基于转录组和WGCNA筛选小麦籽粒蛋白质累积相关调控基因

doi:10.3864/j.issn.0578-1752.2025.06.003

中国农业科学 ›› 2025, Vol. 58 ›› Issue (6): 1065-1082.doi: 10.3864/j.issn.0578-1752.2025.06.003

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于转录组和WGCNA筛选小麦籽粒蛋白质累积相关调控基因

潘丽媛¹(), 王永军¹, 李海军¹, 侯富¹, 李菁¹, 李丽丽¹, 孙苏阳¹^,²^,³()

¹ 江苏徐淮地区淮阴农业科学研究所，江苏淮安 223001
² 江苏省农业科学院生物育种钟山实验室，南京 210014
³ 扬州大学江苏省粮食作物现代产业技术协同创新中心，江苏扬州 225009

收稿日期:2024-08-04 接受日期:2024-09-20 出版日期:2025-03-25 发布日期:2025-03-25
通信作者:
孙苏阳，E-mail：ssy6688@163.com
联系方式: 潘丽媛，E-mail：panly89@126.com。
基金资助:
江苏省种业振兴“揭榜挂帅”项目(JBGS〔2021〕051); 淮安市农业科学研究院发展基金(HNY202007); 淮安市农业科学研究院高层次引进人才科研启动发展基金(0052023016B); 生物育种钟山实验室项目(ZSBBL-KY2023-02-4)

Screening Regulatory Genes Related to Wheat Grain Protein Accumulation Based on Transcriptome and WGCNA Analysis

PAN LiYuan¹(), WANG YongJun¹, LI HaiJun¹, HOU Fu¹, LI Jing¹, LI LiLi¹, SUN SuYang¹^,²^,³()

¹ Huaiyin Institute of Agricultural Sciences of Xuhuai Region in Jiangsu, Huai’an 223001, Jiangsu
² Zhongshan Laboratory of Biological Breeding, Jiangsu Academy of Agricultural Sciences, Nanjing 210014
³ Jiangsu Provincial Collaborative Innovation Center of Modern Industrial Technology for Grain Crops, Yangzhou University, Yangzhou 225009, Jiangsu

Received:2024-08-04 Accepted:2024-09-20 Published:2025-03-25 Online:2025-03-25

摘要/Abstract

摘要：

【目的】小麦作为重要的粮食作物，鉴定参与籽粒蛋白质合成的基因调控网络，并确定关键候选基因，为小麦品质育种和改良提供理论依据。【方法】以淮麦48的6个发育时期（花后5、10、15、20、25和30 d）的籽粒为研究材料，总结小麦籽粒蛋白质累积规律，利用WGCNA方法分析转录组数据和籽粒蛋白质含量表型数据，构建加权基因共表达网络，筛选关键枢纽转录因子（transcript factors，TFs）。【结果】小麦籽粒蛋白质含量积累过程表现为先减少后升高的趋势，并且在花后25 d达到最低值（12.16%），相邻发育时期的蛋白质含量差异显著。在相邻发育时期之间共鉴定到25 427个差异表达基因（differentially expressed genes，DEGs）。通过聚类分析可分为5组（A—E），其中，B组的差异表达基因数量最多（10 906个），包含49个家族的1 022个转录因子，其中，NAC家族的TFs数量最多（107个）。通过WGCNA分析，发现5个与蛋白质含量显著相关的共表达模块。turquoise模块与蛋白质含量的正相关性最高（r=0.80，P=1×10^-4）。通过整合差异表达基因和加权基因共表达网络，在2个模块（turquoise和blue）中发现了6个正向调控的枢纽TFs，分别来自MIKC-MADS、TCP、TALE和CPP家族。进一步对淮麦48的蛋白质含量与不同时期的基因表达量进行相关性分析，发现其中5个枢纽TFs的表达量与蛋白质含量存在显著正相关性，并且TraesCS5B03G0740100和TraesCS7D03G0590500在穗和籽粒中特异性高表达。【结论】鉴定到与小麦蛋白质含量累积相关的重要模块turquoise和blue模块，筛选到6个枢纽TFs，并发现2个重要的枢纽基因表达量与蛋白质含量呈显著正相关，且在穗和籽粒组织中特异性高表达，可作为小麦籽粒蛋白质积累调控的候选基因。

关键词: 普通小麦, 籽粒发育, 蛋白质含量, 加权基因共表达网络分析, 转录因子

Abstract:

【Objective】 Common wheat, as an important food crop, plays a crucial role in global food security. Identifying the gene regulatory networks involved in wheat grain protein synthesis and determining key candidate genes will provide theoretical support for quality breeding and improvement of wheat. 【Method】 The study used wheat grains at six developmental stages (5, 10, 15, 20, 25, and 30 days post-anthesis) as research materials to summarize the pattern of protein accumulation in wheat grains. Transcriptome data and grain protein content phenotypic data were analyzed using the WGCNA (Weighted Gene Co-expression Network Analysis) method to construct weighted gene co-expression networks and identify key hub transcription factor (TFs) genes.【Result】 The accumulation of protein content in wheat grains showed a trend of initial decline followed by an increase, reaching its lowest value (12.16%) at 25 days post-anthesis, with significant differences in protein content between adjacent developmental stages. A total of 25 427 differentially expressed genes (DEGs) were identified between adjacent developmental stages. Cluster analysis divided these DEGs into five groups (A-E), with group B containing the highest number of DEGs (10 906). A total of 1 022 transcription factors (TFs) from 49 families were identified, with the NAC family containing the most TFs (107). WGCNA analysis identified five co-expression modules significantly associated with protein content. The turquoise module showed the highest positive correlation with protein content (r=0.80, P=1×10^-⁴). By integrating differentially expressed genes and weighted gene co-expression networks, six positively regulated hub TFs from the MIKC-MADS, TCP, TALE, and CPP families were identified in two modules (turquoise and blue). Further correlation analysis between the protein content phenotype of Huaimai 48 and gene expression levels at different time points revealed that the expression levels of five hub TFs were significantly positively correlated with the protein content phenotype. Specifically, TraesCS5B03G0740100 and TraesCS7D03G0590500 showed specific high expression in spike and grain tissues.【Conclusion】 The study identified important modules (turquoise and blue) related to wheat protein content accumulation, screened six hub TFs, and identified that the expression levels of two hub TFs are significantly positively correlated with protein content and are specifically highly expressed in spike and grain tissues. These genes can serve as candidate genes for regulating protein accumulation in wheat grains.

Key words: common wheat, seed development, protein content, weighted gene co-expression network analysis, transcript factors

潘丽媛, 王永军, 李海军, 侯富, 李菁, 李丽丽, 孙苏阳. 基于转录组和WGCNA筛选小麦籽粒蛋白质累积相关调控基因[J]. 中国农业科学, 2025, 58(6): 1065-1082.

PAN LiYuan, WANG YongJun, LI HaiJun, HOU Fu, LI Jing, LI LiLi, SUN SuYang. Screening Regulatory Genes Related to Wheat Grain Protein Accumulation Based on Transcriptome and WGCNA Analysis[J]. Scientia Agricultura Sinica, 2025, 58(6): 1065-1082.

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基因ID Gene ID	正向引物 Forward primer (5′-3′)	反向引物 Reverse primer (5′-3′)
TraesCS3A03G0074600	CGGAGATCATTGCGTAGGG	GGAATTAGCACGTGTGGCG
TraesCS3A03G0885700	TCTTCCTTCATCGCTTCCATC	TCCTCAGGGTGCCATCTATCA
TraesCS3A03G0888300	TACCCAGTTTTAGTAGACGCCC	CTCGTTTTGTCCCAGTGTGC
TraesCS3A03G0891600	GAAGTGCGACGTGGATATAAGG	GAAGGTACTGAGAGAGGCGAGA
TraesCS3A03G0891900	GAAGGCTTTTGGGGATGCT	GCTATCCAATCGCTCTCCTCTT
TraesCS3A03G0892500	TCGTTCGGATGACAGTGCTT	CTATCCCACATTTGGAAACAGG
TraesCS4B03G0043800	GAACAAATCCCAGCCATCCA	ACGAGTAGGCGTTGGGTATTC
TraesCS4B03G0823800	TCCACCAGATGGCGGAGTT	GGCGTGGGGAAGAGCGT
TraesCS6B03G0310500	GGGGAGGCACTATTGCTTTC	CCAAAGTAGTCCGAAGCGTATC
TraesCS6B03G0312000	CCTGCTTTGATGGCTGGAC	AAGAAGGGGAATGGGGCT
TaActin	GGAATGGTCAAGGCTGGTTT	CGGAGCTCGTTGTAGAATGTGT

样本 Sample	时期 Stage (d)	原始reads数 Raw reads	质控后reads数 Saved reads	质量分数 High quality rate (%)	比对率 Reads aligned (%)	多次比对率 Multialignment (%)	单一比对率 Unique alignment (%)
05D.Rep1	5	53494726	53253286	99.55	88.31	6.18	94.49
05D.Rep2	5	41144322	40944134	99.51	88.17	6.22	94.38
05D.Rep3	5	42043100	41848014	99.54	88.55	6.14	94.69
10D.Rep1	10	43073974	42750556	99.25	82.85	6.39	89.23
10D.Rep2	10	45712962	45445466	99.41	85.54	6.37	91.91
10D.Rep3	10	47715540	47273162	99.07	80.03	6.62	86.65
15D.Rep1	15	46401552	45997986	99.13	73.62	8.65	82.27
15D.Rep2	15	40517866	40147604	99.09	72.56	8.86	81.42
15D.Rep3	15	44238716	43790148	98.99	71.59	9.02	80.61
20D.Rep1	20	37942654	37577108	99.04	70.18	10.93	81.11
20D.Rep2	20	41530148	41087334	98.93	68.35	11.76	80.11
20D.Rep3	20	53574728	52925588	98.79	69.67	14.02	83.69
25D.Rep1	25	52320432	51672172	98.76	66.29	14.04	80.34
25D.Rep2	25	46626444	46097376	98.87	66.08	13.96	80.04
25D.Rep3	25	51864528	51434414	99.17	69.34	12.86	82.20
30D.Rep1	30	54570678	53738308	98.47	61.09	16.36	77.44
30D.Rep2	30	51852354	51090038	98.53	67.55	12.04	79.59
30D.Rep3	30	55777132	55100978	98.79	68.56	12.38	80.94
共计/平均Total/Mean		850401856	842173672	99.05	74.35	10.16	84.51

样本 Sample	已知基因数 No. of known genes	新基因数 No. of new genes	所有基因数 No. of all genes
05D.Rep1	87963	6413	94376
05D.Rep2	86713	6512	93225
05D.Rep3	86541	6394	92935
10D.Rep1	84695	6534	91229
10D.Rep2	87409	6613	94022
10D.Rep3	84971	6509	91480
15D.Rep1	83938	6406	90344
15D.Rep2	80963	6210	87173
15D.Rep3	81302	6156	87458
20D.Rep1	78899	6029	84928
20D.Rep2	78802	5994	84796
20D.Rep3	81241	6308	87549
25D.Rep1	79118	6076	85194
25D.Rep2	79869	6076	85945
25D.Rep3	82747	6337	89084
30D.Rep1	77776	5945	83721
30D.Rep2	81517	6167	87684
30D.Rep3	80690	6187	86877
平均Mean	82509	6270	88779

基因ID Gene ID	类别Description	基因连通性值kME	所属模块The modlue
TraesCS2A03G0362900	MADS-box转录因子 MADS-box transcription factor	0.986	青绿色 Turquoise
TraesCS4D03G0036300	TALE转录因子 TALE transcription factor	0.981	青绿色 Turquoise
TraesCS5A03G0079200	TCP转录因子 TCP transcription factor	0.984	青绿色 Turquoise
TraesCS5B03G0740100	MADS-box转录因子 MADS-box transcription factor	0.987	青绿色 Turquoise
TraesCS7D03G0590500	MADS-box转录因子 MADS-box transcription factor	0.981	青绿色 Turquoise
TraesCS5A03G0135400	CPP转录因子 CPP transcription factor	0.981	蓝色 Blue

表型/基因<BOLD>P</BOLD>henotype/gene	5 d	10 d	15 d	20 d	25 d	30 d	相关性系数r
蛋白质含量GPC (%)	15.59	15.04	14.15	12.81	12.16	13.20
TraesCS2A03G0362900	8.60	5.85	2.99	1.32	0.90	0.56	0.93**
TraesCS4D03G0036300	13.19	7.96	2.72	2.36	1.57	2.39	0.89*
TraesCS5A03G0079200	5.32	3.77	1.95	0.64	0.50	0.54	0.95**
TraesCS5A03G0135400	21.54	48.33	35.01	15.00	11.12	8.03	0.67n.s.
TraesCS5B03G0740100	136.13	83.19	35.66	28.30	19.17	15.06	0.89*
TraesCS7D03G0590500	284.97	153.86	63.75	55.35	33.35	24.53	0.87*

基于转录组和WGCNA筛选小麦籽粒蛋白质累积相关调控基因

Screening Regulatory Genes Related to Wheat Grain Protein Accumulation Based on Transcriptome and WGCNA Analysis

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家族 Family	数目 Number	5种表达模式 Five expression patterns					家族 Family	数目 Number	5种表达模式 Five expression patterns
家族 Family	数目 Number	A	B	C	D	E	家族 Family	数目 Number	A	B	C	D	E
AP2	22	4	8	9	1	0	HRT-like	1	0	1	0	0	0
ARF	14	4	9	1	0	0	HSF	24	5	1	15	0	3
ARR-B	11	1	9	0	0	1	LBD	17	5	5	0	0	7
B3	49	4	30	2	5	8	LSD	1	0	1	0	0	0
BES1	2	1	1	0	0	0	MIKC_MADS	37	1	32	0	2	2
bHLH	75	14	38	8	4	11	M-type_MADS	11	2	3	1	0	5
bZIP	59	12	19	10	3	15	MYB	90	22	34	8	2	24
C2H2	37	9	18	5	2	3	MYB_related	28	14	9	1	2	2
C3H	21	7	10	3	1	0	NAC	107	28	22	33	3	21
CAMTA	1	0	1	0	0	0	NF-YA	10	0	0	0	4	6
CO-like	3	0	2	0	0	1	NF-YB	14	6	4	0	0	4
CPP	3	2	1	0	0	0	NF-YC	9	1	8	0	0	0
DBB	8	2	4	0	0	2	Nin-like	3	3	0	0	0	0
Dof	14	1	4	3	1	5	S1Fa-like	1	1	0	0	0	0
E2F/DP	12	5	5	0	0	2	SBP	11	0	10	0	0	1
EIL	4	2	2	0	0	0	SRS	1	0	0	1	0	0
ERF	47	20	11	4	4	8	TALE	13	3	7	2	1	0
FAR1	15	2	3	3	0	7	TCP	22	1	21	0	0	0
G2-like	47	20	17	2	1	7	Trihelix	22	6	10	1	0	5
GATA	4	0	2	0	2	0	Whirly	6	1	5	0	0	0
GeBP	2	1	1	0	0	0	WOX	9	4	1	3	1	0
GRAS	24	8	11	1	2	2	WRKY	34	4	14	1	4	11
GRF	7	0	6	0	1	0	YABBY	9	0	7	2	0	0
HB-other	3	0	2	0	0	1	ZF-HD	11	2	3	6	0	0
HD-ZIP	47	7	35	3	1	1	总计Total	1022	235	447	128	47	165

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