Identification and Induced Expression Analysis of Transcription Factors NAC in Soybean Resistance to Soybean Mosaic Virus Based on WGCNA

doi:10.13560/j.cnki.biotech.bull.1985.2025-0109

Abstract

Abstract:

Objective To identify the hub genes of transcription factors associated with soybean resistance to soybean mosaic virus (SMV), which may provide a theoretical foundation for elucidating molecular mechanism and developing resistant germplasm. Method Using the transcriptome data of soybean disease-resistant material X149 and disease-susceptible material X97 induced by SMV SC15, PlantTFDB v5.0 database was used to predict the transcription factor genes of the whole genome. Weighted gene co-expression network analysis (WGCNA) was applied to identify the resistance-associated modules and hub transcription factors based on differentially expressed transcription factor genes. String 12.0 was for predicitng transcription factor interacting proteins. RT-qPCR was to analyze the expression patterns of transcription factor genes induced by hormones (ETH, SA, MeJA and ABA). Result The whole genome contained 3 170 genes encoding transcription factors, of which 1 727 were differentially expressed. WGCNA analysis revealed that 1 727 genes were divided into six co-expression modules. Notably, the brown and turquoise modules demonstrated significant correlations with X149 resistance. Within the brown and turquoise modules, GmNAC030 in turquoise module, a NAC transcription factor, showed the highest connectivity and was identified as a core transcription factor. Other NAC transcription factor genes in the turquoise module expression were similar to GmNAC030 included GmNAC043, GmNAC085, GmNAC092, and GmNAC101. Prediction of interacting protein indicated that the interacting proteins of GmNAC030, GmNAC043, and GmNAC092 were primarily transcription factors. Specifically, only the interacting proteins Glyma.02g131700 (bZIP1), Glyma.16g164800 (AP2-EREBP), and Glyma.08G118200 (WRKY48) were located within the module. RT-qPCR analysis demonstrated that GmNAC030 expression was predominantly induced by MeJA, while the other four NAC transcription factors were mainly induced by both MeJA and ETH. Conclusion The expression of five NAC transcription factors, GmNAC030, GmNAC043, GmNAC085, GmNAC092 and GmNAC101, is associated with soybean resistance to SMV and can be induced by MeJA and ETH.

Key words: soybean, soybean mosaic virus, WGCNA, NAC transcription factor, expression analysis

NIU Jing-ping, ZHAO Jing, GUO Qian, WANG Shu-hong, ZHAO Jin-zhong, DU Wei-jun, YIN Cong-cong, YUE Ai-qin. Identification and Induced Expression Analysis of Transcription Factors NAC in Soybean Resistance to Soybean Mosaic Virus Based on WGCNA[J]. Biotechnology Bulletin, 2025, 41(7): 95-105.

Figures/Tables 9

Table 1 Information of primers for RT-qPCR

基因名称 Gene name	基因ID Gene ID	上游引物 Forward primer (5′-3′)	下游引物 Reverse primer (5′-3′)
GmNAC030	Glyma.05G195000	GGTTCAGTTCCAGCACGAGA	CGTGTCCATGTACAATTGGTCG
GmNAC043	Glyma.06G248900	TCTTCCCGCAAACACAACCT	TGACCCATTGCTGCATTCCA
GmNAC085	Glyma.12G149100	CGACATGCTGGAATCGTTGC	GTCCTGCTGCTGCATTGTTC
GmNAC092	Glyma.12G221500	ACAACTCGACGACGTTCTGG	CCCCCTTCACCCAAGTTCTG
GmNAC101	Glyma.13G279900	AACACGCTGCAACAACAACA	ATTCCCCGAAGCGAAATCGG
tublin		GGAGTTCACAGAGGCAGAG	CACTTACGCATCACATAGC

Table 2 Statistics of transcription factors (TF)

转录因子家族 TF family	基因数 Gene number	转录因子家族 TF family	基因数 Gene number
MYB	335	MIKC	84
ERF	305	Dof	79
bHLH	295	C3H	69
MYB_related	266	GATA	60
HB-other	204	ARF	55
WRKY	176	TCP	54
NAC	171	C2H2	53
M_type	144	HSF	52
bZIP	144	FAR1	49
B3	131	ZF-HD	47
GRAS	111	SBP	45
LBD	91	AP2	44
HD-ZIP	41	CPP	12
DBB	34	EIL	11
Nin-like	28	BBR-BPC	10
CO-like	22	GeBP	9
GRF	22	LSD	8
NF-YA	21	Whirly	7
SRS	21	HB-PHD	6
TALE	18	NF-X1	5
YABBY	17	RAV	4
BES1	15	S1Fa-like	4
CAMTA	15	LFY	2
E2F/DP	14	NZZ/SPL	1

Fig. 1 Identification of DEGs for transcription factors

Fig. 2 Clustering diagram of samplesA: Clustering diagram of all 36 samples. The three outlier samples are written in red fonts. B: Clustering diagram of 33 samples after removing the three outlier samples

Fig. 3 Determination of soft threshold power (β value) via WGCNA and identification of modulesA: Determination of soft threshold power (β value). B: Gene module classification, dendrogram indicate genes and different colors indicate different modules. C: Heat map of correlations between resistance trait and modules. The correlation coefficients are shown above each cell, and the P values are shown below

Fig. 4 Visual network analysis of top 30 transcription factor genes in the disease resistance-related module connectivity

Fig. 5 Expression profiles of NAC and validation of NAC by RT-qPCRA: Expression profiles of five NAC at different time point X149 and X97. B: Validation of GmNAC030 in X149 by RT-qPCR; *: P<0.05, **: P<0.01,The same below

Fig. 6 Analysis of NAC protein interaction and expression profiles of interacting proteins in transcriptomeA: Interacting protein Glyma.05g195000. B: Interacting protein Glyma.06G248900. C: Interacting protein Glyma.12G221500. D: Expression profiles of interacting protein in the modules

Fig. 7 Expression analysis of NAC transcription factors induced by hormone

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