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Rapid evolution of a novel protective symbiont into keystone taxon in Caenorhabditis elegans microbiota

  • Samuel, B. S., Rowedder, H., Braendle, C., Félix, M. A. & Ruvkun, G. Caenorhabditis elegans responses to bacteria from its natural habitats. Proc. Natl. Acad. Sci. USA 113, E3941–E3949 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Oliver, K. M., Smith, A. H. & Russell, J. A. Defensive symbiosis in the real world: Advancing ecological studies of heritable, protective bacteria in aphids and beyond. Funct. Ecol. 28, 341–355 (2014).

    Google Scholar 

  • King, K. C. Defensive symbionts. Curr. Biol. 29, R78–R80 (2019).

    CAS 
    PubMed 

    Google Scholar 

  • Foster, K. R., Schluter, J., Coyte, K. Z. & Rakoff-Nahoum, S. The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ford, S. A., Kao, D., Williams, D. & King, K. C. Microbe-mediated host defence drives the evolution of reduced pathogen virulence. Nat. Commun. 7, 13430 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Litvak, Y. et al. Commensal Enterobacteriaceae protect against Salmonella colonization through oxygen competition. Cell Host Microbe 25, 128–139 (2019).

    CAS 
    PubMed 

    Google Scholar 

  • Pimentel, A. C., Cesar, C. S., Martins, M. & Cogni, R. The antiviral effects of the symbiont bacteria Wolbachia in insects. Front. Immunol. 11, 626329 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Becker, M. H., Brucker, R. M., Schwantes, C. R., Harris, R. N. & Minbiole, K. P. C. The bacterially produced metabolite violacein is associated with survival of amphibians infected with a lethal fungus. Appl. Environ. Microbiol. 75, 6635–6638 (2009).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bates, K. A., Bolton, J. S. & King, K. C. A globally ubiquitous symbiont can drive experimental host evolution. Mol. Ecol. 30, 3882–3892 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • Dahan, D., Preston, G. M., Sealey, J. & King, K. C. Impacts of a novel defensive symbiosis on the nematode host microbiome. BMC Microbiol. 20, 1–10 (2020).

    Google Scholar 

  • Banerjee, S., Schlaeppi, K. & van der Heijden, M. G. A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • Zheng, Y. et al. Exploring biocontrol agents from microbial keystone taxa associated to suppressive soil: A new attempt for a biocontrol strategy. Front. Plant Sci. 12, 655673 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Tudela, H., Claus, S. P. & Saleh, M. Next generation microbiome research: Identification of keystone species in the metabolic regulation of host-gut microbiota interplay. Front. Cell Dev. Biol. 9, 719072 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Mateos-Hernández, L. et al. Anti-tick microbiota vaccine impacts Ixodes ricinus performance during feeding. Vaccine 8, 1–21 (2020).

    Google Scholar 

  • Mateos-Hernández, L. et al. Anti-microbiota vaccines modulate the tick microbiome in a taxon-specific manner. Front. Immunol. 12, 704621 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Dirksen, P. et al. The native microbiome of the nematode Caenorhabditis elegans: Gateway to a new host-microbiome model. BMC Biol. 14, 38 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Berg, M. et al. Assembly of the Caenorhabditis elegans gut microbiota from diverse soil microbial environments. ISME J. 10, 1998–2009 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang, F. et al. Caenorhabditis elegans as a model for microbiome research. Front. Microbiol. 8, 485 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • King, K. C. et al. Rapid evolution of microbe-mediated protection against pathogens in a worm host. ISME J. 10, 1915–1924 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • Röttjers, L. & Faust, K. From hairballs to hypotheses–biological insights from microbial networks. FEMS Microbiol. Rev. 42, 761–780 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hou, Y. et al. Hierarchical microbial functions prediction by graph aggregated embedding. Front. Genet. 11, 608512 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Montalvo-Katz, S., Huang, H., Appel, M. D., Berg, M. & Shapira, M. Association with soil bacteria enhances p38-dependent infection resistance in Caenorhabditis elegans. Infect. Immun. 81, 514–520 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 7, 852–857 (2019).

    Google Scholar 

  • Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).

    Google Scholar 

  • Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).

    CAS 
    PubMed 

    Google Scholar 

  • Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • RStudio Team. RStudio: Integrated Development for R (RStudio, PBC, 2020).

    Google Scholar 

  • Bastian, M., Heymann, S. & Jacomy, M. Gephi: An open-source software for exploring and manipulating networks. Third International AAAI Conference on Weblogs and Social Media (2009).

  • Lhomme, S. NetSwan: Network Strengths and Weaknesses Analysis. R Pack Version (2015).

  • Peschel, S., Müller, C. L., von Mutius, E., Boulesteix, A. L. & Depner, M. NetCoMi: Network construction and comparison for microbiome data in R. Brief Bioinform. 22, bbaa290 (2021).

    PubMed 

    Google Scholar 

  • Kanehisa, M. Goto, S, KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 46, D633–D639 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Lin, H. & Peddada, S. D. Analysis of microbial compositions: A review of normalization and differential abundance analysis. npj Biofilms Microbiomes 6, 60 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Ploner, A. Heatplus: Heatmaps with Row and/or Column Covariates and Colored Clusters. R package version 3.2. (2021).

  • Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948).

  • Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).

    ADS 

    Google Scholar 

  • Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42 (1943).

    Google Scholar 

  • Ford, S. A. & King, K. C. Harnessing the power of defensive microbes: Evolutionary implications in nature and disease control. PLoS Pathog. 12, e1005465 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Gibbons, S. M. Keystone taxa indispensable for microbiome recovery. Nat. Microbiol. 5, 1067–1068 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • Wu-Chuang, A. et al. Thermostable keystone bacteria maintain the functional diversity of the Ixodes scapularis microbiome under heat stress. Microb. Ecol. https://doi.org/10.1007/s00248-021-01929-y (2021).

    Article 
    PubMed 

    Google Scholar 

  • Ford, S. A. & King, K. C. In vivo microbial coevolution favors host protection and plastic downregulation of immunity. Mol. Biol. Evol. 38, 1330–1338 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Gao, Q. et al. The microbial network property as a bio-indicator of antibiotic transmission in the environment. Sci. Total Environ. 758, 143712 (2021).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • de Morais, U. L. A look at the way we look at complex networks’ robustness and resilience. https://arxiv.org/ftp/arxiv/papers/1909/1909.06448.pdf (2017).

  • Carlson, J. M. & Doyle, J. Complexity and robustness. Proc. Natl. Acad. Sci. USA 99, 2538–2545 (2002).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Estrada-Peña, A., Cabezas-Cruz, A. & Obregón, D. Resistance of tick gut microbiome to anti-tick vaccines, pathogen infection and antimicrobial peptides. Pathogens 9, 309 (2020).

    PubMed Central 

    Google Scholar 

  • Neelakanta, G., Sultana, H., Fish, D., Anderson, J. F. & Fikrig, E. Anaplasma phagocytophilum induces Ixodes scapularis ticks to express an antifreeze glycoprotein gene that enhances their survival in the cold. J. Clin. Investig. 120, 3179–3190 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dey, A. K., Gel, Y. R. & Poor, H. V. What network motifs tell us about resilience and reliability of complex networks. Proc. Natl. Acad. Sci. USA 116, 19368–19373 (2019).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. 77, 342–356 (2013).

    Google Scholar 

  • Coyte, K. Z., Rao, C., Rakoff-Nahoum, S. & Foster, K. R. Ecological rules for the assembly of microbiome communities. PLoS Biol. 19, e3001116 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: Networks, competition, and stability. Science 350, 663–666 (2015).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • McLoughlin, K., Schluter, J., Rakoff-Nahoum, S., Smith, A. L. & Foster, K. R. Host selection of microbiota via differential adhesion. Cell Host Microbe 19, 550–559 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • Sheridan, K. J. et al. Ergothioneine biosynthesis and functionality in the opportunistic fungal pathogen, Aspergillus fumigatus. Sci. Rep. 6, 1–17 (2016).

    Google Scholar 

  • Rothfork, J. M. et al. Inactivation of a bacterial virulence pheromone by phagocyte-derived oxidants: New role for the NADPH oxidase in host defense. Proc. Natl. Acad. Sci. USA 101, 13867–13872 (2004).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gaupp, R., Ledala, N. & Somerville, G. A. Staphylococcal response to oxidative stress. Front. Cell. Infect. Microbiol. Microbiol. 2, 33 (2012).

    Google Scholar 

  • Matchado, M. S. et al. Network analysis methods for studying microbial communities: A mini review. Comput. Struct. Biotechnol. J. 19, 2687–2698 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jiang, D. et al. Microbiome multi-omics network analysis: Statistical considerations, limitations, and opportunities. Front. Genet. 10, 995 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gao, C. et al. Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities. Nat. Commun. 13, 3867 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mammeri, M. et al. Cryptosporidium parvum infection depletes butyrate producer bacteria in goat kid microbiome. Front. Microbiol. 16, 548737 (2020).

    Google Scholar 

  • Foo, J. L., Ling, H., Lee, Y. S. & Chang, M. W. Microbiome engineering: Current applications and its future. Biotechnol. J. 12, 1600099 (2017).

  • Inda, M. E., Broset, E., Lu, T. K. & de la Fuente-Nunez, C. Emerging frontiers in microbiome engineering. Trends Immunol. 40, 952–973 (2019).


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