Transcriptome analysis-based bermudagrass root RNA sequencing data under drought stress

doi:10.11686/cyxb2023193

Abstract

Abstract:

The root system is the most important organ of bermudagrass in response to drought stress， and the molecular mechanism of its response to different degrees of drought needs to be further clarified. In this study， two bermudagrass genotypes with contrasting drought-resistant （C138） and drought-sensitive （C32） properties， were sampled under normal irrigation （soil relative water content is 80%-90% of the field capacity）， moderate drought （50%-60% of field capacity） and severe drought （20%-30% of field capacity）. From transcriptome sequencing with the Illumina High-throughput sequencing platform， 43581 Unigenes were obtained， 33025 of which were annotated. Compared with normal irrigation， there were 7537 genes differentially expressed in C138 under moderate and severe drought， comprising 1164 up-regulated and 6373 down-regulated， while in C32， there were 4304 up-regulated and 2427 down regulated unigenes. Top20 GO enrichment analysis found that C138 was mainly enriched in oxidoreductase activity， carbohydrate metabolism and cellulose binding under moderate stress； C138 was mainly enriched in phosphate metabolism and protein phosphorylation under severe stress， and C32 was mainly enriched in peroxidase activity and oxidative stress response. KEGG Pathway enrichment analysis found that C138 was mainly enriched in glutathione metabolism， phenylpropyl biosynthesis and the nitrogen metabolism pathway， while C32 was mainly enriched in oxidative phosphorylation， tricarboxylic acid cycle， ribosomal enzymes and carbon metabolism pathways under moderate stress. Under severe drought stress， C138 was mainly enriched in glutathione metabolism， arachidoic acid metabolism and the ribosomal enzyme pathway， while C32 was mainly enriched in the glutathione metabolism and phenylpropyl biosynthesis pathways. Overall， the gene expression response to moderate drought stress in bermudagrass was mainly related to oxidative response. Severe drought stress was mainly related to the Ca²⁺ pathway， abscisic acid signaling pathway， and glutathione metabolism. Thus， glutathione metabolism might be the main KEGG pathway affected in response to drought in bermudagrass. Moreover， compared with C32， C138 had more calcium-dependent protein kinase-related gene expression in response to drought stress and thus had stronger drought resistance. In conclusion， glutathione metabolism and MYB transcription factor-related genes may be critical for drought resistance in bermudagrass and from this work are indicated as the first choice for investigation in a drought-stress gene study.

Key words: drought stress, bermudagrass, root, transcriptome

Shuo LI, Pei-ying LI, Zong-jiu SUN, Wen LI. Transcriptome analysis-based bermudagrass root RNA sequencing data under drought stress[J]. Acta Prataculturae Sinica, 2024, 33(4): 186-198.

Figures/Tables 11

References 32

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编号Code	采集地点Collection location	抗旱类型Drought resistance type
C32	新疆吐鲁番市托克逊县Tuokexun County， Turpan City， Xinjiang Autonomous Region	敏旱型Drought sensitive
C138	新疆喀什疏勒县Shule County， Kashgar City， Xinjiang Autonomous Region	抗旱型Drought resistant

编号Code	采集地点Collection location	抗旱类型Drought resistance type
C32	新疆吐鲁番市托克逊县Tuokexun County， Turpan City， Xinjiang Autonomous Region	敏旱型Drought sensitive
C138	新疆喀什疏勒县Shule County， Kashgar City， Xinjiang Autonomous Region	抗旱型Drought resistant

基因ID Gene ID	正向引物Forward primer （5′-3′）	反向引物Reverse primer （5′-3′）
Actin	GCTCAACCCCAAGGCTAACAG	AGCGTATCCCTCGTAGATG
c458080.graph_c0	CGGTGCTGGTTGGGTAGTCTG	AAATGGCGGCAATAGCGAAAGC
c452636.graph_c0	CCCTGCCCAACGACGAAGAG	CATTGGCTTGGCGTGCTTGAG
c422082.graph_c1	GCCCATCACCGCCAATCCG	CAAACGCATTAGCAACGCAACG
c448662.graph_c0	GTGAGGTCCAGGCGGTGTTG	CGGCGAGTCGGTGTTCCTTG
c443527.graph_c0	TGCCCGTTGACCTGTCTTTCTC	AGCACTTCCTCCGCCATTTCG
c460219.graph_c1	GCGGCGAGCAATGATGTTACAG	AGATGGCTGGCGTTCTTGGC

基因ID Gene ID	正向引物Forward primer （5′-3′）	反向引物Reverse primer （5′-3′）
Actin	GCTCAACCCCAAGGCTAACAG	AGCGTATCCCTCGTAGATG
c458080.graph_c0	CGGTGCTGGTTGGGTAGTCTG	AAATGGCGGCAATAGCGAAAGC
c452636.graph_c0	CCCTGCCCAACGACGAAGAG	CATTGGCTTGGCGTGCTTGAG
c422082.graph_c1	GCCCATCACCGCCAATCCG	CAAACGCATTAGCAACGCAACG
c448662.graph_c0	GTGAGGTCCAGGCGGTGTTG	CGGCGAGTCGGTGTTCCTTG
c443527.graph_c0	TGCCCGTTGACCTGTCTTTCTC	AGCACTTCCTCCGCCATTTCG
c460219.graph_c1	GCGGCGAGCAATGATGTTACAG	AGATGGCTGGCGTTCTTGGC

样品 Sample	过滤后序列长度Clean datas length （bp）	GC含量 GC content （%）	碱基质量>20 Q₂₀ （%）	碱基质量>30 Q₃₀ （%）	样品 Sample	过滤后序列长度Clean datas length （bp）	GC含量 GC content （%）	碱基质量>20 Q₂₀ （%）	碱基质量>30 Q₃₀ （%）
CR138-1	5751481130	52.51	97.67	93.70	MR32-1	7662111708	52.97	97.70	93.80
CR138-2	6361023526	52.89	97.81	94.00	MR32-2	7405754772	52.73	97.77	93.94
CR138-3	5963989470	53.19	97.69	93.80	MR32-3	7729678484	53.38	97.79	94.03
CR32-1	8694639522	51.96	98.29	95.02	SR138-1	6680468588	52.06	97.67	93.72
CR32-2	7119356674	52.58	98.21	94.90	SR138-2	6788905702	52.18	97.37	92.94
CR32-3	6517861366	51.93	97.87	94.05	SR138-3	5945423404	52.11	97.86	94.16
MR138-1	9409664642	52.57	97.75	93.90	SR32-1	6205146120	52.31	97.94	94.31
MR138-2	6314881976	52.21	97.70	93.72	SR32-2	6469804754	52.11	97.86	94.08
MR138-3	6298108628	52.30	97.67	93.69	SR32-3	6935579000	51.64	97.81	94.00