Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (13): 2487-2503.doi: 10.3864/j.issn.0578-1752.2025.13.001

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Screening of Wheat Varieties with Low Nitrogen Tolerance and Genome-Wide Association Studies of Low Nitrogen Stress Tolerance Index

LI Ning1(), GAO LiFeng2(), HUANG Xin1, SHI HuaWei1,3, YANG JinWen1, SHI YuGang1, CHEN Ming2, JIA JiZeng2(), SUN DaiZhen1()   

  1. 1 College of Agriculture, Shanxi Agricultural University, Taigu 030801, Shanxi
    2 Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081
    3 Rural Survey Institute, Shanxi Agricultural University, Taiyuan 030031
  • Received:2025-01-07 Accepted:2025-03-06 Online:2025-07-01 Published:2025-07-05

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Abstract:

【Objective】 The excessive application of nitrogen fertilizers has led to ecological pollution and waste of agricultural resources. Developing nitrogen-efficient wheat varieties and improving nitrogen use efficiency are effective approaches for achieving sustainable agricultural development and environmental protection. Screening low-nitrogen-tolerant germplasm resources and identifying genetic loci and candidate genes associated with low-nitrogen tolerance can provide materials and theoretical foundations for breeding nitrogen-efficient wheat varieties. 【Method】 A natural population consisting of 389 wheat varieties was cultivated under high-nitrogen (HN) and low-nitrogen (LN) treatments in 10 field environments. Grain yield per plant (GYP) was measured to calculate the stress tolerance index (STI), thereby enabling the classification of varieties with differential low-nitrogen tolerance. Genome-wide association studies (GWAS) were conducted using 660K SNP array genotyping data to identify stable genetic loci associated with low-nitrogen tolerance. Candidate genes were prioritized through haplotype analysis, expression profiling, and functional annotation. 【Result】 Twelve wheat varieties with strong low-nitrogen tolerance were identified, including Zhongluo 08-1, Jimai 15, Jinghua 2, Kehong 1, Mianyang 19, Jimai 22, Zhenmai 4, Yumai 35, Fengkang 7, Mianyang 11, Jinmai 31, and Lumai 5. Fourteen loci significantly associated with STI were detected, among which four (qSTI1A.1, qSTI3B, qSTI6A, and qSTI7A.2) overlapped with previously reported low-nitrogen tolerance or yield-related QTLs. Notably, qSTI3B-replicated across three environments-was identified as a key locus governing low-nitrogen tolerance. Functional annotation revealed that its candidate gene, TraesCS3B02G042400, encodes an AP2/EREBP (APETALA2/ethylene-responsive element-binding protein) transcription factor. Haplotype analysis showed significant STI divergence among varieties carrying distinct haplotypes, while expression levels of TraesCS3B02G042400 exhibited nitrogen dose-responsive upregulation. 【Conclusion】 Twelve wheat varieties with strong low-nitrogen tolerance were screened. A stable genetic locus, qSTI3B, and a candidate gene, TraesCS3B02G042400, associated with low-nitrogen tolerance were identified.

Key words: wheat, low nitrogen tolerance, stress tolerance index, genome-wide association study, candidate gene

Table 1

Descriptive statistical analysis of GYP and low nitrogen tolerance index of wheat under different environments"

性状
Trait		 环境
Environment		 平均值
Average		 最小值
Min		 最大值
Max		 标准差
SD		 变异系数
CV (%)
高氮下单株籽粒产量
GYP under HN treatment (g)		 20HB 18.92 9.29 29.71 3.53 18.66
19HB 19.98 9.43 36.78 4.20 21.02
20SX 7.05 2.17 17.39 2.18 30.95
19SX 10.77 2.42 21.93 3.75 34.79
18SX 7.18 3.13 14.43 1.91 26.22
低氮下单株籽粒产量
GYP under LN treatment (g)		 20HB 16.11*** 8.00 25.71 3.10 19.27
19HB 16.00*** 8.74 23.41 3.01 18.77
20SX 5.51*** 1.63 11.68 1.66 30.22
19SX 8.16*** 1.45 15.94 2.70 33.16
18SX 5.51*** 2.34 13.55 1.53 27.58
产量稳定指数
YSI		 20HB 0.86 0.54 1.28 0.11 12.79
19HB 0.82 0.48 1.10 0.14 17.07
20SX 0.80 0.41 1.17 0.15 18.75
19SX 0.77 0.49 1.14 0.14 18.18
18SX 0.78 0.43 1.06 0.15 19.23
胁迫敏感指数
SSI		 20HB 0.95 -1.86 3.11 0.65 68.42
19HB 0.91 -0.50 2.61 0.62 68.13
20SX 0.92 -0.75 2.69 0.58 63.04
19SX 0.93 -0.59 2.12 0.57 61.29
18SX 0.94 -0.26 2.45 0.64 68.09
耐低氮指数
STI		 20HB 0.87 0.25 2.13 0.30 34.48
19HB 0.82 0.21 1.67 0.28 34.15
20SX 0.83 0.07 2.22 0.44 53.01
19SX 0.83 0.03 2.73 0.52 62.65
18SX 0.80 0.16 2.66 0.39 48.75
氮响应值
NRV		 20HB 0.19 -0.22 0.86 0.12 63.16
19HB 0.27 -0.09 1.09 0.18 66.66
20SX 0.30 -0.14 1.44 0.21 70.00
19SX 0.33 -0.13 1.06 0.24 72.72
18SX 0.33 -0.06 1.33 0.21 63.63

Fig. 1

Analysis of the distribution and differences of various low nitrogen tolerance indices in wheat populations under different environments Different lowercase letters indicate significant differences at the 0.05 probability level"

Fig. 2

Correlation analysis of GYP and low nitrogen tolerance index of wheat under different environment A: 18SX; B: 19SX; C: 20SX; D: 19HB; E: 20HB; F: The average of the five environments. GYPhn+ln: The average value of GYP under the two nitrogen treatments. *: P<0.05, **: P<0.01, ***: P<0.001"

Fig. 3

Cluster analysis and distribution of low nitrogen tolerance of wheat natural population under different nitrogen treatments A: Cluster map of evaluation results of low nitrogen tolerance in wheat natural population; B: Distribution of grain yield per plant of different low nitrogen tolerant wheat varieties under different nitrogen treatments. HT: High tolerance; T: Tolerance; M: Moderate; S: Sensitive; HS: High sensitive. The same as below"

Table 2

Analysis of differences in GYP and yield-related traits among different low nitrogen tolerance types"

性状
Traits		 耐低氮类型
Low nitrogen
tolerant type		 HN环境平均值±标准差
Average value in HN environment±
Standard deviation		 LN环境平均值±标准差
Average value in LN environment±
Standard deviation
单株籽粒产量
GYP (g)		 HT 18.42±1.28a 14.63±0.94a
T 15.79±0.99b 12.53±0.83b
M 13.64±0.89c 10.98±0.67c
S 11.20±1.00d 9.15±0.81d
HS 7.69±1.22e 5.84±1.11e
千粒重
TKW (g)		 HT 45.35±2.96a 42.36±2.76ab
T 45.19±3.92a 43.17±3.77a
M 43.83±4.12ab 41.70±4.00bc
S 43.31±4.19b 41.03±4.41c
HS 40.69±5.39c 38.02±5.55d
每穗粒数
GNS		 HT 57.93±5.32a 52.66±5.02a
T 57.19±6.62a 51.57±5.16a
M 56.78±7.52a 51.36±6.23a
S 55.82±7.25a 50.05±6.02a
HS 56.00±12.18a 50.11±8.04a
分蘖数TN HT 8.34±1.04ab 6.88±0.98ab
T 8.43±1.00a 7.07±0.84a
M 8.38±1.11a 6.98±0.93a
S 7.97±1.08bc 6.65±0.85b
HS 7.56±1.08c 5.90±1.05c

Table 3

Significant association SNP of low nitrogen tolerance index"

环境
Environments		 SNP数量
No. of SNPs		 染色体
Chromosomes		 P值
P value		 R2
(%)
18SX 58 1A, 1B, 2A, 3A, 3B, 4D, 5B, 6B, 7D 2.04E-06—9.70E-05 4.87—7.44
19SX 27 1B, 2A, 4A, 5B, 5D, 7D 6.27E-06—9.86E-05 5.33—7.41
20SX 22 1A, 1B, 3A, 3B, 5B, 5D, 6A, 7A, 7B, 7D 1.69E-05—9.88E-05 4.88—7.58
19HB 8 1A, 1B, 2B, 4A, 5A, 6A, 7B 4.43E-05—9.94E-05 4.69—5.67
20HB 15 3A, 3B, 5B, 5D, 6A, 6B 3.54E-05—9.42E-05 4.85—6.69

Table 4

Locus with significant association of STI"

关联位点
Associated locus		 染色体
Chromosomes		 置信区间
Confidence interval (bp)		 环境
Environments		 显著SNP数量
No. of significant SNPs		 峰值SNP
Peak SNP		 物理位置
Physical location
(bp)		 P值
P value		 R2
(%)		 已报道位点
Reported locus
qSTI1A.1 1A 573330409—575076937 18SX 2 AX-108813611 573502309 6.57E-06 6.92 qTNR1A.2[18]
qSTI1A.2 1A 590711547—590986034 20SX 2 AX-109863561 590977148 7.49E-05 5.25
qSTI2A.1 2A 155876092—156876092 18SX 2 AX-110423504 156376092 3.91E-05 5.74
qSTI2A.2 2A 157543327—158435888 18SX 4 AX-109846450 158045085 6.47E-06 5.22
qSTI2A.3 2A 158455885—159864551 18SX 2 AX-108746106 159092995 7.68E-05 5.51
qSTI3A.1 3A 36089540—36658499 18SX 3 AX-110714576 36647841 2.82E-05 5.69
qSTI3A.2 3A 36667916—37820677 18SX 29 AX-111680971 36892106 2.19E-06 7.44
20SX 2 AX-111680971 36892106 3.57E-05 6.34
qSTI3B 3B 21002550—22060773 20HB 3 AX-111217726 21005270 4.82E-05 5.48 MQTL-3B-2[31]
20SX 4 AX-109372197 21958702 1.69E-05 5.96
19SX 3 AX-109483119 21146511 6.30E-05 5.33
qSTI4A.1 4A 742336253—742890671 19SX 16 AX-111002722 742825633 1.04E-05 7.41
qSTI4A.2 4A 744154769—744311695 19SX 3 AX-111184770 744276864 2.81E-05 6.26
qSTI6A 6A 595581230—596160987 20HB 6 AX-110988432 595581230 3.77E-05 5.37 MQTL-6A-7[31]
19HB 3 AX-108764077 595606825 4.43E-05 5.25
qSTI7A.1 7A 54704803—55369001 20SX 3 AX-109038648 55349021 3.86E-05 5.73
qSTI7A.2 7A 67737933—68737933 20SX 2 AX-110092291 68237933 9.10E-05 5.11 MQTL-7A-1[31]
qSTI7D 7D 1196736—1241409 19SX 2 AX-109901882 1196736 7.38E-05 5.43

Fig. 4

The location of qSTI3B on the chromosome"

Table 5

Candidate genes in qSTI3B physical interval"

基因ID
Gene ID		 物理位置
Physical location (bp)		 注释功能
Annotation function
TraesCS3B02G042000 Chr.3B: 21002815—21003875 染色质重塑子8 Chromatin remodeling 8
TraesCS3B02G042100 Chr.3B: 21346119—21347117 F-box结构域蛋白F-box domain containing protein
TraesCS3B02G042200 Chr.3B: 21384803—21387745 F-box结构域蛋白F-box domain containing protein
TraesCS3B02G042300 Chr.3B: 21402835—21405805 单磷酸鸟苷合酶GMP synthase
TraesCS3B02G042400 Chr.3B: 21770070—21771613 AP2/EREBP转录因子AP2/EREBP transcription factor
TraesCS3B02G042500 Chr.3B: 21880115—21880378 含P环的核苷三磷酸水解酶超家族蛋白
P-loop containing nucleoside triphosphate hydrolases superfamily protein
TraesCS3B02G042600 Chr.3B: 22053762—22056996 信号肽酶亚家族蛋白Signal peptidase subunit family protein

Fig. 5

Haplotype analysis of candidate genes ns: No significant difference between them"

Fig. 6

Expression analysis of candidate genes A: Expression levels of candidate genes after nitrogen starvation and nitrogen recovery; B: Expression levels of candidate genes in different tissues of wheat; C: Expression levels of candidate genes in seed embryo and endosperm"

[1]
LIAN X M, XING Y Z, YAN H, XU C G, LI X H, ZHANG Q F. QTLs for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid. Theoretical and Applied Genetics, 2005, 112(1): 85-96.

pmid: 16189659
[2]
CHEN K E, CHEN H Y, TSENG C S, TSAY Y F. Improving nitrogen use efficiency by manipulating nitrate remobilization in plants. Nature Plants, 2020, 6(9): 1126-1135.
[3]
ZHANG Q F. Strategies for developing green super rice. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(42): 16402-16409.
[4]
ZHANG X, DAVIDSON E A, MAUZERALL D L, SEARCHINGER T D, DUMAS P, SHEN Y. Managing nitrogen for sustainable development. Nature, 2015, 528(7580): 51-59.
[5]
ZHU Q C, DE VRIES W, LIU X J, HAO T X, ZENG M F, SHEN J B, ZHANG F S. Enhanced acidification in Chinese croplands as derived from element budgets in the period 1980-2010. Science of the Total Environment, 2018, 618: 1497-1505.
[6]
MCALLISTER C H, BEATTY P H, GOOD A G. Engineering nitrogen use efficient crop plants: the current status. Plant Biotechnology Journal, 2012, 10(9): 1011-1025.

doi: 10.1111/j.1467-7652.2012.00700.x pmid: 22607381
[7]
CHEN X P, CUI Z L, FAN M S, VITOUSEK P, ZHAO M, MA W Q, WANG Z L, ZHANG W J, YAN X Y, YANG J C, et al. Producing more grain with lower environmental costs. Nature, 2014, 514(7523): 486-489.
[8]
WANG C, SHEN Y, FANG X T, XIAO S Q, LIU G Y, WANG L G, GU B J, ZHOU F, CHEN D L, TIAN H Q, CIAIS P, ZOU J W, LIU S W. Reducing soil nitrogen losses from fertilizer use in global maize and wheat production. Nature Geoscience, 2024, 17: 1008-1015.
[9]
张福锁, 王激清, 张卫峰, 崔振岭, 马文奇, 陈新平, 江荣风. 中国主要粮食作物肥料利用率现状与提高途径. 土壤学报, 2008, 45(5): 915-924.
ZHANG F S, WANG J Q, ZHANG W F, CUI Z L, MA W Q, CHEN X P, JIANG R F. Nutrient use efficiencies of major cereal crops in China and measures for improvement. Acta Pedologica Sinica, 2008, 45(5): 915-924. (in Chinese)
[10]
PALME K, LI X G, TEALE W D. Towards second green revolution: engineering nitrogen use efficiency. Journal of Genetics and Genomics, 2014, 41(6): 315-316.

doi: 10.1016/j.jgg.2014.05.003 pmid: 24976120
[11]
MAHJOURIMAJD S, KUCHEL H, LANGRIDGE P, OKAMOTO M. Evaluation of Australian wheat genotypes for response to variable nitrogen application. Plant and Soil, 2016, 399(1): 247-255.
[12]
HITZ K, CLARK A J, VAN SANFORD D A. Identifying nitrogen-use efficient soft red winter wheat lines in high and low nitrogen environments. Field Crops Research, 2017, 200: 1-9.
[13]
FRELS K, GUTTIERI M, JOYCE B, LEAVITT B, BAENZIGER P S. Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat. Field Crops Research, 2018, 217: 82-92.
[14]
CHANDNA R, KAUR G, IQBAL M, KHAN I, AHMAD A. Differential response of wheat genotypes to applied nitrogen: Biochemical and molecular analysis. Archives of Agronomy and Soil Science, 2012, 58(8): 915-929.
[15]
马晔. 山西省主推小麦品种耐低氮性的鉴定与评价[D]. 太谷: 山西农业大学, 2022.
MA Y. Identification and evaluation of low nitrogen tolerance of main wheat varieties in Shanxi Province[D]. Taigu: Shanxi Agricultural University, 2022. (in Chinese)
[16]
李俊杰, 杜蒲芳, 石婷瑞, 侯沛佳, 柴新宇, 赵瑞, 汪妤, 李红霞. 不同基因型小麦苗期耐低氮性评价及筛选. 中国农业科技导报, 2021, 23(7): 21-32.
LI J J, DU P F, SHI T R, HOU P J, CHAI X Y, ZHAO R, WANG Y, LI H X. Screening and evaluation of low nitrogen tolerance from different genotypes wheat at seedling stage. Journal of Agricultural Science and Technology, 2021, 23(7): 21-32. (in Chinese)
[17]
TYAGI B S, FOULKES J, SINGH G, SAREEN S, KUMAR P, BROADLEY M R, GUPTA V, KRISHNAPPA G, OJHA A, KHOKHAR J S, KING I P, SINGH G P. Identification of wheat cultivars for low nitrogen tolerance using multivariable screening approaches. Agronomy, 2020, 10(3): 417.
[18]
SHI H W, CHEN M, GAO L F, WANG Y X, BAI Y M, YAN H S, XU C J, ZHOU Y B, XU Z S, CHEN J, TANG W S, WANG S G, SHI Y G, WU Y X, SUN D Z, JIA J Z, MA Y Z. Genome-wide association study of agronomic traits related to nitrogen use efficiency in wheat. Theoretical and Applied Genetics, 2022, 135(12): 4289-4302.
[19]
ZHANG M X, GAO M G, ZHENG H H, YUAN Y Y, ZHOU X W, GUO Y, ZHANG G Z, ZHAO Y, KONG F M, AN Y R, LI S S. QTL mapping for nitrogen use efficiency and agronomic traits at the seedling and maturity stages in wheat. Molecular Breeding, 2019, 39(5): 71.
[20]
YANG M J, WANG C R, MUHAMMAD ADEEL H, WU Y Y, XIA X C, SHI S B, XIAO Y G, HE Z H. QTL mapping of seedling biomass and root traits under different nitrogen conditions in bread wheat (Triticum aestivum L.). Journal of Integrative Agriculture, 2021, 20(5): 1180-1192.
[21]
REN W, ZHAO L F, LIANG J X, WANG L F, CHEN L M, LI P C, LIU Z G, LI X J, ZHANG Z H, LI J P, HE K H, ZHAO Z, ALI F, MI G H, YAN J B, ZHANG F S, CHEN F J, YUAN L X, PAN Q C. Genome-wide dissection of changes in maize root system architecture during modern breeding. Nature Plants, 2022, 8(12): 1408-1422.

doi: 10.1038/s41477-022-01274-z pmid: 36396706
[22]
HUANG X H, WEI X H, SANG T, ZHAO Q, FENG Q, ZHAO Y, LI C Y, ZHU C R, LU T T, ZHANG Z W, et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genetics, 2010, 42(11): 961-967.

doi: 10.1038/ng.695 pmid: 20972439
[23]
DENG M, LI D Q, LUO J Y, XIAO Y J, LIU H J, PAN Q C, ZHANG X H, JIN M L, ZHAO M C, YAN J B. The genetic architecture of amino acids dissection by association and linkage analysis in maize. Plant Biotechnology Journal, 2017, 15(10): 1250-1263.

doi: 10.1111/pbi.12712 pmid: 28218981
[24]
CAO K, ZHOU Z K, WANG Q, GUO J, ZHAO P, ZHU G R, FANG W C, CHEN C W, WANG X W, WANG X L, TIAN Z X, WANG L R. Genome-wide association study of 12 agronomic traits in peach. Nature Communications, 2016, 7: 13246.
[25]
SHI H W, WANG W C, GAO L F, WU J R, HU C M, YAN H S, SHI Y G, LI N, MA Y Z, ZHOU Y B, XU Z S, CHEN J, TANG W S, CHEN K, SUN D Z, WU Y X, CHEN M. Genome-wide association study of seedling nitrogen-use efficiency-associated traits in common wheat (Triticum aestivum L.). The Crop Journal, 2024, 12(1): 222-231.
[26]
BOUSLAMA M, SCHAPAUGH W T Jr. Stress tolerance in soybeans: I. Evaluation of three screening techniques for heat and drought tolerance 1. Crop Science, 1984, 24(5): 933-937.
[27]
FISCHER R A, MAURER R. Drought resistance in spring wheat cultivars: I. Grain yield responses. Australian Journal of Agricultural Research, 1978, 29(5): 897.
[28]
SIO-SE MARDEH A, AHMADI A, POUSTINI K, MOHAMMADI V. Evaluation of drought resistance indices under various environmental conditions. Field Crops Research, 2006, 98(2/3): 222-229.
[29]
LIU Y Q, WANG H R, JIANG Z M, WANG W, XU R N, WANG Q H, ZHANG Z H, LI A F, LIANG Y, OU S J, LIU X J, CAO S Y, TONG H N, WANG Y H, ZHOU F, LIAO H, HU B, CHU C C. Genomic basis of geographical adaptation to soil nitrogen in rice. Nature, 2021, 590(7847): 600-605.
[30]
MA S W, WANG M, WU J H, GUO W L, CHEN Y M, LI G W, WANG Y P, SHI W M, XIA G M, FU D L, KANG Z S, NI F. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Molecular Plant, 2021, 14(12): 1965-1968.

doi: 10.1016/j.molp.2021.10.006 pmid: 34715393
[31]
MIAO Y P, JING F L, MA J F, LIU Y, ZHANG P P, CHEN T, CHE Z, YANG D L. Major genomic regions for wheat grain weight as revealed by QTL linkage mapping and meta-analysis. Frontiers in Plant Science, 2022, 13:802310.
[32]
赵瑞. 小麦种质资源氮效率评价筛选及相关性状的全基因组关联分析[D]. 杨凌: 西北农林科技大学, 2022.
ZHAO R. Evaluation and screening of nitrogen efficiency of wheat germplasm resources and genome-wide association study of related characters[D]. Yangling: Northwest A & Forestry University, 2022. (in Chinese)
[33]
SHI X L, CUI F, HAN X Y, HE Y L, ZHAO L, ZHANG N, ZHANG H, ZHU H D, LIU Z X, MA B, et al. Comparative genomic and transcriptomic analyses uncover the molecular basis of high nitrogen-use efficiency in the wheat cultivar Kenong 9204. Molecular Plant, 2022, 15(9): 1440-1456.
[34]
JOFUKU K D, DEN BOER B G, VAN MONTAGU M, OKAMURO J K. Control of Arabidopsis flower and seed development by the homeotic gene APETALA2. The Plant Cell, 1994, 6(9): 1211-1225.
[35]
SHARONI A M, NURUZZAMAN M, SATOH K, SHIMIZU T, KONDOH H, SASAYA T, CHOI I R., OMURA T, KIKUCHI S. Gene structures, classification and expression models of the AP2/EREBP transcription factor family in rice. Plant & Cell Physiology, 2011, 52(2): 344-360.
[36]
SHOESMITH J R, SOLOMON C U, YANG X J, WILKINSON L G, SHELDRICK S, VAN EIJDEN E, COUWENBERG S, PUGH L M, ESKAN M, STEPHENS J, BARAKATE A, DREA S, HOUSTON K, TUCKER M R, MCKIM S M. APETALA2 functions as a temporal factor together with BLADE-ON-PETIOLE2 and MADS29 to control flower and grain development in barley. Development, 2021, 148(5): dev194894.
[37]
JIANG W B, ZHANG X J, SONG X W, YANG J F, PANG Y Z. Genome-wide identification and characterization of APETALA2/ ethylene-responsive element binding factor superfamily genes in soybean seed development. Frontiers in Plant Science, 2020, 11: 566647.
[38]
FU J Y, ZHU C Y, WANG C, LIU L J, SHEN Q Q, XU D B, WANG Q. Maize transcription factor ZmEREB20 enhanced salt tolerance in transgenic Arabidopsis. Plant Physiology and Biochemistry, 2021, 159: 257-267.
[39]
REIS R R, MERTZ-HENNING L M, MARCOLINO-GOMES J, RODRIGUES F A, FUGANTI- PAGLIARINI R, KOLTUN A, GONÇALVES L S A, NEPOMUCENO A L, Differential gene expression in response to water deficit in leaf and root tissues of soybean genotypes with contrasting tolerance profiles. Genetics and Molecular Biology, 2020, 43(2): e20180290.
[40]
庞浩婉, 傅乾坤, 杨青青, 张元元, 付凤玲, 于好强. 玉米转录因子ZmEREB93负调控籽粒发育. 中国农业科学, 2022, 55(19): 3685-3696. doi: 10.3864/j.issn.0578-1752.2022.19.001.
PANG H W, FU Q K, YANG Q Q, ZHANG Y Y, FU F L, YU H Q. Maize transcription factor ZmEREB93 negatively regulates kernel development. Scientia Agricultura Sinica, 2022, 55(19): 3685-3696. doi: 10.3864/j.issn.0578-1752.2022.19.001. (in Chinese)
[41]
SEKHAR S, KUMAR J, MOHANTY S, MOHANTY N, PANDA R S, DAS S, SHAW B P, BEHERA L. Identification of novel QTLs for grain fertility and associated traits to decipher poor grain filling of basal spikelets in dense panicle rice. Scientific Reports, 2021, 11(1): 13617.

doi: 10.1038/s41598-021-93134-7 pmid: 34193914
[42]
QIAN Y X, REN Q Y, ZHANG J, CHEN L. Transcriptomic analysis of the maize (Zea mays L.) inbred line B73 response to heat stress at the seedling stage. Gene, 2019, 692: 68-78.
[43]
陈悦, 陈茜, 董伟峰, 才晓溪, 沈阳, 杨珺凯, 贾博为, 孙明哲, 孙晓丽. 水稻 AP2/ERF 转录因子基因OsERF096启动子克隆及活性分析. 土壤与作物, 2022, 11(2): 159-169.
CHEN Y, CHEN Q, DONG W F, CAI X X, SHEN Y, YANG J K, JIA B W, SUN M Z, SUN X L. Cloning and activity analysis of promoter of an AP2/ERF transcription factor gene OsERF096 in rice. Soils and Crops, 2022, 11(2): 159-169. (in Chinese)
[44]
WU Q, XU J Y, ZHAO Y D, WANG Y C, ZHOU L, NING L H, SHABALA S, ZHAO H. Transcription factor ZmEREB97 regulates nitrate uptake in maize (Zea mays) roots. Plant Physiology, 2024, 196(1): 535-550.
[45]
YU Y W, YANG D X, ZHOU S R, GU J T, WANG F R, DONG J G, HUANG R F. The ethylene response factor OsERF109 negatively affects ethylene biosynthesis and drought tolerance in rice. Protoplasma, 2017, 254(1): 401-408.

doi: 10.1007/s00709-016-0960-4 pmid: 27040682
[46]
KIM M J, RUZICKA D, SHIN R, SCHACHTMAN D P. The Arabidopsis AP2/ERF transcription factor RAP2.11 modulates plant response to low-potassium conditions. Molecular Plant, 2012, 5(5): 1042-1057.
[47]
LIU W, LI Q W, WANG Y, WU T, YANG Y F, ZHANG X Z, HAN Z H, XU X F. Ethylene response factor AtERF72 negatively regulates Arabidopsis thaliana response to iron deficiency. Biochemical and Biophysical Research Communications, 2017, 491(3): 862-868.
[48]
XIAO G Q, QIN H, ZHOU J H, QUAN R D, LU X Y, HUANG R F, ZHANG H W. OsERF 2 controls rice root growth and hormone responses through tuning expression of key genes involved in hormone signaling and sucrose metabolism. Plant Molecular Biology, 2016, 90(3): 293-302.
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