Inhibitory interaction networks among coevolved Streptomyces populations from prairie soils
Autoři:
Daniel C. Schlatter aff001; Zewei Song aff001; Patricia Vaz-Jauri aff002; Linda L. Kinkel aff001
Působiště autorů:
Department of Plant Pathology, University of Minnesota, Saint Paul, MN, United States of America
aff001; Clement Estable Biological Research Institute, Avenida Italia, Montevideo, Uruguay
aff002
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0223779
Souhrn
Soil microbes live within highly complex communities, where community composition, function, and evolution are the product of diverse interactions among community members. Analysis of the complex networks of interactions within communities has the potential to shed light on community stability, functioning, and evolution. However, we have little understanding of the variation in interaction networks among coevolved soil populations. We evaluated networks of antibiotic inhibitory interactions among sympatric Streptomyces communities from prairie soil. Inhibition networks differed significantly in key network characteristics from expectations under null models, largely reflecting variation among Streptomyces in the number of sympatric populations that they inhibited. Moreover, networks of inhibitory interactions within Streptomyces communities differed significantly from each other, suggesting unique network structures among soil communities from different locations. Analyses of tri-partite interactions (triads) showed that some triads were significantly over- or under- represented, and that communities differed in ‘preferred’ triads. These results suggest that local processes generate distinct structures among sympatric Streptomyces inhibition networks in soil. Understanding the properties of microbial interaction networks that generate competitive and functional capacities of soil communities will shed light on the ecological and coevolutionary history of sympatric populations, and provide a foundation for more effective management of inhibitory capacities of soil microbial communities.
Klíčová slova:
Antibiotic resistance – Antibiotics – Community ecology – Community structure – Network analysis – Species interactions – Streptomyces – Network motifs
Zdroje
1. Torsvik V, Øvreås L (2002) Microbial diversity and function in soil: from genes to ecosystems. Curr Opin Microbiol 5:240–245 12057676
2. Czaran TL (2002) Chemical warfare between microbes promotes biodiversity. Proc Natl Acad Sci 99:786–790. doi: 10.1073/pnas.012399899 11792831
3. Kinkel LL, Schlatter DC, Xiao K, Baines AD (2014) Sympatric inhibition and niche differentiation suggest alternative coevolutionary trajectories among Streptomycetes. ISME J 8:249–256. doi: 10.1038/ismej.2013.175 24152720
4. Cordero OX, Wildschutte H, Kirkup B, Proehl S. Ngo L, Hussain F, et al (2012) Ecological Populations of Bacteria Act as Socially Cohesive Units of Antibiotic Production and Resistance. Science 337:1228–1231. doi: 10.1126/science.1219385 22955834
5. Lawrence D, Fiegna F, Behrends V, Bundy JG, Phillimore AB, Bell T, et al (2012) Species Interactions Alter Evolutionary Responses to a Novel Environment. PLoS Biol 10:e1001330. doi: 10.1371/journal.pbio.1001330 22615541
6. Fiegna F, Moreno-Letelier A, Bell T, Barraclough TG (2015) Evolution of species interactions determines microbial community productivity in new environments. ISME J 9:1235–1245. doi: 10.1038/ismej.2014.215 25387206
7. Traxler MF, Watrous JD, Alexandrov T, Dorrestein PC, Kolter R (2013) Interspecies Interactions Stimulate Diversification of the Streptomyces coelicolor Secreted Metabolome. mBio 4:e00459-13–e00459-13. doi: 10.1128/mBio.00459-13 23963177
8. Harrison E, Hall JPJ, Paterson S, Spiers AJ, Brockhurst MA (2017) Conflicting selection alters the trajectory of molecular evolution in a tripartite bacteria-plasmid-phage interaction. Mol Ecol 26:2757–2764. doi: 10.1111/mec.14080 28247474
9. Vetsigian K, Jajoo R, Kishony R (2011) Structure and Evolution of Streptomyces Interaction Networks in Soil and In Silico. PLoS Biol 9:e1001184. doi: 10.1371/journal.pbio.1001184 22039352
10. Slattery M, Rajbhandari I, Wesson K (2001) Competition-Mediated Antibiotic Induction in the Marine Bacterium Streptomyces tenjimariensis. Microb Ecol 41:90–96. doi: 10.1007/s002480000084 12032613
11. Vaz Jauri P, Kinkel LL (2014) Nutrient overlap, genetic relatedness and spatial origin influence interaction-mediated shifts in inhibitory phenotype among Streptomyces spp. FEMS Microbiol Ecol 90:264–275. doi: 10.1111/1574-6941.12389 25098381
12. Pérez-Gutiérrez R-A, López-Ramírez V, Islas Á, Alcaraz LD, Hernandez-Gonzalez I, Olivera BC, et al (2013) Antagonism influences assembly of a Bacillus guild in a local community and is depicted as a food-chain network. ISME J 7:487–497. doi: 10.1038/ismej.2012.119 23096405
13. Prasad S, Manasa P, Buddhi S, Singh SM, Shivaji S (2011) Antagonistic interaction networks among bacteria from a cold soil environment. FEMS Microbiol Ecol 78:376–385. doi: 10.1111/j.1574-6941.2011.01171.x 22092175
14. Kelsic ED, Zhao J, Vetsigian K, Kishony R (2015) Counteraction of antibiotic production and degradation stabilizes microbial communities. Nature 521:516–519. doi: 10.1038/nature14485 25992546
15. Gurney J, Aldakak L, Betts A, Gougat-Barbera C, Poisot T, Kaltz O, et al (2017) Network structure and local adaptation in co-evolving bacteria-phage interactions. Mol Ecol 26:1764–1777. doi: 10.1111/mec.14008 28092408
16. Kurvers RHJM, Krause J, Croft DP, Wilson ADM, Wolf M (2014) The evolutionary and ecological consequences of animal social networks: emerging issues. Trends Ecol Evol 29:326–335. doi: 10.1016/j.tree.2014.04.002 24792356
17. Pinter-Wollman N, Hobson EA, Smith JE, Edelman J, Shizuka D, de Silva A, et al (2014) The dynamics of animal social networks: analytical, conceptual, and theoretical advances. Behav Ecol 25:242–255. https://doi.org/10.1093/beheco/art047
18. Proulx S, Promislow D, Phillips P (2005) Network thinking in ecology and evolution. Trends Ecol Evol 20:345–353. doi: 10.1016/j.tree.2005.04.004 16701391
19. Watts DJ, Strogatz SH (1998) Collective dynamics of “small-world” networks. Nature 393:440–442. doi: 10.1038/30918 9623998
20. Dubitzky W, Wolkenhauer O, Cho K-H, Yokota H (2013) Encyclopedia of systems biology. Springer Reference, New York
21. Milo R (2002) Network Motifs: Simple Building Blocks of Complex Networks. Science 298:824–827. doi: 10.1126/science.298.5594.824 12399590
22. Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450–461. doi: 10.1038/nrg2102 17510665
23. Mangan S, Alon U (2003) Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci 100:11980–11985. doi: 10.1073/pnas.2133841100 14530388
24. Stouffer DB (2010) Scaling from individuals to networks in food webs. Funct Ecol 24:44–51. https://doi.org/10.1111/j.1365-2435.2009.01644.x
25. Shizuka D, McDonald DB (2015) The network motif architecture of dominance hierarchies. J R Soc Interface 12:. https://doi.org/10.1098/rsif.2015.0080
26. Trpevski I, Dimitrova T, Boshkovski T, Stikov N, Kocarev L (2016) Graphlet characteristics in directed networks. Sci Rep 6:. https://doi.org/10.1038/srep37057
27. Poudel R, Jumpponen A, Schlatter DC, Paulitz TC, Gardener BB, Kinkel LL, et al (2016) Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management. Phytopathology 106:1083–1096. doi: 10.1094/PHYTO-02-16-0058-FI 27482625
28. Barberán A, Bates ST, Casamayor EO, Fierer N (2011) Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 6:343–351. doi: 10.1038/ismej.2011.119 21900968
29. Bakker MG, Schlatter DC, Otto-Hanson L, Kinkel LL (2014) Diffuse symbioses: roles of plant-plant, plant-microbe and microbe-microbe interactions in structuring the soil microbiome. Mol Ecol 23:1571–1583. doi: 10.1111/mec.12571 24148029
30. Layeghifard M, Hwang DM, Guttman DS (2017) Disentangling Interactions in the Microbiome: A Network Perspective. Trends Microbiol 25:217–228. doi: 10.1016/j.tim.2016.11.008 27916383
31. Essarioui A, Kistler HC, Kinkel LL (2016) Nutrient use preferences among soil Streptomyces suggest greater resource competition in monoculture than polyculture plant communities. Plant Soil 409:329–343. https://doi.org/10.1007/s11104-016-2968-0
32. Kinkel LL, Schlatter DC, Bakker MG, Arenz BE (2012) Streptomyces competition and co-evolution in relation to plant disease suppression. Res Microbiol 163:490–499. doi: 10.1016/j.resmic.2012.07.005 22922402
33. Seipke RF, Kaltenpoth M, Hutchings MI (2012) Streptomyces as symbionts: an emerging and widespread theme? FEMS Microbiol Rev 36:862–876. doi: 10.1111/j.1574-6976.2011.00313.x 22091965
34. Watve MG, Tickoo R, Jog MM, Bhole BD (2001) How many antibiotics are produced by the genus Streptomyces? Arch Microbiol 176:386–390. doi: 10.1007/s002030100345 11702082
35. Omura S, Ikeda H, Ishikawa J, Hanamoto A, Takahashi C, Shinose M, et al (2001) Genome sequence of an industrial microorganism Streptomyces avermitilis: deducing the ability of producing secondary metabolites. Proc Natl Acad Sci U S A 98:12215–12220. doi: 10.1073/pnas.211433198 11572948
36. Smanski MJ, Schlatter DC, Kinkel LL (2016) Leveraging ecological theory to guide natural product discovery. J Ind Microbiol Biotechnol 43:115–128. doi: 10.1007/s10295-015-1683-9 26434742
37. Kinkel LL, Bakker MG, Schlatter DC (2011) A Coevolutionary Framework for Managing Disease-Suppressive Soils. Annu Rev Phytopathol 49:47–67. doi: 10.1146/annurev-phyto-072910-095232 21639781
38. Cha J-Y, Han S, Hong H-J, Cho H, Kim D, Kwon SK, et al (2016) Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J 10:119–129. doi: 10.1038/ismej.2015.95 26057845
39. Tomihama T, Nishi Y, Mori K, Shirao T, Iida T, Uzuhashi S, et al (2016) Rice Bran Amendment Suppresses Potato Common Scab by Increasing Antagonistic Bacterial Community Levels in the Rhizosphere. Phytopathology 106:719–728. doi: 10.1094/PHYTO-12-15-0322-R 27050572
40. Davelos AL, Kinkel LL, Samac DA (2004) Spatial Variation in Frequency and Intensity of Antibiotic Interactions among Streptomycetes from Prairie Soil. Appl Environ Microbiol 70:1051–1058. doi: 10.1128/AEM.70.2.1051-1058.2004 14766588
41. Davelos Baines AL, Xiao K, Kinkel LL (2007) Lack of correspondence between genetic and phenotypic groups amongst soil-borne streptomycetes. FEMS Microbiol Ecol 59:564–575. doi: 10.1111/j.1574-6941.2006.00231.x 17381515
42. R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
43. West DB (2001) Introduction to graph theory, 2nd ed. Prentice Hall, Upper Saddle River, N.J
44. Newman MEJ, Moore C, Watts DJ (2000) Mean-Field Solution of the Small-World Network Model. Phys Rev Lett 84:3201–3204. doi: 10.1103/PhysRevLett.84.3201 11019047
45. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems:1695
46. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57:289–300
47. Sarajlic A, Malod-Dognin N, Yaveroglu ON, Przulj N (2016) Graphlet-based Characterization of Directed Networks. Sci Rep 6:. https://doi.org/10.1038/srep35098
48. Yaveroğlu ÖN, Malod-Dognin N, Davis D, Levnajic Z, Janjic V, Karapandza R, et al (2015) Revealing the Hidden Language of Complex Networks. Sci Rep 4:. https://doi.org/10.1038/srep04547
49. D’Costa VM (2006) Sampling the Antibiotic Resistome. Science 311:374–377. doi: 10.1126/science.1120800 16424339
50. Leisner JJ, Jørgensen NOG, Middelboe M (2016) Predation and selection for antibiotic resistance in natural environments. Evol Appl 9:427–434. doi: 10.1111/eva.12353 26989434
51. Vetsigian K (2017) Diverse modes of eco-evolutionary dynamics in communities of antibiotic-producing microorganisms. Nat Ecol Evol 1:0189. https://doi.org/10.1038/s41559-017-0189
52. Barabási A-L, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113. doi: 10.1038/nrg1272 14735121
53. Schlatter DC, Kinkel LL (2014) Global biogeography of Streptomyces antibiotic inhibition, resistance, and resource use. FEMS Microbiol Ecol 88:386–397. doi: 10.1111/1574-6941.12307 24580017
54. Schlatter DC, Kinkel LL (2015) Do tradeoffs structure antibiotic inhibition, resistance, and resource use among soil-borne Streptomyces? BMC Evol Biol 15:. https://doi.org/10.1186/s12862-015-0470-6
55. Garbeva P, Tyc O, Remus-Emsermann MNP, van der Wal A, Vos M, Silby M, et al (2011) No Apparent Costs for Facultative Antibiotic Production by the Soil Bacterium Pseudomonas fluorescens Pf0-1. PLoS ONE 6:e27266. doi: 10.1371/journal.pone.0027266 22110622
56. Vaz Jauri P, Bakker MG, Salomon CE, Kinkel LL (2013) Subinhibitory Antibiotic Concentrations Mediate Nutrient Use and Competition among Soil Streptomyces. PLoS ONE 8:e81064. doi: 10.1371/journal.pone.0081064 24339897
57. Melnyk AH, Wong A, Kassen R (2015) The fitness costs of antibiotic resistance mutations. Evol Appl 8:273–283. doi: 10.1111/eva.12196 25861385
58. Kirkup BC, Riley MA (2004) Antibiotic-mediated antagonism leads to a bacterial game of rock–paper–scissors in vivo. Nature 428:412–414. doi: 10.1038/nature02429 15042087
59. Kerr B, Riley MA, Feldman MW, Bohannan BJM (2002) Local dispersal promotes biodiversity in a real-life game of rock–paper–scissors. Nature 418:171–174. doi: 10.1038/nature00823 12110887
60. Ross-Gillespie A, Gardner A, Buckling A, West AS, Griffin AS (2009) Density Dependence and Cooporation: Theory and a Test With Bacteria. Evolution 63:2315–2325. doi: 10.1111/j.1558-5646.2009.00723.x 19453724
61. Wiggins BElizabeth, Kinkel LL (2005) Green manures and crop sequences influence alfalfa root rot and pathogen inhibitory activity among soil-borne streptomycetes. Plant Soil 268:271–283. https://doi.org/10.1007/s11104-004-0300-x
62. Wiggins BE, Kinkel LL (2005) Green Manures and Crop Sequences Influence Potato Diseases and Pathogen Inhibitory Activity of Indigenous Streptomycetes. Phytopathology 95:178–185. doi: 10.1094/PHYTO-95-0178 18943988
63. Schlatter D, Kinkel L, Thomashow L, Weller D, Paulitz T (2017) Disease Suppressive Soils: New Insights from the Soil Microbiome. Phytopathology 107:1284–1297. doi: 10.1094/PHYTO-03-17-0111-RVW 28650266
64. Agler MT, Ruhe J, Kroll S, Morhenn C, Kim ST, Weigel D, 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 26788878
65. Ojiambo PS, Scherm H (2006) Biological and Application-Oriented Factors Influencing Plant Disease Suppression by Biological Control: A Meta-Analytical Review. Phytopathology 96:1168–1174. doi: 10.1094/PHYTO-96-1168 18943952
66. Harcombe WR, Riehl WJ, Dukovski I, Granger BR, Betts A, Lang AH, et al (2014) Metabolic Resource Allocation in Individual Microbes Determines Ecosystem Interactions and Spatial Dynamics. Cell Rep 7:1104–1115. doi: 10.1016/j.celrep.2014.03.070 24794435
67. Litchman E, Edwards KF, Klausmeier CA (2015) Microbial resource utilization traits and trade-offs: implications for community structure, functioning, and biogeochemical impacts at present and in the future. Front Microbiol 06: https://doi.org/10.3389/fmicb.2015.00254
68. Ponomarova O, Patil KR (2015) Metabolic interactions in microbial communities: untangling the Gordian knot. Curr Opin Microbiol 27:37–44. doi: 10.1016/j.mib.2015.06.014 26207681
69. Pilosof S, Porter MA, Pascual M, Kéfi S (2017) The multilayer nature of ecological networks. Nat Ecol Evol 1:0101. https://doi.org/10.1038/s41559-017-0101
Článek vyšel v časopise
PLOS One
2019 Číslo 10
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Correction: Low dose naltrexone: Effects on medication in rheumatoid and seropositive arthritis. A nationwide register-based controlled quasi-experimental before-after study
- Combining CDK4/6 inhibitors ribociclib and palbociclib with cytotoxic agents does not enhance cytotoxicity
- Experimentally validated simulation of coronary stents considering different dogboning ratios and asymmetric stent positioning
- Risk factors associated with IgA vasculitis with nephritis (Henoch–Schönlein purpura nephritis) progressing to unfavorable outcomes: A meta-analysis
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy