Genome-wide identification of the alfalfa TCP gene family and analysis of gene transcription patterns in alfalfa （Medicago sativa） under drought stress

doi:10.11686/cyxb2021189

Abstract

Abstract:

Alfalfa （Medicago sativa） is the most important legume forage in the world. However， its yield and geographical distribution are often influenced by drought. Transcription factors play important regulatory roles in alfalfa’s response to drought stress. Teosinte branched 1/cycloidea/pro-liferating cell factors （TCP） is a plant-specific transcription factor with important functions in plant growth， development， and stress resistance. However， its distribution and potential function in the response to drought stress have not been reported. To explore the responses of functional genes to drought stress in alfalfa， we identified members of the TCP gene family in alfalfa using bioinformatic methods. Further analyses were conducted to clarify the phylogeny and determine their gene structure， chromosome location， collinearity， and transcriptional patterns under drought stress. Our analyses identified 40 MsTCP genes that were unevenly distributed among the 20 chromosomes in the alfalfa genome. Among the MsTCP genes， 17 pairs were paralogous genes arising from gene fragment replication events. In a phylogenetic analysis， MsTCP genes formed two large branches and clustered into three subfamilies （PCF， CIN， and CYC/TB1）. Conserved domain analyses revealed that members in the same branch of the phylogenetic tree contain TCP domains with the same number of amino acids， and that the members of the same subfamily have similar conserved motifs and gene structures. Analyses of transcriptome data from alfalfa under drought stress revealed four MsTCP genes （MsTCP23， MsTCP27， MsTCP29， and MsTCP33） that may be involved in the drought response. The results of qRT-PCR analyses confirmed that these four genes were significantly up-regulated in roots and leaves of plants under a simulated drought （polyethylene glycol） treatment， providing further evidence that they participate in the drought response. These results lay the foundation for further research on the response to drought stress in alfalfa， and will be useful for generating new varieties of drought-resistant alfalfa using genetic engineering technology.

Key words: alfalfa, phylogenetic analysis, drought stress, gene expression analysis

Na WEI, Yan-peng LI, Yi-tong MA, Wen-xian LIU. Genome-wide identification of the alfalfa TCP gene family and analysis of gene transcription patterns in alfalfa （Medicago sativa） under drought stress[J]. Acta Prataculturae Sinica, 2022, 31(1): 118-130.

Figures/Tables 10

References 43

1	Almeida D M， Gregorio G B， Oliveira M M， et al. Five novel transcription factors as potential regulators of osnhx1 gene expression in a salt tolerant rice genotype. Plant Molecular Biology， 2016， 93（1/2）： 1-17.
2	Cubas P， Lauter N， Doebley J， et al. The TCP domain： a motif found in proteins regulating plant growth and development. Plant Journal， 2010， 18（2）： 215-222.
3	Doebley J， Stec A. Teosinte branched1 and the origin of maize： evidence for epistasis and the evolution of dominance. Genetics， 1995， 141（1）： 333-346.
4	Luo D， Carpenter R， Vincent C， et al. Origin of floral asymmetry in Antirrhinum. Nature， 1996， 383（6603）： 794-799.
5	Kosugi S， Ohashi Y. PCF1 and PCF2 specifically bind to cis elements in the rice proliferating cell nuclear antigen gene. The Plant Cell， 1997， 9（9）： 1607-1619.
6	Navaud O， Dabos P， Carnus E， et al. TCP transcription factors predate the emergence of land plants. Journal of Molecular Evolution， 2007， 65（1）： 23-33.
7	Martín-Trillo M， Cubas P. TCP genes： a family snapshot ten years later. Trends in Plant Science， 2010， 15（1）： 31-39.
8	Yao X， Hong M， Jian W， et al. Genome-wide comparative analysis and expression pattern of TCP gene families in Arabidopsis thaliana and Oryza sativa. Journal of Integrative Plant Biology， 2007， 49（6）： 885-897.
9	Huo Y， Xiong W， Su K， et al. Genome-wide analysis of the TCP gene family in Switchgrass （Panicum virgatum L.）. International Journal of Genomics， 2019（1）： 1-13.
10	Parapunova V， Busscher M， Busscher-Lange J， et al. Identification， cloning and characterization of the tomato TCP transcription factor family. BMC Plant Biology， 2014， 14（1）： 1-17.
11	Danisman S， Wal F， Dhondt S， et al. Arabidopsis class I and class Ⅱ TCP transcription factors regulate jasmonic acid metabolism and leaf development antagonistically. Plant Physiology， 2012， 159（4）： 1511-1523.
12	Wang H F， Wang H W， Liu R， et al. Genome-wide identification of TCP family transcription factors in Medicago truncatula reveals significant roles of miR319-Targeted TCPs in nodule development. Frontiers in Plant Science， 2018， 9： 774.
13	Sharma R， Kapoor M， Tyagi A K， et al. Comparative transcript profiling of TCP family genes provide insight into gene functions and diversification in rice and Arabidopsis. Journal of Plant Molecular Biology & Biotechnology， 2010， 1（1）： 24-38.
14	Mukhopadhyay P， Tyagi A K. OsTCP19 influences developmental and abiotic stress signaling by modulating ABI4-mediated pathways. Scientific Reports， 2015， 5（1）： 9998.
15	Liu C H， Liang N S， Yu L， et al. Fraxinus mandshurica Rupr. TCP4 transcription factor cloning and expression analysis under stress and hormones. Journal of Beijing Forestry University， 2017，39（6）： 22-31.
	刘春浩，梁楠松，于磊，等. 水曲柳TCP4转录因子克隆及胁迫和激素下的表达分析. 北京林业大学学报， 2017， 39（6）： 22-31.
16	Lei N， Li S X， Peng M. Cloning， expression analysis and plant expression vector construction of cassava MeTCP4 transcription factor. Molecular Plant Breeding， 2018， 16（5）： 1517-1523.
	雷宁，李淑霞，彭明. 木薯MeTCP4转录因子的克隆，表达分析及植物表达载体的构建. 分子植物育种， 2018， 16（5）： 1517-1523.
17	Liu Z， Chen T， Ma L， et al. Global transcriptome sequencing using the illumina platform and the development of EST-SSR markers in autotetraploid alfalfa. PLoS One， 2013， 8（12）： e83549.
18	Tang L， Cai H， Zhai H， et al. Overexpression of Glycine sojaWRKY20 enhances both drought and salt tolerance in transgenic alfalfa （Medicago sativa L.）. Plant Cell， Tissue and Organ Culture， 2014， 118（1）： 77-86.
19	Jin X， Yin X， Ndayambaza B， et al. Genome-wide identification and expression profiling of the ERF gene family in Medicago sativa L. under various abiotic stresses. DNA and Cell Biology， 2019， 38（10）： 1056-1068.
20	Min X， Jin X， Zhang Z， et al. Genome-wide identification of NAC transcription factor family and functional analysis of the abiotic stress-responsive genes in Medicago sativa L.Journal of Plant Growth Regulation， 2020， 39（1）： 324-337.
21	Chen H， Zeng Y， Yang Y， et al. Allele-aware chromosome-level genome assembly and efficient transgene-free genome editing for the autotetraploid cultivated alfalfa. Nature Communications， 2020， 11（1）： 1-11.
22	Artimo P， Jonnalagedda M， Arnold K， et al. ExPASy： SIB bioinformatics resource portal. Nucleic Acids Research， 2012， 40： 597-603.
23	Sara E G， Jaina M， Alex B， et al. The Pfam protein families database in 2019. Nucleic Acids Research， 2019，47（D1）： 427-432.
24	Kumar S， Tamura K， Nei M. MEGA： molecular evolutionary genetics analysis software for microcomputers. Bioinformatics， 1994， 10（2）： 189-191.
25	Yuan J， Amend A， Borkowski J， et al. MULTICLUSTAL： a systematic method for surveying Clustal W alignment parameters. Bioinformatics， 1999， 15（10）： 862-863.
26	Hall T， Biosciences I， Carlsbad C. BioEdit： an important software for molecular biology. GERF Bulletin Biosciences， 2011， 2（1）： 60-61.
27	Bailey T L， Boden M， Buske F A， et al. MEME SUITE： tools for motif discovery and searching. Nucleic Acids Research， 2009， 37（2）： W202-W208.
28	Chen C， Chen H， Zhang Y， et al. TBtools： An integrative toolkit developed for interactive analyses of big biological data. Molecular Plant， 2020， 13（8）： 1194-1202.
29	Wang Y， Tang H， Debarry J D， et al. MCScanX a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Research， 2012， 40（7）： e49.
30	Luo D， Wu Y， Liu J， et al. Comparative transcriptomic and physiological analyses of Medicago sativa L. indicates that multiple regulatory networks are activated during continuous ABA treatment. International Journal of Molecular Sciences， 2018， 20（1）： 47.
31	Min X， Liu Z， Wang Y， et al. Comparative transcriptomic analysis provides insights into the coordinated mechanisms of leaves and roots response to cold stress in Common Vetch. Industrial Crops and Products， 2020， 158： 112949.
32	Nei M. Gene duplication and nucleotide substitution in evolution. Nature， 1969， 221（5175）： 40-42.
33	Zheng L， Bai X T， Li H Y. Genome-wide identification and expression analysis of Sorghum bicolorTCP gene family. Henan Agricultural Sciences， 2019， 537（10）： 36-42.
	郑玲，白雪婷，李会云. 高粱TCP基因家族全基因组鉴定及表达分析. 河南农业科学， 2019， 537（10）： 36-42.
34	Liu Y， Zhang H， Xin D W， et al. Analysis and function prediction of Glycine maxTCP transcription factor domains. Soybean Science， 2012， 31（5）： 707-713.
	刘洋，张慧，辛大伟，等. 大豆TCP转录因子家族结构域分析及功能预测. 大豆科学， 2012， 31（5）： 707-713.
35	Li K J， Tan S S， Sun B， et al. Genome-wide identification and expression analysis of Brassica juncea TCP transcription factor. Journal of Sichuan Agricultural University， 2019， 37（4）： 459-468.
	李坤杰，谭杉杉，孙勃，等. 芥菜TCP转录因子家族全基因组鉴定及表达分析. 四川农业大学学报， 2019， 37（4）： 459-468.
36	Lei D， Wu Y， Su Z， et al. Research progress on the interaction between TCP transcription factors and hormone signals. Molecular Plant Breeding， 2019， 17（9）： 2868-2875.
	雷豆，吴雨，苏周，等. TCP转录因子与激素信号相互作用研究进展. 分子植物育种， 2019， 17（9）： 2868-2875.
37	Sun H D， Xue J Z， Liu Y J， et al. Genome-wide identification and expression pattern analysis of maize TCP transcription factor family. Molecular Plant Breeding， 2021， 19（8）： 2460-2471.
	孙菡笛，薛江芝，刘亚洁，等. 玉米 TCP 转录因子家族的全基因组鉴定及表达模式分析. 分子植物育种， 2021， 19（8）： 2460-2471.
38	Perez M， Guerringue Y， Ranty B， et al. Specific TCP transcription factors interact with and stabilize PRR2 within different nuclear sub-domains. Plant Science， 2019， 287： 110197.
39	Sun X， Wang C， Xiang N， et al. Activation of secondary cell wall biosynthesis by miR319‐targeted TCP4 transcription factor. Plant Biotechnology Journal， 2017， 15（10）： 1284-1294.
40	Ma X， Ma J， Fan D， et al. Genome-wide identification of TCP family transcription factors from Populus euphratica and their involvement in leaf shape regulation. Scientific Reports， 2016， 6： 32795.
41	Zhao M， Peng X， Chen N， et al. Genome-wide identification of the TCP gene family in Broussonetia papyrifera and functional analysis of BpTCP8， 14 and 19 in shoot branching. Plants， 2020， 9（10）： 1301.
42	Leng X， Wei H， Xu X， et al. Genome-wide identification and transcript analysis of TCP transcription factors in grapevine. BMC Genomics， 2019， 20（1）： 1-18.
43	Liu Y， Guan X， Liu S， et al. Genome-wide identification and analysis of TCP transcription factors involved in the formation of leafy head in Chinese cabbage. International Journal of Molecular Sciences， 2018， 19（3）： 847.

引物名称Primer name	正向引物Forward primer （5′-3′）	反向引物Reverse primer （5′-3′）
MsTCP23	CAAGATATGACAATGACAGTGCC	GAACCTTAAACCTTCCTTCCTC
MsTCP27	GTTACAAAGACCAAATCACCCT	AAGCCATTGTTTCCCAATTGAG
MsTCP29	CAAGATATGACAATGACAGTGCC	CTTCCTTCCTTTGACACGAC
MsTCP33	AACCCTAATCAAGAACCAAACC	GTATAGCCCATACAGGAACCA
MsActin	GACAATGGAACTGGAATGG	CAATACCGTGCTCAATGG

引物名称Primer name	正向引物Forward primer （5′-3′）	反向引物Reverse primer （5′-3′）
MsTCP23	CAAGATATGACAATGACAGTGCC	GAACCTTAAACCTTCCTTCCTC
MsTCP27	GTTACAAAGACCAAATCACCCT	AAGCCATTGTTTCCCAATTGAG
MsTCP29	CAAGATATGACAATGACAGTGCC	CTTCCTTCCTTTGACACGAC
MsTCP33	AACCCTAATCAAGAACCAAACC	GTATAGCCCATACAGGAACCA
MsActin	GACAATGGAACTGGAATGG	CAATACCGTGCTCAATGG

基因名 Gene name	基因ID Gene ID	蛋白长度 Protein length （aa）	分子量 Molecular weight （Da）	等电点 pI	亚组 Subgroup	蛋白亲水性 Protein GRAVY	亚细胞定位 Subcellular localization
MsTCP1	MS.gene95781.t1	421	47569.61	6.43	CYC/TB1	-0.764	nucl
MsTCP2	MS.gene073917.t1	143	16100.50	11.55	PCF	-0.535	nucl
MsTCP3	MS.gene34439.t1	326	34413.31	4.84	PCF	-0.351	nucl
MsTCP4	MS.gene029214.t1	260	27872.04	9.72	PCF	-0.575	nucl
MsTCP5	MS.gene91110.t1	232	25464.17	8.04	PCF	-0.819	nucl
MsTCP6	MS.gene41379.t1	340	35 855.73	4.85	PCF	-0.386	nucl
MsTCP7	MS.gene88588.t1	238	26275.98	8.05	PCF	-0.889	nucl
MsTCP8	MS.gene064482.t1	206	22076.72	8.96	PCF	-0.527	nucl
MsTCP9	MS.gene060651.t1	300	32841.10	7.14	CIN	-0.739	nucl
MsTCP10	MS.gene31403.t1	329	36285.54	5.99	CIN	-0.743	nucl
MsTCP11	MS.gene059738.t1	302	33141.45	7.13	CIN	-0.731	nucl
MsTCP12	MS.gene072060.t1	520	55104.71	6.32	PCF	-0.782	nucl
MsTCP13	MS.gene93507.t1	383	43316.86	7.92	CIN	-0.799	nucl
MsTCP14	MS.gene006670.t1	336	37829.29	6.21	CIN	-0.881	nucl
MsTCP15	MS.gene023326.t1	478	53008.41	6.75	CIN	-0.978	nucl
MsTCP16	MS.gene73844.t1	224	24733.21	7.23	CIN	-0.795	nucl
MsTCP17	MS.gene78508.t1	417	45864.22	8.02	PCF	-0.607	nucl
MsTCP18	MS.gene91902.t1	415	43919.25	6.46	PCF	-0.613	nucl
MsTCP19	MS.gene054307.t1	119	12818.73	9.59	PCF	-0.576	nucl
MsTCP20	MS.gene054308.t1	134	14873.93	7.76	PCF	-0.704	nucl
MsTCP21	MS.gene054297.t1	119	12797.68	10.00	PCF	-0.612	nucl
MsTCP22	MS.gene03516.t1	120	12812.73	9.88	PCF	-0.549	nucl
MsTCP23	MS.gene03512.t1	134	14658.83	8.82	PCF	-0.476	nucl
MsTCP24	MS.gene074319.t1	319	36194.52	9.67	CYC/TB1	-1.033	nucl
MsTCP25	MS.gene80495.t1	107	11418.15	10.23	PCF	-0.508	nucl
MsTCP26	MS.gene80502.t1	164	18081.65	7.72	PCF	-0.476	nucl
MsTCP27	MS.gene48573.t1	351	39672.79	8.57	CIN	-0.944	nucl
MsTCP28	MS.gene063503.t1	309	34369.17	9.10	CIN	-0.678	nucl
MsTCP29	MS.gene42033.t1	130	14327.45	9.46	PCF	-0.555	cyto
MsTCP30	MS.gene42025.t1	120	12784.56	10.14	PCF	-0.653	nucl
MsTCP31	MS.gene92019.t1	373	42567.64	6.68	CYC/TB1	-1.135	nucl
MsTCP32	MS.gene36024.t1	435	47331.52	6.73	CIN	-0.831	nucl
MsTCP33	MS.gene019430.t1	331	36115.04	9.42	PCF	-0.447	nucl
MsTCP34	MS.gene044458.t1	249	27766.24	9.25	CIN	-0.668	nucl
MsTCP35	MS.gene36926.t1	189	20950.04	9.55	PCF	-0.708	cyto
MsTCP36	MS.gene032256.t1	433	47041.11	6.59	CIN	-0.839	nucl
MsTCP37	MS.gene033131.t1	419	45864.22	8.02	PCF	-0.895	nucl
MsTCP38	MS.gene007917.t1	431	46871.96	6.61	CIN	-0.823	nucl
MsTCP39	MS.gene038752.t1	388	44616.54	8.50	CYC/TB1	-1.012	nucl
MsTCP40	MS.gene34255.t1	430	46850.01	6.71	CIN	-0.834	nucl

基因名 Gene name	基因ID Gene ID	蛋白长度 Protein length （aa）	分子量 Molecular weight （Da）	等电点 pI	亚组 Subgroup	蛋白亲水性 Protein GRAVY	亚细胞定位 Subcellular localization
MsTCP1	MS.gene95781.t1	421	47569.61	6.43	CYC/TB1	-0.764	nucl
MsTCP2	MS.gene073917.t1	143	16100.50	11.55	PCF	-0.535	nucl
MsTCP3	MS.gene34439.t1	326	34413.31	4.84	PCF	-0.351	nucl
MsTCP4	MS.gene029214.t1	260	27872.04	9.72	PCF	-0.575	nucl
MsTCP5	MS.gene91110.t1	232	25464.17	8.04	PCF	-0.819	nucl
MsTCP6	MS.gene41379.t1	340	35 855.73	4.85	PCF	-0.386	nucl
MsTCP7	MS.gene88588.t1	238	26275.98	8.05	PCF	-0.889	nucl
MsTCP8	MS.gene064482.t1	206	22076.72	8.96	PCF	-0.527	nucl
MsTCP9	MS.gene060651.t1	300	32841.10	7.14	CIN	-0.739	nucl
MsTCP10	MS.gene31403.t1	329	36285.54	5.99	CIN	-0.743	nucl
MsTCP11	MS.gene059738.t1	302	33141.45	7.13	CIN	-0.731	nucl
MsTCP12	MS.gene072060.t1	520	55104.71	6.32	PCF	-0.782	nucl
MsTCP13	MS.gene93507.t1	383	43316.86	7.92	CIN	-0.799	nucl
MsTCP14	MS.gene006670.t1	336	37829.29	6.21	CIN	-0.881	nucl
MsTCP15	MS.gene023326.t1	478	53008.41	6.75	CIN	-0.978	nucl
MsTCP16	MS.gene73844.t1	224	24733.21	7.23	CIN	-0.795	nucl
MsTCP17	MS.gene78508.t1	417	45864.22	8.02	PCF	-0.607	nucl
MsTCP18	MS.gene91902.t1	415	43919.25	6.46	PCF	-0.613	nucl
MsTCP19	MS.gene054307.t1	119	12818.73	9.59	PCF	-0.576	nucl
MsTCP20	MS.gene054308.t1	134	14873.93	7.76	PCF	-0.704	nucl
MsTCP21	MS.gene054297.t1	119	12797.68	10.00	PCF	-0.612	nucl
MsTCP22	MS.gene03516.t1	120	12812.73	9.88	PCF	-0.549	nucl
MsTCP23	MS.gene03512.t1	134	14658.83	8.82	PCF	-0.476	nucl
MsTCP24	MS.gene074319.t1	319	36194.52	9.67	CYC/TB1	-1.033	nucl
MsTCP25	MS.gene80495.t1	107	11418.15	10.23	PCF	-0.508	nucl
MsTCP26	MS.gene80502.t1	164	18081.65	7.72	PCF	-0.476	nucl
MsTCP27	MS.gene48573.t1	351	39672.79	8.57	CIN	-0.944	nucl
MsTCP28	MS.gene063503.t1	309	34369.17	9.10	CIN	-0.678	nucl
MsTCP29	MS.gene42033.t1	130	14327.45	9.46	PCF	-0.555	cyto
MsTCP30	MS.gene42025.t1	120	12784.56	10.14	PCF	-0.653	nucl
MsTCP31	MS.gene92019.t1	373	42567.64	6.68	CYC/TB1	-1.135	nucl
MsTCP32	MS.gene36024.t1	435	47331.52	6.73	CIN	-0.831	nucl
MsTCP33	MS.gene019430.t1	331	36115.04	9.42	PCF	-0.447	nucl
MsTCP34	MS.gene044458.t1	249	27766.24	9.25	CIN	-0.668	nucl
MsTCP35	MS.gene36926.t1	189	20950.04	9.55	PCF	-0.708	cyto
MsTCP36	MS.gene032256.t1	433	47041.11	6.59	CIN	-0.839	nucl
MsTCP37	MS.gene033131.t1	419	45864.22	8.02	PCF	-0.895	nucl
MsTCP38	MS.gene007917.t1	431	46871.96	6.61	CIN	-0.823	nucl
MsTCP39	MS.gene038752.t1	388	44616.54	8.50	CYC/TB1	-1.012	nucl
MsTCP40	MS.gene34255.t1	430	46850.01	6.71	CIN	-0.834	nucl

[1]	Bin WANG, Man-you LI, Xin-pan WANG, Xiu DONG, Jun-bao PANG, Jian LAN. Combined ploughing and tilling to improve degraded alfalfa （Medicago sativa） stands in a semi-arid region [J]. Acta Prataculturae Sinica, 2022, 31(1): 107-117.
[2]	Yan-zhong LI, Jun-qiang YU, Ming LI. Preliminary evaluation of 48 alfalfa varieties for resistance to three diseases [J]. Acta Prataculturae Sinica, 2021, 30(9): 62-75.
[3]	Xue WANG, Xiao-jing LIU, Ya-jiao ZHAO, Jing WANG. Nitrogen utilization and interspecific feedback characteristics of intercropped alfalfa/oat with different root barriers [J]. Acta Prataculturae Sinica, 2021, 30(8): 73-85.
[4]	Gulnazar Ali, Hai-ning TAO, Zi-kui WANG, Yu-ying SHEN. Evaluating the deep-horizon soil water content and water use efficiency in the alfalfa-wheat rotation system on the dryland of Loess Plateau using APSIM [J]. Acta Prataculturae Sinica, 2021, 30(7): 22-33.
[5]	Dan-dan ZHANG, Yuan-qing ZHANG, Jing CHENG, Guang JIN, Bo LI, Dong-cai WANG, Fang XU, Rui-feng SUN. Effects of different roughage combinations on in vitro rumen fermentation characteristics of Jinnan cattle [J]. Acta Prataculturae Sinica, 2021, 30(7): 93-100.
[6]	Zhen-feng ZANG, Jie BAI, Cong LIU, Kan-zhuo ZAN, Ming-xiu LONG, Shu-bin HE. Variety specificity of alfalfa morphological and physiological characteristics in response to drought stress [J]. Acta Prataculturae Sinica, 2021, 30(6): 73-81.
[7]	Xiao-jun SUO, Nian ZHANG, Qian-ping YANG, Hu TAO, Qi XIONG, Xiao-feng LI, Feng ZHANG, Ming-xin CHEN. Effects of peanut vine and alfalfa meal on weight gain performance， internal organ development， and blood indexes of Boer×Macheng crossbred goats [J]. Acta Prataculturae Sinica, 2021, 30(5): 146-154.
[8]	Zhan XIE, Lin MU, Zhi-fei ZHANG, Gui-hua CHEN, Yang LIU, Shuai GAO, Zhong-shan WEI. Effects on fermentation in alfalfa mixed silage of added lactic acid bacteria or organic acid salt combined with urea [J]. Acta Prataculturae Sinica, 2021, 30(5): 165-173.
[9]	Ji-xiang WANG, Huan-yu GONG, Xiang-jian TU, Zhen-xing GUO, Jia-nan ZHAO, Jian SHEN, Zhen-yi LI, Juan SUN. Screening of phosphite-tolerant alfalfa varieties and identification of phosphite tolerance indicators [J]. Acta Prataculturae Sinica, 2021, 30(5): 186-199.
[10]	Qiao-yu LUO, Yan-long WANG, Zhi CHEN, Yong-gui MA, Qi-mei REN, Yu-shou MA. Effect of water stress on proline accumulation and metabolic pathways in Deschampsia caespitosa [J]. Acta Prataculturae Sinica, 2021, 30(5): 75-83.
[11]	Yi-yao HOU, Xiao LI, Rui-cai LONG, Qing-chuan YANG, Jun-mei KANG, Chang-hong GUO. Effect of overexpression of the alfalfa MsHB7 gene on drought tolerance of Arabidopsis [J]. Acta Prataculturae Sinica, 2021, 30(4): 170-179.
[12]	Di ZHANG, Li-fei REN, Guang-bin LIU, Fu-qing LUO, Wen-hao ZHANG, Tian-zuo WANG. Comparative metabolite profiling of alfalfa seeds dried at different temperatures [J]. Acta Prataculturae Sinica, 2021, 30(3): 158-166.
[13]	Kai-qiang LIU, Wen-hui LIU, Zhi-feng JIA, Guo-ling LIANG, Xiang MA. Effects of drought stress on yield and dry matter accumulation and distribution of Avena sativa cv. Qingyan No.1 [J]. Acta Prataculturae Sinica, 2021, 30(3): 177-188.
[14]	Bai-ping SHA, Ying-zhong XIE, Xue-qin GAO, Wei CAI, Bing-zhe FU. Effects of coupling of drip irrigation water and fertilizer on yield and quality of alfalfa in the yellow river irrigation district [J]. Acta Prataculturae Sinica, 2021, 30(2): 102-114.
[15]	Shuang LIU, Fu-ping HUI. Distribution of alfalfa in the Ming and Qing Dynasties and the underlying driving factors [J]. Acta Prataculturae Sinica, 2021, 30(2): 178-189.