Transcriptome analysis of gibberellins and abscisic acid during the flooding response in Fokienia hodginsii

Shunde Su; Tengfei Zhu; Jun Su; Jian Li; Qing Zhao; Xiangyang Kang; Renhua Zheng

doi:10.1371/journal.pone.0263530

Abstract

Flooding is one of the main abiotic stresses suffered by plants. Plants respond to flooding stress through regulating their morphological structure, endogenous hormone biosynthesis, and genetic signaling transduction. We previously found that Fokienia hodginsii varieties originating from Gutian exhibited typical flooding tolerance traits compared to three other provenances (Yongzhou, Sanming, Nanping), expressed as increased height, longer diameter at breast height (DBH), and smaller branch angle. Herein, the changes in endogenous gibberellins (GA) and abscisic acid (ABA) contents were measured under flooding stress in F. hodginsii, and ABA was found to decrease, whereas GA increased with time. Furthermore, the GA and ABA contents of the varieties originating from Gutian and the three other provenances were measured, and the results indicated that F. hodginsii from Gutian could respond more rapidly to flooding stress. The transcriptomes of the varieties originating from Gutian and the other three provenances were compared using RNA sequencing to explore the underlying genetic mechanisms of the flood-resistant phenotypes in F. hodginsii. The results indicated that two flood-stress response genes (TRINITY_DN142_c0_g2 and TRINITY_DN7657_c0_g1) were highly related to both the ABA and GA response in F. hodginsii.

Citation: Su S, Zhu T, Su J, Li J, Zhao Q, Kang X, et al. (2022) Transcriptome analysis of gibberellins and abscisic acid during the flooding response in Fokienia hodginsii. PLoS ONE 17(2): e0263530. https://doi.org/10.1371/journal.pone.0263530

Editor: Frances Sussmilch, University of Tasmania, AUSTRALIA

Received: August 12, 2021; Accepted: January 21, 2022; Published: February 11, 2022

Copyright: © 2022 Su et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The sequence data reported in this study have been deposited in the Genome Sequence Archive in BIG Data Center, Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, under accession numbers CRA005262, that are publicly accessible at https://bigd.big.ac.cn/gsa/browse/CRA005262.

Funding: the "Eagle Program" of Fujian Province, funded by the Department of Human Resources and Social Security of Fujian Province; 2. the "Fujian Cypress Generation 1 Core Breeding Population Construction Research" (No. 2021R1010004), funded by the Department of Science and Technology of Fujian Province. Role played by the funder in the research: is the one who sets the direction of the research and provides financial support. No author receives a salary from the research project, all funds are used for research.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Water is essential for plant growth, but excess water, such as that caused by flooding, may severely limit plant growth. Under flooding conditions, plants are typically submerged or waterlogged [1, 2], and the resulting O₂ deficiency can cause damage [3]. Plants have a variety of adaptive mechanisms to avoid O₂ deficiency, such as aerenchyma formation, adventitious root formation, and the control of shoot, petiole, or internode elongation [1]. The adaptation of plants to long-term flooding also leads to variations in metabolism, hormone content, and enzyme systems [4]. These are considered as powerful drivers of adaptive evolution, leading to a wide range of physiological, molecular, and morphological adaptations [5, 6].

Plants balance the synthesis and transport of plant hormones and regulate the response to waterlogging via complex signaling processes [7–9]. Over the past few decades, numerous studies have shown that ABA and GA have antagonistic effects on many developmental processes in plants, including seed maturation, seed dormancy and germination, root initiation, hypocotyl and stem elongation, and flower transition. In addition, ABA, a well-established stress hormone, plays a key role in plant responses to abiotic stresses such as drought, flooding, salt and low temperature. Interestingly, recent evidence suggests that GA is also involved in plant responses to adverse environmental conditions. Thus, the complex crosstalk network between ABA and GA, mediated by different key regulatory factors, has been extensively investigated and recorded [10]. Under flooding stress, gibberellins (GAs), and abscisic acid (ABA) play the most crucial roles.

The expression of GA is induced directly or indirectly by ethylene. GAs are directly involved in escaping flooding stress in both DELLA (N-terminal D-E-L-L-A amino acid sequence) pathways [11, 12]. Ethylene and ABA are directly responsible for stomatal closure under waterlogging stress, whereas GAs are responsible for stomatal opening. In summary, GA and ABA play influential roles in resisting submergence stress, whereby ABA biosynthesis is reduced and GA signaling is induced for shoot elongation [13–15].

Fokienia hodginsii is mainly distributed in the humid climate zone of the central subtropics and is an important constituent species of subtropical evergreen broad-leaved forests and mixed natural forests of coniferous and broad-leaved species [16]. It is cultivated widely within its range as an excellent timber tree species [17]. It takes about 5–7 years from germination to flowering, with flowering in March-April and fruiting in October-November.

We established multi-generation seed orchards by means of genetic techniques, including seed source tests and determination of offspring of superior trees. During this process, we discovered that F. hodginsii from Gutian had a distinctive phenotype that differed from the other provenances.

According to historical information, the large-scale construction of reservoirs since 1956 submersed most of the region of Gutian, and the provenance of F. hodginsii from Gutian was collected in the late 1970s [18]. Therefore, we hypothesized that the phenotypes of F. hodginsii in the Gutian area may have been shaped by flooding stress. This study will reveal, for the first time, the molecular mechanism of flooding resistance-related phenotypes in F. hodginsii and enrich the understanding of the molecular basis of flooding resistance in woody plants.

Materials and methods

Different provenances of F. hodginsii

In this research, the wild type was collected from Forest Park in Fuzhou, Fujian, China (26°4′27.12″N 119°17′49.20″E); the provenance 1–Yongzhou was collected in Hunan, China (26°25′26.25″N 111°36′25.17″E); the provenance 2–Sanming was collected in Fujian, China (117°38′20.40″N 26°15′50.04″E); the provenance 3–Nanping was collected in Fujian, China (118°4′51.29″ N 27°22′57.88″E); and the provenance 4–Gutian was collected in Fujian, China (118°48′35.72″N 26°33′20.19″E).

Plant growth conditions

The seedling of F. hodginsii were grown under hydroponics in nutrient solution (10 g/L Huaduoduo 2# Peters®10-30-20+TE ICL Specialty Fertilizers) for two weeks. The growth conditions included 22°C,16-h light/8-h dark cycle, 60 μmol·m²·s^-1 photon flux density, and 85% relative humidity.

Flooding assay

In the flooding stress experiment, the 2-week-old seedlings were completely immersed in purified water for 6 h/12 h/24 h/72 h, and there were three replicates per treatment and 15 samples per replicate, all treatments were performed under complete dark conditions that included 22°C and 85% relative humidity.

Simple Sequence Repeat (SSR) assay

Genomic DNA was extracted from the fresh leaves (100mg) of the four provenances of F. hodginsii using the cetyltrimethylammonium bromide (CTAB) method [19]. We used the assembled contig data (at least 200 bp) to search for hexanucleotide repeats of SSR loci. Identified SSR loci were then grouped into different classes. The SSR locus density was determined based on the frequency of SSR loci and the total length of contigs containing SSRs. Eleven pairs of SSR primers were used to analyse [20] (S1 Table). We also evaluated the motif length, loci numbers, and mean repeat numbers for the selected repetitive motifs [21].

The polymerase chain reaction (PCR) was conducted in a final volume of 20 μL containing 1 μL genomic DNA, 1 μL each of forward and reserve primer (10 μM), 10 μL of Dream Taq^TM Green PCR Master Mix (Thermo Fisher Scientific, USA), and 7 μL ddH₂O. The following PCR conditions were used: an initial denaturation of 95°C for 2 min; 35 cycles of 95°C for 30 s, annealing temperature of 53°C for 60 s, and 72°C for 35 s; followed by a 10-min extension at 72°C. The amplified products were evaluated on 5% agarose gel using the BIO-RAD Gel Doc^TM XR+. Fragment sizes were determined using Image Lab software version 6.0.

Quantification of endogenous ABA and GA₃

The contents of endogenous ABA and GA₃ were determined via high-performance liquid chromatography-mass spectrometry (HPLC-MS) [22].

Metabolites extraction.

The samples were precisely weighed to Eppendorf tubes. After adding two small steel balls and 1000 μL of extraction solution (precooled at -20°C, acetonitrile-methanol-water, 2:2:1), the samples were vortexed for 30 s, and then were homogenized for 4 min at 40 Hz and sonicated for 5 min in ice-water bath. The homogenate and sonicate circle were repeated three times, followed by incubation at -40°C for 1 h and centrifugation at 12000 rpm (RCF = 13800(×g), R = 8.6cm) and 4°C for 15 min. An 80 μL aliquot of the clear supernatant was transferred to an auto-sampler vial for UHPLC-MS/MS analysis.

Standard solution preparation.

Stock solutions were individually prepared by dissolving or diluting each standard substance to give a final concentration of 10 mmol/L. An aliquot of each of the stock solutions was transferred to a 10 mL flask to form a working standard solution. A series of calibration standard solutions were then prepared by stepwise dilution of this standard solution.

UHPLC-MRM-MS analysis.

The UHPLC separation was carried out using an ACQUITY UPLC-I/CLASS(Waters), equipped with a Waters ACQUITY UPLC^®BEH C18(100 × 2.1 mm, 1.7 μm, Waters). The mobile phase A was 0.1% formic acid in water, and the mobile phase B was methanol. The column temperature was set at 35°C. The auto-sampler temperature was set at 10°C and the injection volume was 2 μL. Waters Xevo TQ-S triple quadrupole mass spectrometer (Waters) was applied for assay development. Typical ion source parameters were: Capillary voltages = 3.5kV, Cone voltages = 42V, Desolvation Temperature = 650°C, Desolvation gas flow = 1000(L/Hr), Cone gas flow = 150(L/Hr), Nebuliser gas flow = 7.0(Bar) [23]. The MRM parameters for each of the targeted analytes were optimized using flow injection analysis, by injecting the standard solutions of the individual analytes, into the ESI source of the mass spectrometer. Several most sensitive transitions were used in the MRM scan mode to optimize the collision energy for each Q1/Q3 pair. Among the optimized MRM transitions per analyte, the Q1/Q3 pairs that showed the highest sensitivity and selectivity were selected as ‘quantifier’ for quantitative monitoring. The additional transitions acted as ‘qualifier’ for the purpose of verifying the identity of the target analytes. Skyline Software were employed for MRM data acquisition and processing.

Calibration curves.

Calibration solutions were subjected to UPLC-MRM-MS/MS analysis using the methods described above. The y is the peak areas for analyte, and x is the concentration (nmol/L) for analyte. Least squares method was used for the regression fitting. 1/x weighting was applied in the curve fitting since it provided highest accuracy and correlation coefficient (R2). The level was excluded from the calibration if the accuracy of calibration was not within 80% -120%.

Limit of Detection (LOD) and Limit of Quantitation (LOQ).

The calibration standard solution was diluted stepwise, with a dilution factor of 2. These standard solutions were subjected to UHPLC-MRM-MS analysis. The signal-to-noise ratios (S/N) were used to determine the lower limits of detection (LLODs) and lower limits of quantitation (LLOQs). The LLODs and LLOQs were defined as the analyte concentrations that led to peaks with signal-to-noise ratios (S/N) of 3 and 10, respectively, according to the US FDA guideline for bioanalytical method validation.

Total RNA extraction, cDNA reverse transcription, and quantitative real-time (qRT) PCR

A polysaccharide polyphenol plant total RNA extraction kit (TIANGEN) and 500 mg leaves were used to extract the total RNA of the samples, extraction procedure according to instructions and a 1.2% agarose gel was used to detect the quality of the RNA and measured the RNA concentration, absorbance value, 260/230, 260/280 before reverse transcription. The strip was clear and non-dispersive, and the brightness of the 28S band was about twice as bright as the 18S band, confirming that the extract could be used for subsequent analysis.

For the RT-qPCR(QuantStudio™ 7 Flex), 1 μg of total RNA was treated with DNase Ⅰ and used for cDNA synthesis with a YEASEM Hifair®Ⅱ 1st Strand cDNA Synthesis SuperMix for qPCR Kit (CAT:1123ES60). ACT7 was selected as internal marker gene for real-time fluorescence quantification, and the primers were designed using the Primer 3.0 website. The primers used in this study are listed in S1 Table. The PCR was performed with SYBR-Green PCR Mastermix (CAT:11202ES08). The expression levels of the tested genes were estimated using the relative 2^−△△CT method [24].

RNA-Seq analysis and bioinformatics

The samples for RNA-Seq included the 2-week-old seedlings treated with 1 μM GA₃ and 20 μM ABA [25, 26]. The seedling were grown under hydroponics, the 1 μM GA3 and 20 μM ABA treatments through adding the appropriate amount of powder to the culture solution to achieve the required concentration. Three varieties of seedlings from the same provenance were mixed together and frozen in liquid nitrogen. There were three biological replicates per treatment and three samples per replicate. The RNA extractions and RNA-Seq analysis of the leaves from F. hodginsii followed previous methods [27].

RNA-Seq was performed using the Illumina NovaSeq 6000 with 6G of data and Poly (A) RNA from 1 mg total RNA or purified mRNA and purified m6A-containing fragments were used to generate the cDNA libraries, respectively, according to TruSeq RNA Sample Prep Kit protocol. The sample library type was a eukaryotic unstranded specific transcriptome library. The trimming of raw data resulted in only the uniquely aligned reads, and a fold-change in expression of >1.3 and a false-discovery rate (FDR) <0.01 were set as the thresholds for designating the differentially expressed genes [28].

For the raw reads obtained from transcriptome sequencing, cut adapt and Perl scripts were used to remove splice sequences as well as low-quality sequences with a length <100 bp, <5% unknown bases, and bases with a quality value Q≤30 accounting for more than 20% of the whole sequence in the raw data, followed by verification of data quality using FASTQ. Analysis without a reference transcriptome was used, as no reference genome was available or the genome annotation information was incomplete.

The unigenes were annotated and classified according to GO (Gene Ontology) [2]. Based on this, differentially expressed genes were identified and subjected to GO term enrichment analysis. All samples were filtered using the transcriptome assembly software Trinity (Broad Institute, USA), and the resulting clean reads were assembled from scratch to obtain long fragments (contigs), which were assembled into sequences that could not be further extended at both ends, which were then finally normalized to unigenes. The unigenes were then evaluated for assembly quality, including length, GC content (GC%), and N50 [29].

The obtained unigenes were annotated against GO for functional annotation and classification, and differential expression analysis was performed on unigenes from different groups to find differentially expressed genes among the samples from the different localities of F. hodginsii.

Statistical analyses

All data are from biological replicates. Data were analysed using one-way analysis of variance (ANOVA) tests (Tukey’s post-hoc test), * and ** indicate significant differences (P<0.05 and P<0.01, respectively). Pairwise comparison was performed by Welch’s t-test and different upper and lowercase letters indicated the variability analysis of the amount of change in the same object at different times. GraphPad v.8 was used to perform statistical analysis of the data.

Results

Phenotype of F. hodginsii from four provenances

The phenotypic data of F. hodginsii from four provenances collected at roughly the same latitude were analyzed (Fig 1A), and thus the annual rainfall and average temperature were also approximately the same. Based on phenotypic observation and data analysis, we found that F. hodginsii from Gutian was significantly different from the other provenances. The average clear height of F. hodginsii from P1, P2, P3, and P4 was 4.31 m, 4.36 m, 2.58 m, and 9.45 m, respectively, with P4 being the tallest and also significantly different from the other three. In terms of diameter at breast height (DBH), P1 was 26.65 cm, P2 was 29.16 cm, P3 was 30.18 cm, and P4 was 52.56 cm. P4 also had the greatest DBH and was highly significantly different from the others. In terms of branching angle, P4 was 72.53° and was smaller than the other three, which were 85.65°, 82.14°, and 73.75°. There was a highly significant difference between P1 and P4 and a significant difference between P2 and P4, but no significant difference was detected between P3 and P4. In general, these features are consistent with flood tolerance (Fig 1B).

Download:

Fig 1. Geographical distribution and phenotypic differences among F. hodginsii from four provenances.

(A) The geographical distribution of the four provenances, almost at the same latitude. Provenance 1 is from Yongzhou, Hunan (average temperature 18.6°C, annual rainfall 1507 mm); provenance 2 is from Sanming, Fujian (average temperature 18.3°C, annual rainfall 1688 mm); provenance 3 is from Nanping, Fujian (average temperature 18.3°C, annual rainfall 1627 mm); provenance 4 is from Gutian, Fujian (average temperature 18.3°C, annual rainfall 1579 mm). This picture is from USGS National Map Viewer (https://apps.nationalmap.gov/viewer/). (B) The phenotypic characteristics of the four provenances were statistically analyzed based on height (m), diameter at breast height (cm), crown diameter (m), clear height (m), and branch angle (°). Height is measured as the distance from the root on the ground to the top of the tree. DBH measures the diameter of a tree 1.3-m above the ground. Crown diameter is the arithmetic mean of the maximum and minimum numbers. Clear height calculates the height of the trunk. Branch angle represents the angle of the outermost branch of the crown. Data for each index were collected from 15 samples. * and ** indicate significant differences (P<0.05 and P<0.01, respectively) relative to P4 in height, diameter at breast height, crown diameter, clear height, and branch angle, based on one-way ANOVA with multiple comparisons using Tukey-test.

https://doi.org/10.1371/journal.pone.0263530.g001

Changes in endogenous ABA and GA₃ of F. hodginsii under flooding stress

To verify whether the endogenous ABA and GA₃ in F. hodginsii changed in response to flooding stress, the changes in ABA and GA₃ in wild-type F. hodginsii (Fuzhou local provenance) were detected by water flooding at four time points of 6 h, 12 h, 24 h, and 72 h. We found that the ABA content gradually decreased and the GA₃ content gradually increased with time in the wild type after 6 h of flooding treatment (Fig 2).

Download:

Fig 2. The flooding stress treatments increased endogenous GA₃ and decreased endogenous ABA in the F. hodginsii seedings.

The tested seedings were local varieties of Fuzhou and were cultivated hydroponically for 2 weeks. The hydroponic solution was 10 g nutrient solution mixed with 1 L water. For the flooding stress treatments, the seedings were submerged in water for 6 h/12 h/24 h/72 h, the leaves were collected, and the endogenous ABA and GA₃ were determined. Three independent experiments were performed, each with three biological replicates. Data are the means of three biological repetitions ± SD, n = 9. Different capital letters (A, B, C) indicate the significance of GA₃ content at different treatment times compare to 6 h (p < 0.05), while different lowercase letters (a, b, c) indicate the significance of ABA content at different treatment times relative to 6 h (p < 0.05), Tukey-test was used for comparison between data.

https://doi.org/10.1371/journal.pone.0263530.g002

On the basis of these observations, we quantified the endogenous ABA and GA₃ contents of the three varieties of F. hodginsii from Gutian (G007, G008, G011) and the three varieties from Yongzhou (E010, E015, E025), as these two provenances exhibited significant phenotypic differences. The quantitative analysis revealed that the endogenous ABA content of all three varieties of F. hodginsii from Gutian was lower than that of the three varieties from Yongzhou, while in terms of GA₃ content, all three varieties from Gutian had higher contents than the three varieties of Yongzhou (P<0.05) (Fig 3A and 3B).

Download:

Fig 3. Quantification of endogenous GA₃ and ABA in F. hodginsii and changes in response to flooding stress treatments in provenance 1 (P1) and provenance 4 (P4).

(A&C) Three varieties of P1 (E010, E025, E015) and P4 (G007, G008, G011) showing the amount of endogenous ABA indicating the change trend after 12 h/24 h/72 h flooding stress treatments compared with 6 h. (B&D) The amount of endogenous GA₃ in E010, E025, E015, and G007, G008, G011, and the change in GA₃ in the seedings subjected to 12 h/24 h/72 h flooding stress treatments compared with 6 h. The data are shown as means ± SD (n = 3). * and ** indicate significant differences (P<0.05 and P<0.01, respectively) between P1 and P2 in different times, based on one-way ANOVA with multiple comparisons using Tukey-test.

https://doi.org/10.1371/journal.pone.0263530.g003

Next, the six varieties were subjected to flooding treatments for 6 h, 12 h, 24 h, and 72 h to detect the changes in endogenous ABA and GA₃. The results showed that the endogenous ABA content of both provenances showed a decreasing trend with the duration of the flooding treatment, but the three varieties from Gutian changed more rapidly, and the content of endogenous ABA was obviously less than in the three varieties from Yongzhou after 6 h. In terms of the changes in endogenous GA₃, the three varieties from Yongzhou exhibited no significant change and only increased slightly after 24 h of flooding treatment. However, the three varieties from Gutian changed rapidly, reaching a plateau stage after 12 h of flooding treatment, with a relative GA amount that was 3.25 times higher than that in Yongzhou (Fig 3C and 3D).

Transcriptomic analyses reveal different ABA- and GA-related response genes

The transcriptomes of three varieties of F. hodginsii from P4-Gutian (G007, G008, G011) were compared with three varieties from P1-Yongzhou (E010, E015, E025), and a total of 6281 differentially expressed genes were screened. There were 366 upregulated genes and 227 downregulated genes, accounting for 5.8% and 3.6%, respectively. GO functional analysis was performed by identifying the 15 functional categories with the most significant enrichment according to the upregulated and downregulated genes.

In the GO enrichment analysis of differentially upregulated genes in P4 compared to P1, 10 signaling pathways were identified as ABA-related genes (P<0.001) and were mainly related to biosynthesis, mitosis, and hormonal response. Among the upregulated genes, genes in the regulation of mitotic spindle organization and activation of protein kinase activity had a gene ratio of 0.75, which was the highest (Fig 4A). Eleven signaling pathways which were GA-related genes were detected in the GO enrichment analysis of the upregulated genes and were mainly involved in the metabolic and catabolic activities of life and the redox of cells and hormonal response, and the glycine catabolic process had the highest gene ratio of 0.6 (Fig 4B).

Download:

Fig 4. GO term enrichment analysis and screening of GA₃ and ABA related major regulatory genes.

(A) and (B) 6281 differentially expressed genes were screened (P<0.05, fold-change ≥ 1) via the Illumina RNA-Seq results of P1 (E010, E025, E015 from provenance 1) and P4 (G007, G008, G011 from provenance 4). The RNA-Seq samples (n = 9) originated from hydroponically cultivated 2-week-old seedings, which were treated with 1 μM GA₃ and 20 μM ABA for 24 h. The GO analysis was based on the 15 regulatory pathways that had greater significant enrichment of upregulated and downregulated genes (arranged by P-value). The abscissa represents the P-value of the different genes in this functional classification. The ordinate represents different GO function classifications. The size of the dots represents this pathway gene number in relation to the total, and the color represents the enrichment. (C) and (D) Two crosstalk genes (TRINITY_DN142_c0_g2, TRINITY_DN7657_c0_g1) between ABA and GA₃ were selected from the regulation of salicylic acid-mediated, cellular response to salicylic acid stimulus, and regulation of defense response signaling pathway. P1 (E010, E025, E015) and P4 (G007, G008, G011) were treated with flooding stress for 24 h, and the samples from each provenance were combined. The relative expression of the crosstalk genes was indicated via qPCR (2^−△△CT). The data are shown as means ± SD (n = 3). ** indicates significant differences (P<0.01) based on one-way ANOVA with multiple comparisons using Tukey-test.

https://doi.org/10.1371/journal.pone.0263530.g004

Eight related genes were identified by screening overlapping upregulated and downregulated genes in the regulation of salicylic acid mediated, cellular response to salicylic acid stimulus, and regulation of defense response signaling pathways, as indicated in S2 Table. Through designing primers for these eight genes and RT-qPCR, two genes with significant changes in the regulation of ABA and GA₃ responses were selected from the quantification results. After 20 μM ABA treatment for 24 h, the expression of TRINITY_DN142_c0_g2 and TRINITY_DN7657_c0_g1 in P4 was lower than in P1 and there was a highly significant difference (Fig 4C). Following treatment with 1 μM GA₃ for 24 h, theTRINITY_DN142_c0_g2 and TRINITY_DN7657_c0_g1 genes of P1 were more highly expressed than P4 and were highly significantly different (Fig 4D).

Discussion

Land plants are sensitive to waterlogging, which hinders the growth and survival [30, 31]. Numerous studies have reported that physiological and molecular alterations occur in plants under flooding conditions [32–34]. RNA-Seq is an effective method for assessing transcriptomic changes under waterlogging and can assist in elucidating waterlogging tolerance mechanisms in plants.

Previous studies found that F. hodginsii from Gutian possessed some phenotypes related to resistance to flooding, and the phenotypic characteristics of resistance to flooding were also more pronounced than in the other three provenances (Yongzhou, Sanming, Nanping). Genetic diversity analysis of P1, P2, P3, and P4 by SSRs revealed that P4 also had higher genetic diversity than the other three provenances. This suggested that P4 was more adaptable than P1, P2, and P3.

Phytohormones play a central role in flooding stress conditions for plant survival including morphological, anatomical, biochemical, molecular, and signaling mechanisms. GAs and ABA have crosstalk roles during submergence stress in plants [35], whereby ABA biosynthesis is reduced and GA signaling is induced [13]. The endogenous hormone contents and the associated change trends were explored by conducting flooding treatments in three varieties of P1 (E010, E015, E025) and P4 (G007, G008, G011). We found that the ABA content in G007, G008, and G011 was lower than in E010, E015, and E025. In terms of the content of GA₃, G007, G008, and G011 were all higher than E010, E015, and E025. Additionally, G007, G008, and G011 exhibited more visible trends than E010, E015, and E025 in terms of both ABA and GA₃. This is consistent with previous findings [14]. Based on these results, we infer that the degree of flooding tolerance is more prominent and the response to flooding stress is more dramatic in F. hodginsii from Gutian.

Here, 15 regulatory pathways that had greater significant enrichment of upregulated and downregulated genes which were arranged by P-value were identified using RNA-Seq and mainly related to biosynthesis, mitosis, the metabolic and catabolic activities of life, the redox of cells and hormonal response. Three of these signaling pathways are present in both up- and down-regulated gene expression, they were regulation of salicylic acid-mediated, cellular response to salicylic acid stimulus and regulation of defense response, which were closely related to ABA and GA₃.

Many adaptation mechanisms are related to changes in ABA and GA metabolism and signaling [36–38]. ABA has a main role in regulating the stomata by adjusting the size of the guard cells and regulating the water potential in plants. Thus, ABA is considered to be a key hormone in water stress responses [39, 40]. ABA can be perceived in the guard cells, and ABA signaling can lead to a reduction in the turgor and volume of the guard cells via the efflux of anions and potassium ions and the gluconeogenic conversion of malate into starch, which finally results in stomatal closure; a process that has been previously well illustrated [41, 42]. GAs is one of the essential plant hormones regulating growth and development. GAs regulates multiple processes in plant growth and development, mainly by controlling the size and number of cells [43]. These functional modulations allow them to play an important role in plant defense response.

Ethylene (ET) is a gaseous hormone and its accumulation is an important way to response to flooding stress. It has long been acknowledged as the main regulator of plants’ responses to reduced O₂ conditions [44–46]. Numerous studies show that ET signaling uncouples the catabolism of ABA, and constitutes a putative independent hypoxia signaling pathway [47]. Moreover, any effect on ABA signaling affects GA signaling, as ABA promotes the stabilization of the negatively regulated proteins of DELLA [48]. However, none of the above signaling pathways are associated with ethylene. Possible reasons are that ethylene is a gaseous hormone, which is volatile in air, and that it accumulates more in plant roots and fruits and less in leaves [49].

In addition to the plant defense response, two other signaling pathways were associated with salicylic acid (SA). In plants, SA is a common phenolic compound, which regulates cells’ antioxidant mechanism through inducing the expression of stress-related genes to resist to stress [50–52]. Some experiments have shown that Salicylic acid, as a signal substance, can induce changes in physiological characteristics by increasing the activities of ethanol dehydrogenase, proline, POD and CAT. Thus, protecting leaves and root membranes from damage and maintaining photosynthesis in leaves as well as the activities of root [53]. Previous studies have investigated the function of hormones or salicylic acid as a single component in response to flooding stress, these signaling pathways provide a reference and reference for multi-omics studies of flooding stress.

Based on the above signaling pathway, we obtained eight related genes by screening for overlapping upregulated and downregulated genes from the three signaling pathways. Two genes, including TRINITY_DN142_c0_g2 and TRINITY_DN7657_c0_g1, which exhibited significant changes in the regulation of ABA and GA responses, were selected based on the results of the RT-qPCR. Following treatment with 1 μM GA₃ and 20 μM ABA for 24 h, the relative expression of these two genes in F. hodginsii of P1 was significantly higher than in P4. Treatment with 20 μM ABA resulted in the opposite. This indicated that F. hodginsii from Gutian had a better ability to adapt to the flooding stress. The TRINITY_DN142_c0_g2 and TRINITY_DN7657_c0_g1 genes may be central to its flooding resistance and might also constitute the basis of its adaptive evolution.

Conclusion

When plants are exposed to flooding stress, they initiate various defensive measures, such as stomatal closure, shoot growth, and petiole or internode elongation control under both escape and quiescence strategies. In previous experiments, we found that the varieties of F. hodginsii that originated from Gutian were taller and had a greater DBH but smaller branching angle compared with three other provenances originating from Yongzhou, Sanming, and Nanping. These features were consistent with flood tolerance, suggesting that the F. hodginsii varieties originating from Gutian possessed typical flooding tolerance traits compared to the other three provenances. We then used HPLC technology to measure the dynamics in endogenous GA₃ and ABA content, which indicated that ABA decreased but GA₃ increased with time under flooding stress. Furthermore, the GA₃ and ABA contents of the varieties originating from Gutian and the other three provenances were measured, which indicated that the F. hodginsii from Gutian could respond more rapidly to flooding stress. We then immediately compared the transcriptomes of the varieties originating from Gutian with the other three provenances via RNA-Seq. We identified eight related genes by screening for overlapping upregulated and downregulated genes in the regulation of salicylic acid-mediated, cellular response to salicylic acid stimulus, and regulation of defense response signaling pathways. To explore the underlying genetic mechanisms of the flood-resistant phenotypes, based on the primers designed for these eight genes and RT-qPCR, two genes (TRINITY_DN142_c0_g2 and TRINITY_DN7657_c0_g1) with significant changes in the regulation of ABA and GA responses were selected from the quantification results. Both genes were highly related to ABA and GA response and thus may be the reasons for phenotypic variation of F. hodginsii. The analysis of phenotypic differences in F. hodginsii revealed the intrinsic molecular regulatory mechanisms and provided some reference value for resistance breeding and adaptive evolution.

Supporting information

S1 Fig. Genetic diversity and phylogenetic evolution analysis of four provenance.

SSR (Simple Sequence Repeat) molecular markers were used to predict the genetic diversity among provenances 1–4. Using the DNA of provenances 1–4 as templates, SSR 1–11 primers were used to amplify sequences via PCR. Based on the amplified sequences, alleles frequency of provenance 1–4 were explored. The different colors represent the length of fragment separation, and the dendrogram indicates the genetic evolutionary distance.

https://doi.org/10.1371/journal.pone.0263530.s001

(TIF)

S1 Table. List of primers for SSR and RT-qPCR used in this research.

https://doi.org/10.1371/journal.pone.0263530.s002

(DOCX)

S2 Table. Overlapping genes of ABA and GA.

https://doi.org/10.1371/journal.pone.0263530.s003

(DOCX)

Acknowledgments

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

References

1. Hu Z, Qi X, Zhang M, Chen X, Nakazono M. Roles of Phytohormones in Morphological and Anatomical Responses of Plants to Flooding Stress. Plant Hormones under Challenging Environmental Factors. 2016.
- View Article
- Google Scholar
2. Emily CD, Rachael PH, Yasmin AF, Tony S, Claire O, Maria JM, et al. The UniProt-GO Annotation database in 2011. Nucleic Acids Research. 2011;40(D1):565–570.
- View Article
- Google Scholar
3. Colmer TD, Voesenek L. Flooding tolerance: suites of plant traits in variable environments. Funct. Plant Biol. 2009;36(8):665–681. pmid:32688679
- View Article
- PubMed/NCBI
- Google Scholar
4. Pan J, Sharif R, Xu X, Chen X. Mechanisms of Waterlogging Tolerance in Plants: Research Progress and Prospects. Front Plant Sci. 2020;11:627331. pmid:33643336
- View Article
- PubMed/NCBI
- Google Scholar
5. Li JX, Zhu XH, Li Y, et al. Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics. BMC Plant Biol. 2018;18(1):306. pmid:30482158
- View Article
- PubMed/NCBI
- Google Scholar
6. Li L, Cushman SA, He Yi, Ma X, Li Y. Landscape genomics reveals genetic evidence of local adaptation in a widespread tree, the Chinese wingnut (Pterocarya stenoptera). Journal of Systematics and Evolution. 2020.
- View Article
- Google Scholar
7. Benschop JJ, Bou J, Peeters A, et al. Long-term submergence-induced elongation in Rumex palustris requires abscisic acid-dependent biosynthesis of gibberellin1. PLANT PHYSIOL. 2006;141(4):1644–1652. pmid:16766669
- View Article
- PubMed/NCBI
- Google Scholar
8. Hui , Wu , Huizhe , et al. Effects of 1-aminocyclopropane-1-carboxylate and paclobutrazol on the endogenous hormones of two contrasting rice varieties under submergence stress. Plant growth regulation. 2019;87(1):109–121.
- View Article
- Google Scholar
9. Yamauchi T, Tanaka A, Tsutsumi N, Inukai Y, Nakazono M. A Role for Auxin in Ethylene-Dependent Inducible Aerenchyma Formation in Rice Roots. Plants. 2020;9(5):610. pmid:32403344
- View Article
- PubMed/NCBI
- Google Scholar
10. Shukla , Rakesh , Kumar , et al. Waterlogging and submergence stress: affects and acclimation. Critical Reviews in Biotechnology. 2016;36(1/6):956–966. pmid:26177332
- View Article
- PubMed/NCBI
- Google Scholar
11. Colebrook EH, Thomas SG, Phillips AL, et al. The role of gibberellin signalling in plant responses to abiotic stress. Journal of Experimental Biology. 2014;217(1):67–75. pmid:24353205
- View Article
- PubMed/NCBI
- Google Scholar
12. Else MA, Franciszek J, Atkinson CJ, et al. Root signals and stomatal closure in relation to photosynthesis, chlorophyll a fluorescence and adventitious rooting of flooded tomato plants. Annals of Botany. 2009(2):313–323. pmid:19001430
- View Article
- PubMed/NCBI
- Google Scholar
13. Ba Iley-Serres J, Voesenek L A. Life in the balance: a signaling network controlling survival of flooding. Current Opinion in Plant Biology. 2010;13(5):489–494. pmid:20813578
- View Article
- PubMed/NCBI
- Google Scholar
14. Kuroha TN K.; Gamuyao R.; Wang D.R.; Furuta T.; Nakamori M.; Kitaoka T., et al. Ethylene-gibberellin signaling underlies adaptation of rice to periodic flooding. Science. 2018;361(6398):181–186. pmid:30002253
- View Article
- PubMed/NCBI
- Google Scholar
15. Bashar K, Tareq M, Amin M, et al. Phytohormone-Mediated Stomatal Response, Escape and Quiescence Strategies in Plants under Flooding Stress. Agronomy. 2019;9(2).
- View Article
- Google Scholar
16. Yin Q, Chen S, Guo W, et al. Pronounced genetic differentiation in Fokienia hodginsii revealed by simple sequence repeat markers. Ecol Evol. 2018;8(22):10938–10951. pmid:30519418
- View Article
- PubMed/NCBI
- Google Scholar
17. Zhaowei Gao. A Precious Timber Species-Fokienia hodginsii. JOURNAL OF FUJIAN FORESTRY SCIENCE AND TECHNOLOGY. 1994.
- View Article
- Google Scholar
18. https://baike.baidu.com/item/%E5%8F%A4%E7%94%B0%E5%8E%BF/1977135?fr=aladdin.
19. Porebski S, Bailey LG, Baum BR. Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components. Plant Molecular Biology Reporter. 1997;15(1):8–15.
- View Article
- Google Scholar
20. Ding M, Meng K, Fan Q, S . Development and Validation of EST-SSR Markers for Fokienia hodginsii (Cupressaceae). Applications in Plant Sciences. 2017. pmid:28337393
- View Article
- PubMed/NCBI
- Google Scholar
21. Purwoko D, Cartealy IC, Tajuddin T, Dinarti D, Sudarsono S. SSR identification and marker development for sago palm based on NGS genome data. Breed Sci. 2019;69(1):1–10. pmid:31086478
- View Article
- PubMed/NCBI
- Google Scholar
22. Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz E. HPLC-MS-based methods for the study of metabonomics. J Chromatogr B Analyt Technol Biomed Life. 2005;817(1):67–76.
- View Article
- Google Scholar
23. Liu H, Li X, Xiao J, Wang S. A convenient method for simultaneous quantification of multiple phytohormones and metabolites: application in study of rice-bacterium interaction. Plant Methods. 2012;8(1):2. pmid:22243810
- View Article
- PubMed/NCBI
- Google Scholar
24. Chang S, Chen W, Yang J. Another formula for calculating the gene change rate in real-time RT-PCR. Molecular Biology Reports. 2009;36(8):2165. pmid:19109763
- View Article
- PubMed/NCBI
- Google Scholar
25. He J, Duan Y, Hua D, et al. DEXH box RNA helicase-mediated mitochondrial reactive oxygen species production in Arabidopsis mediates crosstalk between abscisic acid and auxin signaling. Plant Cell. 2012;24(5):1815–1833. pmid:22652060
- View Article
- PubMed/NCBI
- Google Scholar
26. Oh MW, Nanjo Y, Komatsu SJP, Letters P. Analysis of Soybean Root Proteins Affected by Gibberellic Acid Treatment Under Flooding Stress. Protein and Peptide Letters. 2014. pmid:24702262
- View Article
- PubMed/NCBI
- Google Scholar
27. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics. 2009;10(1):57. pmid:19015660
- View Article
- PubMed/NCBI
- Google Scholar
28. Li Y, Lin B, Zhu T, Zhang H, Su J. The interactions of PhSPL17 and PhJAZ1 mediate the on- and off-year moso bamboo (Phyllostachys heterocycla) resistance to the Pantana phyllostachysae larval feeding. Pest Manag Sci. 2020;76(4):1588–1595. pmid:31713977
- View Article
- PubMed/NCBI
- Google Scholar
29. Mortazavi A, Williams BA, Mccue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 2008. pmid:18516045
- View Article
- PubMed/NCBI
- Google Scholar
30. Normile DJS. Reinventing Rice to Feed the World.321(5887):330–333.
- View Article
- Google Scholar
31. Emuejevoke V, Onyekachukwu A, El-Esawi M A. Comparative Physiological, Biochemical, and Genetic Responses to Prolonged Waterlogging Stress in Okra and Maize Given Exogenous Ethylene Priming. Frontiers in Physiology. 2017;8:632. pmid:28993735
- View Article
- PubMed/NCBI
- Google Scholar
32. Jackson MB. Ethylene-promoted Elongation: an Adaptation to Submergence Stress. Ann Bot.2008;101(2):229–248. pmid:17956854
- View Article
- PubMed/NCBI
- Google Scholar
33. Kreuzwieser J, Rennenberg H. Molecular and physiological responses of trees to waterlogging stress. Plant Cell & Environment. 2015;37(10).
- View Article
- Google Scholar
34. Zhou W, Chen F, Meng Y, Chandrasekaran U, Shu K. Plant waterlogging/flooding stress responses: From seed germination to maturation. Plant Physiology and Biochemistry. 2020;148:228–236. pmid:31981875
- View Article
- PubMed/NCBI
- Google Scholar
35. Tzatzani TT, Kavroulakis N, Doupis G, Psarras G, Papadakis IE. Nutritional status of ’Hass’ and ’Fuerte’ avocado (Persea americana Mill.) plants subjected to high soil moisture. Journal of Plant Nutrition. 2019(2).
- View Article
- Google Scholar
36. Carvalho RF, Campos ML, Azevedo RA. The Role of Phytochrome in Stress Tolerance. Journal of Integrative Plant Biology. 2011;53(012):920–929. pmid:22040287
- View Article
- PubMed/NCBI
- Google Scholar
37. Peleg Z, Blumwald E. Hormone balance and abiotic stress tolerance in crop plants. Current Opinion in Plant Biology. 2011;14:290–295. pmid:21377404
- View Article
- PubMed/NCBI
- Google Scholar
38. Fukao T, Yeung E, Bailey-Serres. The submergence tolerance regulator SUB1A mediates crosstalk between submergence and drought tolerance in rice. The Plant Cell. 2011;23(1):412–427. pmid:21239643
- View Article
- PubMed/NCBI
- Google Scholar
39. Zhu JK. Abiotic Stress Signaling and Responses in Plants. CELL. 2016. pmid:27716505
- View Article
- PubMed/NCBI
- Google Scholar
40. Fang H, Wang HL, Li HG, Su Y, Shuang L. PeCHYR1, a ubiquitin E3 ligase from Populus euphratica, enhances drought tolerance via ABA-induced stomatal closure by ROS production in Populus. Plant Biotechnology Journal. 2018;16(8).
- View Article
- Google Scholar
41. Schroeder JI, Kwak JM, Allen GJ. Guard cell abscisic acid signalling and engineering drought hardiness in plants. Nature. 2001;410(6826):327–330. pmid:11268200
- View Article
- PubMed/NCBI
- Google Scholar
42. Kim TH, Böhmer M, Hu H, Nishimura N, Schroeder JI. Guard Cell Signal Transduction Network: Advances in Understanding Abscisic Acid, CO2, and Ca2+ Signaling. Annual Review of Plant Biology. 2010;61(1):561–591.
- View Article
- Google Scholar
43. Nelissen H, Rymen B, Jikumaru Y, et al. A Local Maximum in Gibberellin Levels Regulates Maize Leaf Growth by Spatial Control of Cell Division. Current Biology. 2012;22(13):1183–1187. pmid:22683264
- View Article
- PubMed/NCBI
- Google Scholar
44. Alpuerto J. B., Hussain R. M. F., and Fukao T. The key regulator of submergence tolerance, SUB1A, promotes photosynthetic and metabolic recovery from submergence damage in rice leaves. Plant Cell Environ.2016;39: 672–684. pmid:26477688
- View Article
- PubMed/NCBI
- Google Scholar
45. Hartman S., Liu Z., Van Veen H., Vicente J., Reinen E., Martopawiro S., et al. Ethylene-mediated nitric oxide depletion pre-adapts plants to hypoxia stress. Nat. Commun. 2019a;10: 1–9. pmid:30602773
- View Article
- PubMed/NCBI
- Google Scholar
46. Hartman S., Sasidharan R., and Voesenek L. A. The role of ethylene in metabolic acclimations to low oxygen. New Phytol. 2019b;229: 64–70.
- View Article
- Google Scholar
47. González-Guzmán M, Gómez-Cadenas A, Arbona V. Abscisic Acid as an Emerging Modulator of the Responses of Plants to Low Oxygen Conditions. Front Plant Sci. 2021;12:661–789. pmid:33981326
- View Article
- PubMed/NCBI
- Google Scholar
48. Ross J. J., Weston D. E., Davidson S. E., and Reid J. B. Plant hormone interactions: how complex are they? Physiol Plant. 2011;141: 299–309. pmid:21214880
- View Article
- PubMed/NCBI
- Google Scholar
49. Sasidharan R., and Voesenek L. A. Ethylene-mediated acclimations to flooding stress. Plant Physiol. 2015;169: 3–12. pmid:25897003
- View Article
- PubMed/NCBI
- Google Scholar
50. Zhou Z. S., Guo K., Elbaz A. A., and Yang Z. M. Salicylic acid alleviates mercury toxicity by preventing oxidative stress in roots of Medicago sativa. Environ. Exp. Bot. 2009;65: 27–34.
- View Article
- Google Scholar
51. Hayat Q., Hayat S., Irfan M., and Ahmad A. Effect of exogenous salicylic acid under changing environment: a review. Environ. Exp. Bot. 2010;68: 14–25.
- View Article
- Google Scholar
52. Arif Y., Sami F., Siddiqui H., Bajguz A., and Hayat S. Salicylic acid in relation to other phytohormones in plant: a study towards physiology and signal transduction under challenging environment. Environ. Exp. Bot. 2020;175:104040.
- View Article
- Google Scholar
53. Wang G., Fan W., and Peng F. Physiological responses of the young peach tree to water-logging and spraying SA at different timing. J. Fruit Sci. 2015;32: 872–878.
- View Article
- Google Scholar

[ref1] 1. Hu Z, Qi X, Zhang M, Chen X, Nakazono M. Roles of Phytohormones in Morphological and Anatomical Responses of Plants to Flooding Stress. Plant Hormones under Challenging Environmental Factors. 2016.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Emily CD, Rachael PH, Yasmin AF, Tony S, Claire O, Maria JM, et al. The UniProt-GO Annotation database in 2011. Nucleic Acids Research. 2011;40(D1):565–570.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Colmer TD, Voesenek L. Flooding tolerance: suites of plant traits in variable environments. Funct. Plant Biol. 2009;36(8):665–681. pmid:32688679
View Article
PubMed/NCBI
Google Scholar

[8] View Article

[9] PubMed/NCBI

[10] Google Scholar

[ref4] 4. Pan J, Sharif R, Xu X, Chen X. Mechanisms of Waterlogging Tolerance in Plants: Research Progress and Prospects. Front Plant Sci. 2020;11:627331. pmid:33643336
View Article
PubMed/NCBI
Google Scholar

[12] View Article

[13] PubMed/NCBI

[14] Google Scholar

[ref5] 5. Li JX, Zhu XH, Li Y, et al. Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics. BMC Plant Biol. 2018;18(1):306. pmid:30482158
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref6] 6. Li L, Cushman SA, He Yi, Ma X, Li Y. Landscape genomics reveals genetic evidence of local adaptation in a widespread tree, the Chinese wingnut (Pterocarya stenoptera). Journal of Systematics and Evolution. 2020.
View Article
Google Scholar

[20] View Article

[21] Google Scholar

[ref7] 7. Benschop JJ, Bou J, Peeters A, et al. Long-term submergence-induced elongation in Rumex palustris requires abscisic acid-dependent biosynthesis of gibberellin1. PLANT PHYSIOL. 2006;141(4):1644–1652. pmid:16766669
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref8] 8. Hui , Wu , Huizhe , et al. Effects of 1-aminocyclopropane-1-carboxylate and paclobutrazol on the endogenous hormones of two contrasting rice varieties under submergence stress. Plant growth regulation. 2019;87(1):109–121.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref9] 9. Yamauchi T, Tanaka A, Tsutsumi N, Inukai Y, Nakazono M. A Role for Auxin in Ethylene-Dependent Inducible Aerenchyma Formation in Rice Roots. Plants. 2020;9(5):610. pmid:32403344
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref10] 10. Shukla , Rakesh , Kumar , et al. Waterlogging and submergence stress: affects and acclimation. Critical Reviews in Biotechnology. 2016;36(1/6):956–966. pmid:26177332
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref11] 11. Colebrook EH, Thomas SG, Phillips AL, et al. The role of gibberellin signalling in plant responses to abiotic stress. Journal of Experimental Biology. 2014;217(1):67–75. pmid:24353205
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Else MA, Franciszek J, Atkinson CJ, et al. Root signals and stomatal closure in relation to photosynthesis, chlorophyll a fluorescence and adventitious rooting of flooded tomato plants. Annals of Botany. 2009(2):313–323. pmid:19001430
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref13] 13. Ba Iley-Serres J, Voesenek L A. Life in the balance: a signaling network controlling survival of flooding. Current Opinion in Plant Biology. 2010;13(5):489–494. pmid:20813578
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref14] 14. Kuroha TN K.; Gamuyao R.; Wang D.R.; Furuta T.; Nakamori M.; Kitaoka T., et al. Ethylene-gibberellin signaling underlies adaptation of rice to periodic flooding. Science. 2018;361(6398):181–186. pmid:30002253
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. Bashar K, Tareq M, Amin M, et al. Phytohormone-Mediated Stomatal Response, Escape and Quiescence Strategies in Plants under Flooding Stress. Agronomy. 2019;9(2).
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref16] 16. Yin Q, Chen S, Guo W, et al. Pronounced genetic differentiation in Fokienia hodginsii revealed by simple sequence repeat markers. Ecol Evol. 2018;8(22):10938–10951. pmid:30519418
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref17] 17. Zhaowei Gao. A Precious Timber Species-Fokienia hodginsii. JOURNAL OF FUJIAN FORESTRY SCIENCE AND TECHNOLOGY. 1994.
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref18] 18. https://baike.baidu.com/item/%E5%8F%A4%E7%94%B0%E5%8E%BF/1977135?fr=aladdin.

[ref19] 19. Porebski S, Bailey LG, Baum BR. Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components. Plant Molecular Biology Reporter. 1997;15(1):8–15.
View Article
Google Scholar

[65] View Article

[66] Google Scholar

[ref20] 20. Ding M, Meng K, Fan Q, S . Development and Validation of EST-SSR Markers for Fokienia hodginsii (Cupressaceae). Applications in Plant Sciences. 2017. pmid:28337393
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref21] 21. Purwoko D, Cartealy IC, Tajuddin T, Dinarti D, Sudarsono S. SSR identification and marker development for sago palm based on NGS genome data. Breed Sci. 2019;69(1):1–10. pmid:31086478
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref22] 22. Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz E. HPLC-MS-based methods for the study of metabonomics. J Chromatogr B Analyt Technol Biomed Life. 2005;817(1):67–76.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref23] 23. Liu H, Li X, Xiao J, Wang S. A convenient method for simultaneous quantification of multiple phytohormones and metabolites: application in study of rice-bacterium interaction. Plant Methods. 2012;8(1):2. pmid:22243810
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref24] 24. Chang S, Chen W, Yang J. Another formula for calculating the gene change rate in real-time RT-PCR. Molecular Biology Reports. 2009;36(8):2165. pmid:19109763
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref25] 25. He J, Duan Y, Hua D, et al. DEXH box RNA helicase-mediated mitochondrial reactive oxygen species production in Arabidopsis mediates crosstalk between abscisic acid and auxin signaling. Plant Cell. 2012;24(5):1815–1833. pmid:22652060
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref26] 26. Oh MW, Nanjo Y, Komatsu SJP, Letters P. Analysis of Soybean Root Proteins Affected by Gibberellic Acid Treatment Under Flooding Stress. Protein and Peptide Letters. 2014. pmid:24702262
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref27] 27. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics. 2009;10(1):57. pmid:19015660
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref28] 28. Li Y, Lin B, Zhu T, Zhang H, Su J. The interactions of PhSPL17 and PhJAZ1 mediate the on- and off-year moso bamboo (Phyllostachys heterocycla) resistance to the Pantana phyllostachysae larval feeding. Pest Manag Sci. 2020;76(4):1588–1595. pmid:31713977
View Article
PubMed/NCBI
Google Scholar

[99] View Article

[100] PubMed/NCBI

[101] Google Scholar

[ref29] 29. Mortazavi A, Williams BA, Mccue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 2008. pmid:18516045
View Article
PubMed/NCBI
Google Scholar

[103] View Article

[104] PubMed/NCBI

[105] Google Scholar

[ref30] 30. Normile DJS. Reinventing Rice to Feed the World.321(5887):330–333.
View Article
Google Scholar

[107] View Article

[108] Google Scholar

[ref31] 31. Emuejevoke V, Onyekachukwu A, El-Esawi M A. Comparative Physiological, Biochemical, and Genetic Responses to Prolonged Waterlogging Stress in Okra and Maize Given Exogenous Ethylene Priming. Frontiers in Physiology. 2017;8:632. pmid:28993735
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref32] 32. Jackson MB. Ethylene-promoted Elongation: an Adaptation to Submergence Stress. Ann Bot.2008;101(2):229–248. pmid:17956854
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref33] 33. Kreuzwieser J, Rennenberg H. Molecular and physiological responses of trees to waterlogging stress. Plant Cell & Environment. 2015;37(10).
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref34] 34. Zhou W, Chen F, Meng Y, Chandrasekaran U, Shu K. Plant waterlogging/flooding stress responses: From seed germination to maturation. Plant Physiology and Biochemistry. 2020;148:228–236. pmid:31981875
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref35] 35. Tzatzani TT, Kavroulakis N, Doupis G, Psarras G, Papadakis IE. Nutritional status of ’Hass’ and ’Fuerte’ avocado (Persea americana Mill.) plants subjected to high soil moisture. Journal of Plant Nutrition. 2019(2).
View Article
Google Scholar

[125] View Article

[126] Google Scholar

[ref36] 36. Carvalho RF, Campos ML, Azevedo RA. The Role of Phytochrome in Stress Tolerance. Journal of Integrative Plant Biology. 2011;53(012):920–929. pmid:22040287
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref37] 37. Peleg Z, Blumwald E. Hormone balance and abiotic stress tolerance in crop plants. Current Opinion in Plant Biology. 2011;14:290–295. pmid:21377404
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref38] 38. Fukao T, Yeung E, Bailey-Serres. The submergence tolerance regulator SUB1A mediates crosstalk between submergence and drought tolerance in rice. The Plant Cell. 2011;23(1):412–427. pmid:21239643
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref39] 39. Zhu JK. Abiotic Stress Signaling and Responses in Plants. CELL. 2016. pmid:27716505
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref40] 40. Fang H, Wang HL, Li HG, Su Y, Shuang L. PeCHYR1, a ubiquitin E3 ligase from Populus euphratica, enhances drought tolerance via ABA-induced stomatal closure by ROS production in Populus. Plant Biotechnology Journal. 2018;16(8).
View Article
Google Scholar

[144] View Article

[145] Google Scholar

[ref41] 41. Schroeder JI, Kwak JM, Allen GJ. Guard cell abscisic acid signalling and engineering drought hardiness in plants. Nature. 2001;410(6826):327–330. pmid:11268200
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref42] 42. Kim TH, Böhmer M, Hu H, Nishimura N, Schroeder JI. Guard Cell Signal Transduction Network: Advances in Understanding Abscisic Acid, CO2, and Ca2+ Signaling. Annual Review of Plant Biology. 2010;61(1):561–591.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref43] 43. Nelissen H, Rymen B, Jikumaru Y, et al. A Local Maximum in Gibberellin Levels Regulates Maize Leaf Growth by Spatial Control of Cell Division. Current Biology. 2012;22(13):1183–1187. pmid:22683264
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref44] 44. Alpuerto J. B., Hussain R. M. F., and Fukao T. The key regulator of submergence tolerance, SUB1A, promotes photosynthetic and metabolic recovery from submergence damage in rice leaves. Plant Cell Environ.2016;39: 672–684. pmid:26477688
View Article
PubMed/NCBI
Google Scholar

[158] View Article

[159] PubMed/NCBI

[160] Google Scholar

[ref45] 45. Hartman S., Liu Z., Van Veen H., Vicente J., Reinen E., Martopawiro S., et al. Ethylene-mediated nitric oxide depletion pre-adapts plants to hypoxia stress. Nat. Commun. 2019a;10: 1–9. pmid:30602773
View Article
PubMed/NCBI
Google Scholar

[162] View Article

[163] PubMed/NCBI

[164] Google Scholar

[ref46] 46. Hartman S., Sasidharan R., and Voesenek L. A. The role of ethylene in metabolic acclimations to low oxygen. New Phytol. 2019b;229: 64–70.
View Article
Google Scholar

[166] View Article

[167] Google Scholar

[ref47] 47. González-Guzmán M, Gómez-Cadenas A, Arbona V. Abscisic Acid as an Emerging Modulator of the Responses of Plants to Low Oxygen Conditions. Front Plant Sci. 2021;12:661–789. pmid:33981326
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref48] 48. Ross J. J., Weston D. E., Davidson S. E., and Reid J. B. Plant hormone interactions: how complex are they? Physiol Plant. 2011;141: 299–309. pmid:21214880
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref49] 49. Sasidharan R., and Voesenek L. A. Ethylene-mediated acclimations to flooding stress. Plant Physiol. 2015;169: 3–12. pmid:25897003
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref50] 50. Zhou Z. S., Guo K., Elbaz A. A., and Yang Z. M. Salicylic acid alleviates mercury toxicity by preventing oxidative stress in roots of Medicago sativa. Environ. Exp. Bot. 2009;65: 27–34.
View Article
Google Scholar

[181] View Article

[182] Google Scholar

[ref51] 51. Hayat Q., Hayat S., Irfan M., and Ahmad A. Effect of exogenous salicylic acid under changing environment: a review. Environ. Exp. Bot. 2010;68: 14–25.
View Article
Google Scholar

[184] View Article

[185] Google Scholar

[ref52] 52. Arif Y., Sami F., Siddiqui H., Bajguz A., and Hayat S. Salicylic acid in relation to other phytohormones in plant: a study towards physiology and signal transduction under challenging environment. Environ. Exp. Bot. 2020;175:104040.
View Article
Google Scholar

[187] View Article

[188] Google Scholar

[ref53] 53. Wang G., Fan W., and Peng F. Physiological responses of the young peach tree to water-logging and spraying SA at different timing. J. Fruit Sci. 2015;32: 872–878.
View Article
Google Scholar

[190] View Article

[191] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Different provenances of F. hodginsii

Plant growth conditions

Flooding assay

Simple Sequence Repeat (SSR) assay

Quantification of endogenous ABA and GA3

Metabolites extraction.

Standard solution preparation.

UHPLC-MRM-MS analysis.

Calibration curves.

Limit of Detection (LOD) and Limit of Quantitation (LOQ).

Total RNA extraction, cDNA reverse transcription, and quantitative real-time (qRT) PCR

RNA-Seq analysis and bioinformatics

Statistical analyses

Results

Phenotype of F. hodginsii from four provenances

Changes in endogenous ABA and GA3 of F. hodginsii under flooding stress

Transcriptomic analyses reveal different ABA- and GA-related response genes

Discussion

Conclusion

Supporting information

S1 Fig. Genetic diversity and phylogenetic evolution analysis of four provenance.

S1 Table. List of primers for SSR and RT-qPCR used in this research.

S2 Table. Overlapping genes of ABA and GA.

Acknowledgments

References

Quantification of endogenous ABA and GA₃

Changes in endogenous ABA and GA₃ of F. hodginsii under flooding stress