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Lytic to temperate switching of viral communities

Nature volume 531, pages 466–470 (2016)Cite this article

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A Corrigendum to this article was published on 24 August 2016

This article has been updated

Abstract

Microbial viruses can control host abundances via density-dependent lytic predator–prey dynamics. Less clear is how temperate viruses, which coexist and replicate with their host, influence microbial communities. Here we show that virus-like particles are relatively less abundant at high host densities. This suggests suppressed lysis where established models predict lytic dynamics are favoured. Meta-analysis of published viral and microbial densities showed that this trend was widespread in diverse ecosystems ranging from soil to freshwater to human lungs. Experimental manipulations showed viral densities more consistent with temperate than lytic life cycles at increasing microbial abundance. An analysis of 24 coral reef viromes showed a relative increase in the abundance of hallmark genes encoded by temperate viruses with increased microbial abundance. Based on these four lines of evidence, we propose the Piggyback-the-Winner model wherein temperate dynamics become increasingly important in ecosystems with high microbial densities; thus ‘more microbes, fewer viruses’.

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Figure 1: Virus-like particle (VLP) relative abundance declines with increasing host density despite lower microbial diversity and similar host sensitivity to infection, contrary to predictions of lytic models.
Figure 2: The relative decline in virus-like particles (VLPs) with increasing host density is common in disparate environmental systems.
Figure 3: Density dependence does not drive viral predation.
Figure 4: Temperate features in viromes increase with host density.

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Accession codes

Data deposits

The viromes and microbiomes used in this paper are accessible at MG-RAST (http://metagenomics.anl.gov/) under the Piggyback-the-Winner project. Virome accession numbers: 4683670.3, 4683674.3, 4683677.3, 4683680.3, 4683683.3, 4683684.3, 4683686.3, 4683690.3, 4683702.3, 4683703.3, 4683704.3, 4683706.3, 4683712.3, 4683720.3, 4683739.3, 4683744.3, 4683745.3, 4683746.3, 4683747.3, 4683731.3, 4683733.3, 4683734.3, 4683718.3, 4684617.3. Microbiome accession numbers: 4683666.3, 4683667.3, 4683668.3, 4683669.3, 4683671.3, 4683672.3, 4683673.3, 4683675.3, 4683676.3, 4683678.3, 4683679.3, 4683681.3, 4683682.3, 4683685.3, 4683687.3, 4683688.3, 4683689.3, 4683691.3, 4683692.3, 4683693.3, 4683694.3, 4683695.3, 4683696.3, 4683697.3, 4683698.3, 4683699.3, 4683700.3, 4683701.3, 4683705.3, 4683707.3, 4683708.3, 4683709.3, 4683710.3, 4683711.3, 4683713.3, 4683714.3, 4683715.3, 4683716.3, 4683717.3, 4683719.3, 4683721.3, 4683722.3, 4683723.3, 4683724.3, 4683725.3, 4683726.3, 4683727.3, 4683728.3, 4683729.3, 4683732.3, 4683735.3, 4683736.3, 4683737.3, 4683738.3, 4683740.3, 4683741.3, 4683742.3, 4683743.3, 4683748.3, 4683749.3, 4683750.3, 4683751.3, 4683752.3, 4683753.3, 4683754.3, 4684616.3.

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Acknowledgements

This paper is dedicated to the memory of Mike Furlan, mentor, friend, and colleague. We are grateful to the National Oceanographic and Atmospheric Administration Coral Reef Ecosystem Division for supporting this research, and to the captains and crews of the NOAA ship Hi’ialakai and the Hanse Explorer. Thanks to J. Payet for providing viral and microbial abundance data. Sampling was carried out under research permits from the US Fish and Wildlife Service, Palmyra Atoll National Wildlife Refuge, the Environment and Conservation Division of the Republic of Kiribati (n. 021/13) and ICMBio, Brazil (n. 27147-2). This work was funded by the Canadian Institute for Advanced Research Integrated Microbial Biodiversity Program Fellowship Award 141679 (to F.R.) and National Science Foundation grants OISE-1243541 and DEB-1046413 (to F.R.), CNS-1305112 and MCB-1330800 (to R.A.E.), DUE-1323809 (to E.A.D.), Gordon and Betty Moore Foundation Investigator Award GBMF-3781 (to F.R.), and the Brazilian National Research Council (CNPq; to F.T.) and Brazilian National Research Council Science Without Borders Program (CNPq/CAPES; to C.B.S.).

Author information

Author notes

  1. B. Knowles and C. B. Silveira: These authors contributed equally to this work.

  2. Coral Reef Ecosystem Division-PIFSC-NOAA, 1845 Wasp Blvd, Honolulu, Hawaii 96818, USA

  3. Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA

Authors and Affiliations

  1. Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, 92182, California, USA

    B. Knowles, C. B. Silveira, A. G. Cobián-Güemes, E. A. Dinsdale, E. E. George, K. T. Green, A. F. Haas, J. M. Haggerty, E. R. Hester, N. Hisakawa, L. W. Kelly, Y. W. Lim, M. Little, S. D. Quistad, N. L. Robinett, S. E. Sanchez & F. Rohwer

  2. Biology Institute, Rio de Janeiro Federal University, Av. Carlos Chagas Filho 373, Rio de Janeiro, 21941-599, Rio de Janeiro, Brazil

    C. B. Silveira, F. H. Coutinho, L. S. de Oliveira, C. Thompson & F. Thompson

  3. Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Drive, San Diego, 92182, California, USA

    B. A. Bailey, B. Felts, A. Luque, P. Salamon & J. Nulton

  4. Hawaii Institute of Marine Biology, University of Hawaii at Manoa, 46-007 Lilipuna Road, Kaneohe, 96744, Hawaii, USA

    K. Barott

  5. Computational Science Research Center, San Diego State University, 5500 Campanile Drive, San Diego, 92182, California, USA

    V. A. Cantu, A. Luque, A. Luque, K. McNair, G. G. Z. Silva & R. A. Edwards

  6. Radboud University Medical Centre, Radboud Institute for Molecular Life Sciences, Centre for Molecular and Biomolecular Informatics, 6525HP Nijmegen, The Netherlands ,

    K. A. Furby, T. McDole-Somera, S. Sandin, J. Smith & B. Zgliczynski

  7. Viral Information Institute, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA ,

    G. B. Gregoracci

  8. Scripps Institution of Oceanography, 8622 Kennel Way, La Jolla, 92037, California, USA

    E. Sala

  9. Marine Sciences Department, Sao Paulo Federal University - Baixada Santista, Av. Alm. Saldanha da Gama, 89, Santos, 11030-400, São Paulo, Brazil

    C. Sullivan

  10. National Geographic Society, 1145 17th St NW, Washington D.C, 20036, USA

    M. J. A. Vermeij

  11. Department of Biology, University of California San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA

    P. Salamon, M. J. A. Vermeij & F. Rohwer

  12. CARMABI Foundation, Piscaderabaai z/n, Willemstad, Curacao, Netherlands, Antilles

    M. Youle

  13. Aquatic Microbiology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, 1098XH, The Netherlands

    C. Young & R. Brainard

  14. Rainbow Rock, Ocean View, Hawaii, 96737, USA

    F. H. Coutinho, E. A. Dinsdale, R. A. Edwards & R. A. Edwards

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  1. B. Knowles
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  2. C. B. Silveira
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Contributions

F.R., B.K., C.B.S., and F.T. conceptualized the project; B.K., F.R., C.B.S., and M.Y. wrote the manuscript; B.K., C.B.S., V.A.C., A.G.C.-G., K.T.G, K.M., G.G.Z.S., S.D.Q., Y.W.L., S.E.S., F.H.C., E.R.H. , N.L.R., B.A.B., B.F., A.L., P.S., J.N., C.Y., E.E.G., M.L., K.A.F., L.S.O., T.M.-S., J.M.H., B.Z., A.F.H., M.J.A.V., K.B., C.S., R.A.E., and F.R. performed sample collection, processing, experiments, and analysis; N.H. provided graphics and GIS analysis; E.A.D., L.W.K., S.S., J.S., R.B., C.T., G.B.G., J.N., E.S., R.A.E., F.T., and F.R. provided intellectual guidance and funding during the development of the research.

Corresponding authors

Correspondence to B. Knowles or F. Rohwer.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 The observed decline in virus to microbe ratio with increasing host density is not supported by horizontal transfer (for example, of resistance genes) under conditions where strain diversity is predicted to rise.

a, Host competence gene composition likely does not facilitate the expected rise in resistance to viral infection (n = 66; m = −0.25, t = −2.40, d.f. = 64, P = 0.02; R2 = 0.08; microbial abundance log-transformed; linear regression). b, Lysogeny may provide strain diversification similar to the co-evolutionary diversification predicted by Thingstad et al. (2014)9 nested-infection chemostat model.

Source data

Extended Data Figure 2 Meta-analysis of the frequency of lysogenic cells (FLC) from mitomycin C induction experiments yields ambiguous results.

FLC from four published studies is plotted against total cell abundance. Although a sometimes-significant negative relationship exists at a within-study level (microbial abundance log-transformed; Muck et al. (2014)28, n = 9, m = −10.79, t = −1.76, d.f. = 7, P = 0.12; R2 = 0.31; Bongiorni et al. (2005)29, n = 4, m = −17.23, t = −1.91, d.f. = 2, P = 0.20; R2 = 0.65; Payet and Suttle (2013)4, n = 9, m = −48.31, t = −4.80, d.f. = 7, P = 1.96 × 10−3; R2 = 0.77; Williamson et al. (2002)30, n = 5, m = −26.08, t = −1.08, d.f. = 3, P = 0.36; R2 = 0.28; linear regression of each data set examined independently), when examined altogether across the full range of host abundances studied, no significant slope was observed (microbial abundance log-transformed; n = 27, m = −0.11, t = −0.04, d.f. = 25, P = 0.97; R2 = 5.94 × 10−5; linear regression of pooled data).

Source data

Extended Data Figure 3 Decline in virus to microbe ratio (VMR) observed in incubations with elevated host density over time, contrasted with published values and viral decay.

a, Log-transformed VLP density in experimental incubations is plotted against microbial host density over time (dot size) with VMR indicated by dot colour. Data from Mission Bay (MB) and Palmyra (Pal) water with DOC added (+ DOC) or not (− DOC) is complemented by the nutrient-added ‘lytic’ system of Hennes et al. (1995)31 (H + Nutrients) as well as the ‘non-lytic’ dilutions (3%, 10%, 20%, and 30% final concentration seawater diluted by 0.02 μm filtered seawater) of Wilcox and Fuhrman (1994)3; WF 3% SW, WF 10% SW, WF 20% SW, WF 30% SW). n = 1 all incubations and published mean values. b, Significant viral decay was not observed in cell-free viral decay controls in incubation experiments (Palmyra: n = 4, m = 1.64 × 10−3, t = 1.48, d.f. = 2, P = 0.28; R2 = 0.52; Mission Bay: n = 6, m = 4.53 × 10−3, t = 1.87, d.f. = 4, P = 0.14; R2 = 0.47; linear regression with log-transformed viral density).

Source data

Extended Data Figure 4 Temperateness of viral communities increases with host density and viral functional composition change.

a, The relative composition of provirus-like reads, normalized by total sequences in each sample, increases with host density in viral metagenomes (host density log-transformed; n = 24 independent measures). The linear equations and line of best fit from robust regression and bootstrapped 95% and 90% confidence intervals (CIs) for the slope are shown. Goodness of fit metrics are inappropriate for robust regression and are omitted. b, Viromes clustered by functional similarity (crAss cross-assembly), showing higher host density Pacific viromes (*) grouped away from lower host density Atlantic viromes (†); site names coloured by host density.

Source data

Extended Data Table 1 Summary of linear regression analyses of published microbial and viral counts
Full size table
Extended Data Table 2 Summary information on the post-quality control viromes analysed
Full size table
Extended Data Table 3 Summary of model II OLS, MA, and SMA regression analyses
Full size table

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Knowles, B., Silveira, C., Bailey, B. et al. Lytic to temperate switching of viral communities. Nature 531, 466–470 (2016). https://doi.org/10.1038/nature17193

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