Identification and Expression Analysis of Bacterial Wilt Response Genes Based on RNA-seq in Tomato

doi:10.7668/hbnxb.20192621

Abstract

Abstract:

To explore bacterial wilt resistance genes,RNA sequencing was used to characterize the transcriptomes of resistant and susceptible tomato inbred lines with Ralstonia solanacearum inoculation(RsI).The results showed that a total of 75.02 Gb high-quality data were generated in 12 libraries.With the fold change(FC)≥2 and false discovery rate(FDR)<0.01 as the standard,970 and 695 differentially expressed genes(DEGs)were identified in the two tomato lines,respectively.The 1 312 DEGs accounted for 3.71% of the total.Among them,the numbers of up-regulated genes were 457 and 450,respectively,totaling 693;the numbers of down-regulated genes were 513 and 245,respectively,totaling 621.Among these DEGs,836 genotype-specific DEGs were highlighted.These DEGs were mainly divided into 47 functional groups such as metabolism,regulation,response,binding,and catalysis,and 88 metabolic pathways such as DNA replication,secondary metabolite synthesis,plant-pathogen interaction,and signal transduction by GO and KEGG annotation.Specifically,4 NBS resistance genes,6 plant-pathogen interaction genes,11 plant hormone signal transduction genes,22 defense response genes,32 protein kinases,65 transcription factors,and several other important functional genes were involved,indicating that they played important roles in response to Rs.Promoter analysis revealed that these genes possessed multiple defense and stress response elements.The output was confirmed using RT-qPCR for 50 representative genes.It was found that more than half of the genes were consistent with RNA-seq in expression.Solyc02g086980.3 and Solyc04g011670.3 might be involved in the resistance response,whereas Solyc01g073985.1,Solyc09g092580.4,Solyc09g098100.4,and Solyc10g081300.1 might be the opposite.Together,these gene expression profiles serve as fundamental information to understand the potential molecular basis in the response to Rs in tomato,and facilitate the application of related resistance genes in breeding.

Key words: Tomato, Ralstonia solanacearum, Transcriptome, Differentially expressed genes, Gene annotation, Quantitative expression

SHI Jianlei, XIONG Zili, SU Shiwen, WANG Kelei, ZAI Wenshan. Identification and Expression Analysis of Bacterial Wilt Response Genes Based on RNA-seq in Tomato[J]. Acta Agriculturae Boreali-Sinica, 2022, 37(2): 171-182. doi: 10.7668/hbnxb.20192621.

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References 52

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基因 Gene	正向引物(5'—3') Forward primer	反向引物(5'—3') Reverse primer
SlRPL2	GTCATCCTTTCAGGTACAAGCA	CGTTACAAACAACAGCTCCTTC
Solyc01g073985.1	GTTGGCTCGTGGAGGATGAT	TGCTGGCTGCTTCACTTCA
Solyc02g086980.3	GCGTTGAGTTAGGCAGTAAGG	CTAGAAGATGATCCAGTGAGTGAGA
Solyc04g011670.3	CATTATCGTTGATGGTTGCCTGAA	GCGTTCTGCTGATGTCTTCC
Solyc09g092580.4	CAAGCCGCCACTTCACAA	GCCAACATTTCTTCCGTCACT
Solyc09g098100.4	GCGAACAAGTCAAGTGCCATAC	TTCACCTCATTCCACCAAGATTCT
Solyc10g081300.1	TCCAAGCCTAAATTCATCCCACAT	TCCTCCAACATCTTCACATTCTTCA

基因 Gene	正向引物(5'—3') Forward primer	反向引物(5'—3') Reverse primer
SlRPL2	GTCATCCTTTCAGGTACAAGCA	CGTTACAAACAACAGCTCCTTC
Solyc01g073985.1	GTTGGCTCGTGGAGGATGAT	TGCTGGCTGCTTCACTTCA
Solyc02g086980.3	GCGTTGAGTTAGGCAGTAAGG	CTAGAAGATGATCCAGTGAGTGAGA
Solyc04g011670.3	CATTATCGTTGATGGTTGCCTGAA	GCGTTCTGCTGATGTCTTCC
Solyc09g092580.4	CAAGCCGCCACTTCACAA	GCCAACATTTCTTCCGTCACT
Solyc09g098100.4	GCGAACAAGTCAAGTGCCATAC	TTCACCTCATTCCACCAAGATTCT
Solyc10g081300.1	TCCAAGCCTAAATTCATCCCACAT	TCCTCCAACATCTTCACATTCTTCA

样本 Libraries	净读数 Clean reads	净碱基数/Gb Clean bases	GC含量/% GC content	Q30/%
RC02	40 564 140	6.07	43.72	94.82
RC13	46 173 674	6.90	43.73	94.81
RC14	48 085 886	7.18	43.79	95.01
RT06	45 753 134	6.83	43.74	94.56
RT17	41 260 090	6.17	43.27	94.97
RT18	39 092 480	5.84	43.79	94.61
SC04	41 490 160	6.21	44.17	94.98
SC15	43 627 624	6.51	44.10	94.40
SC16	38 440 746	5.74	43.78	94.85
ST08	38 380 600	5.73	43.97	94.13
ST19	39 741 206	5.94	43.78	94.78
ST20	39 487 202	5.90	44.09	94.57

样本 Libraries	净读数 Clean reads	净碱基数/Gb Clean bases	GC含量/% GC content	Q30/%
RC02	40 564 140	6.07	43.72	94.82
RC13	46 173 674	6.90	43.73	94.81
RC14	48 085 886	7.18	43.79	95.01
RT06	45 753 134	6.83	43.74	94.56
RT17	41 260 090	6.17	43.27	94.97
RT18	39 092 480	5.84	43.79	94.61
SC04	41 490 160	6.21	44.17	94.98
SC15	43 627 624	6.51	44.10	94.40
SC16	38 440 746	5.74	43.78	94.85
ST08	38 380 600	5.73	43.97	94.13
ST19	39 741 206	5.94	43.78	94.78
ST20	39 487 202	5.90	44.09	94.57

样本 Libraries	比对读数 Mapped reads	唯一比对读数 Uniquely mapped reads	多比对读数 Multiple mapped reads	峰值片段大小/bp Insert size peak
RC02	38 673 468 (95.34%)	37 000 978 (91.22%)	1 672 490 (4.12%)	298
RC13	43 965 762 (95.22%)	42 226 315 (91.45%)	1 739 447 (3.77%)	298
RC14	45 679 708 (95.00%)	43 498 262 (90.46%)	2 181 446 (4.54%)	292
RT06	43 520 979 (95.12%)	41 628 854 (90.99%)	1 892 125 (4.14%)	298
RT17	39 471 364 (95.66%)	38 040 850 (92.20%)	1 430 514 (3.47%)	286
RT18	37 279 419 (95.36%)	35 490 584 (90.79%)	1 788 835 (4.58%)	298
SC04	39 648 351 (95.56%)	37 777 814 (91.05%)	1 870 537 (4.51%)	298
SC15	41 661 217 (95.49%)	39 393 711 (90.30%)	2 267 506 (5.20%)	298
SC16	37 020 893 (96.31%)	35 358 525 (91.98%)	1 662 368 (4.32%)	281
ST08	36 769 537 (95.80%)	34 961 451 (91.09%)	1 808 086 (4.71%)	298
ST19	38 083 435 (95.83%)	36 273 601 (91.27%)	1 809 834 (4.55%)	298
ST20	37 682 695 (95.43%)	35 757 215 (90.55%)	1 925 480 (4.88%)	298

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