Mining differential gene expression in Fagus crenata seedlings in response to short-term soil drought stress

Mining differential gene expression in Fagus crenata seedlings in response to short-term soil drought stress | 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 Mining differential gene expression in Fagus crenata seedlings in response to short-term soil drought stress Takeshi Torimaru, Hinako Ao, Yasuaki Akaji, Shinji Akada, Ohmiya Yasunori, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4651558/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2025 Read the published version in Plant Species Biology → Version 1 posted You are reading this latest preprint version Abstract Despite concern about the stress drought imposes on tree species under a warming climate, their molecular responses to drought stress have not been well-documented. We analyzed the transcriptional response of seedling leaves after exposure to short-term drought stress in Fagus crenata . After well-watered and water-stressed treatments, we mapped the RNA-seq reads derived from sampled leaves and identified 127 differentially expressed genes (DEGs), of which 89 were up- and 38 down-regulated in water-stressed plants. Several dozen up-regulated DEGs were predicted to encode proteins that would facilitate mitigating processes or avoid the adverse effects caused by drought stress, including stomatal closure, reactive oxygen species (ROS) scavenging, abscisic acid (ABA) accumulation and response, and osmoprotectants. The evidence of down-regulation in several genes in response to drought stress was in accordance with the results of a literature survey. The functional category of sulfate assimilation was enriched in up-regulated DEGs, although there was also evidence of sulfur deficiency in the DEGs. These results suggest the existence of molecular mechanisms in beech that are common in other plant species, representing an acclimation response to drought stress as well as sulfur metabolism under drought stress conditions. This information provides the basis for further species-specific functional genomic research within the context of a warming climate. Forestry differentially expression gene Fagus crenata seedlings growth experiments RNA-seq soil drought transcriptome analysis Figures Figure 1 Figure 2 Introduction Fagus crenata , commonly known as Japanese beech, is a tall deciduous broad-leaved tree in the Fagaceae family; it is endemic to Japan and the dominant species in Japanese cool-temperate deciduous broad-leaved forests. However, it is predicted that global warming will reduce the habitat suitable for Japanese beech forests (Matsui et al. 2004 ). The main agent driving the decline of beech forests is increased evaporation of soil moisture, causing soil drought (Mouri & Shinoda 2005 ) and imposing physiological stress on the plants (i.e. drought stress). Climatic phenomena such as this are occurring more frequently, with time intervals shorter than those of tree turnovers (Hoffmann & Sgrò 2011 ). Given that seedlings are the most vulnerable to environmental stress compared with the other tree life history stages (Leck et al. 2008 ), investigating the short-term response of beech seedlings to drought stress is important for evaluating the ability of natural beech populations to persist in a future warming climate. It is well-known that plants can respond rapidly to drought stress. Their physiological and morphological responses to short-term experimentally stimulated drought include cell elongation in leaves (Acevedo et al. 1971 ), retardation in cell wall and protein synthesis (Cleland 1967 ; Hsiao 1970 ), reductions in chlorophyll content as well as increased concentrations of reactive oxygen species, malondialdehyde (a marker for lipid peroxidation) (Chen et al. 2016 ), osmoprotectants such as free-proline, glycine betain and polyols (Ghosh et al. 2021 ), and reductions in tree size, specific leaf area, leaf relative water content and stomatal conductance (Amrutha et al. 2021 ). These changes in phenotypic traits over short timescales are driven by differential gene expression, which can be measured by assessing the expression levels of mRNA found in plant tissues (Shanker et al. 2014 ). Differential expression analyses comparing water-stressed plants with controls have identified candidate genes relating to acclimation under short-term drought stress in a wide range of species, including oak (Gugger et al. 2017 ), poplar (Yang et al. 2023 ) and pine (Li et al. 2024 ), as well as annual plants such as maize (Le et al. 2012 ) and tomato (Liu et al. 2023 ). Recent advances in next-generation sequencing technology can yield large quantities of mRNA sequence data in a short time period (RNA-seq), and thus provide a cost-efficient and powerful tool for differential expression analyses (Deshpande et al. 2023 ). Thus, experimental research on stimulated drought stress in plants combined with RNA-seq technology can improve our understanding of the genomic background to the acclimation response of phenotypic traits. Studies utilizing transcriptome profiles obtained from RNA-seq in the genus Fagus have focused on responses to drought stress in F. sylvatica (Müller et al. 2017 ) and flowering phenomena in F. crenata (Miyazaki et al. 2014 ; Satake et al. 2019 ): the molecular responses to drought stress remain unexplored in F. crenata . A previous study reported the species’ phenotypical acclimation response to short-term drought stress in terms of biochemical concentrations in leaves (Ao et al. 2023 ). To better understand the species’ potential for a rapid response to short-term stress, we performed a growth experiment under different watering regimes and obtained differential transcriptome profiles for the F. crenata seedlings using RNA-seq. A series of bioinformatic analyses were then conducted, including the mapping of RNA-seq reads to a reference genome, detection of differentially expressed genes (DEGs), annotations of gene ontology (GO) and metabolic pathways, and enrichment analyses. The specific objectives of the study were to ( i ) find putative genes associated with short-term drought stress and ( ii ) infer acclimation responses to short-term drought stress in F. crenata seedlings. Material and Methods Growth conditions and sampling for the drought stress experiment On 13 July 2022, at Mokkadaira (35°54′N, 136°33′E, 957 m altitude), Mt Arajima (Fukui Prefecture, Japan), beech seedlings with two true leaves completely unfurled were dug up by their roots and transferred to polypots (12 cm in diameter × 10 cm in height) with local soil, and taken to the laboratory at Mie University (Mie, Japan). The seedlings were grown on in an environmental experimental room (LP1.9P-S, Nippon Medical & Chemical Instruments Co., Ltd, Japan) and were well-watered until the start of the experiment. From 25 July to 6 August 2022, the seedlings were grown for 12 days in an environmental experimental chamber (LH-241PFD-S, Nippon Medical & Chemical Instruments Co., Ltd). In the chamber, two watering regimes were applied as follows. Well-watered (WW) seedlings continued to be watered (100 ml per pot) once every 3 days, while water-stressed (WS) seedlings were not watered. There were five seedlings in each watering regime. There were no significant differences in seedling height between the treatments at the start of the experiment [WW: 9.3 ± 1.3 cm ( n = 5), WS: 8.5 ± 1.4 cm ( n = 5), t -test: t = − 0.852, P = 0.419]. The positions of the pots were changed randomly every day to mitigate positional effects within the chamber. The environmental conditions in the chamber were: 25°C from 06:00 h to 18:00 h, 15°C from 18:00 h to 06:00 h; a photoperiod of 14 h light (06:00 h to 20:00 h)/10 h darkness (20:00 h to 06:00 h); and a photosynthetic photon flux density (PPFD) for the light period of 135–190 µmol/m 2 s. The relative soil moisture content in the WW and WS treatment pots was measured at the beginning and end of the experiment, using a soil moisture meter (Lutron PMS-714, Taiwan) at a depth of 5cm below the surface. On 27 July 2022, at the start of the experiment, the average values for relative soil moisture content (± SD) were 11.1% (± 0.5) ( n = 5) for WW, and 10.1% (± 0.7) ( n = 5) for WS. On 6 August 2022, at the end of the experiment, the values were 12.4% (± 1.3) ( n = 5) for WW, whereas for WS the soil in the pots had completely dried out (i.e. a value of 0.0%) ( n = 5). These results confirmed that the WS beech seedlings had been subject to more drought stress than the WW seedlings. At around 13:00–14:00 h on the last day of the experiment, two true leaves that had completed unfurled at the beginning of the experiment were collected per plant. These leaves were immediately frozen in liquid nitrogen and stored at − 80°C until used for the RNA analyses. Total RNA was extracted from the leaves using a NucleoSpin ® RNA Plant (TakaraBio Inc., Kusatsu, Japan) according to the manufacturer’s instructions; 1 µl of the resulting RNA extract was used to measure the RNA concentration using a Qubit® 3.0 Fluorometer (ThermoFisher Scientific, Massachusetts, USA), and stored frozen at − 80°C. The RNA samples were sent to Gene Bay Inc. (Tokyo, Japan), and the preparation of stranded RNA-seq libraries and next-generation sequencing by DNBSEQ -T7 (2x150 bp) (MGI Tech Co. Ltd) were performed at Novogene Co. Ltd (Beijing, China). Transcriptome analyses Adaptor sequences and low-quality regions were removed from the raw reads (DRR539888−DRR539897) for each sample using Trimmomatic (Bolger et al. 2014 ). Read quality was checked using FastQC ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). The reads were then mapped to reference sequences for F. crenata (BKZX02000001-BKZX02000625, see also Supplementary Information Appendix 1, Table S1 and Table S2) using STAR (Dobin et al. 2013 ; Dobin & Gingeras 2015 ). The number of reads mapping to each gene was counted using RSEM (Li & Dewey 2011 ). In order to correct for any bias in the number of reads mapped to each gene as a result of variation in RNA quality [indicated by the RNA integrity number (RIN)] between samples, we calculated transcript integrity number (TIN) for each sample using RSeQC ver. 2.6.4 (Wang et al. 2016 ). Weighted local polynomial regression analyses were performed for the genes in each sample to which reads were mapped, with the ordinary logarithm of the number of reads as the response variable and the TIN as the explanatory variable (Wang et al. 2016 ). These corrected values were used in the following analyses to detect any differential gene expression between the treatments. R package TCC (Sun et al. 2013 ) was used to detect differential gene expression between the treatments. The filterByExpr function in edgeR (Robinson et al. 2010 ) was used to filter out low-expression genes (“min.count” and “min.total.count” were set to 10 and 15, respectively). To normalize read counts and detect any differential expression, we applied DESeq2 (Love et al. 2014 ) in TCC, as DESeq2 has been reported to have superior detection capabilities when the number of samples in each treatment is small (< 12 samples) (Schurch et al. 2016 ). For the TCC analysis, the false discovery rate (FDR) set within the normalization procedure for the read count data was 0.05, the proportion of candidate differentially expressed genes removed during normalization (floorPDEG) was 0.05, the number of repeats for the above normalization procedure was 3, and the FDR for the detection of differentially expressed genes was set to 0.05. Genes with an FDR value of a 1, were considered to be DEGs. In addition, based on normalized expression count data, a principal component analysis was used to visualize the results. These analyses were carried out using R 4.3.1 (R Development Core Team 2023 ). A literature search was also carried out based on key terms within the names of the proteins encoded by the DEGs, as well as the phrases ‘plant’, ‘drought stress’, and ‘water deficiency’, which were input into search engines such as Web of Science, PubMed, and Google Scholar. The resulting literature was surveyed for whether the proteins discussed were involved in an acclimation response to drought stress showing the same trends as revealed in this study (i.e. up- or down-regulation). To perform functional enrichment analyses, gene IDs from Arabidopsis thaliana were annotated with the reference genome as follows. A DIAMOND v2.0.15 blastp (Buchfink et al. 2021 ) search (with an E -value < 10 − 5 ) was made against the protein database for A. thaliana ( https://www.arabidopsis.org/download_files/Proteins/Araport11_protein_lists/Araport1_pep_20220914.gz ). The results of the homology search were then input into Blast2GO Basic 6.0 to extract the A. thaliana gene IDs with the lowest E -values. Based on these annotated gene IDs, Metascape v3.5 (Zhou et al. 2019 ) was used to identify significantly enriched GO terms in the WS-specific up- and down-regulated DEGs, in which all the Arabidopsis gene IDs assigned to the beech reference sequence were used as a background. Similarly, enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted using the same Metascape. The FDR for the enrichment analyses was set at 0.05. Results Filtering low-quality reads for RNA sequences extracted from 10 samples of beech seedlings yielded a total of 51.6 Gb paired-end reads, with a total number of 347,371,569 reads (an average of 34,737,157 reads per individual) (Table S3). The reads were mapped to 25,883 of the 35,116 reference sequences, of which 17,533 were mapped to reads originating from all the samples. After correction based on TIN, the average number of reads mapped to the genes per sample was 13,543,365 (9,698,626–17,340,252). There were 127 DEGs, of which 89 genes were up-regulated and 38 were down-regulated in WS plants (Fig. 1 and Fig. S1). This response was also apparent in a principal component analysis, in which samples collected from the WW plants clustered together and those from WS clustered together (Fig. 2 ). Based on the annotated database for the reference genome, the 82 up- and 35 down-regulated DEGs could be inferred to gene functions rather than “hypothetical protein” (Table S4 and Table S5). Furthermore, the literature survey identified 33 up-regulated DEGs that have been reported to encode proteins involved in acclimation responses to abiotic stresses, including drought stress (Table 1 a). However, only two of the current down-regulated DEGs involving acclimation responses were supported by the literature survey (Table 1 b). Among the 8,868 GO terms found in our reference genome, up-regulated DEGs were annotated by 17 GO terms belonging to biological processes (14 terms) and molecular functions (three terms) (Table S6), whereas 14 GO terms comprised biological processes (eight terms) and molecular functions (six terms) in the down-regulated DEGs (Table S7). Based on the membership similarity between the GO terms, they were grouped into six and five clusters in up- and down-regulated DEGs, respectively (Table S6 and Table S7). In the up-regulated DEGs, two clusters contained significantly enriched GO terms, in which the representative terms with the lowest FDR were “cellular response to sulfur starvation” (GO:0010438) and “sulphate assimilation” (GO:0000103) in the first and second clusters, respectively (Table 2 ). No significantly enriched GO term was detected in the down-regulated DEGs. Among the 141 KEGG pathways found in the reference genome, two pathways were detected in the up-regulated DEGs but were not significantly enriched (Table S8). No KEGG pathway was annotated in the down-regulated DEGs. Discussion DEGs of WS F. crenata seedlings Of the 89 up-regulated DEGs in the WS plants, 33 genes could be annotated to encode proteins involved in acclimation responses to drought stress (Table 1 a), whereas there were only two homologous genes associated with acclimation responses in the down-regulated DEGs (Table 1 b). The additional literature survey identified several proteins encoded by genes down-regulated by drought stress, including a probable polygalacturonase (FCV25MIE_18960 in Table S5) in A. thaliana (Bray 2004 ), a putative pentatricopeptide repeat-containing protein (FCV25MIE_10987) (Pan et al. 2018 ) and LIM domain-containing protein WLIM2b (FCV25MIE_35051) (Yang et al. 2019 ) in foxtail millet ( Setaria italica ), and a DUF1666 domain-containing protein (FCV25MIE_10568) in sunflower ( Helianthus annuus ) (Wu et al. 2022 ). Although future studies should clarify the functions of such proteins in response to drought stress, our results suggest at least the existence of a molecular response to drought stress in beech, in common with other plant species. There were several DEGs encoding for proteins related to detoxification of reactive oxygen species (ROS), including 5′-adenylylsulfate reductase (Park et al. 2012 ), homocysteine S-methyltransferase (Qiu et al. 2023 ), gamma-glutamylcyclotransferase (de María et al. 2020 ), Annexin (Konopka-Postupolska et al. 2009 ), S-adenosylmethionine synthase (Zhang et al. 2020b ), ATP sulfurylase (Anjum et al. 2015 ), phenylcoumaran benzylic ether reductase (Niculaes et al. 2014 ), AAA-atpase asd (Xia et al. 2013 )d box domain-containing protein (Song et al. 2017 ) in the up-regulated DEGs (Table 1 a), and serine acetyltransferase (Ahmad et al. 2016 ) in the down-regulated DEGs (Table 1 b). The accumulation of ROS through abiotic stresses, including water deficiency in the plant body, inflicts damage on cellular components through peroxidation of membrane lipids and the oxidation of proteins, DNA and RNA (Choudhury et al. 2017 ). Therefore, it is critical that plants protect themselves from harmful oxidants via detoxifying mechanisms, by using antioxidants and scavenging agents (Jubany-Marí et al. 2010 ). Our results suggest that detoxification of ROS is an available acclimation response to short-term drought stress in F. crenata seedlings. Because abscisic acid (ABA) is a well-known trigger of the drought signaling cascade (Aslam et al. 2022 ), previous studies have reported genes potentially related to ABA metabolism (Long et al. 2019 ) and the ABA response (Umezawa et al. 2010 ). Our study identified an up-regulated DEG predicted to code for the B-box zinc finger protein (Table 1 a), which regulates key genes related to ABA synthesis ( ABA1 , ABA2 , NCED3 ) and those related to the ABA transduction pathway ( HY5 and RD29A ) in the apple ( Malus domestica ) (Liu et al. 2019a ). In addition, the homologous genes related to the ABA response under drought stress are found; serine hydroxymethyltransferase (Liu et al. 2019b ), ninja-family protein AFP3-like (Rabara et al. 2015 ), serine/threonine-protein kinase SAPK3-like (Gao et al. 2018 ), MLO-like protein (Howlader et al. 2017 ) (Table 1 a). In particular, serine hydroxymethyltransferase and MLO-like protein appear to relate to regulation of stomatal closure (Howlader et al. 2017 ; Liu et al. 2019b ), which is in line with the increase seen in ABA concentration in leaves under drought stress conditions, to regulate stomatal closure and minimize water loss from the leaves (Umezawa et al. 2010 ). These DEGs suggest that ABA-dependent responses lead to acclimation to short-term drought stress in the seedlings. Avoidance of water loss from the plant body is often accompanied by changes in morphology and/or concentrations of biochemical molecules in the cells (Jaleel et al. 2009 ; Ghosh et al. 2021 ; Bawa et al. 2023 ). Our up-regulated DEGs encoded proteins involved with leaf morphology, including the transcription factor bHLH25-like (Table 1 a), of which a homolog in rice is known to regulate positively the genes associated with cuticular wax biosynthesis, which mitigates water loss from the plant body (Gu et al. 2021 ), and xyloglucan endotransglucosylase/hydrolase in tomato, which is known to alter the cell-wall extensibility of guard cells mediated by cell-wall remodeling activity, positively regulating stomatal closure (Choi et al. 2011 ). There were also several up-regulated DEGs whose homologs are known to produce enzymes that facilitate the synthesis of osmotic adjustment substances (Table 1 a), such as gamma-glutamylcyclotransferase for 5-oxo-proline (Xu et al. 2021 ), choline monooxygenase for glycine betaine (Russell et al. 1998 ), and NADP-dependent malic enzyme for malate (Sun et al. 2019 ). The accumulation of these substances in the cells increases cellular osmolality, which drives an influx of water or reduces its efflux, thus maintaining cell turgor pressure (Seleiman et al. 2021 ). A previous study has shown the accumulation of free-proline (a major osmoprotectant) in F. crenata seedlings exposed to drought stress for 12 days (Ao et al. 2023 ). This is in line with the present finding that the gene encoding proline-rich protein was down-regulated (Table 1 b), as drought stress causes a drastic reduction in gene expression to avoid full use of the available proline molecules in plant cells, ensuring the availability of adequate proline molecules for osmolytes until their de novo synthesis begins (Gujjar et al. 2018 ). As already shown for gamma-glutamylcyclotransferase, putative up-regulated DEGs encoding for proteins were inferred to regulate multiple acclimation processes (e.g. ROS detoxification, ABA metabolism and responses, and the production of osmoprotectants), including NAC domain-containing protein (Nakashima et al. 2012 ), ankyrin repeat-containing protein (Zhao et al. 2020 ), and glucosyltransferase families (crocetin glucosyltransferase and 7-deoxyloganetin glucosyltransferase-like) (Liu et al. 2021 ) (Table 1 a). In particular, NAC is a well-known transcription factor (TF) located upstream of the stress-tolerance pathways that regulate downstream genes (Nakashima et al. 2012 ). In plants, there are 11 major TFs (NAC, ERF, WRKY, bZIP, MYB, HD-ZIP, ZnF, bHLH, ASR, NF-Y, and HSF) that form a regulatory network in response to drought stress (Hu et al. 2022 ). Our present study found DEGs encoding for bHLH (basic/helix-loop-helix) as well as NAC TFs, whereas Matsuda et al. ( 2011 ) identified beech homologs of MYB genes with transcriptomes that varied with drought stress treatments over several hours. Given that the number of DEGs varies depending on the period and/or strength of drought stress [e.g. Müller et al. ( 2017 ) in F. sylvatica ], more experiments are needed to clarify the regulatory network of TFs that target the downstream gene expression in response to drought stress in F. crenata . GO enrichment and inference of metabolic processes in WS F. crenata seedlings The GO term “cellular response to sulfur deficiency” was significantly enriched in the up-regulated DEGs in our beech seedlings, in accordance with other reports suggesting drought stress inhibits plants’ absorption of sulfur from soils (Lee et al. 2016 ). Sulfur starvation in the plant body has been reported to enhance the activities of several enzymes relating to sulfur assimilation, including ATP sulfurylase (Takahashi et al. 2001 ) and 5′-adenylylsulfate reductase (an enzyme associated with the step following ATP sulfurylase in the sulfur assimilation process) (Takahashi et al. 1997 ). These enzymes are putatively encoded by the genes for FCV25MIE_22545 and FCV25MIE_32112 (Table 1 a), which are annotated by the GO term “sulfate assimilation” (Table 2 ). ATP sulfurylase, an enzyme in the first committed step of sulfur assimilation, is considered to be sensitive to redox regulation in plants (Prioretti et al. 2014 ), and is negatively regulated by the concentration of cysteine, which is the final product of the sulfur assimilation process (Vauclare et al. 2002 ), and that of reduced glutathione (GSH), which is a major redox regulator synthesized from cysteine (Lappartient & Touraine 1996 ). In our study, because the gene encoding serine acetyltransferase, an enzyme that is essential for synthesizing cysteine (i.e. the endpoint of sulfur assimilation), was found to be down-regulated (Table 1 b), cysteine concentrations would be low in the leaves, allowing the up-regulation of genes encoding ATP sulfurylase and 5′-adenylylsulfate reductase. In maize exposed to a restricted water supply for 12 days, leaves showed significant reductions in the activity of serine acetyltransferase, as well as cysteine concentrations, compared with those receiving a sufficient water supply, whereas there were substantial increases in cysteine concentrations, without the decrease in serine acetyltransferase activity, in the roots of the same plants (Ahmad et al. 2016 ). Ahmad et al. ( 2016 ) point out that re-translocation of sulfur occurs from the shoots to the roots in the plant body, while sulfur compounds accumulate in the roots and enable development of the root system and ROS detoxification under drought conditions. Further research should consider simultaneous examination of the transcriptome in leaves and roots, as well as their comparison, to facilitate our understanding of the overall mechanism of acclimation response to drought stress in F. crenata seedlings. In conclusion, we have identified putative genes encoding proteins associated with short-term drought stress in beech seedlings, from which positive regulators against the adverse effects driven by drought stress can be inferred, including ABA metabolism and response, ROS scavenging, osmotic adjustment, and stomatal closure. Functional categories of genes related to sulfur and its metabolic processes were also enriched in the leaf transcriptome of seedlings exposed to drought stress, which might have been involved in the shoot-to-root sulfur translocation as a result of drought stress acclimation. These genes, as well as their annotated functions, could be used to search for range-wide as well as local genetic variation in relation to functional traits, including acclimation potential to drought stress. This would provide crucial information for establishing a species-specific conservation policy within the context of a future warming climate. Declarations Competing interests The authors declare no competing interests. Data Archiving Statement The raw data generated in this study were archived in the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA) under accession number PRJDB15599. Fundings This research was funded by JSPS KAKENHI grant numbers 20H03027 and 20K06124. Author contributions Conceptualization: TT and HA; Methodology: TT and HA; Formal analysis and investigation: TT and HA; Writing - original draft preparation: TT and HA; Writing - review and editing: YA, SA, YO, YM, HK, and NT; Funding acquisition: TT and NT; Resources: YA, SA, YO, YM, and HK. All authors read and approved the final manuscript. Acknowledgements The authors are grateful to members of the Laboratory of Forest Conservation Ecology, Mie University, Tsu, Japan, for assistance in the field, and for their useful comments. 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(2016) FCV25MIE_05115 5′-adenylylsulfate reductase 1 chloroplastic-like ROS-related defense pathway Park et al. (2012) FCV25MIE_32112 5′-adenylylsulfate reductase 3 chloroplastic-like FCV25MIE_08137 Ninja-family protein AFP3-like isoform X1 ABA response Rabara et al. (2015) FCV25MIE_22587 Homocysteine S-methyltransferase 3 ROS scavenging Qiu et al. (2023) FCV25MIE_25115 Serine/threonine-protein kinase SAPK3-like ABA metabolism Gao et al. (2018) FCV25MIE_12920 Gamma-glutamylcyclotransferase 2-1-like ROS scavenging de María et al. (2020) FCV25MIE_30298 Ankyrin repeat-containing protein At5g02620-like isoform X1 Proline accumulation, antioxidant, and activation of stress-related TFs Zhao et al. (2020) FCV25MIE_15802 Ankyrin repeat-containing protein FCV25MIE_30221 Ankyrin repeat and protein kinase domain-containing protein 1 Table 1 Continued Locus_tag in the beech reference genome* Putative protein Involvement in functions related to abiotic stress References FCV25MIE_28347 NAC domain-containing protein 86-like Regulation of osmoprotectant and antioxidants, and regulation of stress-tolerant genes Nakashima et al. (2012) FCV25MIE_05204 Xyloglucan endotransglucosylase/hydrolase protein 9 Stomatal closure via cell-wall remodeling Choi et al. (2011) FCV25MIE_23256 S-adenosylmethionine synthase ROS scavenging and ABA metabolism Zhang et al. (2020b) FCV25MIE_00576 Auxin-responsive protein SAUR68-like Regulation of plant growth Bouzroud et al. (2018) FCV25MIE_17904 Annexin D3 ROS scavenging Konopka-Postupolska et al. (2009) FCV25MIE_17905 Annexin D4 FCV25MIE_14061 Wall-associated receptor kinase-like 10 isoform X1 Regulation of plant growth Zhang et al. (2020a) FCV25MIE_14564 Wall-associated receptor kinase-like 8 FCV25MIE_22545 ATP sulfurylase 1 chloroplastic-like isoform X1 ROS scavenging Anjum et al. (2015) FCV25MIE_19833 UDP-glycosyltransferase 74B1-like ABA response Rabara et al. (2015) FCV25MIE_27360 Crocetin glucosyltransferase chloroplastic-like ABA accumulation, stomatal closure, ROS scavenging, accumulation of osmoprotectant, and up-regulation of stress-related gene expressions Liu et al. (2021) FCV25MIE_15921 7-deoxyloganetin glucosyltransferase-like Table 1 Continued Locus_tag in the beech reference genome * Putative protein Involvement in functions related to abiotic stress References FCV25MIE_32676 MLO-like protein 12 ABA response Howlader et al. (2017) FCV25MIE_23637 Phenylcoumaran benzylic ether reductase Betv6 ROS scavenging Niculaes et al. (2014) FCV25MIE_08783 AAA-atpase asd mitochondrial ROS scavenging Xia et al. (2013) FCV25MIE_13390 U-box domain-containing protein 21-like ROS scavenging Song et al. (2017) FCV25MIE_20137 U-box domain-containing protein 19-like FCV25MIE_30527 Choline monooxygenase chloroplastic Glysine betaine production Russell et al. (1998) FCV25MIE_12792 NADP-dependent malic enzyme isoform X2 Malate production Sun et al. (2019) FCV25MIE_33628 B-box zinc finger protein 32-like ABA response and ROS scavenging Liu et al. (2019a) (b) Locus_tag in the beech reference genome* Putative protein Involvement in functions related to abiotic stress References FCV25MIE_26236 Serine acetyltransferase 3 mitochondrial ROS scavenging Ahmad et al. (2016) FCV25MIE_24515 14 kDa proline-rich protein DC2.15-like Regulation of proline production level Gujjar et al. (2018) * Obtained from our de novo assembly, which has been registered in DDBJ (accession no. BKZX02000001-BKZX02000625). ROS, reactive oxygen species. ABA, abscisic acid. Table 2 Significantly enriched gene ontology (GO) terms in the differentially expressed genes (DEGs) up-regulated by drought stress in Fagus crenata seedlings ID a Category Description Log 10 ( p -value b ) Log 10 (FDR c ) Test group d Reference group e GO:0010438 Biological processes Cellular response to sulfur starvation -6.607 -2.659 3/70 3/10996 GO:0000103 Biological processes Sulfate assimilation -5.071 -1.424 3/70 7/10996 a The GO terms detected were clustered based on their membership similarity (see also Supplementary Information Table S4), and those exhibiting the lowest significant log 10 (false discovery rate; FDR) are shown here. b Based on the cumulative hypergeometric distribution c Based on the Benjamini-Hochberg procedure to account for multiple testing when a given Q number of GO terms was typically identified in the reference genome, as follows. First, all p -values were sorted from small to large. Second, given a p -value of p at rank i , it would be expected that pQ GO terms could be found with the same or a better p -value by chance under a Bonferroni correction. As we only observed i such GO terms, the portion of our observations that were false (i.e. FDR) was min( pQ / i , 1). See Table S6 for full list of FDR. d The number of DEGs annotated by the corresponding GO term/the number of all DEGs annotated by GO terms. e The number of genes annotated by the corresponding GO term in the reference genome/the number of all genes annotated by GO terms in the reference genome. Additional Declarations The authors declare no competing interests. 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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-4651558","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320011178,"identity":"00c23538-2e6e-4d2a-b17e-05cfab6e3741","order_by":0,"name":"Takeshi Torimaru","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYJCCAyCCX4KxAUhJwATZcCrngWmRnEGKFjAwuEGso+zZex8eulFzL3Hz7ebGB4w5FnIMEgmMH34w8OXhtIXnuMHhnGPFidvuHGw2YNwmYQzUwizZw8BWjFOLRBrD4Ry2hMRtNxLbJIBaEvffSGCQBvolsQGvln8JiZtnQLTUNwBt+U1QS25bQuIGCYiWBKDD2PDbcuYYUEtfgvEMkF8St0kYNvA8bLPsMcDtF/b2NubPOd8SZPtntz988HFbnTwDe/LhGz8qjuEMMVSQACZBcWpwLIE4LUighnQto2AUjIJRMFwBAE9NUdja/e0zAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4759-9108","institution":"Mie University","correspondingAuthor":true,"prefix":"","firstName":"Takeshi","middleName":"","lastName":"Torimaru","suffix":""},{"id":320011179,"identity":"0e0caa02-0518-422e-b6ab-094444fb273e","order_by":1,"name":"Hinako Ao","email":"","orcid":"","institution":"Mie University","correspondingAuthor":false,"prefix":"","firstName":"Hinako","middleName":"","lastName":"Ao","suffix":""},{"id":320011180,"identity":"a3e9130b-eadc-4a6a-bba6-ee1d200da58f","order_by":2,"name":"Yasuaki Akaji","email":"","orcid":"","institution":"National Institute for Environmental Studies","correspondingAuthor":false,"prefix":"","firstName":"Yasuaki","middleName":"","lastName":"Akaji","suffix":""},{"id":320011181,"identity":"304c7cf6-0c7b-4880-a6cb-93cca7f89345","order_by":3,"name":"Shinji Akada","email":"","orcid":"","institution":"Hirosaki University","correspondingAuthor":false,"prefix":"","firstName":"Shinji","middleName":"","lastName":"Akada","suffix":""},{"id":320011182,"identity":"32e6c6a6-351c-4fe9-b874-2dc25edafac3","order_by":4,"name":"Ohmiya Yasunori","email":"","orcid":"","institution":"Forest Tree Breeding Center, Forestry and Forest Products Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ohmiya","middleName":"","lastName":"Yasunori","suffix":""},{"id":320011183,"identity":"a823a3f4-ade6-412f-8757-a1c590d0aaf0","order_by":5,"name":"Yousuke Matsuda","email":"","orcid":"","institution":"Mie University","correspondingAuthor":false,"prefix":"","firstName":"Yousuke","middleName":"","lastName":"Matsuda","suffix":""},{"id":320011184,"identity":"cf42a3f6-0fe9-4603-aca1-4a2bffcfab5a","order_by":6,"name":"Hiromitsu Kisanuki","email":"","orcid":"","institution":"Mie University","correspondingAuthor":false,"prefix":"","firstName":"Hiromitsu","middleName":"","lastName":"Kisanuki","suffix":""},{"id":320011185,"identity":"0e545ab1-3690-48a0-800d-d34ed7c75aaf","order_by":7,"name":"Nobuhiro Tomaru","email":"","orcid":"","institution":"Nagoya University","correspondingAuthor":false,"prefix":"","firstName":"Nobuhiro","middleName":"","lastName":"Tomaru","suffix":""}],"badges":[],"createdAt":"2024-06-28 02:33:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4651558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4651558/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1111/1442-1984.12506","type":"published","date":"2025-02-03T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59466074,"identity":"0349120e-ef48-4171-89c5-4cd2dc1d7bb9","added_by":"auto","created_at":"2024-07-02 06:36:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99737,"visible":true,"origin":"","legend":"\u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003e-fold gene expression change versus mean expression level for drought stress-treated \u003cem\u003eFagus crenata\u003c/em\u003e seedlings. Red points are statistically significant after false discovery rate (FDR) adjustment.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4651558/v1/9a3bca1157fdbab4ba9a43bd.png"},{"id":59466076,"identity":"dc98e72c-c310-4626-8f2e-fe06124b07f4","added_by":"auto","created_at":"2024-07-02 06:36:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18892,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component (PC) analysis of gene expression data for the 500 most variable genes in \u003cem\u003eFagus crenata\u003c/em\u003e seedlings. All samples from the well-watered treatment clustered on the right and all those from the water-stressed treatment clustered on the left.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4651558/v1/e95dbd0930200bbc0b2e1e01.png"},{"id":75456675,"identity":"3c8688cd-b41c-4ffe-bfd7-867541f86bf2","added_by":"auto","created_at":"2025-02-04 20:35:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":870253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4651558/v1/44bdcc18-a845-4931-b74e-b925e8be1635.pdf"},{"id":59466078,"identity":"58ecc350-871e-40ff-b19c-80241cb2c3d0","added_by":"auto","created_at":"2024-07-02 06:36:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1028177,"visible":true,"origin":"","legend":"","description":"","filename":"BeechshorttermtranscriptomeSIver.3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4651558/v1/f108c76decaec7f64469763a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMining differential gene expression in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFagus crenata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e seedlings in response to short-term soil drought stress\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eFagus crenata\u003c/em\u003e, commonly known as Japanese beech, is a tall deciduous broad-leaved tree in the Fagaceae family; it is endemic to Japan and the dominant species in Japanese cool-temperate deciduous broad-leaved forests. However, it is predicted that global warming will reduce the habitat suitable for Japanese beech forests (Matsui et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The main agent driving the decline of beech forests is increased evaporation of soil moisture, causing soil drought (Mouri \u0026amp; Shinoda \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and imposing physiological stress on the plants (i.e. drought stress). Climatic phenomena such as this are occurring more frequently, with time intervals shorter than those of tree turnovers (Hoffmann \u0026amp; Sgr\u0026ograve; \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Given that seedlings are the most vulnerable to environmental stress compared with the other tree life history stages (Leck et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), investigating the short-term response of beech seedlings to drought stress is important for evaluating the ability of natural beech populations to persist in a future warming climate.\u003c/p\u003e \u003cp\u003eIt is well-known that plants can respond rapidly to drought stress. Their physiological and morphological responses to short-term experimentally stimulated drought include cell elongation in leaves (Acevedo et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), retardation in cell wall and protein synthesis (Cleland \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Hsiao \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1970\u003c/span\u003e), reductions in chlorophyll content as well as increased concentrations of reactive oxygen species, malondialdehyde (a marker for lipid peroxidation) (Chen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), osmoprotectants such as free-proline, glycine betain and polyols (Ghosh et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and reductions in tree size, specific leaf area, leaf relative water content and stomatal conductance (Amrutha et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These changes in phenotypic traits over short timescales are driven by differential gene expression, which can be measured by assessing the expression levels of mRNA found in plant tissues (Shanker et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Differential expression analyses comparing water-stressed plants with controls have identified candidate genes relating to acclimation under short-term drought stress in a wide range of species, including oak (Gugger et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), poplar (Yang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and pine (Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as well as annual plants such as maize (Le et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and tomato (Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recent advances in next-generation sequencing technology can yield large quantities of mRNA sequence data in a short time period (RNA-seq), and thus provide a cost-efficient and powerful tool for differential expression analyses (Deshpande et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, experimental research on stimulated drought stress in plants combined with RNA-seq technology can improve our understanding of the genomic background to the acclimation response of phenotypic traits.\u003c/p\u003e \u003cp\u003eStudies utilizing transcriptome profiles obtained from RNA-seq in the genus \u003cem\u003eFagus\u003c/em\u003e have focused on responses to drought stress in \u003cem\u003eF. sylvatica\u003c/em\u003e (M\u0026uuml;ller et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and flowering phenomena in \u003cem\u003eF. crenata\u003c/em\u003e (Miyazaki et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Satake et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e): the molecular responses to drought stress remain unexplored in \u003cem\u003eF. crenata\u003c/em\u003e. A previous study reported the species\u0026rsquo; phenotypical acclimation response to short-term drought stress in terms of biochemical concentrations in leaves (Ao et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To better understand the species\u0026rsquo; potential for a rapid response to short-term stress, we performed a growth experiment under different watering regimes and obtained differential transcriptome profiles for the \u003cem\u003eF. crenata\u003c/em\u003e seedlings using RNA-seq.\u0026nbsp;A series of bioinformatic analyses were then conducted, including the mapping of RNA-seq reads to a reference genome, detection of differentially expressed genes (DEGs), annotations of gene ontology (GO) and metabolic pathways, and enrichment analyses. The specific objectives of the study were to (\u003cem\u003ei\u003c/em\u003e) find putative genes associated with short-term drought stress and (\u003cem\u003eii\u003c/em\u003e) infer acclimation responses to short-term drought stress in \u003cem\u003eF. crenata\u003c/em\u003e seedlings.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGrowth conditions and sampling for the drought stress experiment\u003c/h2\u003e \u003cp\u003eOn 13 July 2022, at Mokkadaira (35\u0026deg;54\u0026prime;N, 136\u0026deg;33\u0026prime;E, 957 m altitude), Mt Arajima (Fukui Prefecture, Japan), beech seedlings with two true leaves completely unfurled were dug up by their roots and transferred to polypots (12 cm in diameter \u0026times; 10 cm in height) with local soil, and taken to the laboratory at Mie University (Mie, Japan). The seedlings were grown on in an environmental experimental room (LP1.9P-S, Nippon Medical \u0026amp; Chemical Instruments Co., Ltd, Japan) and were well-watered until the start of the experiment. From 25 July to 6 August 2022, the seedlings were grown for 12 days in an environmental experimental chamber (LH-241PFD-S, Nippon Medical \u0026amp; Chemical Instruments Co., Ltd). In the chamber, two watering regimes were applied as follows. Well-watered (WW) seedlings continued to be watered (100 ml per pot) once every 3 days, while water-stressed (WS) seedlings were not watered. There were five seedlings in each watering regime. There were no significant differences in seedling height between the treatments at the start of the experiment [WW: 9.3 \u0026plusmn; 1.3 cm (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), WS: 8.5 \u0026plusmn; 1.4 cm (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), \u003cem\u003et\u003c/em\u003e-test: \u003cem\u003et\u003c/em\u003e = \u0026minus;\u0026thinsp;0.852, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.419]. The positions of the pots were changed randomly every day to mitigate positional effects within the chamber. The environmental conditions in the chamber were: 25\u0026deg;C from 06:00 h to 18:00 h, 15\u0026deg;C from 18:00 h to 06:00 h; a photoperiod of 14 h light (06:00 h to 20:00 h)/10 h darkness (20:00 h to 06:00 h); and a photosynthetic photon flux density (PPFD) for the light period of 135\u0026ndash;190 \u0026micro;mol/m\u003csup\u003e2\u003c/sup\u003e s. The relative soil moisture content in the WW and WS treatment pots was measured at the beginning and end of the experiment, using a soil moisture meter (Lutron PMS-714, Taiwan) at a depth of 5cm below the surface. On 27 July 2022, at the start of the experiment, the average values for relative soil moisture content (\u0026plusmn; SD) were 11.1% (\u0026plusmn;\u0026thinsp;0.5) (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) for WW, and 10.1% (\u0026plusmn;\u0026thinsp;0.7) (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) for WS. On 6 August 2022, at the end of the experiment, the values were 12.4% (\u0026plusmn;\u0026thinsp;1.3) (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) for WW, whereas for WS the soil in the pots had completely dried out (i.e. a value of 0.0%) (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5). These results confirmed that the WS beech seedlings had been subject to more drought stress than the WW seedlings.\u003c/p\u003e \u003cp\u003eAt around 13:00\u0026ndash;14:00 h on the last day of the experiment, two true leaves that had completed unfurled at the beginning of the experiment were collected per plant. These leaves were immediately frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until used for the RNA analyses. Total RNA was extracted from the leaves using a NucleoSpin\u003csup\u003e\u0026reg;\u003c/sup\u003e RNA Plant (TakaraBio Inc., Kusatsu, Japan) according to the manufacturer\u0026rsquo;s instructions; 1 \u0026micro;l of the resulting RNA extract was used to measure the RNA concentration using a Qubit\u0026reg; 3.0 Fluorometer (ThermoFisher Scientific, Massachusetts, USA), and stored frozen at \u0026minus;\u0026thinsp;80\u0026deg;C. The RNA samples were sent to Gene Bay Inc. (Tokyo, Japan), and the preparation of stranded RNA-seq libraries and next-generation sequencing by DNBSEQ -T7 (2x150 bp) (MGI Tech Co. Ltd) were performed at Novogene Co. Ltd (Beijing, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome analyses\u003c/h2\u003e \u003cp\u003eAdaptor sequences and low-quality regions were removed from the raw reads (DRR539888\u0026minus;DRR539897) for each sample using Trimmomatic (Bolger et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Read quality was checked using FastQC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The reads were then mapped to reference sequences for \u003cem\u003eF. crenata\u003c/em\u003e (BKZX02000001-BKZX02000625, see also Supplementary Information Appendix 1, Table S1 and Table S2) using STAR (Dobin et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dobin \u0026amp; Gingeras \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The number of reads mapping to each gene was counted using RSEM (Li \u0026amp; Dewey \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In order to correct for any bias in the number of reads mapped to each gene as a result of variation in RNA quality [indicated by the RNA integrity number (RIN)] between samples, we calculated transcript integrity number (TIN) for each sample using RSeQC ver. 2.6.4 (Wang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Weighted local polynomial regression analyses were performed for the genes in each sample to which reads were mapped, with the ordinary logarithm of the number of reads as the response variable and the TIN as the explanatory variable (Wang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These corrected values were used in the following analyses to detect any differential gene expression between the treatments.\u003c/p\u003e \u003cp\u003eR package TCC (Sun et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) was used to detect differential gene expression between the treatments. The filterByExpr function in edgeR (Robinson et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was used to filter out low-expression genes (\u0026ldquo;min.count\u0026rdquo; and \u0026ldquo;min.total.count\u0026rdquo; were set to 10 and 15, respectively). To normalize read counts and detect any differential expression, we applied DESeq2 (Love et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in TCC, as DESeq2 has been reported to have superior detection capabilities when the number of samples in each treatment is small (\u0026lt;\u0026thinsp;12 samples) (Schurch et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For the TCC analysis, the false discovery rate (FDR) set within the normalization procedure for the read count data was 0.05, the proportion of candidate differentially expressed genes removed during normalization (floorPDEG) was 0.05, the number of repeats for the above normalization procedure was 3, and the FDR for the detection of differentially expressed genes was set to 0.05. Genes with an FDR value of a\u0026thinsp;\u0026lt;\u0026thinsp;0.05, as well as a |log\u003csub\u003e2\u003c/sub\u003e-fold change| \u0026gt;1, were considered to be DEGs. In addition, based on normalized expression count data, a principal component analysis was used to visualize the results. These analyses were carried out using R 4.3.1 (R Development Core Team \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA literature search was also carried out based on key terms within the names of the proteins encoded by the DEGs, as well as the phrases \u0026lsquo;plant\u0026rsquo;, \u0026lsquo;drought stress\u0026rsquo;, and \u0026lsquo;water deficiency\u0026rsquo;, which were input into search engines such as Web of Science, PubMed, and Google Scholar. The resulting literature was surveyed for whether the proteins discussed were involved in an acclimation response to drought stress showing the same trends as revealed in this study (i.e. up- or down-regulation).\u003c/p\u003e \u003cp\u003eTo perform functional enrichment analyses, gene IDs from \u003cem\u003eArabidopsis thaliana\u003c/em\u003e were annotated with the reference genome as follows. A DIAMOND v2.0.15 blastp (Buchfink et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) search (with an \u003cem\u003eE\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) was made against the protein database for \u003cem\u003eA. thaliana\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arabidopsis.org/download_files/Proteins/Araport11_protein_lists/Araport1_pep_20220914.gz\u003c/span\u003e\u003cspan address=\"https://www.arabidopsis.org/download_files/Proteins/Araport11_protein_lists/Araport1_pep_20220914.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The results of the homology search were then input into Blast2GO Basic 6.0 to extract the \u003cem\u003eA. thaliana\u003c/em\u003e gene IDs with the lowest \u003cem\u003eE\u003c/em\u003e-values. Based on these annotated gene IDs, Metascape v3.5 (Zhou et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used to identify significantly enriched GO terms in the WS-specific up- and down-regulated DEGs, in which all the \u003cem\u003eArabidopsis\u003c/em\u003e gene IDs assigned to the beech reference sequence were used as a background. Similarly, enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted using the same Metascape. The FDR for the enrichment analyses was set at 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFiltering low-quality reads for RNA sequences extracted from 10 samples of beech seedlings yielded a total of 51.6 Gb paired-end reads, with a total number of 347,371,569 reads (an average of 34,737,157 reads per individual) (Table S3). The reads were mapped to 25,883 of the 35,116 reference sequences, of which 17,533 were mapped to reads originating from all the samples. After correction based on TIN, the average number of reads mapped to the genes per sample was 13,543,365 (9,698,626\u0026ndash;17,340,252).\u003c/p\u003e\n\u003cp\u003eThere were 127 DEGs, of which 89 genes were up-regulated and 38 were down-regulated in WS plants (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. S1). This response was also apparent in a principal component analysis, in which samples collected from the WW plants clustered together and those from WS clustered together (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on the annotated database for the reference genome, the 82 up- and 35 down-regulated DEGs could be inferred to gene functions rather than \u0026ldquo;hypothetical protein\u0026rdquo; (Table S4 and Table S5). Furthermore, the literature survey identified 33 up-regulated DEGs that have been reported to encode proteins involved in acclimation responses to abiotic stresses, including drought stress (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). However, only two of the current down-regulated DEGs involving acclimation responses were supported by the literature survey (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eAmong the 8,868 GO terms found in our reference genome, up-regulated DEGs were annotated by 17 GO terms belonging to biological processes (14 terms) and molecular functions (three terms) (Table S6), whereas 14 GO terms comprised biological processes (eight terms) and molecular functions (six terms) in the down-regulated DEGs (Table S7). Based on the membership similarity between the GO terms, they were grouped into six and five clusters in up- and down-regulated DEGs, respectively (Table S6 and Table S7). In the up-regulated DEGs, two clusters contained significantly enriched GO terms, in which the representative terms with the lowest FDR were \u0026ldquo;cellular response to sulfur starvation\u0026rdquo; (GO:0010438) and \u0026ldquo;sulphate assimilation\u0026rdquo; (GO:0000103) in the first and second clusters, respectively (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). No significantly enriched GO term was detected in the down-regulated DEGs.\u003c/p\u003e\n\u003cp\u003eAmong the 141 KEGG pathways found in the reference genome, two pathways were detected in the up-regulated DEGs but were not significantly enriched (Table S8). No KEGG pathway was annotated in the down-regulated DEGs.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eDEGs of WS\u003c/em\u003e F. crenata \u003cem\u003eseedlings\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eOf the 89 up-regulated DEGs in the WS plants, 33 genes could be annotated to encode proteins involved in acclimation responses to drought stress (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), whereas there were only two homologous genes associated with acclimation responses in the down-regulated DEGs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The additional literature survey identified several proteins encoded by genes down-regulated by drought stress, including a probable polygalacturonase (FCV25MIE_18960 in Table S5) in \u003cem\u003eA. thaliana\u003c/em\u003e (Bray \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), a putative pentatricopeptide repeat-containing protein (FCV25MIE_10987) (Pan et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and LIM domain-containing protein WLIM2b (FCV25MIE_35051) (Yang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in foxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e), and a DUF1666 domain-containing protein (FCV25MIE_10568) in sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e) (Wu et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although future studies should clarify the functions of such proteins in response to drought stress, our results suggest at least the existence of a molecular response to drought stress in beech, in common with other plant species.\u003c/p\u003e \u003cp\u003eThere were several DEGs encoding for proteins related to detoxification of reactive oxygen species (ROS), including 5\u0026prime;-adenylylsulfate reductase (Park et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), homocysteine S-methyltransferase (Qiu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), gamma-glutamylcyclotransferase (de Mar\u0026iacute;a et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Annexin (Konopka-Postupolska et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), S-adenosylmethionine synthase (Zhang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), ATP sulfurylase (Anjum et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), phenylcoumaran benzylic ether reductase (Niculaes et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), AAA-atpase asd (Xia et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)d box domain-containing protein (Song et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in the up-regulated DEGs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), and serine acetyltransferase (Ahmad et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in the down-regulated DEGs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The accumulation of ROS through abiotic stresses, including water deficiency in the plant body, inflicts damage on cellular components through peroxidation of membrane lipids and the oxidation of proteins, DNA and RNA (Choudhury et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, it is critical that plants protect themselves from harmful oxidants via detoxifying mechanisms, by using antioxidants and scavenging agents (Jubany-Mar\u0026iacute; et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Our results suggest that detoxification of ROS is an available acclimation response to short-term drought stress in \u003cem\u003eF. crenata\u003c/em\u003e seedlings.\u003c/p\u003e \u003cp\u003eBecause abscisic acid (ABA) is a well-known trigger of the drought signaling cascade (Aslam et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), previous studies have reported genes potentially related to ABA metabolism (Long et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the ABA response (Umezawa et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Our study identified an up-regulated DEG predicted to code for the B-box zinc finger protein (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), which regulates key genes related to ABA synthesis (\u003cem\u003eABA1\u003c/em\u003e, \u003cem\u003eABA2\u003c/em\u003e, \u003cem\u003eNCED3\u003c/em\u003e) and those related to the ABA transduction pathway (\u003cem\u003eHY5\u003c/em\u003e and \u003cem\u003eRD29A\u003c/em\u003e) in the apple (\u003cem\u003eMalus domestica\u003c/em\u003e) (Liu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). In addition, the homologous genes related to the ABA response under drought stress are found; serine hydroxymethyltransferase (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e), ninja-family protein AFP3-like (Rabara et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), serine/threonine-protein kinase SAPK3-like (Gao et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), MLO-like protein (Howlader et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In particular, serine hydroxymethyltransferase and MLO-like protein appear to relate to regulation of stomatal closure (Howlader et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e), which is in line with the increase seen in ABA concentration in leaves under drought stress conditions, to regulate stomatal closure and minimize water loss from the leaves (Umezawa et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These DEGs suggest that ABA-dependent responses lead to acclimation to short-term drought stress in the seedlings.\u003c/p\u003e \u003cp\u003eAvoidance of water loss from the plant body is often accompanied by changes in morphology and/or concentrations of biochemical molecules in the cells (Jaleel et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ghosh et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bawa et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our up-regulated DEGs encoded proteins involved with leaf morphology, including the transcription factor bHLH25-like (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), of which a homolog in rice is known to regulate positively the genes associated with cuticular wax biosynthesis, which mitigates water loss from the plant body (Gu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and xyloglucan endotransglucosylase/hydrolase in tomato, which is known to alter the cell-wall extensibility of guard cells mediated by cell-wall remodeling activity, positively regulating stomatal closure (Choi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). There were also several up-regulated DEGs whose homologs are known to produce enzymes that facilitate the synthesis of osmotic adjustment substances (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), such as gamma-glutamylcyclotransferase for 5-oxo-proline (Xu et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), choline monooxygenase for glycine betaine (Russell et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and NADP-dependent malic enzyme for malate (Sun et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The accumulation of these substances in the cells increases cellular osmolality, which drives an influx of water or reduces its efflux, thus maintaining cell turgor pressure (Seleiman et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A previous study has shown the accumulation of free-proline (a major osmoprotectant) in \u003cem\u003eF. crenata\u003c/em\u003e seedlings exposed to drought stress for 12 days (Ao et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is in line with the present finding that the gene encoding proline-rich protein was down-regulated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), as drought stress causes a drastic reduction in gene expression to avoid full use of the available proline molecules in plant cells, ensuring the availability of adequate proline molecules for osmolytes until their \u003cem\u003ede novo\u003c/em\u003e synthesis begins (Gujjar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs already shown for gamma-glutamylcyclotransferase, putative up-regulated DEGs encoding for proteins were inferred to regulate multiple acclimation processes (e.g. ROS detoxification, ABA metabolism and responses, and the production of osmoprotectants), including NAC domain-containing protein (Nakashima et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), ankyrin repeat-containing protein (Zhao et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and glucosyltransferase families (crocetin glucosyltransferase and 7-deoxyloganetin glucosyltransferase-like) (Liu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In particular, NAC is a well-known transcription factor (TF) located upstream of the stress-tolerance pathways that regulate downstream genes (Nakashima et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In plants, there are 11 major TFs (NAC, ERF, WRKY, bZIP, MYB, HD-ZIP, ZnF, bHLH, ASR, NF-Y, and HSF) that form a regulatory network in response to drought stress (Hu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our present study found DEGs encoding for bHLH (basic/helix-loop-helix) as well as NAC TFs, whereas Matsuda et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) identified beech homologs of \u003cem\u003eMYB\u003c/em\u003e genes with transcriptomes that varied with drought stress treatments over several hours. Given that the number of DEGs varies depending on the period and/or strength of drought stress [e.g. M\u0026uuml;ller et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in \u003cem\u003eF. sylvatica\u003c/em\u003e], more experiments are needed to clarify the regulatory network of TFs that target the downstream gene expression in response to drought stress in \u003cem\u003eF. crenata\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eGO enrichment and inference of metabolic processes in WS\u003c/em\u003e F. crenata \u003cem\u003eseedlings\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e The GO term \u0026ldquo;cellular response to sulfur deficiency\u0026rdquo; was significantly enriched in the up-regulated DEGs in our beech seedlings, in accordance with other reports suggesting drought stress inhibits plants\u0026rsquo; absorption of sulfur from soils (Lee et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sulfur starvation in the plant body has been reported to enhance the activities of several enzymes relating to sulfur assimilation, including ATP sulfurylase (Takahashi et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and 5\u0026prime;-adenylylsulfate reductase (an enzyme associated with the step following ATP sulfurylase in the sulfur assimilation process) (Takahashi et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). These enzymes are putatively encoded by the genes for FCV25MIE_22545 and FCV25MIE_32112 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), which are annotated by the GO term \u0026ldquo;sulfate assimilation\u0026rdquo; (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ATP sulfurylase, an enzyme in the first committed step of sulfur assimilation, is considered to be sensitive to redox regulation in plants (Prioretti et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and is negatively regulated by the concentration of cysteine, which is the final product of the sulfur assimilation process (Vauclare et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and that of reduced glutathione (GSH), which is a major redox regulator synthesized from cysteine (Lappartient \u0026amp; Touraine \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In our study, because the gene encoding serine acetyltransferase, an enzyme that is essential for synthesizing cysteine (i.e. the endpoint of sulfur assimilation), was found to be down-regulated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), cysteine concentrations would be low in the leaves, allowing the up-regulation of genes encoding ATP sulfurylase and 5\u0026prime;-adenylylsulfate reductase. In maize exposed to a restricted water supply for 12 days, leaves showed significant reductions in the activity of serine acetyltransferase, as well as cysteine concentrations, compared with those receiving a sufficient water supply, whereas there were substantial increases in cysteine concentrations, without the decrease in serine acetyltransferase activity, in the roots of the same plants (Ahmad et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Ahmad et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) point out that re-translocation of sulfur occurs from the shoots to the roots in the plant body, while sulfur compounds accumulate in the roots and enable development of the root system and ROS detoxification under drought conditions. Further research should consider simultaneous examination of the transcriptome in leaves and roots, as well as their comparison, to facilitate our understanding of the overall mechanism of acclimation response to drought stress in \u003cem\u003eF. crenata\u003c/em\u003e seedlings.\u003c/p\u003e \u003cp\u003eIn conclusion, we have identified putative genes encoding proteins associated with short-term drought stress in beech seedlings, from which positive regulators against the adverse effects driven by drought stress can be inferred, including ABA metabolism and response, ROS scavenging, osmotic adjustment, and stomatal closure. Functional categories of genes related to sulfur and its metabolic processes were also enriched in the leaf transcriptome of seedlings exposed to drought stress, which might have been involved in the shoot-to-root sulfur translocation as a result of drought stress acclimation. These genes, as well as their annotated functions, could be used to search for range-wide as well as local genetic variation in relation to functional traits, including acclimation potential to drought stress. This would provide crucial information for establishing a species-specific conservation policy within the context of a future warming climate.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eData Archiving Statement\u003c/h2\u003e \u003cp\u003eThe raw data generated in this study were archived in the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA) under accession number PRJDB15599.\u003c/p\u003e \u003ch2\u003eFundings\u003c/h2\u003e \u003cp\u003eThis research was funded by JSPS KAKENHI grant numbers 20H03027 and 20K06124.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eConceptualization: TT and HA; Methodology: TT and HA; Formal analysis and investigation: TT and HA; Writing - original draft preparation: TT and HA; Writing - review and editing: YA, SA, YO, YM, HK, and NT; Funding acquisition: TT and NT; Resources: YA, SA, YO, YM, and HK. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors are grateful to members of the Laboratory of Forest Conservation Ecology, Mie University, Tsu, Japan, for assistance in the field, and for their useful comments. Some computations were performed on the National Institute of Genetics (NIG) supercomputer at the Research Organization of Information and Systems (ROIS), National Institute of Genetics, Shizuoka, Japan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcevedo E, Hsiao TC, Henderson DW (1971) Immediate and subsequent growth responses of maize leaves to changes in water status. Plant Physiol 48:631-636. https://doi.org/10.1104/pp.48.5.631\u003c/li\u003e\n\u003cli\u003eAhmad N, Malagoli M, Wirtz M, Hell R (2016) Drought stress in maize causes differential acclimation responses of glutathione and sulfur metabolism in leaves and roots. 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Nat Commun 10:1523. https://doi.org/10.1038/s41467-019-09234-6\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Based on a literature survey, the (a) up-regulated and (b) down-regulated differentially expressed genes of \u003cem\u003eFagus crenata\u003c/em\u003e seedlings whose functions in response to abiotic stress can be inferred\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"906\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\" valign=\"bottom\"\u003e\n \u003cp\u003e(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eLocus_tag in the beech reference genome*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003ePutative protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\"\u003e\n \u003cp\u003eInvolvement in functions related to abiotic stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_12444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003eSerine hydroxymethyltransferase 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\"\u003e\n \u003cp\u003eROS scavenging and stomatal closure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\"\u003e\n \u003cp\u003eLiu et al. (2019b)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_07246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003eTranscription factor bHLH25-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\" rowspan=\"2\"\u003e\n \u003cp\u003eCuticular wax biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\" rowspan=\"2\"\u003e\n \u003cp\u003eGu et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.82045929018789%\"\u003e\n \u003cp\u003eFCV25MIE_33838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.1795407098121%\"\u003e\n \u003cp\u003eTranscription factor bHLH113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_16266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003eDNA damage-repair/toleration protein DRT102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\"\u003e\n \u003cp\u003eDNA repair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\"\u003e\n \u003cp\u003eLi et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_05115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003e5\u0026prime;-adenylylsulfate reductase 1 chloroplastic-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\" rowspan=\"2\"\u003e\n \u003cp\u003eROS-related defense pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\" rowspan=\"2\"\u003e\n \u003cp\u003ePark et al. (2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.82045929018789%\"\u003e\n \u003cp\u003eFCV25MIE_32112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.1795407098121%\"\u003e\n \u003cp\u003e5\u0026prime;-adenylylsulfate reductase 3 chloroplastic-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_08137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003eNinja-family protein AFP3-like isoform X1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\"\u003e\n \u003cp\u003eABA response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\"\u003e\n \u003cp\u003eRabara et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_22587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003eHomocysteine S-methyltransferase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\"\u003e\n \u003cp\u003eROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\"\u003e\n \u003cp\u003eQiu et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_25115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase SAPK3-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\"\u003e\n \u003cp\u003eABA metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\"\u003e\n \u003cp\u003eGao et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_12920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003eGamma-glutamylcyclotransferase 2-1-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\"\u003e\n \u003cp\u003eROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\"\u003e\n \u003cp\u003ede Mar\u0026iacute;a et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.880794701986755%\"\u003e\n \u003cp\u003eFCV25MIE_30298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.98896247240618%\"\u003e\n \u003cp\u003eAnkyrin repeat-containing protein At5g02620-like isoform X1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.26269315673289%\" rowspan=\"3\"\u003e\n \u003cp\u003eProline accumulation, antioxidant, and activation of stress-related TFs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.867549668874172%\" rowspan=\"3\"\u003e\n \u003cp\u003eZhao et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.82045929018789%\"\u003e\n \u003cp\u003eFCV25MIE_15802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.1795407098121%\"\u003e\n \u003cp\u003eAnkyrin repeat-containing protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.82045929018789%\"\u003e\n \u003cp\u003eFCV25MIE_30221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.1795407098121%\"\u003e\n \u003cp\u003eAnkyrin repeat and protein kinase domain-containing protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e \u003cem\u003eContinued\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"927\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eLocus_tag in the beech reference genome*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003ePutative protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\"\u003e\n \u003cp\u003eInvolvement in functions related to abiotic stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_28347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eNAC domain-containing protein 86-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\"\u003e\n \u003cp\u003eRegulation of osmoprotectant and antioxidants, and regulation of stress-tolerant genes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\"\u003e\n \u003cp\u003eNakashima et al. (2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_05204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eXyloglucan endotransglucosylase/hydrolase protein 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\"\u003e\n \u003cp\u003eStomatal closure via cell-wall remodeling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\"\u003e\n \u003cp\u003eChoi et al. (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_23256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eS-adenosylmethionine synthase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\"\u003e\n \u003cp\u003eROS scavenging and ABA metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\"\u003e\n \u003cp\u003eZhang et al. (2020b)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_00576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eAuxin-responsive protein SAUR68-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\"\u003e\n \u003cp\u003eRegulation of plant growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\"\u003e\n \u003cp\u003eBouzroud et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_17904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eAnnexin D3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\" rowspan=\"2\"\u003e\n \u003cp\u003eROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\" rowspan=\"2\"\u003e\n \u003cp\u003eKonopka-Postupolska et al. (2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.40372670807454%\"\u003e\n \u003cp\u003eFCV25MIE_17905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64.59627329192547%\"\u003e\n \u003cp\u003eAnnexin D4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_14061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eWall-associated receptor kinase-like 10 isoform X1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\" rowspan=\"2\"\u003e\n \u003cp\u003eRegulation of plant growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\" rowspan=\"2\"\u003e\n \u003cp\u003eZhang et al. (2020a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.40372670807454%\"\u003e\n \u003cp\u003eFCV25MIE_14564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64.59627329192547%\"\u003e\n \u003cp\u003eWall-associated receptor kinase-like 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_22545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eATP sulfurylase 1 chloroplastic-like isoform X1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\"\u003e\n \u003cp\u003eROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\"\u003e\n \u003cp\u003eAnjum et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_19833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eUDP-glycosyltransferase 74B1-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\"\u003e\n \u003cp\u003eABA response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\"\u003e\n \u003cp\u003eRabara et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.426724137931036%\"\u003e\n \u003cp\u003eFCV25MIE_27360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.62068965517241%\"\u003e\n \u003cp\u003eCrocetin glucosyltransferase chloroplastic-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.603448275862068%\" rowspan=\"2\"\u003e\n \u003cp\u003eABA accumulation, stomatal closure, ROS scavenging, accumulation of osmoprotectant, and up-regulation of stress-related gene expressions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349137931034484%\" rowspan=\"2\"\u003e\n \u003cp\u003eLiu et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.40372670807454%\"\u003e\n \u003cp\u003eFCV25MIE_15921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64.59627329192547%\"\u003e\n \u003cp\u003e7-deoxyloganetin glucosyltransferase-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e \u003cem\u003eContinued\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"920\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eLocus_tag in the beech reference genome\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003ePutative protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eInvolvement in functions related to abiotic stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_32676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003eMLO-like protein 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eABA response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\n \u003cp\u003eHowlader et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_23637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003ePhenylcoumaran benzylic ether reductase Betv6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\n \u003cp\u003eNiculaes et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_08783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003eAAA-atpase asd mitochondrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\n \u003cp\u003eXia et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_13390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003eU-box domain-containing protein 21-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\" rowspan=\"2\"\u003e\n \u003cp\u003eROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\" rowspan=\"2\"\u003e\n \u003cp\u003eSong et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.05154639175258%\"\u003e\n \u003cp\u003eFCV25MIE_20137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64.94845360824742%\"\u003e\n \u003cp\u003eU-box domain-containing protein 19-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_30527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003eCholine monooxygenase chloroplastic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eGlysine betaine production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\n \u003cp\u003eRussell et al. (1998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_12792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003eNADP-dependent malic enzyme isoform X2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eMalate production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\n \u003cp\u003eSun et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_33628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003eB-box zinc finger protein 32-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eABA response and ROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\n \u003cp\u003eLiu et al. (2019a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\" valign=\"bottom\"\u003e\n \u003cp\u003e(b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eLocus_tag in the beech reference genome*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003ePutative protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eInvolvement in functions related to abiotic stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_26236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003eSerine acetyltransferase 3 mitochondrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eROS scavenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\" valign=\"bottom\"\u003e\n \u003cp\u003eAhmad et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFCV25MIE_24515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.23913043478261%\"\u003e\n \u003cp\u003e14 kDa proline-rich protein DC2.15-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.869565217391305%\"\u003e\n \u003cp\u003eRegulation of proline production level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.41304347826087%\" valign=\"bottom\"\u003e\n \u003cp\u003eGujjar et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u0026nbsp;\u003c/sup\u003eObtained from our \u003cem\u003ede novo\u003c/em\u003e assembly, which has been registered in DDBJ\u0026nbsp;(accession no. BKZX02000001-BKZX02000625).\u003c/p\u003e\n\u003cp\u003eROS, reactive oxygen species.\u003c/p\u003e\n\u003cp\u003eABA, abscisic acid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Significantly enriched\u0026nbsp;gene ontology\u0026nbsp;(GO) terms in the\u0026nbsp;differentially expressed genes\u0026nbsp;(DEGs) up-regulated by drought stress in \u003cem\u003eFagus crenata\u003c/em\u003e seedlings\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"873\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eID\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.265750286368842%\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.689576174112258%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0893470790378%\"\u003e\n \u003cp\u003eLog\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.194730813287514%\"\u003e\n \u003cp\u003eLog\u003csub\u003e10\u003c/sub\u003e(FDR\u003csup\u003ec\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.049255441008018%\"\u003e\n \u003cp\u003eTest group\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.600229095074456%\"\u003e\n \u003cp\u003eReference group\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eGO:0010438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.265750286368842%\"\u003e\n \u003cp\u003eBiological processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.689576174112258%\"\u003e\n \u003cp\u003eCellular response to sulfur starvation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0893470790378%\"\u003e\n \u003cp\u003e-6.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.194730813287514%\"\u003e\n \u003cp\u003e-2.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.049255441008018%\"\u003e\n \u003cp\u003e3/70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.600229095074456%\"\u003e\n \u003cp\u003e3/10996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eGO:0000103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.265750286368842%\"\u003e\n \u003cp\u003eBiological processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.689576174112258%\"\u003e\n \u003cp\u003eSulfate assimilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0893470790378%\"\u003e\n \u003cp\u003e-5.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.194730813287514%\"\u003e\n \u003cp\u003e-1.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.049255441008018%\"\u003e\n \u003cp\u003e3/70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.600229095074456%\"\u003e\n \u003cp\u003e7/10996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e The GO terms detected were clustered based on their membership similarity (see also Supplementary Information Table S4), and those exhibiting the lowest significant log\u003csub\u003e10\u003c/sub\u003e(false discovery rate;\u0026nbsp;FDR) are shown here.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Based on the cumulative hypergeometric distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Based on the Benjamini-Hochberg procedure to account for multiple testing when a given \u003cem\u003eQ\u003c/em\u003e number of GO terms was typically identified in the reference genome, as follows. First, all \u003cem\u003ep\u003c/em\u003e-values were sorted from small to large. Second, given a \u003cem\u003ep\u003c/em\u003e-value of \u003cem\u003ep\u003c/em\u003e at rank \u003cem\u003ei\u003c/em\u003e, it would be expected that \u003cem\u003epQ\u003c/em\u003e GO terms could be found with the same or a better \u003cem\u003ep\u003c/em\u003e-value by chance under a Bonferroni correction. As we only observed \u003cem\u003ei\u0026nbsp;\u003c/em\u003esuch GO terms, the portion of our observations that were false (i.e. FDR) was min(\u003cem\u003epQ\u003c/em\u003e/\u003cem\u003ei\u003c/em\u003e, 1). See Table S6 for full list of FDR.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e The number of DEGs annotated by the corresponding GO term/the number of all DEGs annotated by GO terms.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003csup\u003ee\u003c/sup\u003e The number of genes annotated by the corresponding GO term in the reference genome/the number of all genes annotated by GO terms in the reference genome.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Mie University","isAcceptedByJournal":true,"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":"differentially expression gene, Fagus crenata seedlings, growth experiments, RNA-seq, soil drought, transcriptome analysis","lastPublishedDoi":"10.21203/rs.3.rs-4651558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4651558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite concern about the stress drought imposes on tree species under a warming climate, their molecular responses to drought stress have not been well-documented. We analyzed the transcriptional response of seedling leaves after exposure to short-term drought stress in \u003cem\u003eFagus crenata\u003c/em\u003e. After well-watered and water-stressed treatments, we mapped the RNA-seq reads derived from sampled leaves and identified 127 differentially expressed genes (DEGs), of which 89 were up- and 38 down-regulated in water-stressed plants. Several dozen up-regulated DEGs were predicted to encode proteins that would facilitate mitigating processes or avoid the adverse effects caused by drought stress, including stomatal closure, reactive oxygen species (ROS) scavenging, abscisic acid (ABA) accumulation and response, and osmoprotectants. The evidence of down-regulation in several genes in response to drought stress was in accordance with the results of a literature survey. The functional category of sulfate assimilation was enriched in up-regulated DEGs, although there was also evidence of sulfur deficiency in the DEGs. These results suggest the existence of molecular mechanisms in beech that are common in other plant species, representing an acclimation response to drought stress as well as sulfur metabolism under drought stress conditions. This information provides the basis for further species-specific functional genomic research within the context of a warming climate.\u003c/p\u003e","manuscriptTitle":"Mining differential gene expression in Fagus crenata seedlings in response to short-term soil drought stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 06:36:16","doi":"10.21203/rs.3.rs-4651558/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":"db1ff1fb-a957-4a60-b9c8-dcd41c8e54d9","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33839484,"name":"Forestry"}],"tags":[],"updatedAt":"2025-02-04T20:35:37+00:00","versionOfRecord":{"articleIdentity":"rs-4651558","link":"https://doi.org/10.1111/1442-1984.12506","journal":{"identity":"plant-species-biology","isVorOnly":true,"title":"Plant Species Biology"},"publishedOn":"2025-02-03 00:00:00","publishedOnDateReadable":"February 3rd, 2025"},"versionCreatedAt":"2024-07-02 06:36:16","video":"","vorDoi":"10.1111/1442-1984.12506","vorDoiUrl":"https://doi.org/10.1111/1442-1984.12506","workflowStages":[]},"version":"v1","identity":"rs-4651558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4651558","identity":"rs-4651558","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}