Microbial Ecology

, Volume 72, Issue 4, pp 870–880 | Cite as

Deciphering the Pathobiome: Intra- and Interkingdom Interactions Involving the Pathogen Erysiphe alphitoides

  • Boris Jakuschkin
  • Virgil Fievet
  • Loïc Schwaller
  • Thomas Fort
  • Cécile Robin
  • Corinne VacherEmail author
Environmental Microbiology


Plant-inhabiting microorganisms interact directly with each other, forming complex microbial interaction networks. These interactions can either prevent or facilitate the establishment of new microbial species, such as a pathogen infecting the plant. Here, our aim was to identify the most likely interactions between Erysiphe alphitoides, the causal agent of oak powdery mildew, and other foliar microorganisms of pedunculate oak (Quercus robur L.). We combined metabarcoding techniques and a Bayesian method of network inference to decipher these interactions. Our results indicate that infection with E. alphitoides is accompanied by significant changes in the composition of the foliar fungal and bacterial communities. They also highlight 13 fungal operational taxonomic units (OTUs) and 13 bacterial OTUs likely to interact directly with E. alphitoides. Half of these OTUs, including the fungal endophytes Mycosphaerella punctiformis and Monochaetia kansensis, could be antagonists of E. alphitoides according to the inferred microbial network. Further studies will be required to validate these potential interactions experimentally. Overall, we showed that a combination of metabarcoding and network inference, by highlighting potential antagonists of pathogen species, could potentially improve the biological control of plant diseases.


Plant microbiota Plant-pathogen interaction Pathobiome Microbial network Network inference Disease resistance Biocontrol Powdery mildew 



We thank Xavier Capdevielle, Olivier Fabreguettes, Laure Villate, and Martine Martin-Clotté (INRA, BioGeCo) for technical assistance and advice during preliminary experiments and during the course of the study. We also thank Franck Salin, Thibaut Decourcelle, Adline Delcamp, and Christophe Hubert (CGFB, Bordeaux) for sequencing the samples. The costs of sampling and sequencing were covered by the AIP Bioressource METAPHORE. Computing facilities were provided by the MCIA (Mésocentre de Calcul Intensif Aquitain) of the Université de Pau et des Pays de l’Adour. BJ received a grant from the French Ministry of Research and Education (MENRT no. 2011/AF/57). We thank Cindy E. Morris for helpful discussions about the phyllosphere. We thank Sarah Ouadah and Stéphane Robin for supervising the network analyses. We thank Marie-Laure Desprez-Loustau, Arndt Hampe, Samantha Yeo, and David Bohan and four anonymous reviewers for their very helpful comments. We also thank Julie Sappa from Alex Edelman & Associates for English language revision.

Supplementary material

248_2016_777_MOESM1_ESM.pdf (1002 kb)
ESM 1 Methods S1 – Preliminary experiment assessing the effect of oak leaf storage on fungal community composition. Table S1 - 454 pyrosequencing primer sequences used for fungi. Table S2 - Illumina primer sequences used for bacteria. Figure S1 - Taxonomic distribution of the fungal OTUs. Figure S2 - Taxonomic distribution of the bacterial OTUs (a) at the phylum level and (b) at the order level. Figure S3 – Principal coordinates analysis showing Bray-Curtis dissimilarities in bacterial community composition between the leaves of an oak tree (Quercus robur L.) highly susceptible to the fungal pathogen Erysiphe alphitoides. Table S3 - Fungal and bacterial OTUs significantly associated to samples highly infected with E. alphitoides. Text S1 - Representative sequences of the fungal OTUs associated with infected leaves or interacting directly with E. alphitoides. Text S2 Representative sequences of the bacterial OTUs associated infected leaves or interacting directly with E. alphitoides. (PDF 1002 kb)


  1. 1.
    Turner TR, James EK, Poole PS (2013) The plant microbiome. Genome Biol 14:209. doi: 10.1186/gb-2013-14-6-209 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Faust K, Raes J (2012) Microbial interactions: from networks to models. Nat Rev Microbiol 10:538–550. doi: 10.1038/nrmicro2832 CrossRefPubMedGoogle Scholar
  3. 3.
    Frey-Klett P, Burlinson P, Deveau A et al (2011) Bacterial-fungal interactions: hyphens between agricultural, clinical, environmental, and food microbiologists. Microbiol Mol Biol Rev 75:583–609. doi: 10.1128/MMBR.00020-11 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Kemen E (2014) Microbe-microbe interactions determine oomycete and fungal host colonization. Curr Opin Plant Biol 20:75–81. doi: 10.1016/j.pbi.2014.04.005 CrossRefPubMedGoogle Scholar
  5. 5.
    Vayssier-Taussat M, Albina E, Citti C et al (2014) Shifting the paradigm from pathogens to pathobiome: new concepts in the light of meta-omics. Front Cell Infect Microbiol 4:29. doi: 10.3389/fcimb.2014.00029 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Thiele I, Heinken A, Fleming RMT (2013) A systems biology approach to studying the role of microbes in human health. Curr Opin Biotechnol 24:4–12. doi: 10.1016/j.copbio.2012.10.001 CrossRefPubMedGoogle Scholar
  7. 7.
    Gaggìa F, Mattarelli P, Biavati B (2010) Probiotics and prebiotics in animal feeding for safe food production. Int J Food Microbiol 141:S15–S28. doi: 10.1016/j.ijfoodmicro.2010.02.031 CrossRefPubMedGoogle Scholar
  8. 8.
    Clemente JC, Ursell LK, Parfrey LW, Knight R (2012) The impact of the gut microbiota on human health: an integrative view. Cell 148:1258–1270. doi: 10.1016/j.cell.2012.01.035 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Ursell LK, Van Treuren W, Metcalf JL et al (2013) Replenishing our defensive microbes. Bioessays 35:810–817. doi: 10.1002/bies.201300018 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Berlec A (2012) Novel techniques and findings in the study of plant microbiota: search for plant probiotics. Plant Sci 193–194:96–102. doi: 10.1016/j.plantsci.2012.05.010 CrossRefPubMedGoogle Scholar
  11. 11.
    Newton AC, Gravouil C, Fountaine JM (2010) Managing the ecology of foliar pathogens: ecological tolerance in crops. Ann Appl Biol 157:343–359. doi: 10.1111/j.1744-7348.2010.00437.x CrossRefGoogle Scholar
  12. 12.
    Vacher C, Tamaddoni-Nezhad A, Kamenova S et al (2016) Learning ecological network from NGS data. Adv Ecol Res 54:1–39. doi: 10.1016/bs.aecr.2015.10.004 CrossRefGoogle Scholar
  13. 13.
    Agler MT, Ruhe J, Kroll S et al (2016) Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol 14, e1002352. doi: 10.1371/journal.pbio.1002352 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Okuyama T, Holland JNN (2008) Network structural properties mediate the stability of mutualistic communities. Ecol Lett 11:208–216. doi: 10.1111/j.1461-0248.2007.01137.x CrossRefPubMedGoogle Scholar
  15. 15.
    Thébault E, Fontaine C (2010) Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329:853–856. doi: 10.1126/science.1188321 CrossRefPubMedGoogle Scholar
  16. 16.
    Hui C, Richardson DM, Landi P et al (2016) Defining invasiveness and invasibility in ecological networks. Biol Invasions, online. doi: 10.1007/s10530-016-1076-7 Google Scholar
  17. 17.
    Kurtz ZD, Mueller CL, Miraldi ER et al (2015) Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol 11, e1004226. doi: 10.1371/journal.pcbi.1004226 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Friedman J, Alm EJ (2012) Inferring correlation networks from genomic survey data. PLoS Comput Biol 8, e1002687. doi: 10.1371/journal.pcbi.1002687 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Deng Y, Jiang Y-H, Yang Y et al (2012) Molecular ecological network analyses. BMC Bioinformatics 13:113. doi: 10.1186/1471-2105-13-113 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Schwaller L, Robin S, Stumpf M (2015) Bayesian Inference of Graphical Model Structures Using Trees. arXiv:1504.02723Google Scholar
  21. 21.
    Schwaller L (2015) saturnin: Spanning Trees Used for Network Inference. R Package version 1.0.
  22. 22.
    Mougou A, Dutech C, Desprez-Loustau M-L (2008) New insights into the identity and origin of the causal agent of oak powdery mildew in Europe. For Pathol 38:275–287. doi: 10.1111/j.1439-0329.2008.00544.x Google Scholar
  23. 23.
    Mougou-Hamdane A, Giresse X, Dutech C, Desprez-Loustau M-L (2010) Spatial distribution of lineages of oak powdery mildew fungi in France, using quick molecular detection methods. Ann For Sci 67:212CrossRefGoogle Scholar
  24. 24.
    Glawe DA (2008) The powdery mildews: a review of the world’s most familiar (yet poorly known) plant pathogens. Annu Rev Phytopathol 46:27–51. doi: 10.1146/annurev.phyto.46.081407.104740 CrossRefPubMedGoogle Scholar
  25. 25.
    Kiss L (2003) A review of fungal antagonists of powdery mildews and their potential as biocontrol agents. Pest Manag Sci 59:475–483. doi: 10.1002/ps.689 CrossRefPubMedGoogle Scholar
  26. 26.
    Copolovici L, Väärtnõu F, Estrada MP, Niinemets Ü (2015) Oak powdery mildew (Erysiphe alphitoides) induced volatile emissions scale with the degree of infection in Quercus robur. Tree Physiol 34:1399–1410. doi: 10.1093/treephys/tpu091.Oak CrossRefGoogle Scholar
  27. 27.
    Hajji M, Dreyer E, Marçais B (2009) Impact of Erysiphe alphitoides on transpiration and photosynthesis in Quercus robur leaves. Eur J Plant Pathol 125:63–72. doi: 10.1007/s10658-009-9458-7 CrossRefGoogle Scholar
  28. 28.
    Scotti-Saintagne C, Bodénès C, Barreneche T et al (2004) Detection of quantitative trait loci controlling bud burst and height growth in Quercus robur L. Theor Appl Genet 109:1648–1659. doi: 10.1007/s00122-004-1789-3 CrossRefPubMedGoogle Scholar
  29. 29.
    Schoch CL, Seifert KA, Huhndorf S et al (2012) Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci 109:6241–6246. doi: 10.1073/pnas.1117018109 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Gardes M, Bruns TD (1993) ITS primers with enhanced specificity for basidiomycetes - application to the identification of mycorrhizae and rusts. Mol Ecol 2:113–118CrossRefPubMedGoogle Scholar
  31. 31.
    White T, Bruns T, Lee S, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ (eds) PCR protocols: a guide to methods and applications. Academic Press, Inc, New York, N.Y, pp 315–322Google Scholar
  32. 32.
    Gloor GB, Hummelen R, Macklaim JM et al (2010) Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products. PLoS One 5, e15406. doi: 10.1371/journal.pone.0015406 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Huse SM, Dethlefsen L, Huber JA et al (2008) Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing. PLoS Genet 4, e1000255. doi: 10.1371/journal.pgen.1000255 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. doi: 10.1038/nmeth.f.303 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Nilsson RH, Veldre V, Hartmann M et al (2010) An open source software package for automated extraction of ITS1 and ITS2 from fungal ITS sequences for use in high-throughput community assays and molecular ecology. Fungal Ecol 3:284–287. doi: 10.1016/j.funeco.2010.05.002 CrossRefGoogle Scholar
  36. 36.
    Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10:996–998. doi: 10.1038/nmeth.2604 CrossRefPubMedGoogle Scholar
  37. 37.
    Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461. doi: 10.1093/bioinformatics/btq461 CrossRefPubMedGoogle Scholar
  38. 38.
    Nilsson RH, Kristiansson E, Ryberg M, Hallenberg N (2008) Intraspecific ITS Variability in the Kingdom Fungi as Expressed in the International Sequence Databases and Its Implications for Molecular Species Identification. Evol Bioinforma 4:193–201Google Scholar
  39. 39.
    Nilsson RH, Bok G, Ryberg M et al (2009) A software pipeline for processing and identification of fungal ITS sequences. Source Code Biol Med 4:1. doi: 10.1186/1751-0473-4-1 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Altschul SF, Gish W, Miller W et al (1990) Basic Local Alignment Search Tool. J Mol Biol 215:403–410CrossRefPubMedGoogle Scholar
  41. 41.
    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 73:5261–5267. doi: 10.1128/AEM.00062-07 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Abarenkov K, Nilsson RH, Larsson KH et al (2010) The UNITE database for molecular identification of fungi—recent updates and future perspectives. New Phytol 186:281–285CrossRefPubMedGoogle Scholar
  43. 43.
    Masella AP, Bartram AK, Truszkowski JM et al (2012) PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 13:31. doi: 10.1186/1471-2105-13-31 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Bengtsson J, Eriksson KM, Hartmann M et al (2011) Metaxa: a software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea, bacteria, eukaryotes, mitochondria, and chloroplasts in metagenomes and environmental sequencing datasets. Antonie Van Leeuwenhoek Int J Gen Mol Microbiol 100:471–475. doi: 10.1007/s10482-011-9598-6 CrossRefGoogle Scholar
  45. 45.
    R_Core_Team (2013) R: A Language and Environment for Statistical Computing.
  46. 46.
    McMurdie PJ, Holmes S (2014) Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol 10, e1003531. doi: 10.1371/journal.pcbi.1003531 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Bastian M, Heymann S (2009) Gephi : An Open Source Software for Exploring and Manipulating Networks. Int. AAAI Conf. Weblogs Soc. Media.Google Scholar
  48. 48.
    Jumpponen A, Jones KL (2009) Massively parallel 454 sequencing indicates hyperdiverse fungal communities in temperate Quercus macrocarpa phyllosphere. New Phytol 184:438–448. doi: 10.1111/j.1469-8137.2009.02990.x CrossRefPubMedGoogle Scholar
  49. 49.
    Cordier T, Robin C, Capdevielle X et al (2012) Spatial variability of phyllosphere fungal assemblages: genetic distance predominates over geographic distance in a European beech stand (Fagus sylvatica). Fungal Ecol 5:509–520. doi: 10.1016/j.funeco.2011.12.004 CrossRefGoogle Scholar
  50. 50.
    Douanla-Meli C, Langer E, Talontsi Mouafo F (2013) Fungal endophyte diversity and community patterns in healthy and yellowing leaves of Citrus limon. Fungal Ecol 6:212–222. doi: 10.1016/j.funeco.2013.01.004 CrossRefGoogle Scholar
  51. 51.
    Schlaeppi K, Bulgarelli D (2015) The plant microbiome at work. MPMI 28:212–217CrossRefPubMedGoogle Scholar
  52. 52.
    Sapkota R, Knorr K, Jørgensen LN et al (2015) Host genotype is an important determinant of the cereal phyllosphere mycobiome. New Phytol 207:1134–1144CrossRefPubMedGoogle Scholar
  53. 53.
    U’Ren JM, Riddle JM, Monacell JT et al (2014) Tissue storage and primer selection influence pyrosequencing-based inferences of diversity and community composition of endolichenic and endophytic fungi. Mol Ecol Resour 14:1032–1048. doi: 10.1111/1755-0998.12252 PubMedGoogle Scholar
  54. 54.
    Hibbing ME, Fuqua C, Parsek MR, Peterson SB (2010) Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol 8:15–25. doi: 10.1038/nrmicro2259 CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Foster KR, Bell T (2012) Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol 22:1845–1850. doi: 10.1016/j.cub.2012.08.005 CrossRefPubMedGoogle Scholar
  56. 56.
    Hoffman MT, Arnold AE (2010) Diverse bacteria inhabit living hyphae of phylogenetically diverse fungal endophytes. Appl Environ Microbiol 76:4063–4075. doi: 10.1128/AEM.02928-09 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Venturi V, da Silva DP (2012) Incoming pathogens team up with harmless “resident” bacteria. Trends Microbiol 20:160–164. doi: 10.1016/j.tim.2012.02.003 CrossRefPubMedGoogle Scholar
  58. 58.
    Verkley GJM, Crous PW, Groenewald JZ et al (2004) Mycosphaerella punctiformis revisited: morphology, phylogeny, and epitypification of the type species of the genus Mycosphaerella (Dothideales, Ascomycota). Mycol Res 108:1271–1282. doi: 10.1017/S0953756204001054 CrossRefPubMedGoogle Scholar
  59. 59.
    Gennaro M, Gonthier P, Nicolotti G (2003) Fungal endophytic communities in healthy and declining Quercus robur L. and Q. cerris L. trees in Northern Italy. J Phytopathol 151:529–534. doi: 10.1046/j.1439-0434.2003.00763.x CrossRefGoogle Scholar
  60. 60.
    Yogeswari S, Ramalakshmi S, Neelavathy R, Muthumary J (2012) Identification and comparative studies of different volatile fractions from Monochaetia kansensis by GCMS. Glob J Pharmacol 6:65–71Google Scholar
  61. 61.
    Ragazzi A, Moricca S, Capretti P et al (2001) Endophytic fungi in Quercus cerris: isolation frequency in relation to phenological phase, tree health and the organ affected. Phytopathol Mediterr 40:165–171Google Scholar
  62. 62.
    Witzell J, Martin JA, Blumenstein K (2014) Ecological Aspects of Endophyte-Based Biocontrol of Forest Diseases. In Advances in Endophytic Research, eds. VC Verma, AC Gange, pp. 321–33Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Boris Jakuschkin
    • 1
  • Virgil Fievet
    • 1
  • Loïc Schwaller
    • 2
    • 3
  • Thomas Fort
    • 1
  • Cécile Robin
    • 1
  • Corinne Vacher
    • 1
    Email author
  1. 1.BIOGECO, INRAUniversity of BordeauxBordeauxFrance
  2. 2.AgroParisTechUMR 518 MIAParisFrance
  3. 3.INRA, UMR 518 MIAParisFrance

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