Bacterial, archaeal and micro-eukaryotic communities characterize a disease-suppressive or conducive soil and a cultivar resistant or susceptible to common scab
Language English Country Great Britain, England Media electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
31619759
PubMed Central
PMC6796001
DOI
10.1038/s41598-019-51570-6
PII: 10.1038/s41598-019-51570-6
Knihovny.cz E-resources
- MeSH
- Actinobacteria classification genetics growth & development pathogenicity MeSH
- Archaea classification genetics growth & development pathogenicity MeSH
- Chloroflexi classification genetics growth & development pathogenicity MeSH
- Nitrogen metabolism pharmacology MeSH
- Eukaryotic Cells metabolism MeSH
- Virulence Factors genetics metabolism MeSH
- Genotyping Techniques MeSH
- Microbiota genetics MeSH
- Disease Susceptibility immunology MeSH
- Plant Diseases immunology microbiology MeSH
- Disease Resistance drug effects MeSH
- Proteobacteria classification genetics growth & development pathogenicity MeSH
- Soil Microbiology * MeSH
- RNA, Ribosomal, 16S genetics MeSH
- RNA, Ribosomal, 18S genetics MeSH
- Sulfur metabolism pharmacology MeSH
- Solanum tuberosum drug effects immunology microbiology MeSH
- Iron metabolism pharmacology MeSH
- Crops, Agricultural MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Nitrogen MeSH
- Virulence Factors MeSH
- RNA, Ribosomal, 16S MeSH
- RNA, Ribosomal, 18S MeSH
- Sulfur MeSH
- Iron MeSH
Control of common scab disease can be reached by resistant cultivars or suppressive soils. Both mechanisms are likely to translate into particular potato microbiome profiles, but the relative importance of each is not known. Here, microbiomes of bulk and tuberosphere soil and of potato periderm were studied in one resistant and one susceptible cultivar grown in a conducive and a suppressive field. Disease severity was suppressed similarly by both means yet, the copy numbers of txtB gene (coding for a pathogenicity determinant) were similar in both soils but higher in periderms of the susceptible cultivar from conducive soil. Illumina sequencing of 16S rRNA genes for bacteria (completed by 16S rRNA microarray approach) and archaea, and of 18S rRNA genes for micro-eukarytes showed that in bacteria, the more important was the effect of cultivar and diversity decreased from resistant cultivar to bulk soil to susceptible cultivar. The major changes occurred in proportions of Actinobacteria, Chloroflexi, and Proteobacteria. In archaea and micro-eukaryotes, differences were primarily due to the suppressive and conducive soil. The effect of soil suppressiveness × cultivar resistance depended on the microbial community considered, but differed also with respect to soil and plant nutrient contents particularly in N, S and Fe.
See more in PubMed
Baker, K. & Cook, R. J. Biological control of plant pathogens. (W.H. Freeman and Co., 1974).
Janvier C, et al. Soil health through soil disease suppression: Which strategy from descriptors to indicators? Soil Biol. Biochem. 2007;39:1–23. doi: 10.1016/j.soilbio.2006.07.001. DOI
Kyselková, M. & Moënne-Loccoz, Y. Pseudomonas and other microbes in disease-suppressive soils. in Organic fertilisation, soil quality and human health, sustainable agriculture reviews 9 (ed. Lichtfouse, E.) 9, 93–140 (Springer Science + Business Media B.V., 2012).
Kinkel LL, Bakker MG, Schlatter DC. A coevolutionary framework for managing disease-suppressive soils. Annu. Rev. Phytopathol. 2011;49:47–67. doi: 10.1146/annurev-phyto-072910-095232. PubMed DOI
Lorang JM, Liu D, Anderson NA, Schottel JL. Identification of potato scab inducing and suppressive species of Streptomyces. Phytopathology. 1995;85:261–268. doi: 10.1094/Phyto-85-261. DOI
Meng Q, Yin J, Rosenzweig N, Douches D, Hao JJ. Culture-based assessment of microbial communities in soil suppressive to potato common scab. Plant Dis. 2012;96:712–717. doi: 10.1094/PDIS-05-11-0441. PubMed DOI
Rosenzweig N, Tiedje JM, Quensen JF, Meng Q, Hao JJ. Microbial communities associated with potato common scab-suppressive soil determined by pyrosequencing analyses. Plant Dis. 2012;96:718–725. doi: 10.1094/PDIS-07-11-0571. PubMed DOI
Braun SR, Endelman JB, Haynes KG, Jansky SH. Quantitative trait loci for resistance to common scab and cold-induced sweetening in diploid potato. Plant Genome. 2017;10:1–9. doi: 10.3835/plantgenome2016.10.0110. PubMed DOI
Kobayashi A, Kobayashi YO, Someya N, Ikeda S. Community analysis of root- and tuber-associated bacteria in field-grown potato plants harboring different resistance levels against common scab. Microbes Environ. 2015;30:301–309. doi: 10.1264/jsme2.ME15109. PubMed DOI PMC
Krištůfek V, Diviš J, Omelka M, Kopecký J, Sagová-Marečková M. Site, year and cultivar effects on relationships between periderm nutrient contents and common scab severity. Am. J. Potato Res. 2015;92:473–482. doi: 10.1007/s12230-015-9456-6. DOI
Sagova-Mareckova M, et al. Determination of factors associated with natural soil suppressivity to potato common scab. PLoS One. 2015;10:1–13. doi: 10.1371/journal.pone.0116291. PubMed DOI PMC
Sagova-Mareckova M, Omelka M, Kopecky J. Sequential analysis of soil factors related to common scab of potatoes. FEMS Microbiol. Ecol. 2017;93:fiw201. doi: 10.1093/femsec/fiw201. PubMed DOI
Sarikhani E, Sagova-Mareckova M, Omelka M, Kopecky J. The effect of peat and iron supplements on the severity of potato common scab and bacterial community in tuberosphere soil. FEMS Microbiol. Ecol. 2017;93:fiw206. doi: 10.1093/femsec/fiw206. PubMed DOI
Shi W, et al. The occurrence of potato common scab correlates with the community composition and function of the geocaulosphere soil microbiome. Microbiome. 2019;7:1–18. doi: 10.1186/s40168-018-0604-3. PubMed DOI PMC
Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: Puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–176. doi: 10.1016/j.tplants.2018.10.011. PubMed DOI
Zahn G, Wagai R, Yonemura S. The effects of amoebal bacterivory on carbon and nitrogen dynamics depend on temperature and soil structure interactions. Soil Biol. Biochem. 2016;94:133–137. doi: 10.1016/j.soilbio.2015.11.021. DOI
Mendes R, Garbeva P, Raaijmakers JM. The rhizosphere microbiome: Significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 2013;37:634–663. doi: 10.1111/1574-6976.12028. PubMed DOI
Taffner J, et al. What is the role of Archaea in plants? New insights from the vegetation of alpine bogs. mSphere. 2018;3:e00122–18. doi: 10.1128/mSphere.00122-18. PubMed DOI PMC
Mendes R, et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science (80-.). 2011;332:1097–1100. doi: 10.1126/science.1203980. PubMed DOI
Krištůfek V, Diviš J, Dostálková I, Kalčík J. Accumulation of mineral elements in tuber periderm of potato cultivars differing in susceptibility to common scab. Potato Res. 2000;43:107–114. doi: 10.1007/BF02357951. DOI
Edgar RC. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods. 2013;10:996–998. doi: 10.1038/nmeth.2604. PubMed DOI
Paliy O, Agans R. Application of phylogenetic microarrays to interrogation of human microbiota. FEMS Microbiol. Ecol. 2012;79:2–11. doi: 10.1111/j.1574-6941.2011.01222.x. PubMed DOI PMC
Kyselková M, et al. Comparison of rhizobacterial community composition in soil suppressive or conducive to tobacco black root rot disease. ISME J. 2009;3:1127–38. doi: 10.1038/ismej.2009.61. PubMed DOI
Kyselková M, et al. Evaluation of rhizobacterial indicators of tobacco black root rot suppressiveness in farmers’ fields. Environ. Microbiol. Rep. 2014;6:346–53. doi: 10.1111/1758-2229.12131. PubMed DOI
Bouffaud M-L, et al. Is diversification history of maize influencing selection of soil bacteria by roots? Mol. Ecol. 2012;21:195–206. doi: 10.1111/j.1365-294X.2011.05359.x. PubMed DOI
Brader G, Compant S, Mitter B, Trognitz F, Sessitsch A. Metabolic potential of endophytic bacteria. Curr. Opin. Biotechnol. 2014;27:30–7. doi: 10.1016/j.copbio.2013.09.012. PubMed DOI PMC
Uroz S, et al. Specific impacts of beech and Norway spruce on the structure and diversity of the rhizosphere and soil microbial communities. Sci. Rep. 2016;6:1–11. doi: 10.1038/srep27756. PubMed DOI PMC
Ditt RF, Nester EW, Comai L. Plant gene expression response to Agrobacterium tumefaciens. Proc. Natl. Acad. Sci. 2002;98:10954–10959. doi: 10.1073/pnas.191383498. PubMed DOI PMC
Loudon AH, et al. Interactions between amphibians’ symbiotic bacteria cause the production of emergent anti-fungal metabolites. Front. Microbiol. 2014;5:1–8. doi: 10.3389/fmicb.2014.00441. PubMed DOI PMC
Tomihama T, et al. Rice bran amendment suppresses potato common scab by increasing antagonistic bacterial community levels in the rhizosphere. Phytopathology. 2016;106:719–728. doi: 10.1094/PHYTO-12-15-0322-R. PubMed DOI
Sanguin H, et al. Potential of a 16S rRNA-based taxonomic microarray for analyzing the rhizosphere effects of maize on Agrobacterium spp. and bacterial communities. Appl. Environ. Microbiol. 2006;72:4302–12. doi: 10.1128/AEM.02686-05. PubMed DOI PMC
Donn S, et al. Rhizosphere microbial communities associated with Rhizoctonia damage at the field and disease patch scale. Appl. Soil Ecol. 2014;78:37–47. doi: 10.1016/j.apsoil.2014.02.001. DOI
Breidenbach B, Blaser MB, Klose M, Conrad R. Crop rotation of flooded rice with upland maize impacts the resident and active methanogenic microbial community. Environ. Microbiol. 2016;18:2868–2885. doi: 10.1111/1462-2920.13041. PubMed DOI
Seppey CVW, et al. Distribution patterns of soil microbial eukaryotes suggests widespread algivory by phagotrophic protists as an alternative pathway for nutrient cycling. Soil Biol. Biochem. 2017;112:68–76. doi: 10.1016/j.soilbio.2017.05.002. DOI
Abdallah RZ, Wegner C-E, Liesack W. Community transcriptomics reveals drainage effects on paddy soil microbiome across all three domains of life. Soil Biol. Biochem. 2019;132:131–142. doi: 10.1016/j.soilbio.2019.01.023. DOI
Jousset Alexandre. Rhizotrophs: Plant Growth Promotion to Bioremediation. Singapore: Springer Singapore; 2017. Application of Protists to Improve Plant Growth in Sustainable Agriculture; pp. 263–273.
Fiore-Donno AM, Weinert J, Wubet T, Bonkowski M. Metacommunity analysis of amoeboid protists in grassland soils. Sci. Rep. 2016;6:1–13. doi: 10.1038/srep19068. PubMed DOI PMC
Bonanomi G, et al. Organic farming induces changes in soil microbiota that affect agro-ecosystem functions. Soil Biol. Biochem. 2016;103:327–336. doi: 10.1016/j.soilbio.2016.09.005. DOI
Barnett BA, Holm DG, Koym JW, Wilson RG, Manter DK. Site and clone effects on the potato root-associated core microbiome and its relationship to tuber yield and nutrients. Am. J. Potato Res. 2015;92:1–9. doi: 10.1007/s12230-014-9405-9. DOI
Inceoǧlu Ö, Salles JF, van Elsas JD. Soil and cultivar type shape the bacterial community in the potato rhizosphere. Microb. Ecol. 2012;63:460–470. doi: 10.1007/s00248-011-9930-8. PubMed DOI PMC
Lazarovits G, Hill J, Patterson G, Conn KL, Crump NS. Edaphic soil levels of mineral nutrients, pH, organic matter, and cationic exchange capacity in the geocaulosphere associated with potato common scab. Phytopathology. 2007;97:1071–1082. doi: 10.1094/PHYTO-97-9-1071. PubMed DOI
Lacey MJ, Wilson CR. Relationship of common scab incidence of potatoes grown in Tasmanian ferrosol soils with pH, exchangeable cations and other chemical properties of those soils. J. Phytopathol. 2001;149:679–683. doi: 10.1046/j.1439-0434.2001.00693.x. DOI
Jauri PV, Altier N, Pérez CA, Kinkel L. Cropping History Effects on Pathogen Suppressive and Signaling Dynamics in Streptomyces Communities. Phytobiomes J. 2018;2:14–23. doi: 10.1094/PBIOMES-05-17-0024-R. DOI
Schlatter D, Kinkel L, Thomashow L, Weller D, Paulitz T. Disease suppressive soils: New insights from the soil microbiome. Phytopathology. 2017;107:1284–1297. doi: 10.1094/PHYTO-03-17-0111-RVW. PubMed DOI
Wenzl H, Demel J. Bildskalen für die Beurteilung von Kartoffelschorf und Rhizoctonia-Pocken. Der Pflanzenarzt. 1967;20:77–78.
Sagova-Mareckova M, et al. Innovative methods for soil DNA purification tested in soils with widely differing characteristics. Appl. Environ. Microbiol. 2008;74:2902–2907. doi: 10.1128/AEM.02161-07. PubMed DOI PMC
Gloeckner Volker, Hentschel Ute, Ereskovsky Alexander V., Schmitt Susanne. Unique and species-specific microbial communities in Oscarella lobularis and other Mediterranean Oscarella species (Porifera: Homoscleromorpha) Marine Biology. 2012;160(4):781–791. doi: 10.1007/s00227-012-2133-0. DOI
Muyzer G, De Waal EC, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 1993;59:695–700. PubMed PMC
Stach JEM, Maldonado LA, Ward AC, Goodfellow M, Bull AT. New primers for the class Actinobacteria: Application to marine and terrestrial environments. Environ. Microbiol. 2003;5:828–841. doi: 10.1046/j.1462-2920.2003.00483.x. PubMed DOI
Qu X, Wanner LA, Christ BJ. Using the txtAB operon to quantify pathogenic Streptomyces in potato tubers and soil. Phytopathology. 2008;98:405–12. doi: 10.1094/PHYTO-98-4-0405. PubMed DOI
Franke-Whittle IH, Klammer SH, Insam H. Design and application of an oligonucleotide microarray for the investigation of compost microbial communities. J. Microbiol. Methods. 2005;62:37–56. doi: 10.1016/j.mimet.2005.01.008. PubMed DOI
Greuter D, Loy A, Horn M, Rattei T. probeBase–an online resource for rRNA-targeted oligonucleotide probes and primers: new features 2016. Nucleic Acids Res. 2016;44:D586–9. doi: 10.1093/nar/gkv1232. PubMed DOI PMC
Ludwig W, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71. doi: 10.1093/nar/gkh293. PubMed DOI PMC
Sanguin H, et al. Development and validation of a prototype 16S rRNA-based taxonomic microarray for Alphaproteobacteria. Environ. Microbiol. 2006;8:289–307. doi: 10.1111/j.1462-2920.2005.00895.x. PubMed DOI
Quast C, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6. doi: 10.1093/nar/gks1219. PubMed DOI PMC
Bruce KD, et al. Amplification of DNA from native populations of soil bacteria by using the polymerase chain reaction. Appl. Environ. Microbiol. 1992;58:3413–6. PubMed PMC
Stralis-Pavese N, et al. Optimization of diagnostic microarray for application in analysing landfill methanotroph communities under different plant covers. Environ. Microbiol. 2004;6:347–63. doi: 10.1111/j.1462-2920.2004.00582.x. PubMed DOI
Fehske H., Schneider R., Weiße A., editors. Computational Many-Particle Physics. Berlin, Heidelberg: Springer Berlin Heidelberg; 2008.
Caporaso JG, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA. 2011;108:4516–4522. doi: 10.1073/pnas.1000080107. PubMed DOI PMC
Takahashi S, Tomita J, Nishioka K, Hisada T, Nishijima M. Development of a prokaryotic universal primer for simultaneous analysis of Bacteria and Archaea using next-generation sequencing. PLoS One. 2014;9:e105592. doi: 10.1371/journal.pone.0105592. PubMed DOI PMC
Amaral-Zettler LA, McCliment EA, Ducklow HW, Huse SM. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS One. 2009;4:e6372. doi: 10.1371/journal.pone.0006372. PubMed DOI PMC
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. doi: 10.7717/peerj.2584. PubMed DOI PMC
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 2013;79:5112–5120. doi: 10.1128/AEM.01043-13. PubMed DOI PMC
Schloss PD, et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 2009;75:7537–7541. doi: 10.1128/AEM.01541-09. PubMed DOI PMC
Yilmaz P, et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:D643–D648. doi: 10.1093/nar/gkt1209. PubMed DOI PMC
Yue JC, Clayton MK. A similarity measure based on species proportions. Commun. Stat. - Theory Methods. 2005;34:2123–2131. doi: 10.1080/STA-200066418. DOI
Martin AP. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl. Environ. Microbiol. 2002;68:3673–82. doi: 10.1128/AEM.68.8.3673-3682.2002. PubMed DOI PMC
White JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 2009;5:e1000352. doi: 10.1371/journal.pcbi.1000352. PubMed DOI PMC
McArdle BH, Anderson MJ. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology. 2001;82:290–297. doi: 10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2. DOI
Venables, W. N. & Ripley, B. D. Modern applied statistics with S. (Springer, 2002).