Abstract
Background
Flooding is among the most severe abiotic stresses in plant growth and development. The mechanism of submergence tolerance of cotton in response to submergence stress is unknown.
Results
The transcriptome results showed that a total of 6,893 differentially expressed genes (DEGs) were discovered under submergence stress. Gene Ontology (GO) enrichment analysis showed that DEGs were involved in various stress or stimulus responses. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that DEGs related to plant hormone signal transduction, starch and sucrose metabolism, glycolysis and the biosynthesis of secondary metabolites were regulated by submergence stress. Eight DEGs related to ethylene signaling and 3 ethylene synthesis genes were identified in the hormone signal transduction. For respiratory metabolism, alcohol dehydrogenase (ADH, GH_A02G0728) and pyruvate decarboxylase (PDC, GH_D09G1778) were significantly upregulated but 6-phosphofructokinase (PFK, GH_D05G0280), phosphoglycerate kinase (PGK, GH_A01G0945 and GH_D01G0967) and sucrose synthase genes (SUS, GH_A06G0873 and GH_D06G0851) were significantly downregulated in the submergence treatment. Terpene biosynthetic pathway-related genes in the secondary metabolites were regulated in submergence stress.
Conclusions
Regulation of terpene biosynthesis by respiratory metabolism may play a role in enhancing the tolerance of cotton to submergence under flooding. Our findings showed that the mevalonate pathway, which occurs in the cytoplasm of the terpenoid backbone biosynthesis pathway (ko00900), may be the main response to submergence stress.
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Background
Flooding is among the most severe abiotic stresses that occur during plant growth and development [1]. Flooding is a general term referring to excessively wet conditions, that is where excess water replaces gas-spaces surrounding roots and/or shoots. It mainly includes four aspects: (1) Waterlogging or soil flooding: only the root-zone is flooded, (2) Partial waterlogging or soil flooding: partial flooding of the root-zone, (3) Submergence refers to the entire plant being underwater (4) Partial submergence: the entire root system and part of above-ground organs are under water [2]. Flooding directly affects the diffusion of oxygen in plant tissues/soil, resulting in hypoxia. Hypoxia greatly disrupts respiration and photosynthesis, which leads to a reduction in the ATP supply and has deleterious effects on normal life activities of plants [3]. When plants are completely submerged, they are deprived of oxygen. Most plant species cannot survive prolonged submergence, but they can temporarily adapt to submergence stress through the Low Oxygen Quiescence Syndrome (LOQS) or the Low Oxygen Escape Syndrome (LOES) [4,5,6]. Heavy rainfall and flood disasters have become frequent in recent years, and there is an urgent need to study the submergence tolerance of plants and their mechanisms to maintain the effective adaptation of plants to climate change [7, 8].
Reactive oxygen species (ROS) are a normal product of plant cell metabolism. Reactive oxygen can be used as a signal molecule to respond to stress, and excessive ROS is harmful to plant cells. Under prolonged hypoxia condition, excessive ROS can be accumulated, causing membrane lipid peroxidation and altering the structure of proteins and nucleic acids [9]. Malondialdehyde (MDA) is one of the most important products of membrane lipid peroxidation and constitutes a common parameter of membrane damage. Plants have evolved a complex set of enzymatic and non-enzymatic detoxification mechanisms to eliminate oxidative damage caused by ROS [Observation of the internal structure of leaf tissue Paraffin sections were used to observe the changes in leaf tissue in ZNL2067 and ZL100 from three treatments. After stress treatment, the leaves were quickly cut into 5 mm × 10 mm pieces, and then fixed with FAA fixing solution. The slices were made by paraffin sectioning method and sliced with a microtome (RM2016) with a thickness of 5 μm. Then stained with safranine and solid green, and sealed with neutral gum. Finally, a light microscope (NIKON ECLIPSE E100) was used to observe and take photos. We used CaseViewer software to observe longitudinal Sects. (200 ×) of paraffin sections of leaves. Palisade tissue thickness, spongy tissue thickness, and leaf thickness were measured, and palisade tissue/spongy tissue ratio, CTR and SR were calculated. Three replicates were measured for each treatment, and 5 readings were taken for each replicate. 10 representative cotton plants were randomly selected from three treatments, separated by roots, stems, and leaves, placed in an oven. First, they were dried at 105℃ for 30 min and then at a temperature of 75 ℃ to a constant weight. Finally, the dry matter weight was calculated. The photosynthetic parameters of the main stem functional leaves of ZNL2067 were measured by a Li-6400 portable photosynthesis meter (produced by LI-COR, USA) on the day of submergence for 3 days and reoxygenation for 3 days. Three replicates were measured for each treatment, and 10 readings were taken for each replicate. 0.1 g of leaf tissue was weighed in three treatments, and 1 mL of extract was added to homogenize in an ice bath. After centrifugation at 8000 g for 10 min at 4 ℃, the supernatant was removed and placed on ice with three biological replicates for each sample. Samples were taken to determine the activity of POD and MDA, and a POD assay kit and MDA assay kit (#A084-3–1, #A003-3–1, Jiancheng Bioengineering Institute, Nan**g, Jiangsu, China) were used to measure the enzyme activity. Representative samples of ZNL2067 were randomly selected from the Nor, Sub and Reo treatments. RNA was separately extracted from roots, stems, and leaves, and then, equal amounts of RNA from roots, stems, and leaves were mixed. Each treatment was repeated three times independently. We isolated and purified total RNA using TRIzol (Invitrogen, Carlsbad, CA, USA). RNA was quantified using NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA), and the RNA integrity was assessed using a Bioanalyzer 2100 (Agilent, CA, USA) with a RIN of > 7.0. The cDNA library was built utilizing the methods of Fan et al. [68]. The average insert length in the cDNA library was 300 ± 50 bp. Finally, we performed 2 × 150 bp paired-end sequencing (PE150) on an Illumina NovaSeq™ 6000 (LC-Bio Technology CO., Ltd., Hangzhou, China). We used StringTie (version: stringtie-1.3.4d.Linux_x86_64) to assemble the mapped reads for each sample [69]. All transcriptomes from the samples were merged to reconstruct a comprehensive transcriptome using the gffcompare software (version: gffcompare-0.9.8.Linux_x86_ 64). We estimated the expression levels of all transcripts using StringTie and Ballgown and determined mRNA expression levels by calculating FPKM values. The differentially expressed mRNAs and genes were selected with log2fold change (FC) > 1 or log2 (FC) < -1 and p value < 0.05 by R package edge R [70]. We used TBtools software to display heatmaps [71]. We performed GO enrichment analysis of DEGs using the GOseq R package [72], and the length bias of DEGs was corrected. GO terms (p value < 0.05) were considered significantly enriched by DEGs. KOBAS77 software was used to test the statistical enrichment of DEGs in KEGG pathways. All DEGs were compared against the GO and KEGG [73,74,75]. We selected thirty DEGs to validate the reliability of the transcriptome database. Thirty pairs of primers were designed using the Primer 6.0 software (Table S1), and RT-qPCR was performed [68]. The Actin gene was used as a reference. SPPS(Ver.21) and EXCEL software were used for statistical analysis. One-way analysis of variance (ANOVA) or Duncan's method was used to compare the significant levels of differences between different treatments (α = 0.05).Dry matter determination
Net photosynthetic rate determination
MDA and POD measurements
cDNA library construction and sequencing
Identification of differentially expressed genes (DEGs)
Gene Ontology (GO) and KEGG pathway enrichment analyses
Real-time quantitative PCR (RT-qPCR) validation and analysis
Data Processing
Availability of data and materials
Data generated by RNA sequencing were deposited in the NCBI repository [Accession number: PRJNA629890]. ZNL2067 and ZL100 were from the laboratory of Wuwei Ye, Institute of Cotton Research of Chinese Academy of Agricultural Sciences.
Abbreviations
- DEGs:
-
Differentially expressed genes
- GO:
-
Gene Ontology
- KEGG:
-
Kyoto Encyclopedia of Genes and Genomes
- LDH:
-
Lactate dehydrogenase
- PDC:
-
Pyruvate decarboxylase
- ADH:
-
Alcohol dehydrogenase
- ROS:
-
Reactive oxygen species
- MVA pathway:
-
Mevalonate pathway
- DXP:
-
Pyruvate/glyceraldehyde-3-phosphate
- GA-3P:
-
Glyceraldehyde 3-phosphate
- EMP-TCA:
-
Citric acid cycle
- PFK:
-
6-Phosphofructokinase
- PGK:
-
Phosphoglycerate kinase
- SUS:
-
Sucrose synthase genes
- CS:
-
Citrate synthase
- ACO:
-
Aconitate hydratase
- LCYB:
-
Lycopene β-cyclase
- TPS:
-
Terpene synthase
- 2-OGD:
-
2-Oxoglutarate-dependent dioxygenase
- SM:
-
Squalene monooxygenase
- PCD:
-
Programmed cell death
- PCO:
-
Plant cysteine oxidase
- AACT:
-
Acetyl-CoA acetyltransferase
- HMGS:
-
Hydroxymethylglutaryl-CoA
- HMGCR:
-
Hydroxymethylglutaryl-CoA reductase synthase
- MVK:
-
Mevalonate kinase
- PMK:
-
Phosphomevalonate kinase
- MVD:
-
Mevalonate diphosphate decarboxylase
- IPP:
-
Isopentenyl pyrophosphate
- DXS:
-
1-Deoxy-D-xylulose-5-phosphate synthase
- DXR:
-
1-Deoxy-D-xylulose-5-phosphate reductoisomerase
- CMS:
-
2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase
- CMK:
-
4-Diphosphocytidyl-2-C-methyl-D-erythritol kinase
- MCS:
-
2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase
- HDS:
-
4-Hydroxy-3-methylbut-2-enyl diphosphate synthase
- GPPS:
-
Geranyl diphosphate synthase
- FPPS:
-
Farnesyl diphosphate synthase
- GGPPS:
-
Geranylgeranyl diphosphate synthase
References
Jackson M, Colmer T. Response and adaptation by plants to flooding stress. Ann Bot. 2005;96(4):501–5.
Sasidharan R, Bailey-Serres J, Ashikari M, Atwell BJ, Colmer TD, Fagerstedt K, Fukao T, Geigenberger P, Hebelstrup KH, Hill RD. Community recommendations on terminology and procedures used in flooding and low oxygen stress research. New Phytol. 2017;214(4):1403–7.
Wu Y-S, Yang C-Y. Physiological responses and expression profile of NADPH oxidase in rice (Oryza Sativa) seedlings under different levels of submergence. Rice. 2016;9(1):1–10.
Fukao T, Xu K, Ronald PC, Bailey-Serres J. A variable cluster of ethylene response factor–like genes regulates metabolic and developmental acclimation responses to submergence in rice. Plant Cell. 2006;18(8):2021–34.
Bailey-Serres J, Voesenek L. Flooding stress: acclimations and genetic diversity. Annu Rev Plant Biol. 2008;59:313–39.
Colmer TD, Voesenek L. Flooding tolerance: suites of plant traits in variable environments. Funct Plant Biol. 2009;36(8):665–81.
Pan J, Sharif R, Xu X, Chen X. Mechanisms of Waterlogging Tolerance in Plants: Research Progress and Prospects. Front Plant Sci. 2020;11: 627331.
Mondal S, Khan MIR, Dixit S, Sta. Cruz PC, Septiningsih EM, Ismail AM: Growth, productivity and grain quality of AG1 and AG2 QTLs introgression lines under flooding in direct-seeded rice system. Field Crops Res. 2020;248:107713.
Wang X, Sun L, Li W, Peng M, Chen F, Zhang W, Sun C, Chen S, Hua W, Zhang J. Dissecting the genetic mechanisms of waterlogging tolerance in Brassica napus through linkage map** and a genome-wide association study. Ind Crops Prod. 2020;147: 112269.
Qamer Z, Chaudhary MT, **ongming DU, Hinze L, Azhar MT: Review of oxidative stress and antioxidative defense mechanisms in Gossypium hirsutum L. in response to extreme abiotic conditions. J Cotton Res. 2021;4(1):1–9.
Noctor G, Mhamdi A, Chaouch S, Han Y, Neukermans J, Marquez-Garcia B, Queval G, Foyer CH. Glutathione in plants: an integrated overview. Plant Cell Environ. 2012;35(2):454–84.
Foyer CH, Noctor G. Ascorbate and Glutathione: The Heart of the Redox Hub. Plant Physiol. 2011;155(1):2–18.
Suzuki N, Koussevitzky S, Mittler R. MILLER G: ROS and redox signalling in the response of plants to abiotic stress. Plant, Cell Environ. 2012;35(2):259–70.
Gill SS, Tuteja N. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiol Biochem. 2010;48(12):909–30.
Fothergill-Gilmore LA, Michels PAM. Evolution of glycolysis. Prog Biophys Mol Biol. 1993;59(2):105–235.
Plaxton WC. The organization and regulation of plant glycolysis. Annu Rev Plant Biol. 1996;47(1):185–214.
Ismond KP, Dolferus R, De Pauw M, Dennis ES, Good AG. Enhanced low oxygen survival in Arabidopsis through increased metabolic flux in the fermentative pathway. Plant Physiol. 2003;132(3):1292–302.
Zhang P, Lyu D, Jia L, He J, Qin S. Physiological and de novo transcriptome analysis of the fermentation mechanism of Cerasus sachalinensis roots in response to short-term waterlogging. BMC Genomics. 2017;18(1):649.
Tougou M, Hashiguchi A, Yukawa K, Nanjo Y, Hiraga S, Nakamura T, Nishizawa K, Komatsu S: Responses to flooding stress in soybean seedlings with the alcohol dehydrogenase transgene. Plant Biotechnol. 2012:12.0301 a.
Li C, Jiang D, Wollenweber B, Li Y, Dai T, Cao W. Waterlogging pretreatment during vegetative growth improves tolerance to waterlogging after anthesis in wheat. Plant Sci. 2011;180(5):672–8.
Wang GB, Cao FL, Zhang XY, Zhang WX. Effects of waterlogging on the growth and energy-metabolic enzyme activities of different tree species. J Appl Ecol. 2010;21(3):590–6.
Voesenek LA, Colmer TD, Pierik R, Millenaar FF, Peeters AJ. How plants cope with complete submergence. New Phytol. 2006;170(2):213–26.
Dolferus R, Wolansky M, Carroll R, Miyashita Y, Ismond K, Good A. Functional analysis of lactate dehydrogenase during hypoxic stress in Arabidopsis. Funct Plant Biol. 2008;35(2):131–40.
Li Z, Zhang M, Chow WS, Chen F, **e Z, Fan D. Carbohydrate saving or biomass maintenance: which is the main determinant of the plant’s long-term submergence tolerance? Photosynth Res. 2021;149(1):155–70.
Luo FL, Nagel KA, Scharr H, Zeng B, Schurr U, Matsubara S. Recovery dynamics of growth, photosynthesis and carbohydrate accumulation after de-submergence: a comparison between two wetland plants showing escape and quiescence strategies. Ann Bot. 2011;107(1):49–63.
Anulika NP, Ignatius EO, Raymond ES, Osasere O-I, Abiola AH. The chemistry of natural product: Plant secondary metabolites. Int J Technol Enhanc Emerg Eng Res. 2016;4(8):1–9.
Cane DE. Biosynthesis meets bioinformatics. Science. 2000;287(5454):818–9.
**e J-T, Shao Z-H, Hoek TLV, Chang W-T, Li J, Mehendale S, Wang C-Z, Hsu C-W, Becker LB, Yin J-J. Antioxidant effects of ginsenoside Re in cardiomyocytes. Eur J Pharmacol. 2006;532(3):201–7.
Bange M, Milroy S, Thongbai P. Growth and yield of cotton in response to waterlogging. Field Crop Res. 2004;88(2–3):129–42.
Milroy S, Bange M: Reduction in radiation use efficiency of cotton (Gossypium hirsutum L.) under repeated transient waterlogging in the field. Field Crops Res. 2013;140:51–58.
Jia X, Zhu Y, Zhang R, Zhu Z, Zhao T, Cheng L, Gao L, Liu B, Zhang X, Wang Y. Ionomic and metabolomic analyses reveal the resistance response mechanism to saline-alkali stress in Malus halliana seedlings. Plant Physiol Biochem. 2020;147:77–90.
Chapman JM, Muhlemann JK, Gayomba SR, Muday GK. RBOH-Dependent ROS Synthesis and ROS Scavenging by Plant Specialized Metabolites To Modulate Plant Development and Stress Responses. Chem Res Toxicol. 2019;32(3):370–96.
Hasanuzzaman M, Bhuyan MHMB, Zulfiqar F, Raza A, Mohsin SM, Mahmud JA, et al. Reactive oxygen species and antioxidant defense in plants under abiotic stress: revisiting the crucial role of a universal defense regulator. Antioxidants. 2020;9(8):681.
Wu H, Chen H, Zhang Y, Zhang Y, Zhu D, **ang J. Effects of 1-aminocyclopropane-1-carboxylate and paclobutrazol on the endogenous hormones of two contrasting rice varieties under submergence stress. Plant Growth Regul. 2019;87(1):109–21.
Alpuerto JB, Hussain RMF, 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(3):672–84.
Hartman S, Liu Z, Van Veen H, Vicente J, Reinen E, Martopawiro S, Zhang H, Van Dongen N, Bosman F, Bassel GW. Ethylene-mediated nitric oxide depletion pre-adapts plants to hypoxia stress. Nat Commun. 2019;10(1):1–9.
Xu K, Xu X, Fukao T, Canlas P, Maghirang-Rodriguez R, Heuer S, Ismail AM, Bailey-Serres J, Ronald PC, Mackill DJ. Sub1A is an ethylene-response-factor-like gene that confers submergence tolerance to rice. Nature. 2006;442(7103):705–8.
Hattori Y, Nagai K, Furukawa S, Song XJ, Kawano R, Sakakibara H, Wu J, Matsumoto T, Yoshimura A, Kitano H, et al. The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water. Nature. 2009;460(7258):1026–30.
Tang H, Bi H, Liu B, Lou S, Song Y, Tong S, Chen N, Jiang Y, Liu J, Liu H: WRKY33 interacts with WRKY12 protein to up-regulate RAP2.2 during submergence induced hypoxia response in Arabidopsis thaliana. New Phytol. 2021;229(1):106–125.
Arnell NW, Gosling SN. The impacts of climate change on river flood risk at the global scale. Clim Change. 2016;134(3):387–401.
Phukan UJ, Mishra S, Shukla RK. Waterlogging and submergence stress: affects and acclimation. Crit Rev Biotechnol. 2016;36(5):956–66.
Baxter-Burrell A, Yang Z, Springer PS, Bailey-Serres J. RopGAP4-dependent Rop GTPase rheostat control of Arabidopsis oxygen deprivation tolerance. Science. 2002;296(5575):2026–8.
Tamang BG, Magliozzi JO, Maroof MS, Fukao T. Physiological and transcriptomic characterization of submergence and reoxygenation responses in soybean seedlings. Plant, Cell Environ. 2014;37(10):2350–65.
Daloso DM, Müller K, Obata T, Florian A, Tohge T, Bottcher A, Riondet C, Bariat L, Carrari F, Nunes-Nesi A, et al. Thioredoxin, a master regulator of the tricarboxylic acid cycle in plant mitochondria. Proc Natl Acad Sci U S A. 2015;112(11):E1392-1400.
Martínez-Reyes I, Diebold LP, Kong H, Schieber M, Huang H, Hensley CT, Mehta MM, Wang T, Santos JH, Woychik R, et al. TCA Cycle and Mitochondrial Membrane Potential Are Necessary for Diverse Biological Functions. Mol Cell. 2016;61(2):199–209.
Sweetlove LJ, Beard KF, Nunes-Nesi A, Fernie AR, Ratcliffe RG. Not just a circle: flux modes in the plant TCA cycle. Trends Plant Sci. 2010;15(8):462–70.
Bhatla SC: Secondary Metabolites. In: Plant Physiology, Development and Metabolism. Edited by Bhatla SC, A. Lal M. Singapore: Springer Singapore; 2018: 1099–1166.
Bailey-Serres J, Chang R. Sensing and signalling in response to oxygen deprivation in plants and other organisms. Ann Bot. 2005;96(4):507–18.
Pucciariello C, Parlanti S, Banti V, Novi G, Perata P. Reactive oxygen species-driven transcription in Arabidopsis under oxygen deprivation. Plant Physiol. 2012;159(1):184–96.
Sharma P, Jha AB, Dubey RS, Pessarakli M. Reactive oxygen species, oxidative damage, and antioxidative defense mechanism in plants under stressful conditions. J Botany. 2012;2012:217037.
Baxter A, Mittler R, Suzuki N. ROS as key players in plant stress signalling. J Exp Bot. 2014;65(5):1229–40.
Li M, Li X, Zhou J, Sun Y, Du J, Wang Z, Luo Y, Zhang Y, Chen Q, Wang Y, et al. Genome-wide identification and analysis of terpene synthase (TPS) genes in celery reveals their regulatory roles in terpenoid biosynthesis. Front Plant Sci. 2022;13:1010780.
Chua NK, Coates HW, Brown AJ. Squalene monooxygenase: a journey to the heart of cholesterol synthesis. Prog Lipid Res. 2020;79: 101033.
**g Y, Wang Y, Zhou D, Wang J, Li J, Sun J, et al. Advances in the synthesis of three typical tetraterpenoids including β-carotene, lycopene and astaxanthin. Biotechnol Adv. 2022;61:108033.
Micera M, Botto A, Geddo F, Antoniotti S, Bertea CM, Levi R, et al. Squalene: more than a step toward sterols. Antioxidants. 2020;9(8):688.
Li Y, Shi L-C, Yang J, Qian Z-H, He Y-X, Li M-W. Physiological and transcriptional changes provide insights into the effect of root waterlogging on the aboveground part of Pterocarya stenoptera. Genomics. 2021;113(4):2583–90.
Chuang Y-C, Hung Y-C, Tsai W-C, Chen W-H, Chen H-H. PbbHLH4 regulates floral monoterpene biosynthesis in Phalaenopsis orchids. J Exp Bot. 2018;69(18):4363–77.
Yu Z-X, Li J-X, Yang C-Q, Hu W-L, Wang L-J, Chen X-Y. The jasmonate-responsive AP2/ERF transcription factors AaERF1 and AaERF2 positively regulate artemisinin biosynthesis in Artemisia annua L. Mol Plant. 2012;5(2):353–65.
Lv Z, Wang S, Zhang F, Chen L, Hao X, Pan Q, Fu X, Li L, Sun X, Tang K. Overexpression of a novel NAC domain-containing transcription factor gene (AaNAC1) enhances the content of artemisinin and increases tolerance to drought and Botrytis cinerea in Artemisia annua. Plant Cell Physiol. 2016;57(9):1961–71.
Zhou M-L, Hou H-L, Zhu X-M, Shao J-R, Wu Y-M, Tang Y-X. Soybean transcription factor GmMYBZ2 represses catharanthine biosynthesis in hairy roots of Catharanthus roseus. Appl Microbiol Biotechnol. 2011;91(4):1095–105.
Suttipanta N, Pattanaik S, Kulshrestha M, Patra B, Singh SK, Yuan L. The transcription factor CrWRKY1 positively regulates the terpenoid indole alkaloid biosynthesis in Catharanthus roseus. Plant Physiol. 2011;157(4):2081–93.
Zhang F, **ang L, Yu Q, Zhang H, Zhang T, Zeng J, Geng C, Li L, Fu X, Shen Q. ARTEMISININ BIOSYNTHESIS PROMOTING KINASE 1 positively regulates artemisinin biosynthesis through phosphorylating AabZIP1. J Exp Bot. 2018;69(5):1109–23.
White MD, Klecker M, Hopkinson RJ, Weits DA, Mueller C, Naumann C, O’Neill R, Wickens J, Yang J, Brooks-Bartlett JC. Plant cysteine oxidases are dioxygenases that directly enable arginyl transferase-catalysed arginylation of N-end rule targets. Nat Commun. 2017;8(1):1–9.
Papdi C, Pérez‐Salamó I, Joseph MP, Giuntoli B, Bögre L, Koncz C, Szabados L: The low oxygen, oxidative and osmotic stress responses synergistically act through the ethylene response factor VII genes RAP 2.12, RAP 2.2 and RAP 2.3. Plant J. 2015, 82(5):772–784.
Licausi F, Van Dongen JT, Giuntoli B, Novi G, Santaniello A, Geigenberger P, Perata P. HRE1 and HRE2, two hypoxia-inducible ethylene response factors, affect anaerobic responses in Arabidopsis thaliana. Plant J. 2010;62(2):302–15.
Fernie AR, Carrari F, Sweetlove LJ. Respiratory metabolism: glycolysis, the TCA cycle and mitochondrial electron transport. Curr Opin Plant Biol. 2004;7(3):254–61.
Nunes-Nesi A, Araújo WL, Obata T, Fernie AR. Regulation of the mitochondrial tricarboxylic acid cycle. Curr Opin Plant Biol. 2013;16(3):335–43.
Fan Y, Lu X, Chen X, Wang J, Wang D, Wang S, Guo L, Rui C, Zhang Y, Cui R. Cotton transcriptome analysis reveals novel biological pathways that eliminate reactive oxygen species (ROS) under sodium bicarbonate (NaHCO3) alkaline stress. Genomics. 2021;113(3):1157–69.
Pertea M, Pertea GM, Antonescu CM, Chang T-C, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33(3):290–5.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.
Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, He Y, **a R. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol Plant. 2020;13(8):1194–202.
Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):1–12.
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2007: 36(suppl_1):D480-D484.
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587-d592.
Acknowledgements
We thank Ms. Rui Cun, Wang **g, Xu Nan and Zhang Hong for their data analysis methods.
Funding
This study was supported by China Agriculture Research System of MOF and MARA, Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences.
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Conceived and designed the experiments: WY, LS; methodology: LS, JW, YC, RK; experiment: LS, JW, RC, SW, LZ, CC, LG; analysis of data: LS, YZ, DW, XL,YF, MH, XC; writing-original draft preparation: LS; writing-review and editing: LS, YC, RK; supervision: WY. All authors reviewed the manuscript.
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The upland cotton cultivar (ZNL2067 and ZL100) used in the experiment are from our laboratory and we have permission to use it. Field studies were conducted in accordance with local legislation. This article does not contain any studies with human participants or animals and did not involve any endangered or protected species.
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Sun, L., Wang, J., Cui, Y. et al. Changes in terpene biosynthesis and submergence tolerance in cotton. BMC Plant Biol 23, 330 (2023). https://doi.org/10.1186/s12870-023-04334-4
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DOI: https://doi.org/10.1186/s12870-023-04334-4