Advertisement

Applied Microbiology and Biotechnology

, Volume 102, Issue 8, pp 3805–3818 | Cite as

Integrated network analysis reveals the importance of microbial interactions for maize growth

  • Jiemeng Tao
  • Delong Meng
  • Chong Qin
  • Xueduan Liu
  • Yili Liang
  • Yunhua Xiao
  • Zhenghua Liu
  • Yabing Gu
  • Juan Li
  • Huaqun Yin
Environmental biotechnology

Abstract

Microbes play a critical role in soil global biogeochemical circulation and microbe–microbe interactions have also evoked enormous interests in recent years. Utilization of green manures can stimulate microbial activity and affect microbial composition and diversity. However, few studies focus on the microbial interactions or detect the key functional members in communities. With the advances of metagenomic technologies, network analysis has been used as a powerful tool to detect robust interactions between microbial members. Here, random matrix theory-based network analysis was used to investigate the microbial networks in response to four different green manure fertilization regimes (Vicia villosa, common vetch, milk vetch, and radish) over two growth cycles from October 2012 to September 2014. The results showed that the topological properties of microbial networks were dramatically altered by green manure fertilization. Microbial network under milk vetch amendment showed substantially more intense complexity and interactions than other fertilization systems, indicating that milk vetch provided a favorable condition for microbial interactions and niche sharing. The shift of microbial interactions could be attributed to the changes in some major soil traits and the interactions might be correlated to plant growth and production. With the stimuli of green manures, positive interactions predominated the network eventually and the network complexity was in consistency with maize productivity, which suggested that the complex soil microbial networks might benefit to plants rather than simple ones, because complex networks would hold strong the ability to cope with environment changes or suppress soil-borne pathogen infection on plants. In addition, network analyses discerned some putative keystone taxa and seven of them had directly positive interactions with maize yield, which suggested their important roles in maintaining environmental functions and in improving plant growth.

Keywords

Green manures Microbial networks Microbial interactions Keystone species Maize yield 

Notes

Acknowledgements

The study was supported by the National Nature Science Foundation of China (nos. 31570113 and 41573072). We acknowledge Xiangxi Tabacco Test Base for providing experimental fields for this study and are grateful to the facilities and conditions provided by Key Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University, Changsha, China. Thanks to Profs. Huaqun Yin, Juan Li, and Xueduan Liu who helped design this study and contributed material essential for the study; to Jiemeng Tao who wrote the manuscript; to Chong Qin, Yabing Gu, and Yili Liang for their help finish this experiment; to Yunhua Xiao for data analysis; and to Delong Meng and Zhenghua Liu for language revision.

Compliance with ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

All authors declare that they have no competing interests.

Supplementary material

253_2018_8837_MOESM1_ESM.pdf (1.2 mb)
ESM 1 (PDF 1189 kb)

References

  1. Amaral LAN, Scala A, Barthélémy M, Stanley HE (2000) Classes of small-world networks. P Natl Acad Sci 97(21):11149–11152CrossRefGoogle Scholar
  2. Barabasi A-L, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113.  https://doi.org/10.1038/nrg1272 CrossRefPubMedGoogle Scholar
  3. Barberán A, Bates ST, Casamayor EO, Fierer N (2012) Using network analysis to explore co-occurrence patterns in soil microbial communities. Isme J 6(2):343–351.  https://doi.org/10.1038/ismej.2011.119 CrossRefPubMedGoogle Scholar
  4. Bascompte J (2007) Networks in ecology. Basic Appl Ecol 8(6):485–490.  https://doi.org/10.1016/j.baae.2007.06.003 CrossRefGoogle Scholar
  5. Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. Icwsm.  https://doi.org/10.13140/2.1.1341.1520
  6. Berry D, Widder S (2014) Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol 5(219):219.  https://doi.org/10.3389/fmicb.2014.00219 PubMedPubMedCentralGoogle Scholar
  7. Caporaso JG, Lauber CL, Walters WA, Berglyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6(8):1621–1624.  https://doi.org/10.1038/ismej.2012.8 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ, Zhang C, Lamb J, Edwards S, Sieberts SK (2008) Variations in DNA elucidate molecular networks that cause disease. Nature 452(7186):429–435.  https://doi.org/10.1038/nature06757 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Chen CP, Cheng CH, Huang YH, Chen CT, Lai CM, Menyailo OV, Fan LJ, Yang YW (2014) Converting leguminous green manure into biochar: changes in chemical composition and C and N mineralization. Geoderma s232–234(12):581–588.  https://doi.org/10.1016/j.geoderma.2014.06.021 CrossRefGoogle Scholar
  10. Cherr CM (2006) Green manure approaches to crop production. Agron J 98(2):302–319.  https://doi.org/10.2134/agronj2005.0035 CrossRefGoogle Scholar
  11. Chow C-ET, Kim DY, Sachdeva R, Caron DA, Fuhrman JA (2014) Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME J 8(4):816–829.  https://doi.org/10.1038/ismej.2013.199 CrossRefPubMedGoogle Scholar
  12. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avilacampilo I, Creech M, Gross B (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2(10):2366–2382.  https://doi.org/10.1038/nprot.2007.324 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Deng Y, Jiang YH, Yang Y, He Z, Luo F, Zhou J (2012) Molecular ecological network analyses. BMC Bioinformatics 13(1):113.  https://doi.org/10.1186/1471-2105-13-113 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Deng Y, Zhang P, Qin Y, Tu Q, Yang Y, He Z, Schadt CW, Zhou J (2015) Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation. Environ Microbiol 18(1):205–218.  https://doi.org/10.1111/1462-2920.12981 CrossRefPubMedGoogle Scholar
  15. Ding J, Zhang Y, Deng Y, Cong J, Lu H, Sun X, Yang C, Yuan T, Van Nostrand JD, Li D (2015) Integrated metagenomics and network analysis of soil microbial community of the forest timberline. Sci Rep 5:7994.  https://doi.org/10.1038/srep07994 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Dixon P (2003) VEGAN, a package of R functions for community ecology. J Veg Sci 14(6):927–930.  https://doi.org/10.1111/j.1654-1103.2003.tb02228.x CrossRefGoogle Scholar
  17. Dunne JA, Williams RJ, Martinez ND, Wood RA, Erwin DH (2008) Compilation and network analyses of cambrian food webs. PLoS Biol 6(4):e102.  https://doi.org/10.1371/journal.pbio.0060102 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dupuis J (2008) Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol 4(8):e1000117.  https://doi.org/10.1371/journal.pcbi.1000117 CrossRefGoogle Scholar
  19. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460–2461.  https://doi.org/10.1093/bioinformatics/btq461 CrossRefPubMedGoogle Scholar
  20. Esperschütz J, Gattinger A, Mäder P, Schloter M, Fliessbach A (2007) Response of soil microbial biomass and community structures to conventional and organic farming systems under identical crop rotations. FEMS Microbiol Ecol 61(1):26–37.  https://doi.org/10.1111/j.1574-6941.2007.00318.x CrossRefPubMedGoogle Scholar
  21. Fath BD, Scharler UM, Ulanowicz RE, Hannon B (2007) Ecological network analysis: network construction. Ecol Model 208(1):49–55.  https://doi.org/10.1016/j.ecolmodel.2007.04.029 CrossRefGoogle Scholar
  22. Faust K, Raes J (2012) Microbial interactions: from networks to models. Nat Rev Microbiol 10(8):538–550.  https://doi.org/10.1038/nrmicro2832 CrossRefPubMedGoogle Scholar
  23. Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, Huttenhower C (2012) Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol 8(7):e1002606.  https://doi.org/10.1371/journal.pcbi.1002606 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Gans J, Wolinsky M, Dunbar J (2005) Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science 309(5739):1387–1390.  https://doi.org/10.1126/science.1112665 CrossRefPubMedGoogle Scholar
  25. Gardner TS, Di Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301(5629):102–105 http://www.jstor.org/stable/3834642 CrossRefPubMedGoogle Scholar
  26. Gerstung M, Baudis M, Moch H, Beerenwinkel N (2009) Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics 25(21):2809–2815.  https://doi.org/10.1126/science.1081900 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Guimerà R, Amaral LA (2005) Cartography of complex networks: modules and universal roles. J Stat Mech-Theory E 2005(P02001):nihpa35573.  https://doi.org/10.1088/1742-5468/2005/02/P02001 Google Scholar
  28. Guimera R, Amaral LAN (2005) Functional cartography of complex metabolic networks. Nature 433(7028):895–900.  https://doi.org/10.1038/nature03288 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Guimerà R, Salespardo M, Amaral LA (2006) Classes of complex networks defined by role-to-role connectivity profiles. Nat Phys 3(1):63–69.  https://doi.org/10.1038/nphys489 CrossRefGoogle Scholar
  30. He Z, Xu M, Deng Y, Kang S, Kellogg L, Wu L, Van Nostrand JD, Hobbie SE, Reich PB, Zhou J (2010) Metagenomic analysis reveals a marked divergence in the structure of belowground microbial communities at elevated CO2. Ecol Lett 13(5):564–575.  https://doi.org/10.1111/j.1461-0248.2010.01453.x CrossRefPubMedGoogle Scholar
  31. Heijden MGAVD, Bardgett RD, Straalen NMV (2008) The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol Lett 11(3):296–310.  https://doi.org/10.1111/j.1461-0248.2007.01139.x CrossRefPubMedGoogle Scholar
  32. Horvath S, Dong J (2008) Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol 4(8):e1000117.  https://doi.org/10.1371/journal.pcbi.1000117 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Kim SJ, Kim KS, Choi JS, Kim MT, Yong BL, Park KD, Hur S (2015) Effects of continuous application of green manures on microbial community in paddy soil. Korean Journal of Soil Science and Fertilizer 48(5):528–534CrossRefGoogle Scholar
  34. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1987) Optimization by simulated annealing. Readings in Computer Vision 220(4598):606–615.  https://doi.org/10.1126/science.220.4598.671 Google Scholar
  35. Kitano H (2004) Biological robustness. Nat Rev Genet 5(11):826–837.  https://doi.org/10.1038/nrg1471 CrossRefPubMedGoogle Scholar
  36. Kragelund C, Caterina L, Borger A, Thelen K, Eikelboom D, Tandoi V, Kong Y, Waarde JVD, Krooneman J, Rossetti S (2007) Identity, abundance and ecophysiology of filamentous Chloroflexi species present in activated sludge treatment plants. FEMS Microbiol Ecol 59(3):671–682.  https://doi.org/10.1111/j.1574-6941.2006.00251.x CrossRefPubMedGoogle Scholar
  37. Krause AE, Frank KA, Mason DM, Ulanowicz RE, Taylor WW (2003) Compartments revealed in food-web structure. Nature 426(6964):282–285.  https://doi.org/10.1038/nature02115 CrossRefPubMedGoogle Scholar
  38. Lele S (1993) Euclidean distance matrix analysis (EDMA): estimation of mean form and mean form difference. Math Geol 25(5):573–602.  https://doi.org/10.1007/BF00890247 CrossRefGoogle Scholar
  39. Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, Hooper DU, Huston MA, Raffaelli D, Schmid B (2001) Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294(5543):804–808.  https://doi.org/10.1126/science.1064088 CrossRefPubMedGoogle Scholar
  40. Lu L, Yin S, Liu X, Zhang W, Gu T, Shen Q, Qiu H (2013) Fungal networks in yield-invigorating and -debilitating soils induced by prolonged potato monoculture. Soil Biol Biochem 65:186–194.  https://doi.org/10.1016/j.soilbio.2013.05.025 CrossRefGoogle Scholar
  41. Luo F, Zhong J, Yang Y, Scheuermann RH, Zhou J (2006) Application of random matrix theory to biological networks. Phy Lett A 357(6):420–423.  https://doi.org/10.1016/j.physleta.2006.04.076 CrossRefGoogle Scholar
  42. Luo F, Yang Y, Zhong J, Gao H, Khan L, Thompson DK, Zhou J (2007) Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC bioinformatics 8(1):299.  https://doi.org/10.1186/1471-2105-8-299 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Lupatini M, Suleiman AKA, Jacques RJS, Antoniolli ZI, De A, Ferreira S, Kuramae EE, Roesch LFW (2014) Network topology reveals high connectance levels and few key microbial genera within soils. Front Environ Sci 2.  https://doi.org/10.3389/fenvs.2014.00010
  44. Lyons KG, Schwartz MW (2001) Rare species loss alters ecosystem function – invasion resistance. Ecol Lett 4(4):358–365CrossRefGoogle Scholar
  45. Mantel N (2002) The detection of disease clustering and a generalized regression approach. Appl Microbiol Biot 27(2):209Google Scholar
  46. Montoya JM, Pimm SL, Solé RV (2006) Ecological networks and their fragility. Nature 442(7100):259–264.  https://doi.org/10.1038/nature04927 CrossRefPubMedGoogle Scholar
  47. Morris JJ, Lenski RE, Zinser ER (2012) The black queen hypothesis: evolution of dependencies through adaptive gene loss. MBio 3(2):e00036–e00012.  https://doi.org/10.1128/mBio.00036-12 CrossRefPubMedPubMedCentralGoogle Scholar
  48. Müller-Linow M, Hilgetag CC, Hütt MT (2008) Organization of excitable dynamics in hierarchical biological networks. PLoS Comput Biol 4(9):e1000190.  https://doi.org/10.1371/journal.pcbi.1000190 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Newman ME (2006) Finding community structure in networks using the eigenvectors of matrices. Phy Rev E 74(3 Pt 2):036104.  https://doi.org/10.1103/PhysRevE.74.036104 CrossRefGoogle Scholar
  50. Olesen JM, Bascompte J, Dupont YL, Jordano P (2007) The modularity of pollination networks. P Natl Acad Sci 104(50):19891–19896.  https://doi.org/10.1073/pnas.0706375104 CrossRefGoogle Scholar
  51. Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86(14):3200–3203.  https://doi.org/10.1103/PhysRevLett.86.3200 CrossRefPubMedGoogle Scholar
  52. Pester M, Bittner N, Deevong P, Wagner M, Loy A (2010) A ‘rare biosphere’ microorganism contributes to sulfate reduction in a peatland. Isme J 4(4):1591–1602.  https://doi.org/10.1038/ismej.2010.75 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Power ME, Tilman D, Estes JA, Menge BA, Bond WJ, Mills LS, Daily G, Castilla JC, Lubchenco J, Paine RT (1996) Challenges in the quest for keystones. Bioscience 46(8):609–620.  https://doi.org/10.2307/1312990 CrossRefGoogle Scholar
  54. Prosser JI, Bohannan BJ, Curtis TP, Ellis RJ, Firestone MK, Freckleton RP, Green JL, Green LE, Killham K, Lennon JJ (2007) The role of ecological theory in microbial ecology. Nat Rev Microbiol 5(5):384–392.  https://doi.org/10.1038/nrmicro1643 CrossRefPubMedGoogle Scholar
  55. Ren D, Madsen JS, Sørensen SJ, Burmølle M (2015) High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. Isme J 9(1):81–89.  https://doi.org/10.1038/ismej.2014.96 CrossRefPubMedGoogle Scholar
  56. Rudolf VH, Rasmussen NL (2013) Population structure determines functional differences among species and ecosystem processes. Nat Commun 4(4):2318.  https://doi.org/10.1038/ncomms3318 PubMedGoogle Scholar
  57. Smith MD, Knapp AK (2003) Dominant species maintain ecosystem function with non-random species loss. Ecol Lett 6(6):509–517.  https://doi.org/10.1046/j.1461-0248.2003.00454.x CrossRefGoogle Scholar
  58. Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, Arrieta JM, Herndl GJ (2006) Microbial diversity in the deep sea and the underexplored “rare biosphere”. P Natl Acad Sci 103(32):12115–12120.  https://doi.org/10.1073/pnas.0605127103 CrossRefGoogle Scholar
  59. Tao J, Liu X, Liang Y, Niu J, Xiao Y, Gu Y, Ma L, Meng D, Zhang Y, Huang W (2016) Maize growth responses to soil microbes and soil properties after fertilization with different green manures. Appl Microbiol Biot 101(3):1289–1299.  https://doi.org/10.1007/s00253-016-7938-1 CrossRefGoogle Scholar
  60. Torsvik V, Øvreås L (2002) Microbial diversity and function in soil: from genes to ecosystems. Curr Opin Microbiol 5(3):240–245.  https://doi.org/10.1016/S1369-5274(02)00324-7 CrossRefPubMedGoogle Scholar
  61. Walker B, Kinzig A, Langridge J (1999) Plant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor species. Ecosystems 2(2):95–113.  https://doi.org/10.1007/s100219900062 CrossRefGoogle Scholar
  62. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16):5261–5267.  https://doi.org/10.1128/AEM.00062-07 CrossRefPubMedPubMedCentralGoogle Scholar
  63. Wang Y, Zhang R, Zheng Q, Deng Y, Van Nostrand JD, Zhou J, Jiao N (2015) Bacterioplankton community resilience to ocean acidification: evidence from microbial network analysis. ICES J Mar Sci 73:865–875.  https://doi.org/10.1093/icesjms/fsv187 CrossRefGoogle Scholar
  64. Yang H, Li J, Xiao Y, Gu Y, Liu H, Liang Y, Liu X, Hu J, Meng D, Yin H (2017) An integrated insight into the relationship between soil microbial community and tobacco bacterial wilt disease. Front Microbiol 8:2179.  https://doi.org/10.3389/fmicb.2017.02179 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X (2010) Functional molecular ecological networks. MBio 1(4):1592–1601.  https://doi.org/10.1128/mBio.00169-10 Google Scholar
  66. Zhou J, Deng Y, Luo F, He Z, Yang Y (2011) Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. MBio 2(4):e00122–e00111.  https://doi.org/10.1128/mBio.00122-11 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jiemeng Tao
    • 1
    • 3
  • Delong Meng
    • 1
    • 2
  • Chong Qin
    • 1
    • 2
  • Xueduan Liu
    • 1
    • 2
  • Yili Liang
    • 1
    • 2
  • Yunhua Xiao
    • 3
  • Zhenghua Liu
    • 1
    • 2
  • Yabing Gu
    • 1
    • 2
  • Juan Li
    • 3
  • Huaqun Yin
    • 1
    • 2
  1. 1.School of Minerals Processing and BioengineeringCentral South UniversityChangshaChina
  2. 2.Key Laboratory of Biometallurgy of Ministry of EducationCentral South UniversityChangshaChina
  3. 3.College of agronomyHunan Agricultural UniversityChangshaChina

Personalised recommendations