimulus\u0026rsquo; at 10 d, suggesting that stress response-related genes are most active at 10 d in \u003cem\u003eA. koreana\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of TFs involved in drought stress\u003c/h2\u003e \u003cp\u003eTFs serve as pivotal regulators in plants, playing a crucial role in sensing and orchestrating plant responses to drought stress. Our study identified several drought stress-responsive TF genes in \u003cem\u003eA. koreana\u003c/em\u003e, particularly those belonging to \u003cem\u003ebHLH\u003c/em\u003e, \u003cem\u003eERF\u003c/em\u003e, \u003cem\u003eMYB\u003c/em\u003e, \u003cem\u003eWRKY\u003c/em\u003e, \u003cem\u003eLBD\u003c/em\u003e, and \u003cem\u003eNAC\u003c/em\u003e families (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A high number of genes encoding ethylene-responsive element-binding factors (ERFs) displayed upregulation in response to drought stress, while a significant number of genes encoding basic helix-loop-helix (bHLH) TFs exhibited downregulation. Furthermore, a considerable number of the \u003cem\u003eMYB\u003c/em\u003e TF genes showed both increased and decreased expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, and Additional file 3: Table S3). MYB TFs not only participate in various phytohormone signal transduction pathways triggered by external stimuli, like drought, but also potentially influence the interplay between different plant hormones. Collectively, the diverse expression patterns of different TF family genes observed in this study suggest that \u003cem\u003eA. koreana\u003c/em\u003e employs a complex and extensive regulatory network to respond to drought stress, and that these various TFs play pivotal roles in mediating the drought stress response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation of differentially expressed TF genes using qRT-PCR\u003c/h2\u003e \u003cp\u003eSix TF-encoding DEGs were randomly selected to validate the RNA-seq data. The expression profiles of these six DEGs were compared between the control (0 d) and drought stress (7, 10, and 14 d) treatments by qRT-PCR. The selected DEGs included genes encoding two ERF TFs (Akoreana1SL019838t0001, Akoreana1SL006588t000), one BES1 TF (Akoreana1SL007270t0001), one HD-ZIP TF (Akoreana1SL003569t0001), one LBD1 TF (Akoreana1SL011234t0001), and one MYB TF (Akoreana1SL005911t0001) (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The expression levels of all six TF genes were found to be higher in drought-treated plants than in control plants, indicating the drought-induced upregulation of these genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Furthermore, the expression profiles of genes determined by qRT-PCR across the drought treatments were similar to those determined by RNA-seq, strongly confirming the reliability of our RNA-seq data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cem\u003eAbies\u003c/em\u003e species, including \u003cem\u003eA. koreana\u003c/em\u003e, are sensitive to drought stress [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. \u003cem\u003eA. koreana\u003c/em\u003e has been categorized as an endangered species owing to its severe decline since the early 2000s, primarily due to the drought induced by global warming [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, to ensure the sustained presence and positive contribution of \u003cem\u003eAbies\u003c/em\u003e species to forest ecosystems, conservation efforts are imperative. However, the drought stress response of \u003cem\u003eA. koreana\u003c/em\u003e at the molecular level remains unknown. Transcriptomic analysis has been used to understand the molecular response of \u003cem\u003eA. koreana\u003c/em\u003e to elevated CO\u003csub\u003e2\u003c/sub\u003e and temperature [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] as well as to identify key genes crucial for drought tolerance in other tree species [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, in this study, we analyzed the transcriptome of \u003cem\u003eA. koreana\u003c/em\u003e plants exposed to drought stress. Our results reveal key \u003cem\u003eA. koreana\u003c/em\u003e genes associated with drought tolerance, which could be used to aid tree breeding programs for the enhancement of forest management in the face of climate change-induced drought challenges.\u003c/p\u003e \u003cp\u003eThree-year-old \u003cem\u003eA. koreana\u003c/em\u003e plants were initially planted in pots containing soil with 50% moisture content. Subsequently, watering was withheld for 14 d to induce water deficit. A reduction in SWC was observed from the 7th day of drought treatment compared with the control (0 d). Additionally, the drought-treated plants exhibited delayed growth, a sign of water deficit in plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To investigate the transcriptomic changes in \u003cem\u003eA. koreana\u003c/em\u003e plants under drought conditions, RNA was extracted from the leaves of plants treated with drought stress for 0 (control), 7, 10, and 14 d. The transcriptomic analysis yielded 85,403 contigs, with an average length of 1,364 bp and an N50 value of 2,087 bp (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the average length and N50 value of \u003cem\u003eA. koreana\u003c/em\u003e contigs obtained under drought conditions surpassed those of contigs assembled in other tree species facing similar conditions. The contigs of \u003cem\u003eA. koreana\u003c/em\u003e were longer than those of Tree peony (average length: 825 bp, N50: 1,368 bp), Aleppo pine (average length: 906.47 bp, N50: 1,269 bp), Masson pine (average length: 695 bp, N50: 1,227 bp), \u003cem\u003eAmmopiptanthus mongolicus\u003c/em\u003e (average length: 304 bp, N50: 471 bp), and \u003cem\u003eProsopis juliflora\u003c/em\u003e (average length: 428 bp, N50: 714 bp) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The longer average length and N50 value of \u003cem\u003eA. koreana\u003c/em\u003e contigs obtained in this study strongly suggest that the RNA-seq data represent a complete set of transcripts and a high-quality transcriptome assembly. Based on the GO classification, unigenes involved in cellular processes, metabolic processes, response to stimuli, cell, cell parts, organelles, binding, and catalytic activities were found to be strongly regulated in response to drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests the significant regulation of unigenes associated with various biological processes, cellular components, and molecular functions in response to drought stress, indicating their substantial involvement in the plant response to drought. Similar observations were reported in other tree species exposed to drought conditions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], indicating a commonality in the regulatory responses to drought stress across different tree species.\u003c/p\u003e \u003cp\u003eIn this study, the number of upregulated genes was higher than that of downregulated genes on day 14 but than that of downregulated genes on days 7 and 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The observed changes in the number of upregulated and downregulated genes at different time points during the drought treatment can be explained by the dynamic nature of the plant response to drought stress [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This dynamic response involves the activation and repression of different sets of genes at different stages of stress. The higher number of downregulated genes on days 7 and 10 could reflect an early response mechanism where the plant initially downregulates certain processes to cope with the immediate impact of drought stress. With the continuation of stress, the plant may switch to upregulating specific genes to initiate more advanced adaptive responses, leading to a greater impact on its gene expression profiles. Therefore, the observed increase in the number of upregulated genes on the 14th day of drought may reflect the activation of specific regulatory pathways that mediate the acclimation of plants to prolonged water deficit [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have shown that the response to drought stress is a complex process involving the regulation of a large number of genes, including those encoding TFs that play critical roles in the adaptation to abiotic stresses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For example, TFs belonging to the ERF, MYB, WRKY, NAC, and bHLH families have been reported to be involved in the regulation of gene expression in response to drought stress in various plant species, including \u003cem\u003eA. koreana\u003c/em\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These TFs are known to directly regulate the expression of stress-responsive genes, and their upregulation is associated with enhanced tolerance to drought stress in tress species [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, a substantial number of TF genes were identified in this study, including those encoding bZIP, HD-ZIP, bHLH, ERF, MYB, WRKY, LBD, and NAC TFs, which have been implicated in various physiological processes, such as stomatal movement and hormone signal transduction. Notably, the number of ERF TFs was found to be greater than that of other TFs, and a large number of ERF TFs were upregulated under drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A similarly high number of upregulated \u003cem\u003eERF\u003c/em\u003e genes was observed in \u003cem\u003eA. koreana\u003c/em\u003e under other stresses such as heat and CO\u003csub\u003e2\u003c/sub\u003e stress [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], strongly indicating the significant role of ERF TFs in the adaptation of \u003cem\u003eA. koreana\u003c/em\u003e to different abiotic stresses. MYB, ERF, NAC, and bHLH TF families are known to be large and functionally diverse. Different members of each TF family may play distinct roles and functions in response to stress [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The observed mix of upregulated and downregulated TF genes across the different drought treatments (7, 10, and 14 d) suggests that \u003cem\u003eA. koreana\u003c/em\u003e employs a complex and dynamic regulatory network to respond to drought stress. In addition, the observed upregulation or downregulation of specific \u003cem\u003eMYB\u003c/em\u003e, \u003cem\u003eERF\u003c/em\u003e, \u003cem\u003eNAC\u003c/em\u003e, and \u003cem\u003ebHLH\u003c/em\u003e TF genes could be related to their functions in distinct signaling pathways or cellular processes activated at different stages of drought stress [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The functions of TFs in conferring tolerance to drought stress in woody crops have been extensively analyzed. Overexpression of \u003cem\u003eERF\u003c/em\u003e genes has been shown to enhance tolerance to drought stress in woody crops [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, \u003cem\u003eMdHB-7\u003c/em\u003e, which encodes an HD-Zip TF, was found to promote drought tolerance in apple [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, recent studies show that the PtrbZIP3 (basic leucine zipper) TF as well as NAC, WRKY, bHLH, LBD, and MYB TFs play essential roles in the response to drought stress, and overexpression of genes encoding these TFs increases drought tolerance in woody crops [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this study, we confirmed the reliability of our RNA-seq data by analyzing the expression patterns of six genes encoding TFs (BES1, HD-ZIP, bHLH, ERF, MYB, and LBD) at different time points using qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Overall, the expression of stress-responsive genes, including those encoding TFs, was induced in a time-dependent manner in response to drought stress. Taken together, this study revealed several candidate genes, including those encoding ERF, MYB, LBD, and NAC TFs, associated with drought tolerance in \u003cem\u003eA. koreana\u003c/em\u003e. Although further investigation is needed to gain valuable insights into the role of these genes in the adaptation of \u003cem\u003eA. koreana\u003c/em\u003e plants to drought stress, we predict that the results of this study will aid tree breeding programs and facilitate the enhancement of forest management in the face of climate change-induced drought challenges.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur transcriptomic analysis of \u003cem\u003eA. koreana\u003c/em\u003e plants subjected to drought stress unveiled a robust molecular response. This study sheds light on the dynamic regulation of genes, emphasizing the intricate roles of ERF, MYB, LBD, and NAC TFs in drought adaptation. The observed temporal gene expression patterns offer valuable insights into early and late stress responses. These findings enhance our understanding of the molecular mechanisms employed by \u003cem\u003eA. koreana\u003c/em\u003e to survive drought. We anticipate that the identified candidate genes will inform tree breeding efforts, enhancing forest management strategies to mitigate the damaging effects of climate change-induced drought on \u003cem\u003eA. koreana\u003c/em\u003e and safeguard the future of this endangered species.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003ePlant materials\u003c/h2\u003e\u003cp\u003eSeeds of Korean fir (\u003cem\u003eAbies koreana\u003c/em\u003e Wilson), collected from Mount Halla on Jeju Island, Korea, were subjected to a low temperature (4°C) for 3 months to break seed dormancy, and then sown in seedling trays containing soil. One year after sowing, the seedlings were transplanted into individual soil-filled pots and grown for 3 years in a greenhouse under natural sunlight. Three-year-old \u003cem\u003eA. koreana\u003c/em\u003e obtained from Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science, Jeju, Korea were used in this study as the plant material for drought treatment.\u003c/p\u003e\u003ch2\u003eDrought treatment and soil water content (SWC) determination\u003c/h2\u003e\u003cp\u003eTo induce drought stress, the watering of 3-year-old \u003cem\u003eA. koreana\u003c/em\u003e plants was withheld for 7, 10, and 14 d. SWC was measured by using a handheld soil water content measuring device (DM-18, Takemura Electric, Nara, Japan), according to the manufacturer’s instructions. The status of the plants just prior to the drought treatment was recorded as control (0 d), and SWC was measured at 0, 7, 10, and 14 d.\u003c/p\u003e\u003ch2\u003eRNA extraction and RNA-seq library construction\u003c/h2\u003e\u003cp\u003eThe needles (leaves) of \u003cem\u003eA. koreana\u003c/em\u003e plants were collected for RNA extraction after 0, 7, 10, and 14 d of drought stress treatment. RNA was extracted using TRIzol reagent, according to the manufacturer’s instructions (GibcoBRL, Cleveland, OH, USA). The quality and concentration of RNA samples were assessed using 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). Only the RNA samples of high quality were employed for the generation of RNA-seq libraries, which were prepared using the Illumina® TruSeq™ RNA Sample Preparation Kit (Illumina, Inc., San Diego, CA, USA), in accordance with the manufacturer’s recommended protocols. Subsequently, the RNA-seq libraries were amplified via polymerase chain reaction (PCR), following Illumina guidelines. The resulting libraries, characterized by insert sizes of 200 bp, were sequenced on the Illumina HiSeq 2000 platform to yield 101-bp paired-end reads. Three distinct biological samples (i.e., three different plants) were utilized for RNA extraction and library construction.\u003c/p\u003e\u003ch2\u003eRNA-seq data analysis\u003c/h2\u003e\u003cp\u003eThe raw RNA-seq reads were subjected to initial processing, including the removal of adaptor sequences and low-quantity reads, using Trimmomatic (version 0.32). Subsequently, the trimmed reads from all experimental conditions were integrated and \u003cem\u003ede novo\u003c/em\u003e assembled using the Trinity software (version R20140717) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], with default settings, to generate a suitable set of reference contigs, denoted as unigenes. These unigenes were functionally annotated using BLASTX (BLAST 2.6.0+), based on the NCBI non-redundant protein sequences and Kyoto Encyclopedia for Genes and Genomes (KEGG) database. Furthermore, a functional enrichment analysis of unigenes within the Gene Ontology (GO) categories of \"molecular function\", \"biological process\", and \"cellular component\" was conducted using the Blast2GO program (version 2.5.0) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The quantification of genes assigned to each GO term was performed using the Web Gene Ontology Annotation Plot (WEGO) tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wego.genomics.org.cn/\u003c/span\u003e\u003cspan address=\"http://wego.genomics.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The unigenes were also aligned to the Clusters of Orthologous Groups (COG) database to predict and classify gene functions.\u003c/p\u003e\u003ch2\u003eIdentification of differentially expressed genes (DEGs)\u003c/h2\u003e\u003cp\u003eExpression levels of unigenes were calculated using the fragments per kb per million fragments method [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Subsequently, gene expression profiles obtained from the RNA-seq data were analyzed using the RNA-Seq by Expectation-Maximization (RSEM) software [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], integrated with the Trinity package. Genes showing significant differences in expression between treatment and control groups were identified as DEGs, based on two criteria: fold change (FC) ≥ 2 and P-value \u0026lt; 0.05. Additionally, Venn diagrams generated in Venny v2.1.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were utilized to examine DEGs across the different drought treatments.\u003c/p\u003e\u003ch2\u003eIdentification of drought stress-responsive transcription factors (TFs)\u003c/h2\u003e\u003cp\u003eTo determine the regulation of TFs in response to drought stress, plant-specific TFs were obtained from the Plant Transcription Factor Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://plntfdb.bio.uni-potsdam.de/v3.0/\u003c/span\u003e\u003cspan address=\"http://plntfdb.bio.uni-potsdam.de/v3.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A BLASTX algorithm-based search was conducted using DEG sequences with an E-value cut-off of ≤ 1e\u003csup\u003e− 10\u003c/sup\u003e, and the unigenes were classified according to gene family-related information.\u003c/p\u003e\u003ch2\u003eValidation of DEGs using quantitative real timePCR (qRTPCR)\u003c/h2\u003e\u003cp\u003eTo assess the reliability of the RNA-seq data, six DEGs (\u003cem\u003eAkoreana1SL003569t0001\u003c/em\u003e [\u003cem\u003eHD-ZIP\u003c/em\u003e], \u003cem\u003eAkoreana1SL011234t0001\u003c/em\u003e [\u003cem\u003eLBD\u003c/em\u003e], \u003cem\u003eAkoreana1SL007270t0001\u003c/em\u003e [\u003cem\u003eBES1\u003c/em\u003e], \u003cem\u003eAkoreana1SL019838t0001\u003c/em\u003e [\u003cem\u003eERF\u003c/em\u003e], \u003cem\u003eAkoreana1SL006588t0001\u003c/em\u003e [\u003cem\u003eERF\u003c/em\u003e], and \u003cem\u003eAkoreana1SL005911t0001 [MYB])\u003c/em\u003e were randomly selected, and their expression levels were quantified using qRT-PCR. Total RNA was extracted from the needles of \u003cem\u003eA. koreana\u003c/em\u003e plants subjected to drought stress for 0, 7, 10, and 14 d. RNA extraction, cDNA synthesis, and qRT-PCR analysis were conducted as described previously [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Primer sequences and PCR conditions utilized for examining gene expression are provided in Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Relative expression levels were determined using the 2\u003csup\u003e–∆∆Ct\u003c/sup\u003e method, and error bars represent the standard deviation values for triplicates.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ebHLH: basic Helix-Loop-Helix; COG database: Clusters of Orthologous Groups database; DEG: Differentially Expressed Gene; ERF: Ethylene-Responsive element-binding Factor; GO: Gene Ontology; IUCN: International Union for Conservation of Nature; KEGG database: Kyoto Encyclopedia for Genes and Genomes database; NCBI: National Center for Biotechnology Information; qRT-PCR: quantitative Real Time-Polymerase Chain Reaction; RNA seq: RNA sequencing; RSEM software: RNA-Seq by Expectation-Maximization software; SWC: Soil Water Content; TF: Transcription Factor; WEGO tool: WEb Gene Ontology annotation plot tool\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information for this paper are available at \u003cem\u003eBMC Plant Biology\u003c/em\u003e Online.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science, Jeju, Korea, for providing the 3-year-old Korean fir (\u003cem\u003eAbies koreana\u003c/em\u003e) plants used in this study.\u0026nbsp;The authors deeply appreciate Jong-won Park for providing assistance in the\u0026nbsp;qRT-PCR experiment. We particularly thank Yunjeong Kim, Da Young Lee, and Da Young Park for their assistance in various experiments.\u0026nbsp;This work was supported by\u0026nbsp;a grant from\u0026nbsp;the National Institute of Ecology (NIE), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIE-B-2024-15).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.C.P.\u0026nbsp;designed\u0026nbsp;the\u0026nbsp;experiments;\u0026nbsp;H.C.P.\u0026nbsp;and\u0026nbsp;J.E.H.\u0026nbsp;conducted\u0026nbsp;the\u0026nbsp;experiments and analyzed the transcriptome data; H.C.P. drafted the manuscript and maintained the plant materials; H.C.P\u0026nbsp;and\u0026nbsp;J.E.H discussed the results and finalized the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;a grant from\u0026nbsp;the National Institute of Ecology (NIE), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIE-B-2024-15).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe high-quality trimmed (clean) read data from RNA-seq are available in the NCBI Sequence Read Archive database (accession numbers:\u0026nbsp;SAMN40219202, SAMN40219203, SAMN40219204, SAMN40219205, SAMN40219206, SAMN40219207, SAMN40219208, SAMN40219209, SAMN40219210, SAMN40219211, SAMN40219212, and SAMN40219213).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree-year-old \u003cem\u003eA. koreana\u003c/em\u003e used in this study were provided from Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science, Jeju, Korea.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003eEcological Technology Research Team, Division of Ecological Applications Research, Bureau of Conservation Research, National Institute of Ecology, Seocheon 33657, Republic of Korea. \u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003eDivision of Restoration Research, Research Center for Endangered Species, National Institute of Ecology, Yeongyang 36531, Republic of Korea.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMauri A, De Rigo D, Caudullo G. \u003cem\u003eAbies alba\u003c/em\u003e in Europe: distribution, habitat, usage and threats. 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Bioinformatics. 2011;27:863-864.\u003c/li\u003e\n\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":"Abies koreana, transcriptomic analysis, drought stress, transcription factors, Gene Ontology classification","lastPublishedDoi":"10.21203/rs.3.rs-4690654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4690654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAbies koreana\u003c/em\u003e, an endangered species sensitive to drought, faces severe decline due to climate change-induced water deficit.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, we performed a comprehensive transcriptomic analysis of \u003cem\u003eA. koreana\u003c/em\u003e under drought stress. RNA-seq data analysis revealed 85,403 contigs of extended lengths, indicative of a high-quality assembly. Gene Ontology classification revealed strong drought stress-responsive regulation of unigenes associated with various biological processes, cellular components, and molecular functions. Gene expression levels showed variation at different time points, indicating a nuanced plant response. Numerous transcription factors, notably those belonging to ERF, MYB, LBD, and NAC families, were identified, underscoring their critical roles in drought tolerance. Finally, qRT-PCR validation confirmed the reliability of RNA-seq data.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOverall, this study reveals candidate genes required for drought tolerance in \u003cem\u003eA. koreana\u003c/em\u003e, thus supporting future tree breeding programs for effective forest management.\u003c/p\u003e","manuscriptTitle":"Identification of drought stress-responsive genes in Korean fir (Abies koreana) through comparative RNA-seq analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-07 09:59:03","doi":"10.21203/rs.3.rs-4690654/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":"48377172-ad65-4a31-a94b-d9feb06e71ad","owner":[],"postedDate":"August 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T07:00:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-07 09:59:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4690654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4690654","identity":"rs-4690654","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}