Transcriptome analysis of antioxidant system response in Styrax tonkinensis seedlings under flood- drought abrupt alternation

Transcriptome analysis of antioxidant system response in Styrax tonkinensis seedlings under flood- drought abrupt alternation | 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 Transcriptome analysis of antioxidant system response in Styrax tonkinensis seedlings under flood- drought abrupt alternation Hong Chen, Chao Han, Luomin Cui, Zemao Liu, Fangyuan Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3708391/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Styrax tonkinensis (Pierre) Craib ex Hartwich is a promising oil species with excellent fatty acid composition, making it a potential candidate for biofuel production. However, its expansion in the south provinces of Yangtze River region has been hindered by climate extremes such as flood-drought abrupt alternation (FDAA), which is caused by global warming. This species has low tolerance to waterlogging and drought, further restricting its growth in this region. To investigate the antioxidant system and the molecular response related to peroxisome pathway of S. tonkinensis under FDAA, we conducted FDAA and drought (DT) experiments on two-years old seedlings. We measured various growth indexes, reactive oxygen species content, the activity of two antioxidant enzymes and analyzed transcriptome of its seedlings under FDAA and DT conditions. Results The results displayed that the reduction in fresh weight was mainly observed in the leaves under FDAA condition. Through transcriptome analysis, we assembled a total of 1,111,088 unigenes (1,111,628,179 bp). We analyzed the differentially expressed genes (DEGs) related to reactive oxygen species (ROS) and antioxidant system. Generally, SOD1 and SOD2 genes in S. tonkinensis seedlings were upregulated to combat abiotic stresses. Our findings revealed that ROS accumulation was predominantly observed in leaves rather than roots under FDAA. Under FDAA circumstance, Protein Mpv17 ( MPV17 ) showed the opposite reaction in leaves and roots with upregulation and downregulation, respectively. Conclusions The ROS generation triggered by MPV17 genes was not the main reason for the eventual mortality of the plant. Instead, plant mortality may be attributed to water loss during the waterlogging phase, decreased root water uptake capacity, and continued water loss during the subsequent drought period. This study establishes a scientific foundation for comprehending the morphological, physiological, and molecular facts of S. tonkinensis under FDAA conditions. flood-drought abrupt alternation drought stress antioxidant system enzymes reactive oxygen species Figures Figure 1 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background Currently, climate change is resulting in the uneven precipitation patterns worldwide and climate extremes, further posing a consequential threat to human society and ecosystem [ 1 , 2 ]. The catastrophic events, including mega-floods, mega-droughts and drought-flood abrupt alternations, happen more frequently and intensively due to climate change [ 3 , 4 ]. In 2021, a significant number of 223 flood incidents were recorded globally, with notable occurrences in countries such as China, India, Afghanistan, and Germany. During the same timeframe, extensive droughts prevailed across North America, Africa, and Asia, leading to extended periods of aridity. Both disasters brought reductions in crop yields and significant economic losses [ 5 , 6 ]. In recent decades, China has experienced a series of severe natural disasters characterized by abrupt alternations between drought and flood, greatly influenced by the monsoon climate [ 7 , 8 ]. This new type of extreme hydrological event is known as drought-flood abrupt alternation (DFAA), which means alternating occurrence of two scenarios (droughts and floods) and the state transformation is speedy [ 9 ]. DFAA events perform as two situations, which are transitioning from drought to flood and from flood to drought [ 10 ], leading to more devastating impacts on socioeconomic loss and ecological destruction than a singular occurrence of drought or flood [ 11 ] . Previous DFAA studies are mainly focused on the spatial distribution, physical mechanism, water quality and the proper water resources distribution. Ma et al. [ 12 ] pointed out that DFAA events were becoming expanding in terms of spatial distribution from frequency and intensity aspects. In order to explore the physical mechanism of DFAA, experts discover that the degree of rainfall concentration is an essential reason for DFAA happening by determining the correlation between the DFAA and precipitation indexes [ 13 , 14 ]. Bi et al. [ 15 ] predicted the impacts of DFAA events on surface water quality data in Luanhe River basin for the future three decades. Their findings also revealed that DFAA events would reduce water quality by triggering total nitrogen and total phosphorous pollution. Huang et al. [ 16 ] reported that Guangzhou plain exhibited a prevalent and persistent arid climate throughout the entire year, coupled with a heightened vulnerability to prolonged wet conditions during the Summer-Autumn season. Besides, the frequency of DFAA events was higher during the summer months (June to August) compared to autumn or spring, with no occurrences observed during the winter season [ 15 ], indicating that the occurrence of DFAA events was seasonal. In addition to the hydro-meteorological studies on DFAA, some researchers were addicted to investigate the influences of the compound natural disaster from agricultural perspective. Rice ( Oryza sativa L.), being highly susceptible to the intricate interplay of water and temperature, emerges as the crop most profoundly impacted by DFAA. Consequently, rice has garnered significant research attention [ 2 , 10 , 17 ]. The average yield of rice under DFAA stress was reduced by 12.98% in 2016 and 29.94% in 2017, respectively [ 17 ]. Crop roots can be adversely affected by both water deficits and excess water in soils, stemming from drought and flood disasters. These conditions hinder the efficient absorption of water and essential nutrients by crop roots, consequently disrupting crop growth and reducing overall yield [ 18 ]. Both Xiong et al. [ 10 ] and Zhu et al. [ 2 ] explored the approach of rice yield recovery after DFAA via applying nitrogen. Furthermore, other significant grain and cash crops, such as cotton [ 18 ], wheat [ 19 ] and maize [ 7 ], have also been the subject of agricultural DFAA research. Nevertheless, the existing DFAA studies rarely pay attention to the impacts of DFAA events on tree species. In our study, S. tonkinensis , a deciduous tree species, was utilized as the experimental objective. S. tonkinensis is a valuable tree species known for its economic significance, primarily due to its oil extraction potential, medicinal properties, and ornamental value [ 20 – 22 ]. Researchers have extensively investigated the seeds of this plant due to their remarkably high oil content, focusing on their biodiesel properties, nutritional components, and the ultrastructure of the oil bodies [ 23 – 25 ]. The four primary free fatty acids found were palmitic acid, stearic acid, oleic acid, and linoleic acid. Within the compounds of flavonoids, the predominate components comprised of flavans, flavonoid glycosides, and o-methylated flavonoids [ 26 ]. Meanwhile, this versatile species is highly vulnerable to waterlogging stress, as evidenced by a 100% mortality rate of one-year-old seedlings after undergoing five days of flooding treatment [ 27 ]. It is mainly distributed in the southern China, especially in Yangtze River basin [ 28 ]. Affected by global climate change, the occurrence and strength of DFAA events have notably surged in the middle and lower sections of China's Yangtze River region. Interestingly, this region witnessed a sudden shift from flooding to drought conditions around mid-July, marked by a notable change in daily precipitation patterns [ 29 ]. In the present scenario, biennial S. tonkinensis seedlings were subjected to flood-drought abrupt alternation (FDAA) to observe the morphological, physiological, and molecular responses of the species. To discern the impact of FDAA, we established control groups (CK) and subjected some seedlings to drought stress (DT), allowing for a comprehensive comparison of each treatment. The primary objective of this study was to offer a theoretical foundation for the prospective extensive cultivation of S. tonkinensis in the Yangtze River basin. 2. Materials and Methods 2.1 Plant material and treatment The experimental seeds were collected from Pingxiang, Jiangxi Province, China in 2020, which was mentioned in the previously published article [ 27 ]. Professor Fangyuan Yu assisted in identifying the species in Pingxiang. A voucher specimen of this material has been deposited in Chinese Field Herbarium, Shanghai, China. After two years of cultivation, the treatments were started at 9 am at the end of June 2022. The experiment was divided into three treatments, which were: (1) CK - common water and nutrient management, (2) FDAA - waterlogging for two days and immediately turning to drought for seven days, (3) DT - drought stress for seven days. The organic matter condition and waterlogging approach were mentioned in the previously published article [ 27 ]. To ensure the consistency of sampling time, FDAA treatment was started two days in advance. Each treatment consisted of 30 seedlings. For each treatment, leaf and root samples for physiological and molecular determination were collected from 16 seedlings with destruction. The molecular samples were promptly frozen in liquid nitrogen and the physiological samples were put in ice bag. Subsequently, all of them were transferred to refrigerators at -80℃. The samples were named as CKL (leaf sample in control groups), FDAAL (leaf sample under flood-drought abrupt alternation), DTL (leaf sample under drought stress), CKR (root sample in control groups), FDAAR (root sample under flood-drought abrupt alternation), and DTR (root sample under drought stress) for transcriptome analysis. Four seedlings for each treatment were used for biomass determination. Four biological replicates were applied in the study. Besides, ten seedlings were used for observing and measuring seedling height (H) and ground diameter (D). 2.2 Seeding height, ground diameter and biomass The two measurements of H and D were taken 15 days part. For each treatment, ten random seedlings of S. tonkinensis were chosen to measure the H and D using a tape measure (accuracy of 0.1 cm) and a Vernier caliper (accuracy of 0.01 mm), respectively. Additionally, the height-diameter ratio was calculated. Seedlings were first separated into their respective parts: leaves, stems, and roots. These parts were then carefully arranged in envelopes and subjected to an oven treatment. The oven temperature was set to 105°C for 30 minutes initially. Subsequently, the temperature was adjusted to 70°C to facilitate the drying process until a constant weight was achieved. Finally, precise measurements of the dry weight of each part were taken using an electronic balance with an accuracy of 0.001 g. 2.3 The content of H 2 O 2 , O 2 − and MDA All physiological parameters were assessed using a Lambda 365 spectrometer (PerkinElmer, Waltham, Massachusetts, USA). The determination of hydrogen peroxide (H 2 O 2 ) content was carried out following the guidelines provided by the Hydrogen Peroxide assay kit. For this assay, 0.3 g of leaf or root tissue was extracted in 2.7 mL of normal saline. After centrifugation, 0.1 mL of the resulting supernatant was combined with the provided reagent, and the optical density (OD) was measured at 405 nm. Furthermore, the soluble protein content of each sample was measured to facilitate the subsequent calculation of H 2 O 2 content (mmol·gprot-1). The quantification of superoxide anion (O 2 − ) content and malondialdehyde (MDA) content followed the methods described by Ma et al. [ 30 ] and Cakmak and Horst [ 31 ], respectively. For this analysis, 0.3 g of leaf or root tissue was finely ground in 8 mL of pH 7.8 phosphate buffer solution (PBS) and then subjected to centrifugation. Subsequently, 1 mL of the resulting supernatant was mixed with 0.75 mL of PBS and 0.25 mL of hydroxylamine hydrochloride, and the mixture was placed in a 25 ℃ water bath for 20 minutes. Following this, 2 mL each of 17 mmol·L − 1 4-aminobenzenesulfonic acid and 7 mmol·L − 1 1-Naphthylamine naphthylamine were added to the solution. The sample was then incubated in a 30 ℃ water bath for 30 minutes, and OD at 530 nm was recorded. To determine the MDA content, 0.3 g of leaf or root tissue was extracted using 5 mL of 10% trichloroacetic acid (TCA) and then subjected to centrifugation. Next, 2 mL of the resulting supernatant was mixed with 4 mL of 0.6% thiobarbituric acid and boiled for 20 minutes. After the solution cooled to room temperature, OD at 450 nm, 532 nm, and 600 nm were recorded. These values were then utilized in the calculation to determine the MDA content. 2.4 Histochemical detection of H 2 O 2 and O 2 - In the study conducted by Kaur et al. [ 32 ], the localization of H 2 O 2 and O 2 − in leaf samples was investigated using histochemical detection methods. To visualize the location of H 2 O 2 , the leaves were immersed in a solution containing 3,3'-diaminobenzidine (DAB) while exposed to light for 12 hours at room temperature. To capture the location of O 2 − , a solution of 6 mM nitrozolium blue tetrachloride (NBT) mixed in sodium citrate buffer was utilized. Subsequently, the treated leaves were incubated at room temperature for a duration of 12 hours. Eventually, both leaf samples for histochemical detection of H 2 O 2 and O 2 − were transferred to ethanol and boiled at 100℃ to eliminate chlorophyll interference. To prevent dehydration, the treated leaves were then placed in a 20% glycerol solution. 2.5 Enzyme activities To assess superoxide dismutase (SOD; EC 1.15.1.1) activity, the supernatant used for analysis was obtained by grinding and centrifuging 0.3 g of leaf or root tissue in 8 mL PBS at pH 7.8 Then, 0.05 mL of the supernatant was subjected to a reaction with specific chemical reagents following the NBT-illumination method [ 33 ]. The absorbance at 560 nm (OD560 nm) was recorded, and SOD activity was expressed as U·g − 1 FW. In another extraction process, enzyme extract was obtained from the sample using a pH 7.0 buffer and 8 mL of PBS to measure catalase (CAT; EC 1.11.3.6) activity. The 0.02 mL of leaf supernatant and 0.1 mL of root supernatant was used for H 2 O 2 degradation, which was modified according to Ma et al. [ 30 ] . 2.6 RNA extraction and cDNA library construction RNA extraction from leaf and root samples was carried out using the Ambion Plant RNA Kit, adhering to the protocol recommended by the manufacturer. For the evaluation of RNA integrity, the Agilent 2100 Bioanalyzer manufactured by Agilent Technologies in Santa Clara, CA, USA, was employed for the analysis. Libraries were generated using the TruSeq Stranded mRNA LT Sample Prep Kit from Illumina, based in San Diego, CA, USA, in accordance with the manufacturer's instructions. 2.7 Quality control, de novo assembly and functional annotation Transcriptome sequencing and analysis were conducted by OE Biotech Co., Ltd. in Shanghai, China, utilizing the Illumina HiSeq 4000 Sequencing platform. The raw reads underwent processing with Trimmomatic [ 34 ] to eliminate reads containing poly-N and low-quality sequences, aiming to obtain clean reads. Trinity was used to assisting in de novo assembly of clean reads in the paired-end method [ 35 ], generating expressed sequence tag clusters (contigs) and transcripts. By comparing the length and similarity of transcript, the longest one for each cluster was chosen for subsequent analysis. To annotate unigenes function, they were aligned with databases such as the Swiss-Prot protein (SwissProt), clusters of orthologous groups (KOG), and evolutionary genealogy of genes: non-supervised orthologous groups (eggNOG) using basic local alignment search tool (BLAST) [ 36 ] with a threshold E-value of 10 − 5 . Functional annotations were assigned to the unigenes based on the proteins showing the highest sequence similarity. Furthermore, gene ontology (GO) classification was performed based on the SwissProt annotation, establishing the mapping relationship between SwissProt and GO terms. Additionally, the unigenes were mapped to the Kyoto encyclopedia of genes and genomes (KEGG) database [ 37 ] to annotate their potential metabolic pathways. 2.8 Differential expression analysis of unigenes and qRT-PCR analysis The DESeq2 method was employed to normalize the gene count data for each sample, and the expression level was estimated via the base mean value, represented as fragments per kilobase per million mapped reads (FPKM). Additionally, the fold change (difference multiple) was calculated, and the significance of the differences was assessed using the negative binomial (NB) distribution test. To identify the differentially expressed genes (DEGs), the results from the difference multiple and significance tests were used for screening, following the approach described by Love et al. [ 38 ]. DEGs were deemed statistically significant if they had a p-value less than 0.05 and | log2FC | greater than 1, as the method proposed by Anders and Huber [ 39 ] . For validation of the RNA-seq results, a subset of transcripts ( MPV17, PMP34, PEX3, PEX14, SOD1, SOD2, CAT, POD ) associated with the antioxidant system were selected and verified. The primers for each of the DEGs were provided in Tab.S1. The quantitative real-time PCR (qRT-PCR) reactions were performed on a StepOne Real-Time PCR System utilizing SYBR Green Dye from Applied Biosystems (Foster City, USA) and Takara (Dalian, China). The 2^ −ΔΔCt method with 18S ribosomal RNA serving as an internal control was applied to determine the relative gene expression. 2.9 Statistical analysis The data analysis comprised initial basic descriptive analysis, followed by an analysis of variance (ANOVA) to assess the differences between groups. Subsequently, Duncan and Pearson R correlation tests were conducted using SPSS version 23.0 for Windows (SPSS Science, Chicago, IL, USA). In evaluating significance between treatments, p-values less than 0.05 were considered indicative of statistically significant differences. 3. Results 3.1 Impacts of FDAA on the growth of S. tonkinensis As shown in Fig. S1 , the stems and twigs of S. tonkinensis seedlings became curved and the leaves dropped under both FDAA and DT stress. The experimental period was during the rapid growth period of S. tonkinensis seedlings. S. tonkinensis seedlings under normal water and fertilizer management (CK) grew rapidly, with an increment of 13.83% in H and 5.41% in D. However, both FDAA and DT treatments inhibited the growth of S. tonkinensis seedlings, as evidenced by reduced H and D. The growth of H and D in the FDAA treatment was inhibited by 2.2 cm (3.48%) and 0.31 mm (4.56%), respectively. Compared to FDAA, DT stress caused even greater growth reductions in seedling height and ground diameter (Table 1 ). Regarding biomass, both FDAA and DT treatments significantly inhibited fresh weight growth, including root, stem, leaf, and total biomass. In the FDAA treatment, the reduction in fresh weight growth was mainly observed in the leaves, decreasing from 8.074 ± 0.49a g to 2.257 ± 0.42b g, as compared to the CK. However, DT resulted in greater fresh weight loss in roots and stems than FDAA, especially in the roots, which experienced almost a 50% reduction. As for dry weight, no noteworthy distinctions were noted among the treatments (Table 2 ). Table 1 The variation of seedlings height and ground diameter of S. tonkinensis in response to FDAA and DT between pre-treatment and post-treatment. Values are mean ± SD, n = 4. Different lowercase letters within each treatment indicate significant differences ( P < 0.05). Treatment Pre-treatment Post-treatment Seedling height (cm) Ground diameter (mm) Height-diameter ratio (%) Seedling height (cm) Ground diameter (mm) Height-diameter ratio (%) CK 59.3 ± 2.30a 6.65 ± 0.18a 89.05 ± 2.11a 67.5 ± 2.29a 7.01 ± 0.19a 96.37 ± 1.98a FDAA 63.2 ± 2.50a 6.80 ± 0.33a 94.03 ± 3.86a 61.0 ± 2.35ab 6.49 ± 0.30a 95.02 ± 3.97a DT 62.9 ± 3.04a 6.80 ± 0.37a 93.45 ± 3.59a 59.6 ± 2.40b 5.73 ± 0.26b 105.06 ± 24.41a Table 2 The root, stem, leaf and total biomass of S. tonkinensis in response to FDAA and DT between pre-treatment and post-treatment. Values are mean ± SD, n = 4. Different lowercase letters within each treatment indicate significant differences ( P < 0.05). FW, estimated fresh weight; DW, estimated dry weight. Treatment Fresh Weight (g) Dry Weight (g) Root Stem Leaf Total Root Stem Leaf Total CK 5.314 ± 0.38a 7.794 ± 0.41a 8.074 ± 0.49a 21.182 ± 0.55a 1.408 ± 0.12a 2.110 ± 0.08a 1.896 ± 0.12a 5.414 ± 0.14a FDAA 3.254 ± 0.20b 5.936 ± 0.44b 2.257 ± 0.42b 11.448 ± 0.85b 1.203 ± 0.15a 2.427 ± 0.19a 1.682 ± 0.21a 5.321 ± 0.53a DT 2.822 ± 0.26b 5.272 ± 0.40b 2.671 ± 0.25b 10.765 ± 0.38b 1.761 ± 0.39a 2.549 ± 0.23a 2.254 ± 0.24a 6.564 ± 0.78a 3.2 Impacts of FDAA on ROS and lipid peroxidation of S. tonkinensis Under FDAA and DT stress, the O 2 − content in both leaves and roots was elevated compared to the CK (Fig. 1 A). Histochemical detection of O 2 − (Fig. 1 A) further confirmed that the leaves in the FDAA group were most severely attacked by O 2 − with a concentration of 36.97 ± 3.40a µg·g − 1 FW, which was over twice as much as that in the CK group. The blue dyeing on leaves of the DT group also indicated an increase in O 2 − content level (22.82 ± 2.39a µg·g − 1 FW). Moreover, the H 2 O 2 content in leaves increased significantly under both FDAA and DT stress, with separate increments of 21.76 mmol·gprot − 1 FW and 23.36 mmol·gprot − 1 FW (Fig. 1 B), as also evidenced by the brown dots on leaves in Fig. S1 . In general, both O 2 − and H 2 O 2 contents in roots increased slightly under FDAA and DT stress, but without significant differences when compared to the CK. Furthermore, compared to the CK, the leaves in both FDAA and DT groups experienced severe lipid peroxidation, indicated by the dramatic elevation in MDA content. However, the variation in MDA content in roots among treatments was not significant (Fig. 1 C). 3.3 Impacts of FDAA on two antioxidant enzymes of S. tonkinensis Generally, DT led to the highest SOD activity in leaves (763.76 ± 21.83a U·g − 1 FW) and roots (284.58 ± 16.14a U·g − 1 FW) compared to other two treatments. The SOD activity in leaves and roots of FDAA was 20.85% and 52.49% lower than that of DT, respectively. Compared to CK, FDAA not only improved the SOD activity in leaves but also in roots (Fig. 2 A). Regarding CAT activity, FDAA stress contributed to the maximal CAT activity in both leaves (794.44 ± 63.55a U·g − 1 ·min − 1 FW) and roots (158.22 ± 2.87a U·g − 1 ·min − 1 FW). The CAT activity of DT was slightly lower than that of FDAA in two organs, but still higher than that of CK (Fig. 2 B). 3.4 Quality control, de novo assembly, and total gene expression After the completion of transcriptome sequencing for 24 samples, a cumulative total of 156.98 G of high-quality data were acquired. The individual sample datasets exhibited effective data sizes spanning from 5.97 to 6.81 G, with Q30 bases accounting for a range of 93.07–93.66%. Moreover, the collective average GC content was measured at 46.99%, as detailed in Tab.S2. The assembly process resulted in the creation of 1,111,088 distinct unigenes, with an overall length of 1,111,628,179 bp and an average length of 1004.86 bp, as outlined in Tab.S3. The dataset consisted of sequences spanning lengths between 301 to 400, with the highest count of sequences (30,690) falling within this range. Additionally, there were 14,355 sequences with lengths exceeding 2,000, which secured the second-highest count (Fig. S2 B). Validation of the FPKM values was conducted and is visually depicted in Fig. S2 A. For the determination of FPKM values across 24 samples derived from CK, DT, and FDAA groups, the DESeq2 method was employed. The distribution of FPKM values among these 24 samples is illustrated in Fig. S3 . 3.5 Functional annotation and classification The BLAST program against five publicly accessible protein databases was applied to elucidate and characterize potential functions, employing a threshold E-value of 10 − 5 . The results revealed substantial matches with known proteins in the SwissProt, KEGG, KOG, eggNOG, and GO databases, yielding a total of 50,402 (45.37%), 18,284 (16.46%), 39,673 (35.71%), 64,050 (57.66%), and 44,622 (40.17%) annotated unigenes, respectively. A comprehensive total of 44,622 assembled unigenes were systematically categorized across three principal functional domains in GO framework. These domains encompassed biological processes (37,140 unigenes, 83.23%), cellular components (40,281 unigenes, 82.35%), and molecular functions (38,829 unigenes, 87.02%) as depicted in Fig. 3 A. The biological process category was further subdivided into 23 distinctive sub-categories. Among these, the two most prominently represented sub-categories were "cellular process" and "metabolic process," housing a substantial 30,585 unigenes (69.65%) and 25,362 unigenes (68.29%) respectively. Within the cellular component category, allocation to 14 sub-categories transpired. The preponderance of unigenes were affiliated with the "cell" category (37,026 unigenes, 91.92%), closely followed by the "cell part" category (36,948 unigenes, 91.73%). Meanwhile, the molecular function domain exhibited a distribution across 16 sub-categories. Notably, the two most prevailing sub-categories were "binding" (25,937 unigenes, 66.80%) and "catalytic activity" (22,935 unigenes, 59.07%). A total of 18,284 unigenes were categorized into five KEGG categories, 29 sub-categories, and 136 KEGG pathways (Fig. 3 B). In “environmental information processing” category, the “signal transduction” pathway (743 unigenes, 4.06%) may be related to S. tonkinensis responding to FDAA and DT stress. In “metabolism” category, a total of 3,544 unigenes (19.38%) were assigned in the “carbohydrate metabolism” pathway, followed by “amino acid metabolism” (1,951 unigenes, 10.67%) and “energy metabolism” (1,779 unigens, 9.73%) pathways. A comprehensive count of 39,673 unigenes underwent allocation across 25 KOG classifications, with the greatest representation observed in the "general function prediction only" category (7,509 unigenes, 18.93%). This was pursued by a notable presence in the "posttranslational modification, protein turnover, chaperones" category (4,871 unigenes, 12.28%), and subsequently in the "signal transduction mechanisms" category (3,728 unigenes, 9.40%) as visually represented in Fig. 3 C. Moreover, “signal transduction mechanisms” might be connected to the response of S. tonkinensis to FDAA and DT stress. 3.6 Analysis of gene expression To explore the expression patterns of differently expressed genes (DEGs) and specific pathways under FDAA and DT stress, the transcriptome profiles from treatments were compared. Compared to CKL, FDAAL possessed 2,251 up-regulated DEGs and 2,390 down-regulated DEGs. A total of 7,012 DEGs and 9,304 DEGs were positively regulated and negatively regulated in FDAAL VS DTL group, respectively. DTR had over 15,000 up-regulated DEGs when compared to CKR. Furthermore, the leaves of S. tonkinensis exhibited higher number of down-regulated DEGs compared to the roots, regardless of the treatment conditions (Fig. 4 ). The DEGs related to ROS and antioxidant system were analyzed, and their FPKMs were verified by qRT-PCR in Fig. S4 . Under FDAA condition, peroxin-3 ( PEX3 ) in leaves of S. tonkinensis exhibited the highest expression level (18.60 ± 1.38a), and showed a significant difference compared to other samples. Compared to CKL, PEX3 in DTL was apparently down-regulated. No significant differences were observed among the root samples. The FPKM of PMP34 in FDAAL (41.87 ± 22.74a) went up slightly, while it in DTL (13.28 ± 2.39b) was reduced massively when compared to CKL (38.83 ± 5.58a). In the case of CKR, both FDAAR and DTR declined the PMP34 FPKM as an adaptation to abiotic stresses. Both leaves and roots of S. tonkinensis improved SOD1 and SOD2 expression level to scavenge ROS. FDAA stress was more inclined to trigger SOD1 expression while DT stress tended to induce SOD2 expression. For leaves, the variation of CAT expression was unobvious. However, DT stress induced the highest FPKM (627.11 ± 87.14a) of CAT in the roots. In addition, DT stress triggered the expression of POD no matter in leaves or roots of S. tonkinensis . On the contrary, FDAAL and FDAAR showed lower FPKM values of POD than that of CKL and CKR, respectively. In general, drought stress contributed to the most varied expression of DEGs in the peroxisome pathway in the roots, including 40 types of DEGs. In the case of Protein Mpv17 ( MPV17 ), which encodes a peroxisomal protein to produce ROS, was up-regulated with one unigene in leaves, while down-regulated with one unigene in roots under FDAA. Under DT stress, three DEGs of MPV17 were negatively regulated on the average in leave. On the contrary, six DEGs of MPV17 were all positively regulated in roots (Fig. 5 ). Through KEGG enrichment analysis, the “flavonoid biosynthesis” pathway was most enriched (3.86 enrichment score) with hitting 12 DEGs in the leaves of S. tonkinensis under FDAA stress. Furthermore, the “Plant hormone signal transduction” pathway and “MAPK signaling pathway – plant” pathway ranked 4th and 7th in terms of enrichment scores, respectively, and they were associated with abiotic adaption. For roots, FDAA stress induced the “photosynthesis - antenna proteins” pathway most enriched (20.63 enrichment score) with 28 DEGs, followed by the “photosynthesis” pathway with 12.84 enrichment score via hitting 50 DEGs. Concerning DT stress, the leaves of S. tonkinensis in DTL VS CKL had the same most enrichment pathways in FDAAR VS CKR. In DTR VS CKR, the “photosynthesis - antenna proteins”, “aflatoxin biosynthesis” and “photosynthesis” pathways ranked top3. The “peroxisome” pathway acquired 1.16 enrichment score with 106 DEGs when comparing FDAAR and DTR samples. In general, FDAA and DT stress obviously affected the enrichment of the “photosynthesis - antenna proteins” and “photosynthesis” pathways to different extent. Furthermore, the pathways related to environmental adaption were also enriched, such as the “Plant hormone signal transduction” and “MAPK signaling pathway – plant” pathways (Fig. 6 ). As displayed in Fig. 7 , no matter in FDAA stress or DT stress, the DEGs in the “response to stimulus” (biological process) and “antioxidant activity” (molecular function) pathways were triggered obviously. 4. Discussion FDAA, which stands for the combination of two abiotic stresses, leads to drought damage in plants following waterlogging destruction. To comprehensively investigate how S. tonkinensis seedlings respond to FDAA, we analyzed morphological variations, growth conditions, ROS generation, antioxidant enzyme activity, relative gene expression, and key DEGs associated with antioxidant activity. 4.1 Impacts of FDAA on the morphological variation and growth condition of S. tonkinensis The study was conducted during the growing season for S. tonkinensis seedlings. Under normal water management, these seedlings exhibited rapid growth, with a 13.83% increase in H and a 5.41% increase in D. Nevertheless, the growth of S. tonkinensis seedlings was significantly inhibited by FDAA and DT treatments. Notably, DT stress had a more pronounced inhibitory effect on H and D, reducing them by 5.25% and 15.74%, respectively. Ünyayar et al. [ 40 ] reported that drought stress led to a decline in shoot growth in drought-sensitive Lycopersicon peruvianum . Drought stress impaired the shoots growth and roots growth of potato [ 41 ]. In addition, the growth and development of cotton was hindered by waterlogging, due to the obstacle to absorbing water and nutrient [ 42 ]. FDAA combines the waterlogging and drought stress, which might have superimposed obstruction for the growth of S. tonkinensis seedlings. DFAA stress also had a negative effect on rice growth, eventually reducing the rice yield[ 17 ]. In terms of the morphological changes, the stems tended to bend, and the leaves exhibited drooping due to water loss. Based on the biomass results, both FDAA and DT stresses not only decreased the total fresh weight but also reduced the fresh weight of each organ. The reduction in absolute water content was particularly concentrated in the leaves exposed to FDAA, while DT stress resulted in water loss primarily in the stems and roots. It can be inferred that FDAA represents a form of superimposed damage for S. tonkinensis seedlings. During the waterlogging period, the water absorption capacity of roots was inhibited, hindering vertical water transport to stems and leaves. Simultaneously, transpiration continued, resulting in water loss from leaves [ 43 ]. Subsequently, drainage would provide temporary relief for S. tonkinensis seedlings [ 44 ]. However, the following drought stress led to continuous water loss from leaves. As for the dry weight, no obvious difference occurred between treatments, indicating that the short-term FDAA and DT stresses had slight effects on the accumulation of dry biomass. 4.2 Impacts of FDAA on the ROS generation and antioxidant enzyme activity of S. tonkinensis ROS are molecules characterized by high reactivity that contain oxygen atoms and are generated as by-products of various cellular processes. These ROS play important roles in cell signaling and defense mechanisms but can also be toxic when their levels exceed the cellular capacity to detoxify them [ 45 , 46 ]. ROS are generated in different cellular sites within plant cells, including peroxisomes, mitochondria, chloroplasts, and the apoplast [ 46 ]. Under abiotic stress conditions, accumulation of an excess of ROS occurs as a result of electron leakage from complexes I and III, resulting in the generation of O 2 − . This O 2 − is subsequently catalyzed by Mn-SOD and Cu/Zn-SOD to produce H 2 O 2 [ 47 ]. Peroxisomes in various plant species house notable types of SODs, including Cu/Zn-SOD and Mn-SOD, establishing them as crucial locations for H 2 O 2 production [ 48 , 49 ]. In non-photosynthetic plant organs, especially in roots, mitochondria are frequently regarded as the primary sites for the generation of ROS. This is because roots rely on mitochondrial respiration for energy production [ 45 ]. Zheng et al. [ 50 ], He et al. [ 51 ], and Da-Silva and do Amarante [ 52 ] all provided evidence that waterlogging, a condition where plant roots were submerged in water for an extended period, leading to an elevation in concentrations of ROS in watermelon, cucumber and soybean. Drought stress can also cause the imbalance between ROS and antioxidant ability, further generating excessive ROS and leading to leaf senescence [ 53 ]. Under drought stress, ROS was accumulated to a high level in Arabidopsis [ 54 ]. In our study, both FDAA and DT stress conditions led to a noteworthy rise in the concentrations of O 2 − and H 2 O 2 in the leaves of S. tonkinensis seedlings. These ROS are associated with oxidative stress and can have detrimental effects on plant cells [ 55 ]. While there was a slight increase in O 2 − and H 2 O 2 content in the roots as well, these increases were not statistically significant compared to CK, reflecting that the leaves of the plants were more severely affected by oxidative stress in response to FDAA and DT stress. It appeared that FDAA was causing an increase in the generation of ROS, particularly in the leaves of S. tonkinensis seedlings, due to the ROS harm could be superimposed. Noctor [ 49 ] claimed that photorespiration produced the majority of H 2 O 2 under drought stress circumstances. The antioxidant system preserves plants from oxidative damage under diverse environmental stresses [ 46 ]. Under waterlogging and drought stress, the antioxidant system helps to scavenge ROS that accumulate in plant tissues due to limited oxygen availability and water deficit, respectively [ 56 , 57 ]. The antioxidant enzymes are upregulated to detoxify ROS and maintain cellular redox homeostasis [ 58 ]. SOD and CAT are two important enzymes involved in the defense mechanisms of plants against drought and flooding stress [ 59 , 60 ]. SOD plays a crucial role in scavenging ROS generated during drought stress. It converts superoxide radicals into H 2 O 2 , which is then detoxified by CAT [ 61 ]. In summary, SOD plays a crucial role in scavenging ROS, while CAT aids in the detoxification of H 2 O 2 , thereby maintaining the balance between ROS production and scavenging. In our study, it was observed the elevated activities of SOD and CAT in two organs of S. tonkinensis seedlings under FDAA and DT stress conditions. These findings were consistent with previous research on strawberries, where an increase in SOD and CAT activities in strawberry leaves exposed to drought stress [ 62 ]. Similar responses were also observed in Bupleurum chinense under drought stress [ 63 ]. In a study conducted on potato genotypes under water deficit conditions, it was observed that the activity of SOD, including Fe-SOD isoforms, resulted in an enhancement of water use efficiency (WUE) [ 64 ]. Bansal and Srivastava [ 65 ] also discovered that waterlogging triggered an increase in CAT and SOD activities in Cajanus cajan . Furthermore, the cultivar with higher waterlogging-resistance or drought resistance exhibit higher antioxidant enzyme activity [ 66 , 67 ]. Besides, a drought-resistant variety exhibits a more effective mechanism for scavenging ROS, as evidenced by a significant boost in the activity of the antioxidant enzyme SOD [ 66 ]. Compared to FDAA stress, SOD activities in leaves and roots were higher under DT condition. However, CAT activities presented the opposite performance. Combined the ROS condition, it was inferred that SOD activity was inhibited by FDAA stress to scavenge less O 2 − in leaves of S. tonkinensis seedlings, which was displayed in Fig. 2 A and Fig. S1 . Additionally, the higher CAT activity was accord with lower H 2 O 2 content (Fig. 1 B and Fig. S1 ), due to the essential role of CAT in breaking down H 2 O 2 into water and oxygen [ 61 ]. 4.3 Impacts of FDAA on the key DEGs related to peroxisome pathway of S. tonkinensis Multiple transcriptomic investigations have demonstrated a robust correlation between peroxisomal H 2 O 2 and oxidative stress. This suggests that the balance of redox homeostasis, which is connected to the NAD and NADP systems, could potentially regulate this interaction [ 68 – 70 ]. Three genes in Arabidopsis thaliana , encoding catalase have been discovered. The expression of catalase-2 ( CAT2 ) is linked to the photorespiration pathway, while catalase-1 ( CAT1 ) expression is connected to fatty acid β-oxidation. catalase-3 ( CAT3 ), on the other hand, is associated with senescence processes [ 71 , 72 ]. In the study, the expression levels of CAT were elevated in the leaves and roots of S. tonkinensis seedlings under both FDAA and DT stresses, except for the roots under FDAA condition, which was not obvious DEGs. In rice chloroplasts, the overexpression of a pea manganese SOD gene ( MnSOD ) controlled by an oxidative stress-inducible promoter SWPA2 has been found to enhance the drought tolerance of transgenic rice [ 73 ]. In general, Cu/Zn family superoxide dismutase ( SOD1 ) and Fe/Mn family superoxide dismutase ( SOD2 ) genes in S. tonkinensis seedlings were upregulated to combat abiotic stresses. Nevertheless, SOD2 presented obviously downregulated in the leaves under DT condition. In A. thaliana plants, exposure to salt stress leads to the upregulation of three peroxisome-associated genes: thiolase (PED1 ), peroxin-10 ( PEX10 ), and peroxin-1 ( PEX1 ) [ 74 ]. In S. tonkinensis seedlings, peroxin-11 ( PEX11 ) gene, which medicates peroxisome proliferation was obviously upregulated in roots after FDAA treatment [ 75 ]. Furthermore, PEX3 in leaves of S. tonkinensis exhibited the highest expression level (18.60 ± 1.38a), and showed a significant difference compared to other samples. However, the expression level of peroxin-14 ( PEX14 ) gene displayed the opposite trend in leaves under DT stress. MVP17 is able to encode a peroxisomal protein producing ROS, which might regulate the activity of antioxidant enzymes [ 76 , 77 ]. Under FDAA circumstance, MPV17 genes showed the opposite reaction in leaves and roots with upregulation and downregulation, respectively. S. tonkinensis seedlings exhibited varied gene expression patterns in response to abiotic stresses, which subsequently influenced various physiological parameters. The morphological changes in leaves, roots, and overall seedlings served as tangible reflections of how S. tonkinensis seedlings responded to these stresses (Fig. 8 ). In more specific terms, the downregulation of the MPV17 gene in the roots of S. tonkinensis seedlings resulted in reduced ROS production. Under DT stress, three DEGs of MPV17 were negatively regulated on the average in leaves of S. tonkinensis seedlings. On the contrary, six DEGs of MPV17 were all positively regulated in roots of S. tonkinensis seedlings (Tab.S4). This suggests that excessive ROS production in the peroxisomes of the roots was not the primary cause of root necrosis under FDAA conditions. It can be inferred that ROS-induced damage primarily accumulated in the leaves of S. tonkinensis seedlings during FDAA stress. However, the roots seemed to undergo some recovery during the transition from waterlogging to drought stress, enabling them to engage in aerobic respiration. When considering the biomass index, it becomes evident that substantial water loss, particularly in the leaves, may be the primary factor contributing to the eventual demise of S. tonkinensis seedlings under FDAA conditions. This is because the cumulative damage resulting from water loss exacerbates the stress on the seedlings. 5. Conclusion The entire study progressively reveals the responses of S. tonkinensis seedlings to rapid shifts from waterlogging to drought stress, examining their responses at the morphological, physiological, and molecular levels. Additionally, it compares the effects of DT stress and FDAA stress on the seedlings. Furthermore, we observed that the accumulation of ROS induced by waterlogging and drought stress during this rapid transition is additive, with the primary damage occurring predominantly in the leaf tissues. The ultimate cause of plant mortality may be attributed to water loss during the waterlogging phase, diminished root water uptake capacity, and continued water loss during the subsequent drought period. Abbreviations ANOVA: analysis of variance; BLAST: basic local alignment search tool; CAT: catalase; CAT1 :catalase-1; CAT2 : catalase-2; CAT3 : catalase-3; CK: control groups; CKL: leaf sample in control groups; CKR: root sample in control groups; D: ground diameter; DAB: 3,3'-diaminobenzidine; DEGs: differentially expressed genes; DT: drought stress; DTL: leaf sample under drought stress; DTR: root sample under drought stress; eggnog: evolutionary genealogy of genes: non-supervised orthologous groups; FDAA: flood-drought abrupt alternation; FDAAL: leaf sample under flood-drought abrupt alternation; FDAAR: root sample under flood-drought abrupt alternation; FPKM: fragments per kilobase per million mapped reads; GO: gene ontology; H: seedling height; H 2 O 2 : hydrogen peroxide; KEGG: Kyoto encyclopedia of genes and genomes; KOG: clusters of orthologous groups; MDA: malondialdehyde; MnSOD : manganese SOD gene; MPV17 :Protein Mpv17; NB: negative binomial; NBT: nitrozolium blue tetrachloride; O 2 - :superoxide anion; PBS: phosphate buffer solution; PED1 :thiolase; PEX1 :peroxin-1; PEX10 : peroxin-10; PEX11 :peroxin-11; PEX14 :peroxin-14; PMP34 : peroxisomal adenine nucleotide transporter; ROS: reactive oxygen species; SOD: superoxide dismutase; SOD1 :Cu/Zn family superoxide dismutase ; SOD2 :Fe/Mn family superoxide dismutase; SwissProt: Swiss-Prot protein; TCA: trichloroacetic acid. Declarations Acknowledgments We would like to acknowledge Chen Chen, Ming Ni and Guangtao Zhang for their experimental assistance. Authors’ contributions HC and FY designed the research. HC carried out all experiments, analyzed the data and wrote manuscript. HC, LC and ZL assisted at conducting experiments, analyzing data. FY guided in editing manuscript. The authors read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China [Grant number 3197140894] and Jiangsu Graduate Scientific Research Innovation Project [Grant number KYCX21_0914]. Availability of data and materials The RNA transcripts accession number is: CRA012350 , which can be found in the following link: https://bigd.big.ac.cn/gsa/browse/CRA012350 Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Fabian PS, Kwon HH, Vithanage M, Lee JH. Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: a review. Environ Res. 2023;225:115617. Zhu J, Li A, Zhang J, Sun C, Tang G, Chen L, et al. Effects of nitrogen application after abrupt drought-flood alternation on rice root nitrogen uptake and rhizosphere soil microbial diversity. Environ Exp Bot. 2022;201:105007. Hochman A, Marra F, Messori G, Pinto JG, Raveh-Rubin S, Yosef Y, et al. Extreme weather and societal impacts in the eastern Mediterranean. Earth Syst Dynam. 2022;13:749–77. Nakamura T, Sato T. A possible linkage of Eurasian heat wave and East Asian heavy rainfall in Relation to the rapid arctic warming. Environ Res. 2022;209:112881. Van Camp M, Viron O, Dassargues A, Delobbe L, Chanard K, Gobron K. Extreme hydrometeorological events, a challenge for gravimetric and seismology networks. Earths Future. 2022;10. Williams AP, Cook BI, Smerdon JE. Rapid intensification of the emerging southwestern North American megadrought in 2020–2021. Nat Clim Chang. 2022;12:232–4. Bi W, Weng B, Yan D, Wang M, Wang H, Jing L, et al. Soil phosphorus loss increases under drought-flood abrupt alternation in summer maize planting area. Agr Water Manage. 2022;262:107426. Shi W, Huang S, Zhang K, Liu B, Liu D, Huang Q, et al. Quantifying the superimposed effects of drought-flood abrupt alternation stress on vegetation dynamics of the Wei River Basin in China. J Hydrol. 2022;612:128105. Yang Y, Weng B, Bi W, Xu T, Yan D, Ma J. Climate change impacts on drought-flood abrupt alternation and water quality in the Hetao Area, China. Water. 2019;11:652. Xiong Q, Zhong L, Du J, Zhu C, Peng X, He X, et al. Ribosome profiling reveals the effects of nitrogen application translational regulation of yield recovery after abrupt drought-flood alternation in rice. Plant Physiol Bioch. 2020;155:42–58. Zhang Y, You Q, Ullah S, Chen C, Shen L, Liu Z. Substantial increase in abrupt shifts between drought and flood events in China based on observations and model simulations. Sci Total Environ. 2023;876:162822. Ma Y, Yang Y, Wang C. How essential of the balance between large and small scale features to reproduce precipitation during a sudden sharp turn from drought to flood. Clim Dyn. 2019;52:5013–29. Duan W, Zou S, Christidis N, Schaller N, Chen Y, Sahu N, et al. Changes in temporal inequality of precipitation extremes over China due to anthropogenic forcings. npj Clim Atmos Sci. 2022;5:33. Zhou P, Liu Z. Likelihood of concurrent climate extremes and variations over China. Environ Res Lett. 2018;13:094023. Bi W, Weng B, Yuan Z, Yang Y, Xu T, Yan D, et al. Evolution of drought–flood abrupt alternation and its impacts on surface water quality from 2020 to 2050 in the Luanhe River Basin. IJERPH. 2019;16:691. Huang S, Hou B, Chang J, Huang Q, Chen Y. Copulas-based probabilistic characterization of the combination of dry and wet conditions in the Guanzhong Plain, China. J Hydrol. 2014;519:3204–13. Gao Y, Hu T, Wang Q, Yuan H, Yang J. Effect of drought-flood abrupt alternation on rice yield and yield components. Crop Sci. 2019;59:280–92. Qian L, Meng H, Chen X, Tang R. Evaluating agricultural drought and flood abrupt alternation: a case study of cotton in the middle-and-lower Yangtze River, China. Agr Water Manage. 2023;283:108313. Brisson N, Rebiere B, Zimmer D, Renault P. Response of the root system of a winter wheat crop to waterlogging. Plant Soil. 2002;243:43–55. Hu WL, Li ZL, Chen QJ, Sun YW, Zhai S, Lu F, et al. Triterpenes and lignans from the leaves of Styrax tonkinensis . Biochem Syst Ecol. 2019;86:103891. Le X, Shida S, Saito Y, Momohara I. Effect of heat treatment on bending strength and decay resistance of Styrax tonkinensis wood. Wood Preservation. 2006;32:7–12. Wang F, Zhang L, Zhang Q, Wang S-M, Fang Z-F. Neolignan and phenylpropanoid compounds from the resin of Styrax tonkinensis . J Asian Nat Prod Res. 2021;23:527–35. Chen C, Chen H, Han C, Liu Z, Yu F, Wu Q. 24-Epibrassinolide promotes fatty acid accumulation and the expression of related genes in Styrax tonkinensis seeds. Int J Mol Sci. 2022;23:8897. Sasaki C, Negoro H, Asada C, Nakamura Y. Microwave-assisted glucose production from bode ( Styrax tonkinensis ) woody biomass for bioethanol production. J Mater Cycles Waste Manag. 2019;21:201–4. Wu Q, Cao Y, Chen C, Gao Z, Yu F, Guy RD. Transcriptome analysis of metabolic pathways associated with oil accumulation in developing seed kernels of Styrax tonkinensis , a woody biodiesel species. BMC Plant Biol. 2020;20:121. Wu Q, Zhao X, Chen C, Zhang Z, Yu F. Metabolite profiling and classification of developing Styrax tonkinensis kernels. Metabolites. 2020;10:21. Chen H, Wu Q, Ni M, Chen C, Han C, Yu F. Transcriptome analysis of endogenous hormone response mechanism in roots of Styrax tonkinensis under waterlogging. Front Plant Sci. 2022;13:896850. Zhang Z, Luo Y, Wang X, Yu F. Fruit Spray of 24-Epibrassinolide and fruit shade alter pericarp photosynthesis activity and seed lipid accumulation in Styrax tonkinensis . J Plant Growth Regul. 2018;37:1066–84. Shan L, Zhang L, Song J, Zhang Y, She D, Xia J. Characteristics of dry-wet abrupt alternation events in the middle and lower reaches of the Yangtze River Basin and the relationship with ENSO. J Geogr Sci. 2018;28:1039–58. Ma C, Liu H, Guo H, Musante C, Coskun SH, Nelson BC, et al. Defense mechanisms and nutrient displacement in Arabidopsis thaliana upon exposure to CeO 2 and In 2 O 3 nanoparticles. Environ Sci: Nano. 2016;3:1369–79. Cakmak I, Horst WJ. Effect of aluminium on lipid peroxidation, superoxide dismutase, catalase, and peroxidase activities in root tips of soybean ( Glycine max ). Physiol Plant. 1991;83:463–8. Kaur N, Sharma I, Kirat K, Pati P. Detection of reactive oxygen species in Oryza sativa L. (Rice). Bio-protocol. 2016;6. Beauchamp C, Fridovich I. Superoxide dismutase: improved assays and an assay applicable to acrylamide gels. Anal Biochem. 1971;44:276–87. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2007;36 Database:D480–4. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106. Ünyayar S, Keleş Y, Çekiç FÖ. The antioxidative response of two tomato species with different drought tolerances as a result of drought and cadmium stress combinations. Plant Soil Environ. 2005;51:57–64. Lahlou O, Ouattar S, Ledent J-F. The effect of drought and cultivar on growth parameters, yield and yield components of potato. Agronomie. 2003;23:257–68. Milroy SP, Bange MP, Thongbai P. Cotton leaf nutrient concentrations in response to waterlogging under field conditions. Field Crop Res. 2009;113:246–55. Aroca R, Porcel R, Manuel Ruiz-Lozano J. Regulation of root water uptake under abiotic stress conditions. J Exp Bot. 2012;63:43–57. Puyang X, An M, Xu L, Han L, Zhang X. Antioxidant responses to waterlogging stress and subsequent recovery in two Kentucky bluegrass ( Poa pratensis L.) cultivars. Acta Physiol Plant. 2015;37:197. Bose J, Rodrigo-Moreno A, Shabala S. ROS homeostasis in halophytes in the context of salinity stress tolerance. J Exp Bot. 2014;65:1241–57. Hasanuzzaman M, Raihan MRH, Masud AAC, Rahman K, Nowroz F, Rahman M, et al. Regulation of reactive oxygen species and antioxidant defense in plants under salinity. Int J Mol Sci. 2021;22:9326. Singh A, Kumar A, Yadav S, Singh IK. Reactive oxygen species-mediated signaling during abiotic stress. Plant Gene. 2019;18:100173. Del Río LA, López-Huertas E. ROS Generation in peroxisomes and its role in cell signaling. Plant Cell Physiol. 2016;076. Noctor G. Drought and oxidative load in the leaves of C3 plants: a predominant role for photorespiration? Ann Bot. 2002;89:841–50. Zheng J, Ying Q, Fang C, Sun N, Si M, Yang J, et al. Alternative oxidase pathway is likely involved in waterlogging tolerance of watermelon. Sci Hortic. 2021;278:109831. He L, Li B, Lu X, Yuan L, Yang Y, Yuan Y, et al. The effect of exogenous calcium on mitochondria, respiratory metabolism enzymes and ion transport in cucumber roots under hypoxia. Sci Rep. 2015;5:11391. Da-Silva CJ, do Amarante L. Time-course biochemical analyses of soybean plants during waterlogging and reoxygenation. Environ Exp Bot. 2020;180:104242. Munné-Bosch S, Alegre L. Die and let live: leaf senescence contributes to plant survival under drought stress. Funct Plant Biol. 2004;31:203. Lee S, Park C-M. Regulation of reactive oxygen species generation under drought conditions in Arabidopsis . Plant Signal Behav. 2012;7:599–601. Foyer CH, Noctor G. Redox signaling in plants. Antioxid Redox Signal. 2013;18:2087–90. Cruz de Carvalho MH. Drought stress and reactive oxygen species: production, scavenging and signaling. Plant Signal Behav. 2008;3:156–65. Kim MJ, Ciani S, Schachtman DP. A peroxidase contributes to ROS production during Arabidopsis root response to potassium deficiency. Mol Plant. 2010;3:420–7. Mishra N, Jiang C, Chen L, Paul A, Chatterjee A, Shen G. Achieving abiotic stress tolerance in plants through antioxidative defense mechanisms. Front Plant Sci. 2023;14:1110622. Mittler R. Oxidative stress, antioxidants and stress tolerance. Trends Plant Sci. 2002;7:405–10. Pisoschi AM, Pop A. The role of antioxidants in the chemistry of oxidative stress: a review. Eur J Med Chem. 2015;97:55–74. Zainab Q, Tanees CM, Du X, Lori H, Tehseen AM. Review of oxidative stress and antioxidative defense mechanisms in Gossypium hirsutum L. in response to extreme abiotic conditions. J Cotton Res. 2021;4:9. Sun C, Li X, Hu Y, Zhao P, Xu T, Sun J, et al. Proline, sugars, and antioxidant enzymes respond to drought stress in the leaves of strawberry plants. Korean J Hortic Sci Tech. 2015;33:625–32. Yang L, Yang L, Lan Y, Zhao Y, Han M, Yang L. Exogenous abscisic acid reduces saikosaponin accumulation by inhibiting saikosaponin synthesis pathway gene expression under drought stress in Bupleurum chinense DC. Ind Crop Prod. 2020;154:112686. Rahimi M, Kordrostami M, Mohamadhasani F, Chaeikar SS. Antioxidant gene expression analysis and evaluation of total phenol content and oxygen-scavenging system in tea accessions under normal and drought stress conditions. BMC Plant Biol. 2021;21:494. Bansal R, Srivastava JP. Erratum to: Antioxidative defense system in pigeonpea roots under waterlogging stress. Acta Physiol Plant. 2012;34:1595–5. Bano A, Ullah F, Nosheen A. Role of abscisic acid and drought stress on the activities of antioxidant enzymes in wheat. Plant Soil Environ. 2012;58:181–5. Wei W, Li D, Wang L, Ding X, Zhang Y, Gao Y, et al. Morpho-anatomical and physiological responses to waterlogging of sesame ( Sesamum indicum L). Plant Sci. 2013;208:102–11. Jones DP, Sies H. The redox code. Antioxid Redox Signal. 2015;23:734–46. Sandalio LM, Romero-Puertas MC. Peroxisomes sense and respond to environmental cues by regulating ROS and RNS signalling networks. Ann Bot. 2015;116:475–85. Yun B-W, Spoel SH, Loake GJ. Synthesis of and signalling by small, redox active molecules in the plant immune response. Biochim Biophys Acta-Gen Subj. 2012;1820:770–6. Mhamdi A, Noctor G, Baker A. Plant catalases: Peroxisomal redox guardians. Arch Biochem Biophys. 2012;525:181–94. Mhamdi A, Queval G, Chaouch S, Vanderauwera S, Van Breusegem F, Noctor G. Catalase function in plants: a focus on Arabidopsis mutants as stress-mimic models. J Exp Bot. 2010;61:4197–220. Wang FZ, Wang QB, Kwon SY, Kwak SS, Su WA. Enhanced drought tolerance of transgenic rice plants expressing a pea manganese superoxide dismutase. J Plant Physiol. 2005;162:465–72. Charlton WL, Matsui K, Johnson B, Graham IA, Ohme-Takagi M, Baker A. Salt-induced expression of peroxisome-associated genes requires components of the ethylene, jasmonate and abscisic acid signaling pathways. Plant Cell Environ. 2005;28:513–24. Schrader M, Reuber BE, Morrell JC, Jimenez-Sanchez G, Obie C, Stroh TA, et al. Expression of PEX11β mediates peroxisome proliferation in the absence of extracellular stimuli. J Biol Chem. 1998;273:29607–14. Iida R, Yasuda T, Tsubota E, Takatsuka H, Matsuki T, Kishi K. Human Mpv17-like protein is localized in peroxisomes and regulates expression of antioxidant enzymes. Biochem Bioph Res Co. 2006;344:948–54. Zwacka RM, Reuter A, Pfaff E, Moll J, Gorgas K, Karasawa M, et al. The glomerulosclerosis gene Mpv17 encodes a peroxisomal protein producing reactive oxygen species. EMBO J. 1994;13:5129–34. Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.xlsx Fig.S1.png Fig.S2.jpg Fig.S3.jpg Fig.S4.jpg Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Feb, 2024 Reviews received at journal 31 Jan, 2024 Reviewers agreed at journal 11 Jan, 2024 Reviews received at journal 11 Jan, 2024 Reviews received at journal 10 Jan, 2024 Reviewers agreed at journal 09 Jan, 2024 Reviewers agreed at journal 21 Dec, 2023 Reviewers agreed at journal 09 Dec, 2023 Reviewers invited by journal 06 Dec, 2023 Editor assigned by journal 06 Dec, 2023 Submission checks completed at journal 06 Dec, 2023 First submitted to journal 05 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-3708391","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":256766181,"identity":"c476c26e-898c-4033-9a56-006a9ebd7650","order_by":0,"name":"Hong Chen","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Chen","suffix":""},{"id":256766182,"identity":"00d70061-1dd1-4dc7-8cb0-9770a343be68","order_by":1,"name":"Chao Han","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Han","suffix":""},{"id":256766183,"identity":"a763d15d-a1d6-4a9e-b504-3eb64f5c29ca","order_by":2,"name":"Luomin Cui","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Luomin","middleName":"","lastName":"Cui","suffix":""},{"id":256766184,"identity":"36c38005-a5fc-487c-85f1-5337b79acf85","order_by":3,"name":"Zemao Liu","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zemao","middleName":"","lastName":"Liu","suffix":""},{"id":256766185,"identity":"42e40e40-95bf-465f-8021-045fdfa4d871","order_by":4,"name":"Fangyuan Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBAC9gYg8YGBjR/EkSBKC88BBgbGGQxskg0kaWHmYWAgRYtEjpm07Q4+CYMDzAdv8zDY5RGnJfcMG1ALW7I1D0NyMUEt9mAtbWx1Bgd4zKR5GA4kNhBli2UbyBb+byRoYQRr4WEjUgvPs2LLXqAWycNsxpZzDJKJ0MKevPHGz7ZjEnzHmx/eeFNhR1gLg0CGAZA8BowdEM+AoHog4D/+AEjWEKN0FIyCUTAKRioAALfqMoqWIhfCAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Fangyuan","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2023-12-05 07:29:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3708391/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3708391/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47882447,"identity":"fe57973a-2bef-44b8-b2d8-3a10622f378f","added_by":"auto","created_at":"2023-12-08 20:36:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":867748,"visible":true,"origin":"","legend":"\u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e content (A), H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content (B) and MDA content (C) in roots (brown) and leaves (green) of \u003cem\u003eS. tonkinensis\u003c/em\u003e in response to FDAA and DT. Values are mean ± SD, n = 4. Different lowercase letters within each treatment indicate significant differences (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). FW, estimated fresh weight.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/ec3835a9450c57097c833966.jpg"},{"id":47883159,"identity":"57475e37-b34e-4279-8cde-6e5b21a9c2c5","added_by":"auto","created_at":"2023-12-08 20:52:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":473047,"visible":true,"origin":"","legend":"\u003cp\u003eGO (A), KEGG (B) and KOG (C) classifications in roots and leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003eunigenes in response to FDAA and DT.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/3a2fece1efad7786c032f7ed.png"},{"id":47883155,"identity":"db5e367a-17e8-4105-b99a-9175ccbe9250","added_by":"auto","created_at":"2023-12-08 20:52:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1153753,"visible":true,"origin":"","legend":"\u003cp\u003eNumber and distribution of up-regulated and down-regulated differently expressed genes in roots and leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings between different treatments (CK, FDAA and DT).\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/c23e7a4a172f5a96403f4b80.jpg"},{"id":47884024,"identity":"bd45eeef-0e73-4e44-80c3-d761bbd5f981","added_by":"auto","created_at":"2023-12-08 21:08:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":456031,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical cluster analysis (HCA) of DEGs comparison in FDAAL VS CKL (A), FDAAR VS CKR (B), DTL VS CKL (C), and DTR VS CKR (D) in the peroxisome pathway of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings under FDAA and DT stress. Red blocks represent high expression of DEGs, and blue blocks represent a low expression of DEGs. These DEGs need to meet the threshold that p \u0026lt; 0.05 and |log2FC| \u0026gt; 1.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/3429160456f1f219745cd657.png"},{"id":47882457,"identity":"ce9fe1bf-e3c5-4ba7-a06a-eca692fe5b61","added_by":"auto","created_at":"2023-12-08 20:36:37","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3562693,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of top 20 pathways of \u003cem\u003eS. tonkinensis\u003c/em\u003e DEGs for six groups (FDAAL VS CKL, FDAAR VS CKR, DTL VS CKL, DTR VS CKR, FDAAL VS DTL, and FDAAR VS DTR) in response to FDAA and DT.\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/c326317bc67b4c8bbf4b4ecb.jpg"},{"id":47882458,"identity":"4951d746-e0bd-4360-bd5e-abc65e23bee0","added_by":"auto","created_at":"2023-12-08 20:36:38","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":18057714,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of up/down differentially expressed genes (DEGs)\u003cem\u003e \u003c/em\u003eof \u003cem\u003eS. tonkinensis\u003c/em\u003e unigenes for six groups (FDAAL VS CKL, FDAAR VS CKR, DTL VS CKL, DTR VS CKR, FDAAL VS DTL, and FDAAR VS DTR) in response to FDAA and DT.\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/64f4ca51d621027f0e299316.jpg"},{"id":47882455,"identity":"96bb59a2-b497-43cf-ba0b-ec0af7f9e321","added_by":"auto","created_at":"2023-12-08 20:36:37","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":292477,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the variation of essential DEGs in the peroxisome pathway, some physiological indicators related to this pathway, and the morphological variation under FDAA and DT stress in \u003cem\u003eS. tonkinensis \u003c/em\u003eseedlings. Values are mean ± SD, n = 4. Different lowercase letters within each treatment indicate significant differences (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). FW, estimated fresh weight.\u003c/p\u003e","description":"","filename":"Fig.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/705a34945eaba2dce0818c5a.jpg"},{"id":47882460,"identity":"bbf8d8bb-d6aa-420c-b951-b1cab6efafe2","added_by":"auto","created_at":"2023-12-08 20:36:42","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":71969709,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/cca65cc5db4c2b37f4f8a317.xlsx"},{"id":47883730,"identity":"022d0f92-439b-4cb5-ba9d-857bdf7a7d9c","added_by":"auto","created_at":"2023-12-08 21:00:37","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":735957,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.png","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/dfdc67b5214c77a11dae76d1.png"},{"id":47882940,"identity":"e4acc308-e9d3-4499-bd3c-9e6a74973a5b","added_by":"auto","created_at":"2023-12-08 20:44:37","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":531833,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/63dba09929e57fa7dea46302.jpg"},{"id":47882454,"identity":"16647259-3d25-440d-81f5-ca726e0f1170","added_by":"auto","created_at":"2023-12-08 20:36:37","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1807011,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/58c2d02ce26d0dfb69030380.jpg"},{"id":47882946,"identity":"62ef8553-88d1-4ced-9d5e-8ae0baaaafe9","added_by":"auto","created_at":"2023-12-08 20:44:37","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2497430,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3708391/v1/3e0ffa1f4777f62fb892a479.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome analysis of antioxidant system response in Styrax tonkinensis seedlings under flood- drought abrupt alternation","fulltext":[{"header":"1. Background","content":"\u003cp\u003eCurrently, climate change is resulting in the uneven precipitation patterns worldwide and climate extremes, further posing a consequential threat to human society and ecosystem [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The catastrophic events, including mega-floods, mega-droughts and drought-flood abrupt alternations, happen more frequently and intensively due to climate change [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2021, a significant number of 223 flood incidents were recorded globally, with notable occurrences in countries such as China, India, Afghanistan, and Germany. During the same timeframe, extensive droughts prevailed across North America, Africa, and Asia, leading to extended periods of aridity. Both disasters brought reductions in crop yields and significant economic losses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In recent decades, China has experienced a series of severe natural disasters characterized by abrupt alternations between drought and flood, greatly influenced by the monsoon climate [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This new type of extreme hydrological event is known as drought-flood abrupt alternation (DFAA), which means alternating occurrence of two scenarios (droughts and floods) and the state transformation is speedy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. DFAA events perform as two situations, which are transitioning from drought to flood and from flood to drought [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], leading to more devastating impacts on socioeconomic loss and ecological destruction than a singular occurrence of drought or flood [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003ePrevious DFAA studies are mainly focused on the spatial distribution, physical mechanism, water quality and the proper water resources distribution. Ma et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] pointed out that DFAA events were becoming expanding in terms of spatial distribution from frequency and intensity aspects. In order to explore the physical mechanism of DFAA, experts discover that the degree of rainfall concentration is an essential reason for DFAA happening by determining the correlation between the DFAA and precipitation indexes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Bi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] predicted the impacts of DFAA events on surface water quality data in Luanhe River basin for the future three decades. Their findings also revealed that DFAA events would reduce water quality by triggering total nitrogen and total phosphorous pollution. Huang et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported that Guangzhou plain exhibited a prevalent and persistent arid climate throughout the entire year, coupled with a heightened vulnerability to prolonged wet conditions during the Summer-Autumn season. Besides, the frequency of DFAA events was higher during the summer months (June to August) compared to autumn or spring, with no occurrences observed during the winter season [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], indicating that the occurrence of DFAA events was seasonal.\u003c/p\u003e \u003cp\u003eIn addition to the hydro-meteorological studies on DFAA, some researchers were addicted to investigate the influences of the compound natural disaster from agricultural perspective. Rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.), being highly susceptible to the intricate interplay of water and temperature, emerges as the crop most profoundly impacted by DFAA. Consequently, rice has garnered significant research attention [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The average yield of rice under DFAA stress was reduced by 12.98% in 2016 and 29.94% in 2017, respectively [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Crop roots can be adversely affected by both water deficits and excess water in soils, stemming from drought and flood disasters. These conditions hinder the efficient absorption of water and essential nutrients by crop roots, consequently disrupting crop growth and reducing overall yield [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Both Xiong et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and Zhu et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] explored the approach of rice yield recovery after DFAA via applying nitrogen. Furthermore, other significant grain and cash crops, such as cotton [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], wheat [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and maize [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], have also been the subject of agricultural DFAA research. Nevertheless, the existing DFAA studies rarely pay attention to the impacts of DFAA events on tree species.\u003c/p\u003e \u003cp\u003eIn our study, \u003cem\u003eS. tonkinensis\u003c/em\u003e, a deciduous tree species, was utilized as the experimental objective. \u003cem\u003eS. tonkinensis\u003c/em\u003e is a valuable tree species known for its economic significance, primarily due to its oil extraction potential, medicinal properties, and ornamental value [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Researchers have extensively investigated the seeds of this plant due to their remarkably high oil content, focusing on their biodiesel properties, nutritional components, and the ultrastructure of the oil bodies [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The four primary free fatty acids found were palmitic acid, stearic acid, oleic acid, and linoleic acid. Within the compounds of flavonoids, the predominate components comprised of flavans, flavonoid glycosides, and o-methylated flavonoids [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Meanwhile, this versatile species is highly vulnerable to waterlogging stress, as evidenced by a 100% mortality rate of one-year-old seedlings after undergoing five days of flooding treatment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is mainly distributed in the southern China, especially in Yangtze River basin [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Affected by global climate change, the occurrence and strength of DFAA events have notably surged in the middle and lower sections of China's Yangtze River region. Interestingly, this region witnessed a sudden shift from flooding to drought conditions around mid-July, marked by a notable change in daily precipitation patterns [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present scenario, biennial \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings were subjected to flood-drought abrupt alternation (FDAA) to observe the morphological, physiological, and molecular responses of the species. To discern the impact of FDAA, we established control groups (CK) and subjected some seedlings to drought stress (DT), allowing for a comprehensive comparison of each treatment. The primary objective of this study was to offer a theoretical foundation for the prospective extensive cultivation of \u003cem\u003eS. tonkinensis\u003c/em\u003e in the Yangtze River basin.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant material and treatment\u003c/h2\u003e \u003cp\u003eThe experimental seeds were collected from Pingxiang, Jiangxi Province, China in 2020, which was mentioned in the previously published article [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Professor Fangyuan Yu assisted in identifying the species in Pingxiang. A voucher specimen of this material has been deposited in Chinese Field Herbarium, Shanghai, China. After two years of cultivation, the treatments were started at 9 am at the end of June 2022. The experiment was divided into three treatments, which were: (1) CK - common water and nutrient management, (2) FDAA - waterlogging for two days and immediately turning to drought for seven days, (3) DT - drought stress for seven days. The organic matter condition and waterlogging approach were mentioned in the previously published article [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To ensure the consistency of sampling time, FDAA treatment was started two days in advance. Each treatment consisted of 30 seedlings. For each treatment, leaf and root samples for physiological and molecular determination were collected from 16 seedlings with destruction. The molecular samples were promptly frozen in liquid nitrogen and the physiological samples were put in ice bag. Subsequently, all of them were transferred to refrigerators at -80℃. The samples were named as CKL (leaf sample in control groups), FDAAL (leaf sample under flood-drought abrupt alternation), DTL (leaf sample under drought stress), CKR (root sample in control groups), FDAAR (root sample under flood-drought abrupt alternation), and DTR (root sample under drought stress) for transcriptome analysis. Four seedlings for each treatment were used for biomass determination. Four biological replicates were applied in the study. Besides, ten seedlings were used for observing and measuring seedling height (H) and ground diameter (D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Seeding height, ground diameter and biomass\u003c/h2\u003e \u003cp\u003eThe two measurements of H and D were taken 15 days part. For each treatment, ten random seedlings of \u003cem\u003eS. tonkinensis\u003c/em\u003e were chosen to measure the H and D using a tape measure (accuracy of 0.1 cm) and a Vernier caliper (accuracy of 0.01 mm), respectively. Additionally, the height-diameter ratio was calculated.\u003c/p\u003e \u003cp\u003eSeedlings were first separated into their respective parts: leaves, stems, and roots. These parts were then carefully arranged in envelopes and subjected to an oven treatment. The oven temperature was set to 105\u0026deg;C for 30 minutes initially. Subsequently, the temperature was adjusted to 70\u0026deg;C to facilitate the drying process until a constant weight was achieved. Finally, precise measurements of the dry weight of each part were taken using an electronic balance with an accuracy of 0.001 g.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The content of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and MDA\u003c/h2\u003e \u003cp\u003eAll physiological parameters were assessed using a Lambda 365 spectrometer (PerkinElmer, Waltham, Massachusetts, USA). The determination of hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) content was carried out following the guidelines provided by the Hydrogen Peroxide assay kit. For this assay, 0.3 g of leaf or root tissue was extracted in 2.7 mL of normal saline. After centrifugation, 0.1 mL of the resulting supernatant was combined with the provided reagent, and the optical density (OD) was measured at 405 nm. Furthermore, the soluble protein content of each sample was measured to facilitate the subsequent calculation of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content (mmol\u0026middot;gprot-1).\u003c/p\u003e \u003cp\u003eThe quantification of superoxide anion (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) content and malondialdehyde (MDA) content followed the methods described by Ma et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and Cakmak and Horst [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], respectively. For this analysis, 0.3 g of leaf or root tissue was finely ground in 8 mL of pH 7.8 phosphate buffer solution (PBS) and then subjected to centrifugation. Subsequently, 1 mL of the resulting supernatant was mixed with 0.75 mL of PBS and 0.25 mL of hydroxylamine hydrochloride, and the mixture was placed in a 25 ℃ water bath for 20 minutes. Following this, 2 mL each of 17 mmol\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e 4-aminobenzenesulfonic acid and 7 mmol\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e 1-Naphthylamine naphthylamine were added to the solution. The sample was then incubated in a 30 ℃ water bath for 30 minutes, and OD at 530 nm was recorded.\u003c/p\u003e \u003cp\u003eTo determine the MDA content, 0.3 g of leaf or root tissue was extracted using 5 mL of 10% trichloroacetic acid (TCA) and then subjected to centrifugation. Next, 2 mL of the resulting supernatant was mixed with 4 mL of 0.6% thiobarbituric acid and boiled for 20 minutes. After the solution cooled to room temperature, OD at 450 nm, 532 nm, and 600 nm were recorded. These values were then utilized in the calculation to determine the MDA content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Histochemical detection of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003eIn the study conducted by Kaur et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], the localization of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in leaf samples was investigated using histochemical detection methods. To visualize the location of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, the leaves were immersed in a solution containing 3,3'-diaminobenzidine (DAB) while exposed to light for 12 hours at room temperature. To capture the location of O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, a solution of 6 mM nitrozolium blue tetrachloride (NBT) mixed in sodium citrate buffer was utilized. Subsequently, the treated leaves were incubated at room temperature for a duration of 12 hours. Eventually, both leaf samples for histochemical detection of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e were transferred to ethanol and boiled at 100℃ to eliminate chlorophyll interference. To prevent dehydration, the treated leaves were then placed in a 20% glycerol solution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Enzyme activities\u003c/h2\u003e \u003cp\u003eTo assess superoxide dismutase (SOD; EC 1.15.1.1) activity, the supernatant used for analysis was obtained by grinding and centrifuging 0.3 g of leaf or root tissue in 8 mL PBS at pH 7.8 Then, 0.05 mL of the supernatant was subjected to a reaction with specific chemical reagents following the NBT-illumination method [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The absorbance at 560 nm (OD560 nm) was recorded, and SOD activity was expressed as U\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW.\u003c/p\u003e \u003cp\u003eIn another extraction process, enzyme extract was obtained from the sample using a pH 7.0 buffer and 8 mL of PBS to measure catalase (CAT; EC 1.11.3.6) activity. The 0.02 mL of leaf supernatant and 0.1 mL of root supernatant was used for H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e degradation, which was modified according to Ma et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 RNA extraction and cDNA library construction\u003c/h2\u003e \u003cp\u003eRNA extraction from leaf and root samples was carried out using the Ambion Plant RNA Kit, adhering to the protocol recommended by the manufacturer. For the evaluation of RNA integrity, the Agilent 2100 Bioanalyzer manufactured by Agilent Technologies in Santa Clara, CA, USA, was employed for the analysis. Libraries were generated using the TruSeq Stranded mRNA LT Sample Prep Kit from Illumina, based in San Diego, CA, USA, in accordance with the manufacturer's instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Quality control, \u003cem\u003ede novo\u003c/em\u003e assembly and functional annotation\u003c/h2\u003e \u003cp\u003eTranscriptome sequencing and analysis were conducted by OE Biotech Co., Ltd. in Shanghai, China, utilizing the Illumina HiSeq 4000 Sequencing platform. The raw reads underwent processing with Trimmomatic [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] to eliminate reads containing poly-N and low-quality sequences, aiming to obtain clean reads. Trinity was used to assisting in de novo assembly of clean reads in the paired-end method [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], generating expressed sequence tag clusters (contigs) and transcripts. By comparing the length and similarity of transcript, the longest one for each cluster was chosen for subsequent analysis.\u003c/p\u003e \u003cp\u003eTo annotate unigenes function, they were aligned with databases such as the Swiss-Prot protein (SwissProt), clusters of orthologous groups (KOG), and evolutionary genealogy of genes: non-supervised orthologous groups (eggNOG) using basic local alignment search tool (BLAST) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] with a threshold E-value of 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. Functional annotations were assigned to the unigenes based on the proteins showing the highest sequence similarity. Furthermore, gene ontology (GO) classification was performed based on the SwissProt annotation, establishing the mapping relationship between SwissProt and GO terms. Additionally, the unigenes were mapped to the Kyoto encyclopedia of genes and genomes (KEGG) database [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] to annotate their potential metabolic pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Differential expression analysis of unigenes and qRT-PCR analysis\u003c/h2\u003e \u003cp\u003eThe DESeq2 method was employed to normalize the gene count data for each sample, and the expression level was estimated via the base mean value, represented as fragments per kilobase per million mapped reads (FPKM). Additionally, the fold change (difference multiple) was calculated, and the significance of the differences was assessed using the negative binomial (NB) distribution test. To identify the differentially expressed genes (DEGs), the results from the difference multiple and significance tests were used for screening, following the approach described by Love et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. DEGs were deemed statistically significant if they had a p-value less than 0.05 and | log2FC | greater than 1, as the method proposed by Anders and Huber [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eFor validation of the RNA-seq results, a subset of transcripts (\u003cem\u003eMPV17, PMP34, PEX3, PEX14, SOD1, SOD2, CAT, POD\u003c/em\u003e) associated with the antioxidant system were selected and verified. The primers for each of the DEGs were provided in Tab.S1. The quantitative real-time PCR (qRT-PCR) reactions were performed on a StepOne Real-Time PCR System utilizing SYBR Green Dye from Applied Biosystems (Foster City, USA) and Takara (Dalian, China). The 2^\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method with 18S ribosomal RNA serving as an internal control was applied to determine the relative gene expression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data analysis comprised initial basic descriptive analysis, followed by an analysis of variance (ANOVA) to assess the differences between groups. Subsequently, Duncan and Pearson R correlation tests were conducted using SPSS version 23.0 for Windows (SPSS Science, Chicago, IL, USA). In evaluating significance between treatments, p-values less than 0.05 were considered indicative of statistically significant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Impacts of FDAA on the growth of \u003cem\u003eS. tonkinensis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, the stems and twigs of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings became curved and the leaves dropped under both FDAA and DT stress. The experimental period was during the rapid growth period of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings. \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings under normal water and fertilizer management (CK) grew rapidly, with an increment of 13.83% in H and 5.41% in D. However, both FDAA and DT treatments inhibited the growth of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings, as evidenced by reduced H and D. The growth of H and D in the FDAA treatment was inhibited by 2.2 cm (3.48%) and 0.31 mm (4.56%), respectively. Compared to FDAA, DT stress caused even greater growth reductions in seedling height and ground diameter (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding biomass, both FDAA and DT treatments significantly inhibited fresh weight growth, including root, stem, leaf, and total biomass. In the FDAA treatment, the reduction in fresh weight growth was mainly observed in the leaves, decreasing from 8.074\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49a g to 2.257\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42b g, as compared to the CK. However, DT resulted in greater fresh weight loss in roots and stems than FDAA, especially in the roots, which experienced almost a 50% reduction. As for dry weight, no noteworthy distinctions were noted among the treatments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe variation of seedlings height and ground diameter of \u003cem\u003eS. tonkinensis\u003c/em\u003e in response to FDAA and DT between pre-treatment and post-treatment. Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n\u0026thinsp;=\u0026thinsp;4. Different lowercase letters within each treatment indicate significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePre-treatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePost-treatment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeedling height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGround diameter (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeight-diameter ratio (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeedling height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGround diameter (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeight-diameter ratio (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.86a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105.06\u0026thinsp;\u0026plusmn;\u0026thinsp;24.41a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe root, stem, leaf and total biomass of \u003cem\u003eS. tonkinensis\u003c/em\u003e in response to FDAA and DT between pre-treatment and post-treatment. Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n\u0026thinsp;=\u0026thinsp;4. Different lowercase letters within each treatment indicate significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). FW, estimated fresh weight; DW, estimated dry weight.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eFresh Weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eDry Weight (g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeaf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRoot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeaf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.314\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.794\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.074\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.182\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.408\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.110\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.896\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.414\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.254\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.936\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.257\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.448\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.203\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.427\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.682\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.321\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.822\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.272\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.671\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.765\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.761\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.549\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.254\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.564\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Impacts of FDAA on ROS and lipid peroxidation of \u003cem\u003eS. tonkinensis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eUnder FDAA and DT stress, the O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e content in both leaves and roots was elevated compared to the CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Histochemical detection of O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) further confirmed that the leaves in the FDAA group were most severely attacked by O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e with a concentration of 36.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40a \u0026micro;g\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW, which was over twice as much as that in the CK group. The blue dyeing on leaves of the DT group also indicated an increase in O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e content level (22.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39a \u0026micro;g\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW). Moreover, the H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content in leaves increased significantly under both FDAA and DT stress, with separate increments of 21.76 mmol\u0026middot;gprot\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW and 23.36 mmol\u0026middot;gprot\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), as also evidenced by the brown dots on leaves in Fig.\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. In general, both O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e contents in roots increased slightly under FDAA and DT stress, but without significant differences when compared to the CK. Furthermore, compared to the CK, the leaves in both FDAA and DT groups experienced severe lipid peroxidation, indicated by the dramatic elevation in MDA content. However, the variation in MDA content in roots among treatments was not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Impacts of FDAA on two antioxidant enzymes of \u003cem\u003eS. tonkinensis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eGenerally, DT led to the highest SOD activity in leaves (763.76\u0026thinsp;\u0026plusmn;\u0026thinsp;21.83a U\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW) and roots (284.58\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14a U\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW) compared to other two treatments. The SOD activity in leaves and roots of FDAA was 20.85% and 52.49% lower than that of DT, respectively. Compared to CK, FDAA not only improved the SOD activity in leaves but also in roots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Regarding CAT activity, FDAA stress contributed to the maximal CAT activity in both leaves (794.44\u0026thinsp;\u0026plusmn;\u0026thinsp;63.55a U\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW) and roots (158.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87a U\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW). The CAT activity of DT was slightly lower than that of FDAA in two organs, but still higher than that of CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Quality control, \u003cem\u003ede novo\u003c/em\u003e assembly, and total gene expression\u003c/h2\u003e \u003cp\u003eAfter the completion of transcriptome sequencing for 24 samples, a cumulative total of 156.98 G of high-quality data were acquired. The individual sample datasets exhibited effective data sizes spanning from 5.97 to 6.81 G, with Q30 bases accounting for a range of 93.07\u0026ndash;93.66%. Moreover, the collective average GC content was measured at 46.99%, as detailed in Tab.S2. The assembly process resulted in the creation of 1,111,088 distinct unigenes, with an overall length of 1,111,628,179 bp and an average length of 1004.86 bp, as outlined in Tab.S3. The dataset consisted of sequences spanning lengths between 301 to 400, with the highest count of sequences (30,690) falling within this range. Additionally, there were 14,355 sequences with lengths exceeding 2,000, which secured the second-highest count (Fig.\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). Validation of the FPKM values was conducted and is visually depicted in Fig.\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA. For the determination of FPKM values across 24 samples derived from CK, DT, and FDAA groups, the DESeq2 method was employed. The distribution of FPKM values among these 24 samples is illustrated in Fig.\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Functional annotation and classification\u003c/h2\u003e \u003cp\u003eThe BLAST program against five publicly accessible protein databases was applied to elucidate and characterize potential functions, employing a threshold E-value of 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. The results revealed substantial matches with known proteins in the SwissProt, KEGG, KOG, eggNOG, and GO databases, yielding a total of 50,402 (45.37%), 18,284 (16.46%), 39,673 (35.71%), 64,050 (57.66%), and 44,622 (40.17%) annotated unigenes, respectively.\u003c/p\u003e \u003cp\u003eA comprehensive total of 44,622 assembled unigenes were systematically categorized across three principal functional domains in GO framework. These domains encompassed biological processes (37,140 unigenes, 83.23%), cellular components (40,281 unigenes, 82.35%), and molecular functions (38,829 unigenes, 87.02%) as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. The biological process category was further subdivided into 23 distinctive sub-categories. Among these, the two most prominently represented sub-categories were \"cellular process\" and \"metabolic process,\" housing a substantial 30,585 unigenes (69.65%) and 25,362 unigenes (68.29%) respectively. Within the cellular component category, allocation to 14 sub-categories transpired. The preponderance of unigenes were affiliated with the \"cell\" category (37,026 unigenes, 91.92%), closely followed by the \"cell part\" category (36,948 unigenes, 91.73%). Meanwhile, the molecular function domain exhibited a distribution across 16 sub-categories. Notably, the two most prevailing sub-categories were \"binding\" (25,937 unigenes, 66.80%) and \"catalytic activity\" (22,935 unigenes, 59.07%).\u003c/p\u003e \u003cp\u003eA total of 18,284 unigenes were categorized into five KEGG categories, 29 sub-categories, and 136 KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In \u0026ldquo;environmental information processing\u0026rdquo; category, the \u0026ldquo;signal transduction\u0026rdquo; pathway (743 unigenes, 4.06%) may be related to \u003cem\u003eS. tonkinensis\u003c/em\u003e responding to FDAA and DT stress. In \u0026ldquo;metabolism\u0026rdquo; category, a total of 3,544 unigenes (19.38%) were assigned in the \u0026ldquo;carbohydrate metabolism\u0026rdquo; pathway, followed by \u0026ldquo;amino acid metabolism\u0026rdquo; (1,951 unigenes, 10.67%) and \u0026ldquo;energy metabolism\u0026rdquo; (1,779 unigens, 9.73%) pathways.\u003c/p\u003e \u003cp\u003eA comprehensive count of 39,673 unigenes underwent allocation across 25 KOG classifications, with the greatest representation observed in the \"general function prediction only\" category (7,509 unigenes, 18.93%). This was pursued by a notable presence in the \"posttranslational modification, protein turnover, chaperones\" category (4,871 unigenes, 12.28%), and subsequently in the \"signal transduction mechanisms\" category (3,728 unigenes, 9.40%) as visually represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eC. Moreover, \u0026ldquo;signal transduction mechanisms\u0026rdquo; might be connected to the response of \u003cem\u003eS. tonkinensis\u003c/em\u003e to FDAA and DT stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Analysis of gene expression\u003c/h2\u003e \u003cp\u003eTo explore the expression patterns of differently expressed genes (DEGs) and specific pathways under FDAA and DT stress, the transcriptome profiles from treatments were compared. Compared to CKL, FDAAL possessed 2,251 up-regulated DEGs and 2,390 down-regulated DEGs. A total of 7,012 DEGs and 9,304 DEGs were positively regulated and negatively regulated in FDAAL VS DTL group, respectively. DTR had over 15,000 up-regulated DEGs when compared to CKR. Furthermore, the leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e exhibited higher number of down-regulated DEGs compared to the roots, regardless of the treatment conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe DEGs related to ROS and antioxidant system were analyzed, and their FPKMs were verified by qRT-PCR in Fig.\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003eS4\u003c/span\u003e. Under FDAA condition, peroxin-3 (\u003cem\u003ePEX3\u003c/em\u003e) in leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e exhibited the highest expression level (18.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38a), and showed a significant difference compared to other samples. Compared to CKL, \u003cem\u003ePEX3\u003c/em\u003e in DTL was apparently down-regulated. No significant differences were observed among the root samples. The FPKM of \u003cem\u003ePMP34\u003c/em\u003e in FDAAL (41.87\u0026thinsp;\u0026plusmn;\u0026thinsp;22.74a) went up slightly, while it in DTL (13.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39b) was reduced massively when compared to CKL (38.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58a). In the case of CKR, both FDAAR and DTR declined the \u003cem\u003ePMP34\u003c/em\u003e FPKM as an adaptation to abiotic stresses. Both leaves and roots of \u003cem\u003eS. tonkinensis\u003c/em\u003e improved \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eSOD2\u003c/em\u003e expression level to scavenge ROS. FDAA stress was more inclined to trigger \u003cem\u003eSOD1\u003c/em\u003e expression while DT stress tended to induce \u003cem\u003eSOD2\u003c/em\u003e expression. For leaves, the variation of \u003cem\u003eCAT\u003c/em\u003e expression was unobvious. However, DT stress induced the highest FPKM (627.11\u0026thinsp;\u0026plusmn;\u0026thinsp;87.14a) of \u003cem\u003eCAT\u003c/em\u003e in the roots. In addition, DT stress triggered the expression of \u003cem\u003ePOD\u003c/em\u003e no matter in leaves or roots of \u003cem\u003eS. tonkinensis\u003c/em\u003e. On the contrary, FDAAL and FDAAR showed lower FPKM values of \u003cem\u003ePOD\u003c/em\u003e than that of CKL and CKR, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn general, drought stress contributed to the most varied expression of DEGs in the peroxisome pathway in the roots, including 40 types of DEGs. In the case of Protein Mpv17 (\u003cem\u003eMPV17\u003c/em\u003e), which encodes a peroxisomal protein to produce ROS, was up-regulated with one unigene in leaves, while down-regulated with one unigene in roots under FDAA. Under DT stress, three DEGs of \u003cem\u003eMPV17\u003c/em\u003e were negatively regulated on the average in leave. On the contrary, six DEGs of \u003cem\u003eMPV17\u003c/em\u003e were all positively regulated in roots (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough KEGG enrichment analysis, the \u0026ldquo;flavonoid biosynthesis\u0026rdquo; pathway was most enriched (3.86 enrichment score) with hitting 12 DEGs in the leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e under FDAA stress. Furthermore, the \u0026ldquo;Plant hormone signal transduction\u0026rdquo; pathway and \u0026ldquo;MAPK signaling pathway \u0026ndash; plant\u0026rdquo; pathway ranked 4th and 7th in terms of enrichment scores, respectively, and they were associated with abiotic adaption. For roots, FDAA stress induced the \u0026ldquo;photosynthesis - antenna proteins\u0026rdquo; pathway most enriched (20.63 enrichment score) with 28 DEGs, followed by the \u0026ldquo;photosynthesis\u0026rdquo; pathway with 12.84 enrichment score via hitting 50 DEGs. Concerning DT stress, the leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e in DTL VS CKL had the same most enrichment pathways in FDAAR VS CKR. In DTR VS CKR, the \u0026ldquo;photosynthesis - antenna proteins\u0026rdquo;, \u0026ldquo;aflatoxin biosynthesis\u0026rdquo; and \u0026ldquo;photosynthesis\u0026rdquo; pathways ranked top3. The \u0026ldquo;peroxisome\u0026rdquo; pathway acquired 1.16 enrichment score with 106 DEGs when comparing FDAAR and DTR samples. In general, FDAA and DT stress obviously affected the enrichment of the \u0026ldquo;photosynthesis - antenna proteins\u0026rdquo; and \u0026ldquo;photosynthesis\u0026rdquo; pathways to different extent. Furthermore, the pathways related to environmental adaption were also enriched, such as the \u0026ldquo;Plant hormone signal transduction\u0026rdquo; and \u0026ldquo;MAPK signaling pathway \u0026ndash; plant\u0026rdquo; pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). As displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003e, no matter in FDAA stress or DT stress, the DEGs in the \u0026ldquo;response to stimulus\u0026rdquo; (biological process) and \u0026ldquo;antioxidant activity\u0026rdquo; (molecular function) pathways were triggered obviously.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFDAA, which stands for the combination of two abiotic stresses, leads to drought damage in plants following waterlogging destruction. To comprehensively investigate how \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings respond to FDAA, we analyzed morphological variations, growth conditions, ROS generation, antioxidant enzyme activity, relative gene expression, and key DEGs associated with antioxidant activity.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Impacts of FDAA on the morphological variation and growth condition of \u003cem\u003eS. tonkinensis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe study was conducted during the growing season for \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings. Under normal water management, these seedlings exhibited rapid growth, with a 13.83% increase in H and a 5.41% increase in D. Nevertheless, the growth of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings was significantly inhibited by FDAA and DT treatments. Notably, DT stress had a more pronounced inhibitory effect on H and D, reducing them by 5.25% and 15.74%, respectively. \u0026Uuml;nyayar et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] reported that drought stress led to a decline in shoot growth in drought-sensitive \u003cem\u003eLycopersicon peruvianum\u003c/em\u003e. Drought stress impaired the shoots growth and roots growth of potato [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In addition, the growth and development of cotton was hindered by waterlogging, due to the obstacle to absorbing water and nutrient [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. FDAA combines the waterlogging and drought stress, which might have superimposed obstruction for the growth of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings. DFAA stress also had a negative effect on rice growth, eventually reducing the rice yield[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In terms of the morphological changes, the stems tended to bend, and the leaves exhibited drooping due to water loss. Based on the biomass results, both FDAA and DT stresses not only decreased the total fresh weight but also reduced the fresh weight of each organ. The reduction in absolute water content was particularly concentrated in the leaves exposed to FDAA, while DT stress resulted in water loss primarily in the stems and roots. It can be inferred that FDAA represents a form of superimposed damage for \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings. During the waterlogging period, the water absorption capacity of roots was inhibited, hindering vertical water transport to stems and leaves. Simultaneously, transpiration continued, resulting in water loss from leaves [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Subsequently, drainage would provide temporary relief for \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, the following drought stress led to continuous water loss from leaves. As for the dry weight, no obvious difference occurred between treatments, indicating that the short-term FDAA and DT stresses had slight effects on the accumulation of dry biomass.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Impacts of FDAA on the ROS generation and antioxidant enzyme activity of \u003cem\u003eS. tonkinensis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eROS are molecules characterized by high reactivity that contain oxygen atoms and are generated as by-products of various cellular processes. These ROS play important roles in cell signaling and defense mechanisms but can also be toxic when their levels exceed the cellular capacity to detoxify them [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. ROS are generated in different cellular sites within plant cells, including peroxisomes, mitochondria, chloroplasts, and the apoplast [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Under abiotic stress conditions, accumulation of an excess of ROS occurs as a result of electron leakage from complexes I and III, resulting in the generation of O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e. This O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e is subsequently catalyzed by Mn-SOD and Cu/Zn-SOD to produce H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Peroxisomes in various plant species house notable types of SODs, including Cu/Zn-SOD and Mn-SOD, establishing them as crucial locations for H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e production [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In non-photosynthetic plant organs, especially in roots, mitochondria are frequently regarded as the primary sites for the generation of ROS. This is because roots rely on mitochondrial respiration for energy production [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Zheng et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], He et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and Da-Silva and do Amarante [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] all provided evidence that waterlogging, a condition where plant roots were submerged in water for an extended period, leading to an elevation in concentrations of ROS in watermelon, cucumber and soybean. Drought stress can also cause the imbalance between ROS and antioxidant ability, further generating excessive ROS and leading to leaf senescence [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Under drought stress, ROS was accumulated to a high level in \u003cem\u003eArabidopsis\u003c/em\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, both FDAA and DT stress conditions led to a noteworthy rise in the concentrations of O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in the leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings. These ROS are associated with oxidative stress and can have detrimental effects on plant cells [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. While there was a slight increase in O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content in the roots as well, these increases were not statistically significant compared to CK, reflecting that the leaves of the plants were more severely affected by oxidative stress in response to FDAA and DT stress. It appeared that FDAA was causing an increase in the generation of ROS, particularly in the leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings, due to the ROS harm could be superimposed. Noctor [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] claimed that photorespiration produced the majority of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e under drought stress circumstances.\u003c/p\u003e \u003cp\u003eThe antioxidant system preserves plants from oxidative damage under diverse environmental stresses [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Under waterlogging and drought stress, the antioxidant system helps to scavenge ROS that accumulate in plant tissues due to limited oxygen availability and water deficit, respectively [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The antioxidant enzymes are upregulated to detoxify ROS and maintain cellular redox homeostasis [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. SOD and CAT are two important enzymes involved in the defense mechanisms of plants against drought and flooding stress [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. SOD plays a crucial role in scavenging ROS generated during drought stress. It converts superoxide radicals into H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, which is then detoxified by CAT [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In summary, SOD plays a crucial role in scavenging ROS, while CAT aids in the detoxification of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, thereby maintaining the balance between ROS production and scavenging. In our study, it was observed the elevated activities of SOD and CAT in two organs of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings under FDAA and DT stress conditions. These findings were consistent with previous research on strawberries, where an increase in SOD and CAT activities in strawberry leaves exposed to drought stress [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Similar responses were also observed in \u003cem\u003eBupleurum chinense\u003c/em\u003e under drought stress [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In a study conducted on potato genotypes under water deficit conditions, it was observed that the activity of SOD, including Fe-SOD isoforms, resulted in an enhancement of water use efficiency (WUE) [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Bansal and Srivastava [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] also discovered that waterlogging triggered an increase in CAT and SOD activities in \u003cem\u003eCajanus cajan\u003c/em\u003e. Furthermore, the cultivar with higher waterlogging-resistance or drought resistance exhibit higher antioxidant enzyme activity [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Besides, a drought-resistant variety exhibits a more effective mechanism for scavenging ROS, as evidenced by a significant boost in the activity of the antioxidant enzyme SOD [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Compared to FDAA stress, SOD activities in leaves and roots were higher under DT condition. However, CAT activities presented the opposite performance. Combined the ROS condition, it was inferred that SOD activity was inhibited by FDAA stress to scavenge less O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings, which was displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Fig.\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Additionally, the higher CAT activity was accord with lower H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Fig.\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), due to the essential role of CAT in breaking down H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e into water and oxygen [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Impacts of FDAA on the key DEGs related to peroxisome pathway of \u003cem\u003eS. tonkinensis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eMultiple transcriptomic investigations have demonstrated a robust correlation between peroxisomal H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and oxidative stress. This suggests that the balance of redox homeostasis, which is connected to the NAD and NADP systems, could potentially regulate this interaction [\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Three genes in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, encoding catalase have been discovered. The expression of catalase-2 (\u003cem\u003eCAT2\u003c/em\u003e) is linked to the photorespiration pathway, while catalase-1 (\u003cem\u003eCAT1\u003c/em\u003e) expression is connected to fatty acid β-oxidation. catalase-3 (\u003cem\u003eCAT3\u003c/em\u003e), on the other hand, is associated with senescence processes [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In the study, the expression levels of \u003cem\u003eCAT\u003c/em\u003e were elevated in the leaves and roots of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings under both FDAA and DT stresses, except for the roots under FDAA condition, which was not obvious DEGs. In rice chloroplasts, the overexpression of a pea manganese \u003cem\u003eSOD\u003c/em\u003e gene (\u003cem\u003eMnSOD\u003c/em\u003e) controlled by an oxidative stress-inducible promoter \u003cem\u003eSWPA2\u003c/em\u003e has been found to enhance the drought tolerance of transgenic rice [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. In general, Cu/Zn family superoxide dismutase (\u003cem\u003eSOD1\u003c/em\u003e) and Fe/Mn family superoxide dismutase (\u003cem\u003eSOD2\u003c/em\u003e) genes in \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings were upregulated to combat abiotic stresses. Nevertheless, \u003cem\u003eSOD2\u003c/em\u003e presented obviously downregulated in the leaves under DT condition. In \u003cem\u003eA. thaliana\u003c/em\u003e plants, exposure to salt stress leads to the upregulation of three peroxisome-associated genes: thiolase \u003cem\u003e(PED1\u003c/em\u003e), peroxin-10 (\u003cem\u003ePEX10\u003c/em\u003e), and peroxin-1 (\u003cem\u003ePEX1\u003c/em\u003e) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. In \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings, peroxin-11 (\u003cem\u003ePEX11\u003c/em\u003e) gene, which medicates peroxisome proliferation was obviously upregulated in roots after FDAA treatment [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Furthermore, \u003cem\u003ePEX3\u003c/em\u003e in leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e exhibited the highest expression level (18.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38a), and showed a significant difference compared to other samples. However, the expression level of peroxin-14 (\u003cem\u003ePEX14\u003c/em\u003e) gene displayed the opposite trend in leaves under DT stress. \u003cem\u003eMVP17\u003c/em\u003e is able to encode a peroxisomal protein producing ROS, which might regulate the activity of antioxidant enzymes [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Under FDAA circumstance, \u003cem\u003eMPV17\u003c/em\u003e genes showed the opposite reaction in leaves and roots with upregulation and downregulation, respectively.\u003c/p\u003e \u003cp\u003e \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings exhibited varied gene expression patterns in response to abiotic stresses, which subsequently influenced various physiological parameters. The morphological changes in leaves, roots, and overall seedlings served as tangible reflections of how \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings responded to these stresses (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In more specific terms, the downregulation of the \u003cem\u003eMPV17\u003c/em\u003e gene in the roots of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings resulted in reduced ROS production. Under DT stress, three DEGs of \u003cem\u003eMPV17\u003c/em\u003e were negatively regulated on the average in leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings. On the contrary, six DEGs of \u003cem\u003eMPV17\u003c/em\u003e were all positively regulated in roots of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings (Tab.S4). This suggests that excessive ROS production in the peroxisomes of the roots was not the primary cause of root necrosis under FDAA conditions. It can be inferred that ROS-induced damage primarily accumulated in the leaves of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings during FDAA stress. However, the roots seemed to undergo some recovery during the transition from waterlogging to drought stress, enabling them to engage in aerobic respiration. When considering the biomass index, it becomes evident that substantial water loss, particularly in the leaves, may be the primary factor contributing to the eventual demise of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings under FDAA conditions. This is because the cumulative damage resulting from water loss exacerbates the stress on the seedlings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe entire study progressively reveals the responses of \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings to rapid shifts from waterlogging to drought stress, examining their responses at the morphological, physiological, and molecular levels. Additionally, it compares the effects of DT stress and FDAA stress on the seedlings. Furthermore, we observed that the accumulation of ROS induced by waterlogging and drought stress during this rapid transition is additive, with the primary damage occurring predominantly in the leaf tissues. The ultimate cause of plant mortality may be attributed to water loss during the waterlogging phase, diminished root water uptake capacity, and continued water loss during the subsequent drought period.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANOVA: analysis of variance; BLAST: basic local alignment search tool;\u0026nbsp;CAT: catalase;\u0026nbsp;\u003cem\u003eCAT1\u003c/em\u003e:catalase-1;\u0026nbsp;\u003cem\u003eCAT2\u003c/em\u003e: catalase-2;\u0026nbsp;\u003cem\u003eCAT3\u003c/em\u003e: catalase-3;\u0026nbsp;CK: control groups;\u0026nbsp;CKL: leaf sample in control groups;\u0026nbsp;CKR: root sample in control groups;\u0026nbsp;D: ground diameter;\u0026nbsp;DAB: 3,3\u0026apos;-diaminobenzidine; DEGs: differentially expressed genes; DT: drought stress;\u0026nbsp;DTL: leaf sample under drought stress;\u0026nbsp;DTR: root sample under drought stress; eggnog: evolutionary genealogy of genes: non-supervised orthologous groups;\u0026nbsp;FDAA: flood-drought abrupt alternation;\u0026nbsp;FDAAL: leaf sample under flood-drought abrupt alternation;\u0026nbsp;FDAAR: root sample under flood-drought abrupt alternation; FPKM: fragments per kilobase per million mapped reads; GO: gene ontology;\u0026nbsp;H: seedling height;\u0026nbsp;H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e: hydrogen peroxide; KEGG: Kyoto encyclopedia of genes and genomes; KOG: clusters of orthologous groups;\u0026nbsp;MDA: malondialdehyde; \u003cem\u003eMnSOD\u003c/em\u003e: manganese \u003cem\u003eSOD\u0026nbsp;\u003c/em\u003egene;\u0026nbsp;\u003cem\u003eMPV17\u003c/em\u003e:Protein Mpv17;\u0026nbsp;NB: negative binomial;\u0026nbsp;NBT: nitrozolium blue tetrachloride;\u0026nbsp;O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e:superoxide anion;\u0026nbsp;PBS: phosphate buffer solution;\u0026nbsp;\u003cem\u003ePED1\u003c/em\u003e:thiolase;\u0026nbsp;\u003cem\u003ePEX1\u003c/em\u003e:peroxin-1; \u003cem\u003ePEX10\u003c/em\u003e: peroxin-10; \u003cem\u003ePEX11\u003c/em\u003e:peroxin-11; \u003cem\u003ePEX14\u003c/em\u003e:peroxin-14;\u0026nbsp;\u003cem\u003ePMP34\u003c/em\u003e: peroxisomal adenine nucleotide transporter; ROS: reactive oxygen species;\u0026nbsp;SOD: superoxide dismutase;\u0026nbsp;\u003cem\u003eSOD1\u003c/em\u003e:Cu/Zn family superoxide dismutase\u003cem\u003e; SOD2\u003c/em\u003e:Fe/Mn family superoxide dismutase; SwissProt: Swiss-Prot protein; TCA: trichloroacetic acid.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Chen Chen, Ming Ni and Guangtao Zhang for their experimental assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHC and FY designed the research. HC carried out all experiments, analyzed the data and wrote manuscript. HC, LC and ZL assisted at conducting experiments, analyzing data. FY guided in editing manuscript. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [Grant number 3197140894] and Jiangsu Graduate Scientific Research Innovation Project [Grant number KYCX21_0914].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA transcripts accession number is: \u003cstrong\u003eCRA012350\u003c/strong\u003e, which can be found in the following link: https://bigd.big.ac.cn/gsa/browse/CRA012350\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFabian PS, Kwon HH, Vithanage M, Lee JH. Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: a review. Environ Res. 2023;225:115617.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu J, Li A, Zhang J, Sun C, Tang G, Chen L, et al. Effects of nitrogen application after abrupt drought-flood alternation on rice root nitrogen uptake and rhizosphere soil microbial diversity. Environ Exp Bot. 2022;201:105007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHochman A, Marra F, Messori G, Pinto JG, Raveh-Rubin S, Yosef Y, et al. Extreme weather and societal impacts in the eastern Mediterranean. Earth Syst Dynam. 2022;13:749\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakamura T, Sato T. A possible linkage of Eurasian heat wave and East Asian heavy rainfall in Relation to the rapid arctic warming. Environ Res. 2022;209:112881.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Camp M, Viron O, Dassargues A, Delobbe L, Chanard K, Gobron K. Extreme hydrometeorological events, a challenge for gravimetric and seismology networks. Earths Future. 2022;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams AP, Cook BI, Smerdon JE. Rapid intensification of the emerging southwestern North American megadrought in 2020\u0026ndash;2021. Nat Clim Chang. 2022;12:232\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi W, Weng B, Yan D, Wang M, Wang H, Jing L, et al. Soil phosphorus loss increases under drought-flood abrupt alternation in summer maize planting area. Agr Water Manage. 2022;262:107426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi W, Huang S, Zhang K, Liu B, Liu D, Huang Q, et al. Quantifying the superimposed effects of drought-flood abrupt alternation stress on vegetation dynamics of the Wei River Basin in China. J Hydrol. 2022;612:128105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Weng B, Bi W, Xu T, Yan D, Ma J. Climate change impacts on drought-flood abrupt alternation and water quality in the Hetao Area, China. Water. 2019;11:652.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong Q, Zhong L, Du J, Zhu C, Peng X, He X, et al. Ribosome profiling reveals the effects of nitrogen application translational regulation of yield recovery after abrupt drought-flood alternation in rice. Plant Physiol Bioch. 2020;155:42\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, You Q, Ullah S, Chen C, Shen L, Liu Z. Substantial increase in abrupt shifts between drought and flood events in China based on observations and model simulations. Sci Total Environ. 2023;876:162822.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Y, Yang Y, Wang C. How essential of the balance between large and small scale features to reproduce precipitation during a sudden sharp turn from drought to flood. Clim Dyn. 2019;52:5013\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan W, Zou S, Christidis N, Schaller N, Chen Y, Sahu N, et al. Changes in temporal inequality of precipitation extremes over China due to anthropogenic forcings. npj Clim Atmos Sci. 2022;5:33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou P, Liu Z. Likelihood of concurrent climate extremes and variations over China. Environ Res Lett. 2018;13:094023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi W, Weng B, Yuan Z, Yang Y, Xu T, Yan D, et al. Evolution of drought\u0026ndash;flood abrupt alternation and its impacts on surface water quality from 2020 to 2050 in the Luanhe River Basin. IJERPH. 2019;16:691.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang S, Hou B, Chang J, Huang Q, Chen Y. Copulas-based probabilistic characterization of the combination of dry and wet conditions in the Guanzhong Plain, China. J Hydrol. 2014;519:3204\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Y, Hu T, Wang Q, Yuan H, Yang J. Effect of drought-flood abrupt alternation on rice yield and yield components. Crop Sci. 2019;59:280\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian L, Meng H, Chen X, Tang R. Evaluating agricultural drought and flood abrupt alternation: a case study of cotton in the middle-and-lower Yangtze River, China. Agr Water Manage. 2023;283:108313.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrisson N, Rebiere B, Zimmer D, Renault P. Response of the root system of a winter wheat crop to waterlogging. Plant Soil. 2002;243:43\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu WL, Li ZL, Chen QJ, Sun YW, Zhai S, Lu F, et al. Triterpenes and lignans from the leaves of \u003cem\u003eStyrax tonkinensis\u003c/em\u003e. Biochem Syst Ecol. 2019;86:103891.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe X, Shida S, Saito Y, Momohara I. Effect of heat treatment on bending strength and decay resistance of \u003cem\u003eStyrax tonkinensis\u003c/em\u003e wood. Wood Preservation. 2006;32:7\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang F, Zhang L, Zhang Q, Wang S-M, Fang Z-F. Neolignan and phenylpropanoid compounds from the resin of \u003cem\u003eStyrax tonkinensis\u003c/em\u003e. J Asian Nat Prod Res. 2021;23:527\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen C, Chen H, Han C, Liu Z, Yu F, Wu Q. 24-Epibrassinolide promotes fatty acid accumulation and the expression of related genes in \u003cem\u003eStyrax tonkinensis\u003c/em\u003e seeds. Int J Mol Sci. 2022;23:8897.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki C, Negoro H, Asada C, Nakamura Y. Microwave-assisted glucose production from bode (\u003cem\u003eStyrax tonkinensis\u003c/em\u003e) woody biomass for bioethanol production. J Mater Cycles Waste Manag. 2019;21:201\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Cao Y, Chen C, Gao Z, Yu F, Guy RD. Transcriptome analysis of metabolic pathways associated with oil accumulation in developing seed kernels of \u003cem\u003eStyrax tonkinensis\u003c/em\u003e, a woody biodiesel species. BMC Plant Biol. 2020;20:121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Zhao X, Chen C, Zhang Z, Yu F. Metabolite profiling and classification of developing \u003cem\u003eStyrax tonkinensis\u003c/em\u003e kernels. Metabolites. 2020;10:21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Wu Q, Ni M, Chen C, Han C, Yu F. Transcriptome analysis of endogenous hormone response mechanism in roots of \u003cem\u003eStyrax tonkinensis\u003c/em\u003e under waterlogging. Front Plant Sci. 2022;13:896850.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Luo Y, Wang X, Yu F. Fruit Spray of 24-Epibrassinolide and fruit shade alter pericarp photosynthesis activity and seed lipid accumulation in \u003cem\u003eStyrax tonkinensis\u003c/em\u003e. J Plant Growth Regul. 2018;37:1066\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan L, Zhang L, Song J, Zhang Y, She D, Xia J. Characteristics of dry-wet abrupt alternation events in the middle and lower reaches of the Yangtze River Basin and the relationship with ENSO. J Geogr Sci. 2018;28:1039\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa C, Liu H, Guo H, Musante C, Coskun SH, Nelson BC, et al. Defense mechanisms and nutrient displacement in Arabidopsis thaliana upon exposure to CeO\u003csub\u003e2\u003c/sub\u003e and In\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles. Environ Sci: Nano. 2016;3:1369\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCakmak I, Horst WJ. Effect of aluminium on lipid peroxidation, superoxide dismutase, catalase, and peroxidase activities in root tips of soybean (\u003cem\u003eGlycine max\u003c/em\u003e). Physiol Plant. 1991;83:463\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur N, Sharma I, Kirat K, Pati P. Detection of reactive oxygen species in \u003cem\u003eOryza sativa\u003c/em\u003e L. (Rice). Bio-protocol. 2016;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeauchamp C, Fridovich I. Superoxide dismutase: improved assays and an assay applicable to acrylamide gels. Anal Biochem. 1971;44:276\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2007;36 Database:D480\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Uuml;nyayar S, Keleş Y, \u0026Ccedil;eki\u0026ccedil; F\u0026Ouml;. The antioxidative response of two tomato species with different drought tolerances as a result of drought and cadmium stress combinations. Plant Soil Environ. 2005;51:57\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahlou O, Ouattar S, Ledent J-F. The effect of drought and cultivar on growth parameters, yield and yield components of potato. Agronomie. 2003;23:257\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilroy SP, Bange MP, Thongbai P. Cotton leaf nutrient concentrations in response to waterlogging under field conditions. Field Crop Res. 2009;113:246\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAroca R, Porcel R, Manuel Ruiz-Lozano J. Regulation of root water uptake under abiotic stress conditions. J Exp Bot. 2012;63:43\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuyang X, An M, Xu L, Han L, Zhang X. Antioxidant responses to waterlogging stress and subsequent recovery in two Kentucky bluegrass (\u003cem\u003ePoa pratensis\u003c/em\u003e L.) cultivars. Acta Physiol Plant. 2015;37:197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBose J, Rodrigo-Moreno A, Shabala S. ROS homeostasis in halophytes in the context of salinity stress tolerance. J Exp Bot. 2014;65:1241\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasanuzzaman M, Raihan MRH, Masud AAC, Rahman K, Nowroz F, Rahman M, et al. Regulation of reactive oxygen species and antioxidant defense in plants under salinity. Int J Mol Sci. 2021;22:9326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh A, Kumar A, Yadav S, Singh IK. Reactive oxygen species-mediated signaling during abiotic stress. Plant Gene. 2019;18:100173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel R\u0026iacute;o LA, L\u0026oacute;pez-Huertas E. ROS Generation in peroxisomes and its role in cell signaling. Plant Cell Physiol. 2016;076.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoctor G. Drought and oxidative load in the leaves of C3 plants: a predominant role for photorespiration? Ann Bot. 2002;89:841\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng J, Ying Q, Fang C, Sun N, Si M, Yang J, et al. Alternative oxidase pathway is likely involved in waterlogging tolerance of watermelon. Sci Hortic. 2021;278:109831.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe L, Li B, Lu X, Yuan L, Yang Y, Yuan Y, et al. The effect of exogenous calcium on mitochondria, respiratory metabolism enzymes and ion transport in cucumber roots under hypoxia. Sci Rep. 2015;5:11391.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDa-Silva CJ, do Amarante L. Time-course biochemical analyses of soybean plants during waterlogging and reoxygenation. Environ Exp Bot. 2020;180:104242.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunn\u0026eacute;-Bosch S, Alegre L. Die and let live: leaf senescence contributes to plant survival under drought stress. Funct Plant Biol. 2004;31:203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee S, Park C-M. Regulation of reactive oxygen species generation under drought conditions in \u003cem\u003eArabidopsis\u003c/em\u003e. Plant Signal Behav. 2012;7:599\u0026ndash;601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoyer CH, Noctor G. Redox signaling in plants. Antioxid Redox Signal. 2013;18:2087\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz de Carvalho MH. Drought stress and reactive oxygen species: production, scavenging and signaling. Plant Signal Behav. 2008;3:156\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim MJ, Ciani S, Schachtman DP. A peroxidase contributes to ROS production during \u003cem\u003eArabidopsis\u003c/em\u003e root response to potassium deficiency. Mol Plant. 2010;3:420\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra N, Jiang C, Chen L, Paul A, Chatterjee A, Shen G. Achieving abiotic stress tolerance in plants through antioxidative defense mechanisms. Front Plant Sci. 2023;14:1110622.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMittler R. Oxidative stress, antioxidants and stress tolerance. Trends Plant Sci. 2002;7:405\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePisoschi AM, Pop A. The role of antioxidants in the chemistry of oxidative stress: a review. Eur J Med Chem. 2015;97:55\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZainab Q, Tanees CM, Du X, Lori H, Tehseen AM. Review of oxidative stress and antioxidative defense mechanisms in \u003cem\u003eGossypium hirsutum\u003c/em\u003e L. in response to extreme abiotic conditions. J Cotton Res. 2021;4:9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun C, Li X, Hu Y, Zhao P, Xu T, Sun J, et al. Proline, sugars, and antioxidant enzymes respond to drought stress in the leaves of strawberry plants. Korean J Hortic Sci Tech. 2015;33:625\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Yang L, Lan Y, Zhao Y, Han M, Yang L. Exogenous abscisic acid reduces saikosaponin accumulation by inhibiting saikosaponin synthesis pathway gene expression under drought stress in \u003cem\u003eBupleurum chinense\u003c/em\u003e DC. Ind Crop Prod. 2020;154:112686.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahimi M, Kordrostami M, Mohamadhasani F, Chaeikar SS. Antioxidant gene expression analysis and evaluation of total phenol content and oxygen-scavenging system in tea accessions under normal and drought stress conditions. BMC Plant Biol. 2021;21:494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal R, Srivastava JP. Erratum to: Antioxidative defense system in pigeonpea roots under waterlogging stress. Acta Physiol Plant. 2012;34:1595\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBano A, Ullah F, Nosheen A. Role of abscisic acid and drought stress on the activities of antioxidant enzymes in wheat. Plant Soil Environ. 2012;58:181\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei W, Li D, Wang L, Ding X, Zhang Y, Gao Y, et al. Morpho-anatomical and physiological responses to waterlogging of sesame (\u003cem\u003eSesamum indicum\u003c/em\u003e L). Plant Sci. 2013;208:102\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones DP, Sies H. The redox code. Antioxid Redox Signal. 2015;23:734\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandalio LM, Romero-Puertas MC. Peroxisomes sense and respond to environmental cues by regulating ROS and RNS signalling networks. Ann Bot. 2015;116:475\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun B-W, Spoel SH, Loake GJ. Synthesis of and signalling by small, redox active molecules in the plant immune response. Biochim Biophys Acta-Gen Subj. 2012;1820:770\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMhamdi A, Noctor G, Baker A. Plant catalases: Peroxisomal redox guardians. Arch Biochem Biophys. 2012;525:181\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMhamdi A, Queval G, Chaouch S, Vanderauwera S, Van Breusegem F, Noctor G. Catalase function in plants: a focus on Arabidopsis mutants as stress-mimic models. J Exp Bot. 2010;61:4197\u0026ndash;220.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang FZ, Wang QB, Kwon SY, Kwak SS, Su WA. Enhanced drought tolerance of transgenic rice plants expressing a pea manganese superoxide dismutase. J Plant Physiol. 2005;162:465\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharlton WL, Matsui K, Johnson B, Graham IA, Ohme-Takagi M, Baker A. Salt-induced expression of peroxisome-associated genes requires components of the ethylene, jasmonate and abscisic acid signaling pathways. Plant Cell Environ. 2005;28:513\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchrader M, Reuber BE, Morrell JC, Jimenez-Sanchez G, Obie C, Stroh TA, et al. Expression of \u003cem\u003ePEX11β\u003c/em\u003e mediates peroxisome proliferation in the absence of extracellular stimuli. J Biol Chem. 1998;273:29607\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida R, Yasuda T, Tsubota E, Takatsuka H, Matsuki T, Kishi K. Human Mpv17-like protein is localized in peroxisomes and regulates expression of antioxidant enzymes. Biochem Bioph Res Co. 2006;344:948\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZwacka RM, Reuter A, Pfaff E, Moll J, Gorgas K, Karasawa M, et al. The glomerulosclerosis gene \u003cem\u003eMpv17\u003c/em\u003e encodes a peroxisomal protein producing reactive oxygen species. EMBO J. 1994;13:5129\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"flood-drought abrupt alternation, drought stress, antioxidant system enzymes, reactive oxygen species","lastPublishedDoi":"10.21203/rs.3.rs-3708391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3708391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eStyrax tonkinensis\u003c/em\u003e (Pierre) Craib ex Hartwich is a promising oil species with excellent fatty acid composition, making it a potential candidate for biofuel production. However, its expansion in the south provinces of Yangtze River region has been hindered by climate extremes such as flood-drought abrupt alternation (FDAA), which is caused by global warming. This species has low tolerance to waterlogging and drought, further restricting its growth in this region. To investigate the antioxidant system and the molecular response related to peroxisome pathway of \u003cem\u003eS. tonkinensis\u003c/em\u003e under FDAA, we conducted FDAA and drought (DT) experiments on two-years old seedlings. We measured various growth indexes, reactive oxygen species content, the activity of two antioxidant enzymes and analyzed transcriptome of its seedlings under FDAA and DT conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results displayed that the reduction in fresh weight was mainly observed in the leaves under FDAA condition. Through transcriptome analysis, we assembled a total of 1,111,088 unigenes (1,111,628,179 bp). We analyzed the differentially expressed genes (DEGs) related to reactive oxygen species (ROS) and antioxidant system. Generally, \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eSOD2\u003c/em\u003e genes in \u003cem\u003eS. tonkinensis\u003c/em\u003e seedlings were upregulated to combat abiotic stresses. Our findings revealed that ROS accumulation was predominantly observed in leaves rather than roots under FDAA. Under FDAA circumstance, Protein Mpv17 (\u003cem\u003eMPV17\u003c/em\u003e) showed the opposite reaction in leaves and roots with upregulation and downregulation, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe ROS generation triggered by \u003cem\u003eMPV17\u003c/em\u003e genes was not the main reason for the eventual mortality of the plant. Instead, plant mortality may be attributed to water loss during the waterlogging phase, decreased root water uptake capacity, and continued water loss during the subsequent drought period. This study establishes a scientific foundation for comprehending the morphological, physiological, and molecular facts of \u003cem\u003eS. tonkinensis\u003c/em\u003e under FDAA conditions.\u003c/p\u003e","manuscriptTitle":"Transcriptome analysis of antioxidant system response in Styrax tonkinensis seedlings under flood- drought abrupt alternation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-08 20:36:32","doi":"10.21203/rs.3.rs-3708391/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-01T16:55:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-01T02:55:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1a26a834-199c-472e-8f1e-e8fd1a2429a2","date":"2024-01-12T01:34:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-11T09:34:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-10T05:20:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"06eb8720-9b6b-4083-8b44-e2a778c3222f","date":"2024-01-09T06:37:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10405bb4-f103-494a-9626-1a4ccbb79477","date":"2023-12-21T06:21:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5bdb9a1a-ad6d-4869-9c56-b70b56fc4fc8","date":"2023-12-09T13:18:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-06T13:00:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-06T11:46:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-06T11:45:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2023-12-05T07:22:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53478cdc-dba6-4868-be4d-f3d200f8f0c0","owner":[],"postedDate":"December 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-10T10:51:43+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-08 20:36:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3708391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3708391","identity":"rs-3708391","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}