Analysis of metabolic pathways and differentially expressed genes of Trifolium pratense responding to waterlogging stress

doi:10.11686/cyxb2022139

Abstract

Abstract:

Waterlogging stress is an important abiotic stress that affects plant growth， development and distribution， and research on plant waterlogging stress is key to addressing plant production management under extreme heavy rainfall in recent years. Trifolium pratense is a high-quality legume forage， but is poorly tolerant to waterlogging， and long-term waterlogging can lead to root rot and death. In order to investigate the molecular response mechanism of T. pratense under waterlogging stress， the transcriptome was sequenced from seedling leaves of the waterlogging-tolerant variety “Hong Long” at 0， 8 and 24 h using an Illumina high-throughput sequencing platform， and the sequenced data were compared with the reference genome for differentially expressed genes （DEGs） and functional annotation. The results showed that， compared with the control 0 h， “Hong Long” had 5065 DEGs after 8 h of waterlogging stress， among which 2442 genes were up-regulated and 2623 genes were down-regulated； After 24 h of waterlogging stress， there were 9022 DEGs， among which 4279 genes were up-regulated and 4743 genes were down-regulated. The gene ontology （GO） enrichment results showed that DEGs were mainly enriched in terms of metabolic process， cellular process， biological regulation， cell and catalytic activity； The kyoto encyclopedia of genes and genomes （KEGG） enrichment results showed that DEGs were significantly enriched in pathways such as plant hormone signal transduction， plant-pathogen interaction， carbon metabolism and glyoxylate and dicarboxylate metabolism， in which antioxidant enzymes such as peroxidase and formic dehydrogenase were highly expressed in the glyoxylate and dicarboxylate metabolism pathways. This study also found that the differentially expressed AP2/ERF， WRKY， bHLH， NAC， bZIP and other important transcription factors also played important roles in the response of T. pratense to waterlogging stress. Finally， the expression pattern of DEGs was verified by qRT-PCR and found to be consistent with the RNA-Seq results， confirming the accuracy of the sequencing results. In this study， functional annotation， metabolic pathways and transcription factors of DEGs were analyzed based on the transcriptome information， which provided a preliminary understanding of the molecular response mechanisms of T. pratense to waterlogging stress and provided basic data and theoretical direction for the subsequent functional mining of candidate genes.

Key words: Trifolium pratense, waterlogging stress, transcriptomic sequencing, metabolic pathways, differentially expressed genes

Pan-pan SHANG, Bing ZENG, Ming-hao QU, Ming-yang LI, Xing-yun YANG, Yu-qian ZHENG, Bing-na SHEN, Lei BI, Cheng YANG, Bing ZENG. Analysis of metabolic pathways and differentially expressed genes of Trifolium pratense responding to waterlogging stress[J]. Acta Prataculturae Sinica, 2023, 32(4): 112-128.

Figures/Tables 15

References 59

1	Chen M J， Jia S X. Chinese forage plants. Beijing： China Agriculture Press， 2002.
	陈默君，贾慎修. 中国饲用植物. 北京：中国农业出版社， 2002.
2	Jia S X. Forage plants of China-Vol.6. Beijing： China Agriculture Press， 1997.
	贾慎修. 中国饲用植物志-第六卷. 北京：中国农业出版社， 1997.
3	Chen B S. Forage crop cultivation. Beijing： China Agriculture Press， 2001.
	陈宝书. 牧草饲料作物栽培学. 北京：中国农业出版社， 2001.
4	Meng X J， Yu L P， Cheng W D， et al. Influence of rhizobia inoculation and nitrogen fertilizer application on isoflavonoides content of Minshan red clover. Pratacultural Science， 2010， 27（5）： 97-100.
	孟祥君，俞联平，程文定，等. 接种根瘤菌与施肥对岷山红三叶异黄酮含量的影响. 草业科学， 2010， 27（5）： 97-100.
5	Yuan J B， Lu J Z. Determination of soybean isoflavone with ultra-violet spectrophotometry. Soybean Science， 2004（2）： 147-150.
	袁金斌，卢建中. 紫外分光光度法测定大豆总异黄酮的含量. 大豆科学， 2004（2）： 147-150.
6	Wang Z M， Yue M Q， Du W H， et al. Excellent legume for feeding and medicine-Minshan Trifolium pratense. Pratacultural Science， 2005， 22（4）： 33-35.
	王志明，岳民勤，杜文华，等. 集饲用和药用价值于一体的牧草新秀-岷山红三叶. 草业科学， 2005， 22（4）： 33-35.
7	Phelan P， Moloney A P， McGeough E J， et al. Forage legumes for grazing and conserving in ruminant production systems. Critical Reviews in Plant Sciences， 2015， 34（4）： 281-326.
8	Mckenna P， Cannon N， Conway J， et al. Red clover （Trifolium pratense） in conservation agriculture： A compelling case for increased adoption. International Journal of Agricultural Sustainability， 2018， 16： 342-366.
9	Tan S D， Zhu M Y， Zhang K R， et al. Response and adaptation of plants to submergence stress. Chinese Journal of Ecology， 2009， 28（9）： 1871-1877.
	谭淑端，朱明勇，张克荣，等. 植物对水淹胁迫的响应与适应. 生态学杂志， 2009， 28（9）： 1871-1877.
10	Bodegom P M V， Sorrell B K， Oosthoek A， et al. Separating the effects of partial submergence and soil oxygen demand on plant physiology. Ecology， 2008， 89（1）： 193-204.
11	Silvertown J， Dodd M E， Gowing D， et al. Hydrologically defined niches reveal a basis for species richness in plant communities. Nature， 1999， 400： 61-63.
12	Normile D. Reinventing rice to feed the world. Science， 2008， 321： 330-333.
13	Mcmanmon M， Crawford R M. Metabolic theory of flooding tolerance-significance of enzyme distribution and behaviour. New Phytologist， 1971， 70（2）： 299-306.
14	Visser E， Voesenek L. Acclimation to soil flooding-sensing and signal-transduction. Plant and Soil， 2005， 274（1/2）： 197-214.
15	Voesenek L A C J， Sasidharan R. Ethylene- and oxygen signalling- drive plant survival during flooding. Plant Biology， 2013， 15（3）： 426-435.
16	Zhang F S. Environmental stress and plant rhizosphere nutrition. Beijing： China Agriculture Press， 1998.
	张福锁. 环境胁迫与植物根际营养. 北京：中国农业出版社， 1998.
17	Klok E J， Wilson I W， Wilson D， et al. Expression profile analysis of the low-oxygen response in Arabidopsis root cultures. Plant Cell， 2002， 14（10）： 2481-2494.
18	Zhang J， Tang L， Zhang Y J， et al. Application of transcriptome sequencing technique in the study of waterlogging stress in plant. Molecular Plant Breeding， 2019， 17（4）： 1191-1202.
	张健，唐露，张雅洁，等. 转录组测序技术在植物淹水胁迫研究中的应用. 分子植物育种， 2019， 17（4）： 1191-1202.
19	Loucks C E S， Deen W， Gaudin A C M， et al. Genotypic differences in red clover （Trifolium pratense） response under severe water deficit. Plant and Soil， 2018， 425（1/2）： 401-414.
20	He W， Fan Y， Wang L， et al. Analysis of the grey incidence of wild Trifolium pratense drought resistance in the three gorges reservoir area. Acta Prataculturae Sinica， 2009， 18（3）： 255-259.
	何玮，范彦，王琳，等. 三峡库区野生红三叶苗期抗旱性灰色关联分析. 草业学报， 2009， 18（3）： 255-259.
21	Pu X J， Tian J S， Tian X H， et al. Construction of the AFLP linkage map and QTL analysis of powdery mildew resistance in red clover. Acta Prataculturae Sinica， 2018， 27（4）： 79-88.
	蒲小剑，田久胜，田新会，等. 红三叶遗传图谱构建及抗白粉病基因QTL定位. 草业学报， 2018， 27（4）： 79-88.
22	Zhang H S， Gao Q， Zhang T T， et al. Comprehensive evaluation of copper tolerance of 30 germplasm resources of red clover （Trifolium pratense）. Acta Prataculturae Sinica， 2021， 30（12）： 117-128.
	张鹤山，高秋，张婷婷，等. 30份红三叶种质资源耐铜性综合评价. 草业学报， 2021， 30（12）： 117-128.
23	Wang Z， Gerstein M， Snyder M. RNA-Seq： A revolutionary tool for transcriptomics. Nature Reviews Genetics， 2009， 10（1）： 57-63.
24	Ren S， Sun M， Yan H， et al. Identification and distribution of NBS-encoding resistance genes of Dactylis glomerata L. and its expression under abiotic and biotic stress. Biochemical Genetics， 2020， 58（6）： 824-847.
25	Qiao D D， Zhang Y J， Xiong X M， et al. Transcriptome analysis on responses of orchardgrass （Dactylis glomerata L.） leaves to a short term flooding. Hereditas， 2020， 157（1）： 1-16.
26	Zeng B， Zhang Y， Zhang A， et al. Transcriptome profiling of two Dactylis glomerata L. cultivars with different tolerance in response to submergence stress. Phytochemistry， 2020， 175： 112378.
27	Reis-Filho J S. Next-generation sequencing. Breast Cancer Research， 2009， 11（3）： 1-7.
28	Pertea M， Pertea G M， Antonescu C M， et al. Stringtie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology， 2015， 33（3）： 290-295.
29	Love M I， Huber W， Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology， 2014， 15（12）： 550.
30	Altschul S F， Madden T L， Schaffer A A， et al. Gapped BLAST and PSI-BLAST： A new generation of protein database search programs. Nucleic Acids Research， 1997， 25（17）： 3389-3402.
31	Ashburner M， Ball C A， Blake J A， et al. Gene ontology： Tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics， 2000， 25（1）： 25-29.
32	Minoru K， Susumu G， Shuichi K， et al. The KEGG resource for deciphering the genome. Nucleic Acids Research， 2004， 32： 277-280.
33	Uniprot C T. UniProt： The universal protein knowledgebase. Nucleic Acids Research， 2018， 32： 115-119.
34	Mistry J， Chuguransky S， Williams L， et al. Pfam： The protein families database in 2021. Nucleic Acids Research， 2021， 49： 412-419.
35	Muller J， Szklarczyk D， Julien P， et al. egg NOG v2.0： Extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups， species and functional annotations. Nucleic Acids Research， 2010， 38： 190-195.
36	Bettler E， Krause R， Horn F， et al. NRSAS： Nuclear receptor structure analysis servers. Nucleic Acids Research， 2003， 31（13）： 3400-3403.
37	Pruitt K D， Tatusova T， Maglott D R. NCBI reference sequences （RefSeq）： A curated non-redundant sequence database of genomes， transcripts and proteins. Nucleic Acids Research， 2007， 35： 61-65.
38	Tatusov R L， Galperin M Y， Natale D A， et al. The COG database： A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Research， 2000， 28（1）： 33-36.
39	Ichimura K， Shinozaki K， Tena G， et al. Mitogen-activated protein kinase cascades in plants： A new nomenclature. Trends in Plant Science， 2002， 7（7）： 301-308.
40	Zhang H. Functional analysis of C₂H₂-type zinc finger protein ZFP182 and ZFP36 in ABA-induced antioxidant defense in rice. Nanjing： Nanjing Agricultural University， 2012.
	张宏. 水稻C₂H₂型锌指蛋白ZFP182和ZFP36在ABA诱导的抗氧化防护中的功能分析. 南京：南京农业大学， 2012.
41	Dahl C C， Baldwin I T. Deciphering the role of ethylene in plant-herbivore interactions. Journal of Plant Growth Regulation， 2007， 26（2）： 201-209.
42	Loon L C， Geraats B P J， Linthorst H J M. Ethylene as a modulator of disease resistance in plants. Trends in Plant Science， 2006， 11（4）： 184-191.
43	Pozo M J， Van Loon L C， Pieterse C. Jasmonates-signals in plant-microbe interactions. Trends in Plant Science， 2004， 23（3）： 211-222.
44	Aslam S， Gul N， Mir M A， et al. Role of jasmonates， calcium， and glutathione in plants to combat abiotic stresses through precise signaling cascade. Frontiers in Plant Science， 2021， 12： 668029.
45	Hu X J， Mao D H. Genome-wide expression analysis of the phytohormones signalling pathway in rice seedlings by using RNA-Seq. Research of Agricultural Modernization， 2019， 40（5）： 878-890.
	胡潇婕，毛东海. 基于RNA-Seq技术分析植物激素信号途径在水稻幼苗中对低温胁迫的应答规律. 农业现代化研究， 2019， 40（5）： 878-890.
46	Gao H X， Zhu L， Liu T Q， et al. Transcriptomic analysis of plant hormone response to low temperature stress in rice. Molecular Plant Breeding， 2021， 19（13）： 4188-4197.
	高红秀，朱琳，刘天奇，等. 水稻植物激素响应低温胁迫反应的转录组分析. 分子植物育种， 2021， 19（13）： 4188-4197.
47	Zhou W， Yang Y， Zheng C， et al. Flooding represses soybean seed germination by mediating anaerobic respiration， glycometabolism and phytohormones biosynthesis. Environmental and Experimental Botany， 2021， 188： 104491.
48	Li Y， Shi L， Yang J， et al. Physiological and transcriptional changes provide insights into the effect of root waterlogging on the aboveground part of Pterocarya stenoptera. Genomics， 2021， 113（4）： 2583-2590.
49	Wang X， He Y， Zhang C， et al. Physiological and transcriptional responses of Phalaris arundinacea under waterlogging conditions. Journal of Plant Physiology， 2021， 261（9）： 153428.
50	Park S， Kim Y， Lee C， et al. Comparative transcriptome profiling of two sweet potato cultivars with contrasting flooding stress tolerance levels. Plant Biotechnology Reports， 2020， 14（6）： 743-756.
51	Bromke M A. Amino acid biosynthesis pathways in diatoms. Metabolites， 2013， 3（2）： 294-311.
52	Yalage D S M， Gambetta J M， Steel C C， et al. Elucidating the interaction of carbon， nitrogen， and temperature on the biosynthesis of Aureobasidium pullulans antifungal volatiles. Environmental Microbiology Reports， 2021， 13（4）： 482-494.
53	Wang L， Yang Y R， Liu Y Q， et al. Transcriptome analysis of Chysanthemum×grandiflora in salt stress based on high-through-put sequencing. Molecular Plant Breeding， 2020， 18（5）： 1419-1427.
	王琳，杨伊如，刘艳秋，等. 基于高通量测序的露地菊（Chysanthemum×grandiflora）盐胁迫转录组分析. 分子植物育种， 2020， 18（5）： 1419-1427.
54	Qi X. The analysis of the differentially expressed genes in alfalfaunder cold stress at transcriptome level. Beijing： Chinese Academy of Agricultural Sciences， 2017.
	齐晓. 紫花苜蓿在转录组水平响应低温胁迫的差异表达基因研究. 北京：中国农业科学院， 2017.
55	Kramer R. Genetic and physiological approaches for the production of amino acids. Journal of Biotechnology， 1996， 45（1）： 1-21.
56	Li Y H. Study on the regulation of metabolic pathway in aromatic amino acids biosynthesis. Beijing： Academy of Military Medical Sciences， 2003.
	李永辉. 芳香族氨基酸生物合成代谢途径调控研究. 北京：中国人民解放军军事医学科学院， 2003.
57	Lu Y. Analyses of rice genome sequences and expressed sequence tags and studies of glyoxylate cycle in submerged seedlings in rice. Shanghai： University of Chinese Academy of Sciences （Shanghai Institutes for Biological Sciences）， 2006.
	陆颖. 水稻基因组和EST序列的测定和分析以及水淹条件下乙醛酸循环相关基因功能的研究. 上海：中国科学院研究生院（上海生命科学研究院）， 2006.
58	Willekens H， Chamnongpol S， Davey M， et al. Catalase is a sink for H₂O₂ and is indispensable for stress defence in C₃ plants. The EMBO Journal， 1997， 16（16）： 4806-4816.
59	Mei Y， Chen L M. Research progresses on gene regulation and physiological role of plant formate dehygrogenase. China Biotechnology， 2010，30（5）： 133-139.
	梅岩，陈丽梅. 植物甲酸脱氢酶基因的表达调控及其生理功能研究进展. 中国生物工程杂志， 2010， 30（5）： 133-139.

名称Primer	正向引物 Forward primers （5′-3′）	反向引物Reverse primers （5′-3′）
gene623	GGGCAGAGTCCGTGGTATGATAATC	AAAGATCGCTGGGTTGCTCGTG
gene26262	CAACAACAAACACCACCACCAACG	TTGAGTTACGACGGAGCAACACTG
gene11075	TCTCTACGGTATGATCAACGATGCA	CCCCCATGGTCTTCTCCTAACA
gene44440	CACCACAACCACAATCACAACCATC	GCTCCGCTACAACGTGCTAGTG
gene21760	CCCCAACTTGAGTCAGAAGCATC	TCCCTTTGATGTGCTGCACC
gene21720	CGGATGAGTTAGGCGGATGGTTG	CGACATCTCCTCTTCTCCCAACATC
gene21305	TCATCCCCTAAGAGGCCATACAGAG	CTGCTTCAGCGGTATCAAATGTTCC
gene3571	TGACTTGCTCAACAATCCAGACCTC	GGCATGATGGCTTCGGTGACTG
GAPDH	TCTGACCGTTAGACTTGAGAAGG	CTTGAGCTTACCCTCAGACTCCT

名称Primer	正向引物 Forward primers （5′-3′）	反向引物Reverse primers （5′-3′）
gene623	GGGCAGAGTCCGTGGTATGATAATC	AAAGATCGCTGGGTTGCTCGTG
gene26262	CAACAACAAACACCACCACCAACG	TTGAGTTACGACGGAGCAACACTG
gene11075	TCTCTACGGTATGATCAACGATGCA	CCCCCATGGTCTTCTCCTAACA
gene44440	CACCACAACCACAATCACAACCATC	GCTCCGCTACAACGTGCTAGTG
gene21760	CCCCAACTTGAGTCAGAAGCATC	TCCCTTTGATGTGCTGCACC
gene21720	CGGATGAGTTAGGCGGATGGTTG	CGACATCTCCTCTTCTCCCAACATC
gene21305	TCATCCCCTAAGAGGCCATACAGAG	CTGCTTCAGCGGTATCAAATGTTCC
gene3571	TGACTTGCTCAACAATCCAGACCTC	GGCATGATGGCTTCGGTGACTG
GAPDH	TCTGACCGTTAGACTTGAGAAGG	CTTGAGCTTACCCTCAGACTCCT

样品Samples	测序数据Clean reads	测序下机数据Clean bases	GC含量GC content （%）	Q20 （%）	Q30 （%）
H₀-1	21397140	6395717310	41.76	97.95	93.95
H₀-2	21857534	6532928926	41.69	98.02	94.09
H₀-3	19569973	5851935352	41.57	97.98	93.94
H₈-1	23040082	6890947266	42.35	98.12	94.36
H₈-2	19333333	5775427470	43.44	98.06	94.31
H₈-3	26332328	7873279856	41.72	98.16	94.44
H₂₄-1	18119992	5414773508	42.65	98.21	94.65
H₂₄-2	39179129	11705420840	42.69	98.06	94.27
H₂₄-3	20547211	6143134312	41.70	98.10	94.30

样品Samples	测序数据Clean reads	测序下机数据Clean bases	GC含量GC content （%）	Q20 （%）	Q30 （%）
H₀-1	21397140	6395717310	41.76	97.95	93.95
H₀-2	21857534	6532928926	41.69	98.02	94.09
H₀-3	19569973	5851935352	41.57	97.98	93.94
H₈-1	23040082	6890947266	42.35	98.12	94.36
H₈-2	19333333	5775427470	43.44	98.06	94.31
H₈-3	26332328	7873279856	41.72	98.16	94.44
H₂₄-1	18119992	5414773508	42.65	98.21	94.65
H₂₄-2	39179129	11705420840	42.69	98.06	94.27
H₂₄-3	20547211	6143134312	41.70	98.10	94.30

样品 Samples	比对数据 Total reads	比对上的数据 Mapped reads	比对到唯一位置的数据 Unique mapped reads	比对到多处位置的数据 Multiple mapped reads	比对到正链的数据 Reads map to ‘+’	比对到负链的数据 Reads map to ‘-’
H₀-1	42794280	35486057	31690935	3795122	16073999	16721352
H₀-2	43715068	36362495	32008861	4353634	15860703	16888759
H₀-3	39139946	32347565	28590712	3756853	14374026	15045589
H₈-1	46080164	37362124	33004646	4357478	16597665	17394638
H₈-2	38666666	30216711	26296339	3920372	12726728	13856534
H₈-3	52664656	43972928	39050460	4922468	19761602	20580516
H₂₄-1	36239984	28099032	24677641	3421391	12290831	13074940
H₂₄-2	78358258	63574337	55516961	8057376	27856971	29552246
H₂₄-3	41094422	34030303	30035515	3994788	15052963	15928683