Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (9): 1690-1702.doi: 10.3724/SP.J.1006.2021.04137

• RESEARCH PAPERS • Previous Articles     Next Articles

Effects of waterlogging on rhizosphere microorganisms communities of different soybean varieties

YU Tao-Bing(), SHI Qi-Han, NIAN-Hai *(), LIAN Teng-Xiang*()   

  1. College of Agriculture, South China Agricultural University / Guangdong Subcenter of National Soybean Improvement Center, Guangzhou 510642, Guangdong, China
  • Received:2020-06-23 Accepted:2020-09-13 Online:2021-09-12 Published:2020-09-22
  • Contact: NIAN-Hai ,LIAN Teng-Xiang E-mail:277885643@qq.com;hnian@scau.edu.cn;liantx@scau.edu.cn
  • Supported by:
    National Key Research and Development Program of China “Physiological Basis and Agronomic Management for High-quality and High-yield of Field Cash Crops”(2018YFD1000900)

RichHTML387

24

PDF

854

Abstract:

Waterlogging affects the composition of rhizosphere microbial community of different soybean varieties. The tolerance of soybean plant with different genotypes to waterlogging is quite different. In this study, waterlogging tolerant soybean genotype (Qihuang 34) and waterlogging sensitive soybean genotype (Jidou 17) were selected. The bacterial diversity, community composition, and network characteristics in the rhizosphere of the two genotypes under different waterlogging time were analyzed via fluorescence quantitative qPCR and Illumina Miseq high-throughput sequencing. The results showed that the biomass of waterlogging tolerant genotype and bacterial abundance in its rhizosphere were significantly higher than those for waterlogging sensitive genotype. The PCoA analysis showed that the difference in microbial community composition between waterlogging tolerant and sensitive soybean genotypes changed with waterlogging time (P < 0.05). Under the condition of waterlogging, Yonghaparkia and Unclassified-WD2101, OTU274 (Clostridium) and OTU2334 (Desulfosporosinus) enriched in the rhizosphere of the waterlogging tolerant genotype. The enrichment of these bacteria might be related to waterlogging tolerance. This study provides evidence of the microbial potential in the rhizosphere of soybean against waterlogging.

Key words: soybean, waterlogging tolerance, rhizosphere microorganism, 16S rRNA, network analysis

Fig. 1

Effects of different waterlogging time on plant Chao 1 richness (a), Shannon diversity (b), biomass (c), and bacterial abundance (d) in W-T and W-S genotype soybeans Boxes superscripted by different letters indicate significant differences between two varieties under the same treatment (P < 0.05). W-TCK: waterlogging-tolerant variety without waterlogging; W-SCK: waterlogging-sensitive variety without waterlogging; W-T1D: waterlogging-tolerant variety under waterlogging for one day; W-S1D: waterlogging-sensitive variety under waterlogging for one day; W-T5D: waterlogging-tolerant variety under waterlogging for five days; W-S5D: waterlogging-sensitive variety under waterlogging for five days. "

Fig. 2

Relative abundance of phylum under different waterlogging time Treatments are the same as those given in Fig. 1. "

Fig. 3

Relative abundance of the genera of W-T and W-S genotype soybeans responding to 0 d (a), 1 d (b), and 5 d (c) waterlogging time The error bars show the calculated standard variation of six replicates. Corrected P-values were calculated by the Benjamini-Hochberg false discovery rate approach at (P< 0.05). Treatments are the same as those given inFig. 1. "

Fig. 4

Enrichment and depletion of OTUs included in the waterlogging one day and five days compared with the no-waterlogging soybean controls in soybean a: comparison analysis of W-T variety under waterlogging for one day and no waterlogging; b: comparison analysis of W-T variety under waterlogging for five days and no waterlogging; c: comparison analysis of W-S variety under waterlogging for one day and no waterlogging; d: comparison analysis of W-S variety under waterlogging for five days and no waterlogging; e: Venn analysis of rhizosphere soil bacteria at genus level under different waterlogging time. Treatments are the same as those given inFig. 1. "

Fig. 5

Relative abundance of the OTU that associated to waterlogging tolerance soybean genotype W-T: waterlog-tolerant soybean genotypes; W-S: waterlog-sensitive soybean genotypes. Bars superscripted with different letters indicate significant differences between the two varieties under the same treatment at P< 0.05. Treatments are the same as those given inFig. 1. "

Fig. 6

Principal coordinate analysis of bacterial community in rhizosphere soil under different waterlogging time Treatments are the same as those given in Fig. 1. "

Table 1

Effects of soybean genotypes on rhizosphere bacterial community structure assessed by permutational multivariate analysis of variance (PERMANOVA)"

配对比较
Pairwise comparison 		统计值
F-value 		决定系数
R2 		P值
P-value
W-TCK vs. W-SCK 1.657 0.142 0.045*
W-T1D vs. W-S1D 1.970 0.165 0.003**
W-T5D vs. W-S5D 1.972 0.165 0.020*

Fig. 7

Co-occurrence network of the rhizosphere bacterial community for different waterlogging time and soybean genotypes a: co-occurrence network of the rhizosphere bacterial community of W-T variety without waterlogging; b: co-occurrence network of the rhizosphere bacterial community of W-S variety without waterlogging; c: co-occurrence network of the rhizosphere bacterial community of W-T variety under waterlogging for one day; d: co-occurrence network of the rhizosphere bacterial community of W-S variety under waterlogging for one day; e: co-occurrence network of the rhizosphere bacterial community of W-T variety under waterlogging for five days; f: co-occurrence network of the rhizosphere bacterial community of W-S variety under waterlogging for five days. Different color nodes represent different phyla. The red connection line indicates positive correlation between two nodes, and the blue connection line indicates negative correlation between two nodes. Treatments are the same as those given in Fig. 1. "

Table 2

Topological properties of rhizosphere bacterial networks"

网络指标
Network metrics 		W-SCK W-TCK W-S1D W-T1D W-S5D W-T5D
节点数Number of nodes 81 79 79 74 89 86
边数Number of edges 338 346 536 317 521 579
正相关数Number of positive correlation 218 216 433 224 321 353
负相关数Number of negative correlations 120 130 103 93 200 226
平均路径长度Average path length 3.816 3.058 2.626 3.688 3.192 3.195
图密度Graph density 0.104 0.112 0.174 0.117 0.133 0.158
网络直径Network diameter 10 7 7 9 9 9
平均聚类系数Average clustering coefficient 0.641 0.594 0.72 0.688 0.638 0.649
平均加权度Average degree 2.301 2.931 8.219 3.459 2.723 3
连接部件Number of modules 4 4 5 5 3 3
模块化Modularity 8.518 6.313 0.613 2.164 4.412 3.709
[1] Loreti E, Van V H, Perata P. Plant responses to flooding stress. Plant Biol, 2016, 33:64-71.
[2] Duggan B L, Domitruk D R, Fowler D B. Yield component variation in winter grown under drought stress. Can J Plant Sci, 2000, 80:739-745.
doi: 10.4141/P00-006
[3] Bagci S A, Ekiz H, Yilmaz A, Cakmak I. Effects of zinc deficiency and drought on grain yield of field-grown wheat cultivars in Central Anatolia. J Agron Crop Sci, 2007, 193:198-206.
doi: 10.1111/jac.2007.193.issue-3
[4] Jiang D, Fan X, Dai T, Cao W. Nitrogen fertilizer rate and post anthesis waterlogging effects on carbohydrate and nitrogen dynamics in wheat. Plant Soil, 2008, 304:301-314.
doi: 10.1007/s11104-008-9556-x
[5] Yang M, Wangr F. Effects of tea and fungus intercropping on soil microbial community of tea. South China Agric, 2010, 11:13-16.
[6] Fukao T, Bailey S J. Submergence tolerance conferred by Sub1A is mediated by SLR1 and SLRL1 restriction of gibberellin responses in rice. Proc Natl Acad Sci USA, 2008, 105:16814-16819.
doi: 10.1073/pnas.0807821105
[7] Setter T L, Waters I. Review of prospects for germplasm improvement for waterlogging tolerance in wheat barley and oats. Plant Soil, 2003, 253:1-34.
doi: 10.1023/A:1024573305997
[8] Herzog M, Striker G G, Colmer T D, Pedersen O. Mechanisms of waterlogging tolerance in wheat: a review of root and shoot physiology. Plant Cell Environ, 2016, 39:1068-1086.
doi: 10.1111/pce.12676
[9] Grayston S, Wang J, Campbell C D S, Edwards A C. Selective influence of plant species on microbial diversity in the rhizosphere. Soil Biol Biochem, 1998, 30:369-378.
doi: 10.1016/S0038-0717(97)00124-7
[10] Sairam R K, Dharmar K, Chinnusamy V, Meena R C. Waterlogging-induced increase in sugar mobilization, fermentation, and related gene expression in the roots of mung bean ( Vigna radiata). J Plant Physiol, 2009, 166:602-616.
doi: 10.1016/j.jplph.2008.09.005
[11] Ngumbi E, Kloepper J. Bacterial-mediated drought tolerance: current and future prospects. Appl Soil Ecol, 2016, 105:109-125.
doi: 10.1016/j.apsoil.2016.04.009
[12] Nguyen L T T, Osanai Y, Lai K, Anderson I C, Bange M P, Tissue D T, Singh B K. Responses of the soil microbial community to nitrogen fertilizer regimes and historical exposure to extreme weather events: flooding or prolonged-drought. Soil Biol Biochem, 2018, 118:227-236.
doi: 10.1016/j.soilbio.2017.12.016
[13] Fleury D, Jefffferies S, Kuchel H, Langridge P. Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot, 2010, 61:3211-3222.
doi: 10.1093/jxb/erq152
[14] Quiza L, St-Arnaud M, Yergeau E. Harnessing phytomicrobiome signaling for rhizosphere microbiome engineering. Front Plant Sci, 2015, 6:507-516.
[15] Coleman D D, Tringe S G. Building the crops of tomorrow: advantages of symbiont-based approaches to improving abiotic stress tolerance. Front Microbiol, 2014, 5:283-295.
doi: 10.3389/fmicb.2014.00283 pmid: 24936202
[16] Budak H, Akpinar B A, Unver T, Turktas M. Proteome changes in wild and modern wheat leaves upon drought stress by two-dimensional electrophoresis and nanoLC-ESI-MS/MS. Plant Mol Biol, 2013, 83:89-103.
doi: 10.1007/s11103-013-0024-5
[17] Swamy B P M, Kumar A. Genomics-based precision breeding approaches to improve drought tolerance in rice. Biotechnol Adv, 2013, 31:1308-1318.
doi: 10.1016/j.biotechadv.2013.05.004
[18] Waterer D, Benning N T, Wu G, Luo X, Liu X, Gusta M. Evaluation of abiotic stress tolerance of genetically modified potatoes ( Solanum tuberosumcv. Desiree). Mol Breed, 2010, 25:527-540.
doi: 10.1007/s11032-009-9351-2
[19] Sanaullah M, Blagodatskaya E, Chabbi A, Rumpel C, Kuzyakov Y. Drought effects on microbial biomass and enzyme activities in the rhizosphere of grasses depend on plant community composition. Appl Soil Ecol, 2011, 48:38-44.
doi: 10.1016/j.apsoil.2011.02.004
[20] Canarini A, Dijkstra F. Dry-rewetting cycles regulate wheat carbon rhizodeposition, stabilization and nitrogen cycling. Soil Biol Biochem, 2015, 81:195-203.
doi: 10.1016/j.soilbio.2014.11.014
[21] Grayston S J, Wang S, Campbell C D, Edwards A C. Selective influence of plant species on microbial diversity in the rhizosphere. Soil Biol Biochem, 1998, 30:369-378.
doi: 10.1016/S0038-0717(97)00124-7
[22] Lau J A, Lennon J T. Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc Natl Acad Sci USA, 2012, 35:14058-14062.
[23] Kaisermann A, Vries F T, Grifths R I, Bardgett R D. Legacy effects of drought on plant-soil feedbacks and plant-plant interactions. New Phytol, 2017, 215:1413-1424.
doi: 10.1111/nph.2017.215.issue-4
[24] Castrillo G, Teilxeira P, Paredes S, Theresa F L, Laura L, Meghan E, Feltcher O M, Finkel N W, Breakfield P M, Corbin D J, Javier P A, Jeffery L. Root microbiota drive direct integration of phosphate stress and immunity. Nature, 2017, 543:513-518.
doi: 10.1038/nature21417 pmid: 28297714
[25] Saleem M, Arshad M, Hussain S, Bhatti A S. Perspective of plant growth promoting rhizobacteria (PGPR) containing ACC deaminase in stress agriculture. J Ind Microbiol Biotechnol, 2007, 34:635-648.
doi: 10.1007/s10295-007-0240-6
[26] Barnawal D, Bharti N, Maji D, Chanotiya C S, Kalra A. 1-Aminocyclopropane-1-carboxylic acid (ACC) deaminase-containing rhizobacteria protect Ocimum sanctum plants during waterlogging stress via reduced ethylene generation. Plant Physiol Biochem, 2012, 58:227-235.
doi: 10.1016/j.plaphy.2012.07.008
[27] Grichko V P, Glick B R. Flooding tolerance of transgenic tomato plants expressing the bacterial enzyme ACC deaminase controlled by the 35S, rolD or PRB-1b promoter. Plant Physiol Biochem, 2001, 39: 19-25.
doi: 10.1016/S0981-9428(00)01217-1
[28] Zahir Z A, Munir A, Asghar H N, Shaharoona B, M Arshad. Effectiveness of rhizobacteria containing ACC-deaminase for growth promotion of pea ( Pisum sativum) under drought conditions. Microbiol Biotechnol, 2007, 18:958-963.
[29] Mayak S, Tirosh T, Glick B R. Plant growth-promoting bacteria confer resistance in tomato plants to salt stress. Plant Physiol Biochem, 2004, 42:565-572.
doi: 10.1016/j.plaphy.2004.05.009
[30] Lebeis S L, Paredes S H, Lundberg D S, Breakfield N, Gehring J, McDonald M, Malfatti S T, Tijana G, Corbin D, Susannah G, Jeffery L. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science, 2015, 349:860-864.
doi: 10.1126/science.aaa8764
[31] Lian T X, Ma Q B, Shi Q H, Cai Z D, Zhang Y F, Cheng Y B, Nian H. High aluminum stress drives different rhizosphere soil enzyme activities and bacterial community structure between aluminum-tolerant and aluminum-sensitive soybean genotypes. Plant Soil, 2019, 440:409-425.
doi: 10.1007/s11104-019-04089-8
[32] 赵青松, 闫龙, 刘兵强, 邸锐, 史晓蕾, 赵双进, 张孟臣, 杨春燕. 高产广适优质大豆品种冀豆17. 大豆科学, 2015, 34:736-739.
Zhao Q S, Yan L, Liu B Q, Di R, Shi X L, Zhao S J, Zhang M C, Yang C Y. High yield wide adaptability and high quality soybean variety Jidou 17. Soybean Sci, 2015, 34:736-739 (in Chinese with English abstract).
[33] 徐冉, 王彩洁, 张礼凤, 李伟, 戴海英, 张军. 高产优质多抗广适大豆新品种齐黄34的选育. 山东农业科学, 2013, 45(3):107-108.
Xu R, Wang C J, Zhang L F, Li W, Dai H Y, Zhang J. Breeding of a new soybean variety Qihuang 34 with high yield, high quality and multi resistance. Shandong Agric Sci, 2013, 45(3):107-108 (in Chinese with English abstract).
[34] Castrillo G, Teilxeira P, Paredes S, Law T, Lorenzo L, Feltcher M, Finkel O, Breakfield N, Mieczkowski P, Jones C, Paz J, Dangl J. Root microbiota drive direct integration of phosphate stress and immunity. Nature, 2017, 543:513-518.
doi: 10.1038/nature21417 pmid: 28297714
[35] Caporaso J G, Kuczynski J, Stombaugh J, Bittinger K, Bushman F D, Costello E K, Noah F, Antonio G P, Julia K G, Jeffrey I G, Gavin A H, Scott T K, Dan K, Jeremy E K. QIIME allows analysis of high-throughput community sequencing data. Nat Methods, 2010, 7:335-336.
doi: 10.1038/nmeth.f.303 pmid: 20383131
[36] Magoc T, Salzberg S L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics, 2011, 27:2957-2963.
doi: 10.1093/bioinformatics/btr507
[37] Edgar R, Haas B, Clemente J, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 2011, 27:2194-2200.
doi: 10.1093/bioinformatics/btr381
[38] Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006, 22:1658-1659.
doi: 10.1093/bioinformatics/btl158
[39] Abarenkov K, Nilsson R H, Larsson K H, Alexander I J, Eberhardt U, Erland S, Klaus H, Rasmus K, Ellen L, Taina P, Robin S, Andy F S, Taylor L, Björn M U, Trude V. The UNITE database for molecular identification of fungi recent updates and future perspectives. New Phytol, 2010, 186:281-285.
doi: 10.1111/nph.2010.186.issue-2
[40] Wang Q, Garrity G M, Tiedje J M, Cole J R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol, 2007, 73:5261-5267.
doi: 10.1128/AEM.00062-07
[41] Muyzer G, Waal E D, Uitterlinden A G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction amplified genes coding for 16S rRNA. Appl Environmen Microbiol, 1993, 59:695-700.
[42] Li J, Lin J, Pei C, Kaitao L, Thomas C J, Guang D T. Variation of soil bacterial communities along a chronosequence of Eucalyptus plantation. Peer J, 2018, 6:5648-5657.
[43] Revelle W. Procedures for personality and psychological research. Nort Univ, 2017, 7:136-142.
[44] Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. San Jose CA, 2009, 361-362.
[45] Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol, 2014, 5:219-232.
[46] Agler M T, Ruhe J, Kroll S, Morhenn C, Kim S T, Weigel D, Eric M K. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol, 2016, 14:e1002352.
doi: 10.1371/journal.pbio.1002352
[47] Azarbad H, Constant P, Giard L C, Bainard L D, Yergeau E. Water stress history and wheat genotype modulate rhizosphere microbial response to drought. Soil Biol Biochem, 2018, 126:228-236.
doi: 10.1016/j.soilbio.2018.08.017
[48] 郭太忠, 袁刘正, 赵月强, 柳家友, 谷川. 渍涝对玉米产量和根际土壤微生物的影响. 湖北农业科学, 2014, 53:505-507.
Guo T Z, Yuan L Z, Zhao Y Q, Liu J Y, Gu C. Effects of waterlogging on maize yield and the rhizosphere soil microorganism. Hubei Agric Sci, 2014, 53:505-507 (in Chinese with English abstract).
[49] 曾成城, 陈锦平, 魏虹, 刘媛, 马文超, 王婷, 周翠. 水淹生境下秋华柳对Cd污染土壤微生物数量及酶活性的影响. 生态学报, 2017, 37:4327-4334.
Zeng C C, Chen J P, Wei H, Liu Y, Mao W C, Wang T, Zhou C. Effects of Salix variegata on soil microorganisms and enzymatic activity in contaminated soils under flooding conditions. Acta Ecol Sin, 2017, 37:4327-4334 (in Chinese with English abstract).
[50] 赵可夫. 植物对水涝胁迫的适应. 生物学通报, 2003, 38(12):11-14.
Zhao K F. Adaptation of plants to waterlogging stress. Biol Bull, 2003, 38(12):11-14 (in Chinese with English abstract).
[51] Went F W. Effect of root system on tomato stem growth. Plant Physiol, 1943, 18:51-65.
pmid: 16653830
[52] 倪君蒂, 李振国. 淹水对大豆生长的影响. 大豆科学, 2000, 19:42-48.
Ni J D, Li Z G. Effects of flooding on soybean growth. Soybean Sci, 2000, 19:42-48 (in Chinese).
[53] 宋晓慧, 滕占林, 箫长亮, 李冬梅, 李文滨, 张代平. 淹水胁迫对不同耐涝性大豆品种苗期根部形态及叶部生理指标的影响. 大豆科学, 2014, 32:130-132.
Song X H, Teng Z L, Xiao C L, Li D M, Li W B, Zhang D P. Effect of waterlogging on root morphology and foliar physiological indexes of soybean varieties. Soybean Sci, 2014, 32:130-132 (in Chinese with English abstract).
[54] Constant P, Chowdhury S P, Hesse L, Pratscher J, Conrad R. Genome data mining and soil survey for the novel group 5 [NiFe]-hydrogenase to explore the diversity and ecological importance of presumptive high affinity H2-oxidizing bacteria. Appl Environ Microbiol, 2011, 77:6027-6035.
doi: 10.1128/AEM.00673-11
[55] Duarte C I, Mertz H L M, Aurelian D S, Nepomuceno A, Moraes L, Alexandra C, Marcolino G J, Richter C, Colnago L A. Flooded soybean metabolomic analysis reveals important primary and secondary metabolites involved in the hypoxia stress response and tolerance. Environ Exp Bot, 2018, 153:176-187.
doi: 10.1016/j.envexpbot.2018.05.018
[56] Greening C, Biswas A, Carere C R, Jackson C J, Taylor M C, Stott M B, Cook G M, Morales S E. Genomic and metagenomic surveys of hydrogenase distribution indicate H is a widely utilised energy source for microbial growth and survival. ISME J, 2016, 10:761-777.
doi: 10.1038/ismej.2015.153 pmid: 26405831
[57] Evans S E, Wallenstein M D. Soil microbial community response to drying and rewetting stress: does historical precipitation regime matter. Biogeochemistry, 2012, 109:101-116.
doi: 10.1007/s10533-011-9638-3
[58] Preece C, Peñuelas J. Rhizodeposition under drought and consequences for soil communities and ecosystem resilience. Plant Soil, 2016, 409:1-17.
doi: 10.1007/s11104-016-3090-z
[59] Unger I M, Kennedy A C, Muzika R M. Flooding effects on soil microbial communities. Appl Soil Ecol, 2009, 42:1-8.
doi: 10.1016/j.apsoil.2009.01.007
[60] Fierer N, Schimel J, Holden P. Variations in microbial community composition through two soil depth profiles. Soil Biol Biochem, 2003, 35:167-176.
doi: 10.1016/S0038-0717(02)00251-1
[61] Moyano F E, Manzoni S, Chenu C. Responses of soil heterotrophic respiration to moisture availability: an exploration of processes and models. Soil Biol Biochem, 2013, 59:72-85.
doi: 10.1016/j.soilbio.2013.01.002
[62] Meisner A, Leizeaga A, Rousk J, Bååth E. Partial drying accelerates bacterial growth recovery to rewetting. Soil Biol Biochem, 2017, 112:269-276.
doi: 10.1016/j.soilbio.2017.05.016
[63] Fuchslueger L, Bahn M, Fritz K, Hasibeder R, Richter A. Experimental drought reduces the transfer of recently fixed plant carbon to soil microbes and alters the bacterial community composition in a mountain meadow. New Phytol, 2014, 201:916-927.
doi: 10.1111/nph.2014.201.issue-3
[64] Kozlowski T T. Flooding and plant growth. Ann Bot-London, 1994, 91:107.
doi: 10.1093/aob/mcg014
[65] Coutinho I D, Baker J M, Ward J L, Beale M H, Creste S, Cavalheiro A. Metabolite profiling of sugarcane genotypes and identification of flavonoid glycosides and phenolic acids. J Agric Food Chem, 2016, 64:4198-4206.
doi: 10.1021/acs.jafc.6b01210
[66] Tokala R K, Strap J L, Jung C M, Jung D L, Crawford M S, Lee A, Deobald J, Franklin B. Novel plant-microbe rhizosphere interaction involvingStreptomyces lydicus WYEC108 and the pea plant (Pisum sativum). Appl Environ Microbiol, 2002, 68:2161-2171.
doi: 10.1128/AEM.68.5.2161-2171.2002
[67] Yamanaka K, Oikawa H, Ogawa H, Hideaki T, Shohei S, Teruhiko B, Kenji U. Desferrioxamine E produced by Streptomyces griseus stimulates growth and development of Streptomyces tanashiensis. Microbiology, 2005, 151:2899-2905.
doi: 10.1099/mic.0.28139-0
[68] 黄佩蓓, 焦念志, 冯浩, 舒青龙. 海洋浮霉状菌多样性与生态学功能研究进展. 微生物学报, 2014, 41:1891-1902.
Huang P B, Jiao N Z, Feng H, Shu Q L. Research progress on Planctomycetes’ diversity and ecological function in marine environments. Acta Microbiol Sin, 2014, 41:1891-1902 (in Chinese with English abstract).
[69] Gloeckner F O, Bauer M, Teeling H, Lombardot T, Ludwig W, Gade D, Beck A, Borzym K, Heitmann K, Rabus R, Schlesner H, Amann R, Reinhardt R. Complete genome sequence of the marine planctomycete Pirellulasp. Strain 1. Proc Natl Acad Sci USA, 2003, 103:292-310.
[70] Strobel G. Harnessing endophytes for industrial microbiology. Curr Opin Microbiol, 2006, 9:240-244.
pmid: 16647289
[71] Somers E, Vanderleyden J, Srinivasan M. Rhizosphere bacterial signalling: a love parade beneath our feet. Crit Rev Microbiol, 2004, 30:205-240.
pmid: 15646398
[72] Zhang Y H P, Lynd L R. Cellulose utilization by Clostridium thermocellum: bioenergetics and hydrolysis product assimilation. Proc Natl Acad Sci USA, 2005, 56:168-176.
[73] Pester M, Bittner N, Deevong P, Wagner M, Loy A. A ‘rare biosphere’ microorganism contributes to sulfate reduction in a peatland. ISME J, 2010, 4:1591-1602.
doi: 10.1038/ismej.2010.75
[74] Freilich S, Kreimer A, Meilijson I, Gophna U, Sharan R, Ruppin E. The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res, 2010, 38:3857-3868.
doi: 10.1093/nar/gkq118 pmid: 20194113
[75] Shi Q, Liu Y, Shi A, Cai Z, Nian H, Martin H, Lian T. Rhizosphere soil fungal communities of aluminum-tolerant and -sensitive soybean genotypes respond differently to aluminum stress in an acid soil. Front Microbiol, 2020, 11:1177.
doi: 10.3389/fmicb.2020.01177
[76] Xiao X, Liang Y, Zhou S, Zhuang S, Sun B. Fungal community reveals less dispersal limitation and potentially more connected network than that of bacteria in bamboo forest soils. Mol Ecol, 2018, 27:550-563.
doi: 10.1111/mec.2018.27.issue-2
[77] Jones J D G, Dang J L. The plant immune system. Nature, 2006, 444:323-329.
doi: 10.1038/nature05286
[78] Peter N D, John P R. Plant immunity: towards an integrated view of plant-pathogen interactions. Nat Rev Genet, 2010, 11:539-548.
[79] Van der Ent S, Van Hulten M, Pozo M J, Czechowski T, Udvardi M K, Pieterse C M J, Ton J. Priming of plant innate immunity by rhizobacteria and β-aminobutyric acid: differences and similarities in regulation. New Phytol, 2009, 183:419-431.
doi: 10.1111/nph.2009.183.issue-2
[1] CHEN Ling-Ling, LI Zhan, LIU Ting-Xuan, GU Yong-Zhe, SONG Jian, WANG Jun, QIU Li-Juan. Genome wide association analysis of petiole angle based on 783 soybean resources (Glycine max L.) [J]. Acta Agronomica Sinica, 2022, 48(6): 1333-1345.
[2] YANG Huan, ZHOU Ying, CHEN Ping, DU Qing, ZHENG Ben-Chuan, PU Tian, WEN Jing, YANG Wen-Yu, YONG Tai-Wen. Effects of nutrient uptake and utilization on yield of maize-legume strip intercropping system [J]. Acta Agronomica Sinica, 2022, 48(6): 1476-1487.
[3] YU Chun-Miao, ZHANG Yong, WANG Hao-Rang, YANG Xing-Yong, DONG Quan-Zhong, XUE Hong, ZHANG Ming-Ming, LI Wei-Wei, WANG Lei, HU Kai-Feng, GU Yong-Zhe, QIU Li-Juan. Construction of a high density genetic map between cultivated and semi-wild soybeans and identification of QTLs for plant height [J]. Acta Agronomica Sinica, 2022, 48(5): 1091-1102.
[4] LI A-Li, FENG Ya-Nan, LI Ping, ZHANG Dong-Sheng, ZONG Yu-Zheng, LIN Wen, HAO Xing-Yu. Transcriptome analysis of leaves responses to elevated CO2 concentration, drought and interaction conditions in soybean [Glycine max (Linn.) Merr.] [J]. Acta Agronomica Sinica, 2022, 48(5): 1103-1118.
[5] PENG Xi-Hong, CHEN Ping, DU Qing, YANG Xue-Li, REN Jun-Bo, ZHENG Ben-Chuan, LUO Kai, XIE Chen, LEI Lu, YONG Tai-Wen, YANG Wen-Yu. Effects of reduced nitrogen application on soil aeration and root nodule growth of relay strip intercropping soybean [J]. Acta Agronomica Sinica, 2022, 48(5): 1199-1209.
[6] WANG Hao-Rang, ZHANG Yong, YU Chun-Miao, DONG Quan-Zhong, LI Wei-Wei, HU Kai-Feng, ZHANG Ming-Ming, XUE Hong, YANG Meng-Ping, SONG Ji-Ling, WANG Lei, YANG Xing-Yong, QIU Li-Juan. Fine mapping of yellow-green leaf gene (ygl2) in soybean (Glycine max L.) [J]. Acta Agronomica Sinica, 2022, 48(4): 791-800.
[7] LI Rui-Dong, YIN Yang-Yang, SONG Wen-Wen, WU Ting-Ting, SUN Shi, HAN Tian-Fu, XU Cai-Long, WU Cun-Xiang, HU Shui-Xiu. Effects of close planting densities on assimilate accumulation and yield of soybean with different plant branching types [J]. Acta Agronomica Sinica, 2022, 48(4): 942-951.
[8] DU Hao, CHENG Yu-Han, LI Tai, HOU Zhi-Hong, LI Yong-Li, NAN Hai-Yang, DONG Li-Dong, LIU Bao-Hui, CHENG Qun. Improving seed number per pod of soybean by molecular breeding based on Ln locus [J]. Acta Agronomica Sinica, 2022, 48(3): 565-571.
[9] ZHOU Yue, ZHAO Zhi-Hua, ZHANG Hong-Ning, KONG You-Bin. Cloning and functional analysis of the promoter of purple acid phosphatase gene GmPAP14 in soybean [J]. Acta Agronomica Sinica, 2022, 48(3): 590-596.
[10] WANG Juan, ZHANG Yan-Wei, JIAO Zhu-Jin, LIU Pan-Pan, CHANG Wei. Identification of QTLs and candidate genes for 100-seed weight trait using PyBSASeq algorithm in soybean [J]. Acta Agronomica Sinica, 2022, 48(3): 635-643.
[11] ZHANG Guo-Wei, LI Kai, LI Si-Jia, WANG Xiao-Jing, YANG Chang-Qin, LIU Rui-Xian. Effects of sink-limiting treatments on leaf carbon metabolism in soybean [J]. Acta Agronomica Sinica, 2022, 48(2): 529-537.
[12] SONG Li-Jun, NIE Xiao-Yu, HE Lei-Lei, KUAI Jie, YANG Hua, GUO An-Guo, HUANG Jun-Sheng, FU Ting-Dong, WANG Bo, ZHOU Guang-Sheng. Screening and comprehensive evaluation of shade tolerance of forage soybean varieties [J]. Acta Agronomica Sinica, 2021, 47(9): 1741-1752.
[13] CAO Liang, DU Xin, YU Gao-Bo, JIN Xi-Jun, ZHANG Ming-Cong, REN Chun-Yuan, WANG Meng-Xue, ZHANG Yu-Xian. Regulation of carbon and nitrogen metabolism in leaf of soybean cultivar Suinong 26 at seed-filling stage under drought stress by exogenous melatonin [J]. Acta Agronomica Sinica, 2021, 47(9): 1779-1790.
[14] ZHANG Ming-Cong, HE Song-Yu, QIN Bin, WANG Meng-Xue, JIN Xi-Jun, REN Chun-Yuan, WU Yao-Kun, ZHANG Yu-Xian. Effects of exogenous melatonin on morphology, photosynthetic physiology, and yield of spring soybean variety Suinong 26 under drought stress [J]. Acta Agronomica Sinica, 2021, 47(9): 1791-1805.
[15] ZENG Wei-Ying, LAI Zhen-Guang, SUN Zu-Dong, YANG Shou-Zhen, CHEN Huai-Zhu, TANG Xiang-Min. Identification of the candidate genes of soybean resistance to bean pyralid (Lamprosema indicata Fabricius) by BSA-Seq and RNA-Seq [J]. Acta Agronomica Sinica, 2021, 47(8): 1460-1471.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd