Differentially expressed genes and related pathways in root systems of Dactylis glomerata under flooding stress

doi:10.11686/cyxb2023117

Abstract

Abstract:

In recent years， flooding has occurred frequently in southern China， and has seriously restricted the development of grass and animal husbandry industries. Dactylis glomerata is an important ecological grass species and a high-quality forage. However， it has poor flooding tolerance， so it is seldom grown in areas that are frequently affected by flooding. In this study， to explore the flooding response mechanism of D. glomerata， the physiological indexes and gene expression patterns in the roots of seedlings of the D. glomerata cultivar ‘Anba’ were analyzed at 0， 8， and 24 hours of flooding stress. The results showed that the contents of soluble sugars， soluble protein， and malondialdehyde in the roots increased significantly under flooding stress， and the relative conductivity decreased initially and then increased significantly during the 24 hours flooding treatment. After 8 hours of flooding stress （compared with 0 hours）， there were 5788 differentially expressed genes in the roots of D. glomerata， including 2872 up-regulated genes and 2916 down-regulated genes. After 24 hours of flooding stress， there were 8807 differentially expressed genes in the roots of D. glomerata， including 4123 up-regulated genes and 4684 down-regulated genes. Gene ontology enrichment analyses showed that these differentially expressed genes were mainly involved in polysaccharide metabolism， microtubule binding， cellulose metabolism， and the antioxidant response. The results of Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the pathways responding to flooding in roots of D. glomerata were phenylpropanoid biosynthesis， carbon metabolism， glutathione metabolism， amino acid biosynthesis， starch and sucrose metabolism， and glycolysis/gluconeogenesis. Further analysis of differentially expressed genes involved in phenylpropanoid biosynthesis， carbon metabolism， and glutathione metabolism pathways suggested that HXK1， HXK2， ADH1， GST， and APX2 encode products with important roles in the response of D. glomerata to flooding stress. Genes encoding MYB， NB-ARC， WRKY， GRAS， and AP2 transcription factors were highly expressed under flooding stress， suggesting that these transcription factors are closely related to flooding tolerance in D. glomerata. The results of this study provide basic data for further exploration of the molecular mechanism of flooding tolerance of D. glomerata， and also provide theoretical support for breeding to improve flooding tolerance.

Key words: Dactylis glomerata, flooding stress, roots, transcriptome, differentially expressed genes, metabolic pathway

Bing ZENG, Pan-pan SHANG, Bing-na SHEN, Yin-chen WANG, Ming-hao QU, Yang YUAN, Lei BI, Xing-yun YANG, Wen-wen LI, Xiao-li ZHOU, Yu-qian ZHENG, Wen-qiang GUO, Yan-long FENG, Bing ZENG. Differentially expressed genes and related pathways in root systems of Dactylis glomerata under flooding stress[J]. Acta Prataculturae Sinica, 2024, 33(2): 93-111.

Figures/Tables 14

References 64

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基因编号 Gene ID	正向引物Forward primer （5'-3'）	反向引物Reverse primer （5'-3'）
DG4C05841	GAGAAGGTGACCGAGAAC	GAGTGGGTGAAGAGGATG
DG3C00580	CGGCTACAGAAGAGGAGAGT	TGAAGAACAACAACGACAGACA
DG3C06505	CACTACTCCAAGACATGCCCGAATG	AAGCAGCACCGATCCATCACAAC
DG3C01167	TGCTCTGAACGACAACTT	ATGAACTCTTCTTCTCTCTGG
DG7C01296	ACTGCTGTGAAAGTGGTGCTGATC	TTCGCCGAGGATTCTGTTACAACTG
DG7C02648	TCAACAAGATGCGGTCCAACTGAG	GTACTTACACCACGTCGAGTTCCAC
Actin	GATCTTCGCCTCGCCAGGTTATC	ATATCGCCGTGCTTCATCCATGTC

基因编号 Gene ID	正向引物Forward primer （5'-3'）	反向引物Reverse primer （5'-3'）
DG4C05841	GAGAAGGTGACCGAGAAC	GAGTGGGTGAAGAGGATG
DG3C00580	CGGCTACAGAAGAGGAGAGT	TGAAGAACAACAACGACAGACA
DG3C06505	CACTACTCCAAGACATGCCCGAATG	AAGCAGCACCGATCCATCACAAC
DG3C01167	TGCTCTGAACGACAACTT	ATGAACTCTTCTTCTCTCTGG
DG7C01296	ACTGCTGTGAAAGTGGTGCTGATC	TTCGCCGAGGATTCTGTTACAACTG
DG7C02648	TCAACAAGATGCGGTCCAACTGAG	GTACTTACACCACGTCGAGTTCCAC
Actin	GATCTTCGCCTCGCCAGGTTATC	ATATCGCCGTGCTTCATCCATGTC

样品编号 Sample number	原始数据 Raw reads	过滤数据 Clean reads	Q20 （%）	Q30 （%）	GC （%）	总比对率 Total mapping rate		唯一比对率 Unique mapping rate		多比对率 Multiple mapping rate
样品编号 Sample number	原始数据 Raw reads	过滤数据 Clean reads	Q20 （%）	Q30 （%）	GC （%）	A	B （%）	A	C （%）	A	D （%）
AB_0h-1	44620874	42773100	98.04	94.27	53.94	31478723	73.59	30319134	70.88	1159589	2.71
AB_0h-2	46136466	44761494	98.00	94.28	54.79	32380485	72.34	31351666	70.04	1028819	2.30
AB_0h-3	44129532	42808268	97.99	94.22	53.97	30601830	71.49	29397803	68.67	1204027	2.81
AB_8h-1	43448316	42799686	98.17	94.69	54.83	29514489	68.96	28122605	65.71	1391884	3.25
AB_8h-2	41701902	39685822	98.07	94.31	52.56	27539115	69.39	26220686	66.07	1318429	3.32
AB_8h-3	42842338	41560508	98.10	94.44	55.58	29288776	70.47	28052239	67.50	1236537	2.98
AB_24h-1	43175864	41932282	97.94	94.19	56.10	27742677	66.16	25974820	61.94	1767857	4.22
AB_24h-2	47368352	46014046	97.98	94.24	55.61	30684212	66.68	29002064	63.03	1682148	3.66
AB_24h-3	49040614	47826980	97.91	94.05	55.25	31306578	65.46	29261089	61.18	2045489	4.28

样品编号 Sample number	原始数据 Raw reads	过滤数据 Clean reads	Q20 （%）	Q30 （%）	GC （%）	总比对率 Total mapping rate		唯一比对率 Unique mapping rate		多比对率 Multiple mapping rate
样品编号 Sample number	原始数据 Raw reads	过滤数据 Clean reads	Q20 （%）	Q30 （%）	GC （%）	A	B （%）	A	C （%）	A	D （%）
AB_0h-1	44620874	42773100	98.04	94.27	53.94	31478723	73.59	30319134	70.88	1159589	2.71
AB_0h-2	46136466	44761494	98.00	94.28	54.79	32380485	72.34	31351666	70.04	1028819	2.30
AB_0h-3	44129532	42808268	97.99	94.22	53.97	30601830	71.49	29397803	68.67	1204027	2.81
AB_8h-1	43448316	42799686	98.17	94.69	54.83	29514489	68.96	28122605	65.71	1391884	3.25
AB_8h-2	41701902	39685822	98.07	94.31	52.56	27539115	69.39	26220686	66.07	1318429	3.32
AB_8h-3	42842338	41560508	98.10	94.44	55.58	29288776	70.47	28052239	67.50	1236537	2.98
AB_24h-1	43175864	41932282	97.94	94.19	56.10	27742677	66.16	25974820	61.94	1767857	4.22
AB_24h-2	47368352	46014046	97.98	94.24	55.61	30684212	66.68	29002064	63.03	1682148	3.66
AB_24h-3	49040614	47826980	97.91	94.05	55.25	31306578	65.46	29261089	61.18	2045489	4.28

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[4]	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.
[5]	Bing-na SHEN, Pan-pan SHANG, Bing（student） ZENG, Lin-xiang LI, Xing-yun YANG, Lei BI, Yu-qian ZHENG, Ming-hao QU, Wen-wen LI, Xiao-li ZHOU, Jun RAO, Bing(teacher) ZENG. Comparative metabolomics analysis of root systems of two Dactylis glomerata cultivars in response to submergence stress [J]. Acta Prataculturae Sinica, 2023, 32(10): 40-57.
[6]	Lu-juan SUN, Jian-jun HE, Jun-cheng WANG, Li-rong YAO, Er-jing SI, Ke YANG, Bao-chun LI, Xiao-le MA, Xun-wu SHANG, Ya-xiong MENG, Hua-jun WANG. Development of SSR markers based on full-length transcriptome sequencing and genetic diversity analysis of Halogeton glomeratus [J]. Acta Prataculturae Sinica, 2022, 31(8): 199-210.
[7]	Feng-ling GAN, Jie WEI, Sha-sha LI. Response of root-soil friction characteristics of three common grasses to soil water content in purple soil bunds [J]. Acta Prataculturae Sinica, 2022, 31(7): 28-37.
[8]	Xing-yun YANG, Dan-dan QIAO, Ya-jie ZHANG, Shao-qing WANG, Jun-cai REN, Ming-yang LI, Ming-hao QU, Pan-pan SHANG, Cheng YANG, Lin-kai HUANG, Bing ZENG. A differential gene expression analysis of miRNA in Dactylis glomerata in response to flooding stress [J]. Acta Prataculturae Sinica, 2022, 31(6): 150-162.
[9]	Zhi-min YANG, Rui XING, Yun-jia DING, Li-li ZHUANG. Analysis of differentially expressed genes in relation to tiller development and plant height based on transcriptomic sequencing of two tall fescue cultivars [J]. Acta Prataculturae Sinica, 2022, 31(1): 145-163.
[10]	Qiao-yu LUO, Yan-long WANG, Zhi CHEN, Yong-gui MA, Qi-mei REN, Yu-shou MA. Effect of water stress on proline accumulation and metabolic pathways in Deschampsia caespitosa [J]. Acta Prataculturae Sinica, 2021, 30(5): 75-83.
[11]	Jing ZHOU, Si-qi CHEN, Wen-jiao SHI, Fu-lin YANG, Hui LIN, Zhan-xi LIN. Transcriptome analyses of functional genes in young leaves and roots of Giant Juncao [J]. Acta Prataculturae Sinica, 2021, 30(2): 143-155.
[12]	Fang-zhen WANG, Cheng-hang YANG, Zi-hua HE, Zi-ru LIN, Hao-yuan ZENG, Qing MA. Analysis of differentially expressed protein kinase related genes in the xerophyte Pugionium cornutum under salt treatment [J]. Acta Prataculturae Sinica, 2021, 30(10): 116-124.
[13]	SHU Xin-yue, JIANG Bo, MA Li, ZHENG Ai-ping. Transcriptome analysis of Tilletia horrida at different infection time points [J]. Acta Prataculturae Sinica, 2020, 29(9): 190-202.
[14]	QIAN Chen, LIU Zhi-wei, ZHONG Xiao-xian, WU Juan-zi, ZHANG Jian-li, PAN Yu-mei. Transcriptomic analysis of the self-incompatibility mechansim in Paspalum vaginatum by comparison with an artificial self-compatible mutant [J]. Acta Prataculturae Sinica, 2019, 28(5): 132-142.
[15]	GONG Wen-long, WANG Zan, ZHAO Gui-qin, MA Lin, WEI Bao, GONG Pan, LIU Xi-qiang. Development of EST-SSR molecular markers and analysis of genetic diversity of erect milk vetch (Astragalus adsurgens) [J]. Acta Prataculturae Sinica, 2019, 28(11): 147-158.