Abstract
As a heritable sequence-specific adaptive immune system, CRISPR–Cas is a powerful force shaping strain diversity in host–virus systems. While the diversity of CRISPR alleles has been explored, the associated structure and dynamics of host–virus interactions have not. We explore the role of CRISPR in mediating the interplay between host–virus interaction structure and eco-evolutionary dynamics in a computational model and compare the results with three empirical datasets from natural systems. We show that the structure of the networks describing who infects whom and the degree to which strains are immune, are respectively modular (containing groups of hosts and viruses that interact strongly) and weighted-nested (specialist hosts are more susceptible to subsets of viruses that in turn also infect the more generalist hosts with many spacers matching many viruses). The dynamic interplay between these networks influences transitions between dynamical regimes of virus diversification and host control. The three empirical systems exhibit weighted-nested immunity networks, a pattern our theory shows is indicative of hosts able to suppress virus diversification. Previously missing from studies of microbial host–pathogen systems, the immunity network plays a key role in the coevolutionary dynamics.
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Data availability
Simulated and empirical network data are available in the dedicated GitHub repository associated with this paper at: https://github.com/Ecological-Complexity-Lab/CRISPR_networks.
Code availability
The code for the simulations and their analysis is available in the dedicated GitHub repository associated with this paper at: https://github.com/Ecological-Complexity-Lab/CRISPR_networks.
References
Labrie, S. J., Samson, J. E. & Moineau, S. Bacteriophage resistance mechanisms. Nat. Rev. Microbiol. 8, 317–327 (2010).
van Houte, S., Buckling, A. & Westra, E. R. Evolutionary ecology of prokaryotic immune mechanisms. Microbiol. Mol. Biol. Rev. 80, 745–763 (2016).
Mayer, A., Mora, T., Rivoire, O. & Walczak, A. M. Diversity of immune strategies explained by adaptation to pathogen statistics. Proc. Natl Acad. Sci. USA 113, 8630–8635 (2016).
Chevallereau, A., Meaden, S., van Houte, S., Westra, E. R. & Rollie, C. The effect of bacterial mutation rate on the evolution of CRISPR–Cas adaptive immunity. Philos. Trans. R. Soc. Lond. B 374, 20180094 (2019).
Gurney, J., Pleška, M. & Levin, B. R. Why put up with immunity when there is resistance: an excursion into the population and evolutionary dynamics of restriction–modification and CRISPR–Cas. Philos. Trans. R. Soc. Lond. B 374, 20180096 (2019).
Weitz, J. S. et al. Phage–bacteria infection networks. Trends Microbiol. 21, 82–91 (2013).
Gurney, J. et al. Network structure and local adaptation in co-evolving bacteria-phage interactions. Mol. Ecol. 26, 1764–1777 (2017).
Fortuna, M. A. et al. Coevolutionary dynamics shape the structure of bacteria-phage infection networks. Evolution 73, 1001–1011 (2019).
Krasnov, B. R. et al. Phylogenetic signal in module composition and species connectivity in compartmentalized host-parasite networks. Am. Nat. 179, 501–511 (2012).
Pilosof, S. et al. Host–parasite network structure is associated with community-level immunogenetic diversity. Nat. Commun. 5, 5172 (2014).
Dallas, T. & Cornelius, E. Co-extinction in a host-parasite network: identifying key hosts for network stability. Sci. Rep. 5, 13185 (2015).
Pilosof, S., Morand, S., Krasnov, B. R. & Nunn, C. L. Potential parasite transmission in multi-host networks based on parasite sharing. PLoS ONE 10, e0117909 (2015).
Vázquez, D. P., Poulin, R., Krasnov, B. R. & Shenbrot, G. I. Species abundance and the distribution of specialization in host–parasite interaction networks. J. Anim. Ecol. 74, 946–955 (2005).
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).
Flores, C. O., Meyer, J. R., Valverde, S., Farr, L. & Weitz, J. S. Statistical structure of host–phage interactions. Proc. Natl Acad. Sci. USA 108, E288–E297 (2011).
Beckett, S. J. & Williams, H. T. P. Coevolutionary diversification creates nested-modular structure in phage–bacteria interaction networks. Interface Focus 3, 20130033 (2013).
He, Q. et al. Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium falciparum. Nat. Commun. 9, 1817 (2018).
Pilosof, S. et al. Competition for hosts modulates vast antigenic diversity to generate persistent strain structure in plasmodium falciparum. PLoS Biol. 17, e3000336 (2019).
van der Oost, J., Westra, E. R., Jackson, R. N. & Wiedenheft, B. Unravelling the structural and mechanistic basis of CRISPR–Cas systems. Nat. Rev. Microbiol. 12, 479–492 (2014).
Childs, L. M., Held, N. L., Young, M. J., Whitaker, R. J. & Weitz, J. S. Multiscale model of CRISPR-induced coevolutionary dynamics: diversification at the interface of Lamarck and Darwin. Evolution 66, 2015–2029 (2012).
Paez-Espino, D. et al. CRISPR immunity drives rapid phage genome evolution in Streptococcus thermophilus. mBio 6, e00262-15 (2015).
van Houte, S. et al. The diversity-generating benefits of a prokaryotic adaptive immune system. Nature 532, 385–388 (2016).
Daly, R. A. et al. Viruses control dominant bacteria colonizing the terrestrial deep biosphere after hydraulic fracturing. Nat. Microbiol. 4, 352–361 (2019).
Pauly, M. D., Bautista, M. A., Black, J. A. & Whitaker, R. J. Diversified local CRISPR–Cas immunity to viruses of Sulfolobus islandicus. Philos. Trans. R. Soc. Lond. B 374, 20180093 (2019).
Childs, L. M., England, W. E., Young, M. J., Weitz, J. S. & Whitaker, R. J. CRISPR-induced distributed immunity in microbial populations. PLoS ONE 9, e101710 (2014).
Almeida-Neto, M. & Ulrich, W. A straightforward computational approach for measuring nestedness using quantitative matrices. Environ. Model. Softw. 26, 173–178 (2011).
Held, N. L., Herrera, A., Cadillo-Quiroz, H. & Whitaker, R. J. CRISPR associated diversity within a population of Sulfolobus islandicus. PLoS ONE 5, e12988 (2010).
England, W. E., Kim, T. & Whitaker, R. J. Metapopulation structure of CRISPR–Cas immunity in Pseudomonas aeruginosa and its viruses. mSystems 3, e00075-18 (2018).
Marvig, R. L., Sommer, L. M., Molin, S. & Johansen, H. K. Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis. Nat. Genet. 47, 57–64 (2015).
Pirnay, J.-P. et al. Pseudomonas aeruginosa population structure revisited. PLoS ONE 4, e7740 (2009).
Hall, A. R., Scanlan, P. D., Morgan, A. D. & Buckling, A. Host–parasite coevolutionary arms races give way to fluctuating selection. Ecol. Lett. 14, 635–642 (2011).
Levin, B. R., Moineau, S., Bushman, M. & Barrangou, R. The population and evolutionary dynamics of phage and bacteria with CRISPR-mediated immunity. PLoS Genet. 9, e1003312 (2013).
Koskella, B. & Brockhurst, M. A. Bacteria–phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol. Rev. 38, 916–931 (2014).
Weissman, J. L. et al. Immune loss as a driver of coexistence during host-phage coevolution. ISME J. 12, 585–597 (2018).
Braga, L. P. P., Soucy, S. M., Amgarten, D. E., da Silva, A. M. & Setubal, J. C. Bacterial diversification in the light of the interactions with phages: the genetic symbionts and their role in ecological speciation. Front. Ecol. Evol. 6, 6 (2018).
Fontaine, C. et al. The ecological and evolutionary implications of merging different types of networks. Ecol. Lett. 14, 1170–1181 (2011).
Gupta, S. & Day, K. P. A strain theory of malaria transmission. Parasitol. Today 10, 476–481 (1994).
Buckee, C. O., Recker, M., Watkins, E. R. & Gupta, S. Role of stochastic processes in maintaining discrete strain structure in antigenically diverse pathogen populations. Proc. Natl Acad. Sci. USA 108, 15504–15509 (2011).
Artzy-Randrup, Y. et al. Population structuring of multi-copy, antigen-encoding genes in Plasmodium falciparum. eLife 1, e00093 (2012).
Zinder, D., Bedford, T., Gupta, S. & Pascual, M. The roles of competition and mutation in shaping antigenic and genetic diversity in influenza. PLoS Pathog. 9, e1003104 (2013).
Song, C., Rohr, R. P. & Saavedra, S. Why are some plant–pollinator networks more nested than others? J. Anim. Ecol. 86, 1417–1424 (2017).
Chabas, H. et al. Evolutionary emergence of infectious diseases in heterogeneous host populations. PLoS Biol. 16, e2006738 (2018).
Iranzo, J., Lobkovsky, A. E., Wolf, Y. I. & Koonin, E. V. Evolutionary dynamics of the prokaryotic adaptive immunity system CRISPR–Cas in an explicit ecological context. J. Bacteriol. 195, 3834–3844 (2013).
Staniczenko, P. P. A., Kopp, J. C. & Allesina, S. The ghost of nestedness in ecological networks. Nat. Commun. 4, 1391 (2013).
Rosvall, M. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl Acad. Sci. USA 105, 1118–1123 (2008).
Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Spec. Top. 178, 13–23 (2010).
Farage, C., Edler, D., Eklöf, A., Rosvall, M. & Pilosof, S. A dynamical perspective to community detection in ecological networks. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2020.04.14.040519v1 (2020).
Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).
Lancichinetti, A. & Fortunato, S. Community detection algorithms: a comparative analysis. Phys. Rev. E 80, 056117 (2009).
Campbell, K. M. et al. Sulfolobus islandicus meta-populations in Yellowstone National Park hot springs. Environ. Microbiol. 19, 2334–2347 (2017).
Bautista, M. A., Black, J. A., Youngblut, N. D. & Whitaker, R. J. Differentiation and structure in Sulfolobus islandicus rod-shaped virus populations. Viruses 9, 120 (2017).
Acknowledgements
We thank W. England and M. Pauly for data processing and collection and Q. He for guidance on the construction of the phylogenetic trees from the model outputs. R.W. acknowledges the support of the Cystic Fibrosis Foundation (no. CFF C2480) and an Allen Distinguished Investigator Award from the Allen Frontiers Institute. M.P. acknowledges the support of the University of Chicago. We are grateful for the access to the computer cluster of the Research Computing Center of the University of Chicago.
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S.P. conceptualized the study, carried out the formal analysis (networks and data analysis, model dynamics, visualizations) and wrote the original manuscript draft. S.A.A.-C. carried out the formal analysis (networks analysis, model simulation) and reviewed and edited the manuscript. T.W. was involved with the software, methodology and formal analysis (model dynamics). T.K. curated and analysed the data and reviewed and edited the manuscript. S.M. was involved with the software, methodology and funding acquisition, and reviewed and edited the manuscript. R.W. conceptualized the study, curated and analysed the data, acquired the funding, supervised the study and reviewed and edited the manuscript. M.P. conceptualized the study, carried out the formal analysis (model dynamics), acquired the funding and prepared the original manuscript draft.
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Extended data
Extended Data Fig. 1 Viral and host abundance and richness.
a, Host abundance. The 100 most abundant strains are colored, the rest are aggregated and shown in gray. b, Richness (i.e., number of unique strains) of hosts (blue) and viruses (red). The number of unique spacers (spacer richness) is depicted in brown. During VDRs the abundance of both hosts and viruses fluctuates. As a response to virus diversification, host richness eventually increases despite possible declines at the beginning of the VDR resulting from the initial viral attack.
Extended Data Fig. 2 Host phylogenetic tree.
The tree is not inferred, but rather drawn based on exact genealogical data (which strain descends from which) collected during the simulation. Branch length indicates the lifetime of any given host strain. Hosts diversify and go extinct primarily during VDRs, resulting in strain replacement, but strains can also persist from one VDR to the next.
Extended Data Fig. 3 Host and virus diversification and extinction.
Each host (panel a) or viral (panel b) strain is plotted with a line, starting at the time when the strain was generated and ending when the strain went extinct. During VDRs the rate of diversification of both viruses and hosts is higher than during HCRs. While viruses have relatively short persistence (shorter line lengths), hosts have long persistence and can persist during an entire VDR.
Extended Data Fig. 4 Modules in the infection network.
A time series of the number of modules in the infection network for a single simulation. Modularity enables diversification since it allows the temporary coexistence of different groups of viruses.
Extended Data Fig. 5 Host and virus rankings in the weighted nested immunity matrix as a function of age.
The plots show node strength (sum of the corresponding number of matches in the immunity network) of hosts (top) and viruses (bottom) against their age measured from the time of their birth, for two selected times before the start of an HCR (leY, t=231 and right, t=1720). Each data point represents a host (top) or virus (bottom) strain. On the different plots, selected groups of youngest and oldest strains are indicated. The oldest host strains occupy the lower rows (low node strength), and their rankings tend to decrease with age. This is because descendants of a given host inherit all of its spacers and add a new one, which always results in an increase in total matches (host node strength). Because they have acquired additional protection, they can grow in abundance and through resulting enhanced encounters and infections, the failure of existing spacers can add redundancy (more than one spacer to the same virus), further contributing to their ranking. The oldest viruses occupy the leftmost columns, with the highest column sums of matches to hosts, since longer lifetimes provide the opportunity for many encounters, and therefore for both the failure of existing spacers (which adds redundancy to a given entry) and the addition of new spacers (which distributes immunity throughout entries). A successful offspring will have mutated a protospacer that confers escape from a given match of the parent; thus, successful descendants exhibit one less match and are placed to the right. It is worth noting that there is considerable variation around the general trends with age, reflecting the complex interplay of the stochastic acquisition of spacers and protospacers with the abundance dynamics which affect both encounter and mutation rates.
Extended Data Fig. 6 Order of extinctions.
We tested for ‘orderly’ extinctions in which extinction preferentially happens from the viruses to which hosts have most immunity to those that can infect many hosts. For any virus that went extinct we calculated an ‘immunity rank’. Specifically, for a given time step, we calculated the strength of all n virus nodes in the immunity network, s=(s1,s2, …,sj,…,sn), where sj is the node strength of virus j (i.e., the sum of the columns in Fig. 2g in the main text). Viruses with higher values of sj are those that are more to the left in Fig. 2g, and to which hosts have high immunity. We removed duplicate values in s (to avoid ties) and ordered it in ascending order to obtain s’. We then calculated the relative position of sj in s’. A rank of 1 means that the virus that went extinct was highly ranked (e.g., position 5 out of 5 values will render a rank of 1). 50% of viruses (median indicated by a vertical dashed line) had an extinction rank of 0.5.
Extended Data Fig. 7 Viral escape via 1-matches.
A tripartite virus-protospacer-host network depicting escape routes for a single virus. Each host is connected to a single protospacer (colored boxes). The spacer composition of strains is shown. Escape occurs through matching colors.
Extended Data Fig. 8 Reproductive numbers R0 and R1.
At the entrance into an HCR, the average of reproductive numbers \({{\rm{R}}}_{{\rm{j}}}^{0}\) over all virus strains j (weighted by their respective abundances) (light blue line) is considerably below 1. After this initial stage, this mean measure hovers around 1 (horizontal dashed line) and exhibits considerably larger values towards the end of this period before the transition to a VDR. Growth based purely on hosts made available by a single escape mutation is shown here as the weighted-mean of \({{\rm{R}}}_{{\rm{j}}}^{1}\) across viruses j (in pink). This quantity exhibits an increasing trend during the HCR, which also raises the potential \({{\rm{R}}}_{{\rm{pot}}}^{{\rm{j}}}\), defined as the sum of \({{\rm{R}}}_{0}^{{\rm{j}}}\) and \({{\rm{R}}}_{1}^{{\rm{j}}}\) (not shown, for clarity).
Extended Data Fig. 9 Modularity of empirical host-spacer networks.
Each row represents a different data set: Sulfolobus islandicus hosts from Yellowstone (Top). Pseudomonas aeruginosa hosts from Copenhagen (middle). S. islandicus hosts from the Mutnovsky Volcano in Russia, 2010 (bottom). Panels a, c and e, are host-spacer networks in which interactions within host-spacer modules are colored. Panels b, d and f, are distributions of the map equation (L) obtained from networks shuffled by randomly distributing interactions. Value of the observed L is depicted with a vertical dashed line.
Extended Data Fig. 10 Regime definition.
Each point in the virus abundance time series in panel a is first converted to relative abundance (panel b) and then classified into a HCR (green) or VDR (purple). This classification is based on the second difference (panel d) (for comparison we also show the first difference in panel c). Momentary virus growth periods (marked with an arrow) are not classified as VDR. The final classification is shown using vertical lines. HCRs start with a purple line and VDRs with a green line.
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Pilosof, S., Alcalá-Corona, S.A., Wang, T. et al. The network structure and eco-evolutionary dynamics of CRISPR-induced immune diversification. Nat Ecol Evol 4, 1650–1660 (2020). https://doi.org/10.1038/s41559-020-01312-z
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DOI: https://doi.org/10.1038/s41559-020-01312-z
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