Lytic to temperate switching of viral communities

Journal name:
Nature
Volume:
531,
Pages:
466–470
Date published:
DOI:
doi:10.1038/nature17193
Received
Accepted
Published online

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’.

At a glance

Figures

  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 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.

    a, Log-transformed VLP versus microbial densities have an m < 1 relationship (n = 223 independent measures); the dashed reference line depicts a 10:1 relationship. b, Steady-state microbial and viral abundances and schematic microbial growth rate predicted by three modified Lotka–Volterra models: Piggyback-the-Winner (red), Thingstad et al. (2014; black9), and Weitz and Dushoff (2008; blue8). c, Shannon microbial species diversity versus host density (H′; n = 66 independent measures). d, Abundance of CRISPR elements in the microbial metagenomes (n = 66 independent measures). All slopes (m), R2, and P values describe linear regressions testing against a slope of 0, except a which shows the P value from a two-sided t-test against a slope ≠ 1. Black best-fit lines with grey 99% prediction intervals from linear regressions are shown (a, c, and d).

  2. The relative decline in virus-like particles (VLPs) with increasing host density is common in disparate environmental systems.
    Figure 2: The relative decline in virus-like particles (VLPs) with increasing host density is common in disparate environmental systems.

    Published microbial and VLP densities, and calculated virus to microbe ratio (with all environments pooled; final panel) are plotted by ecosystem. n = 23, 139, 27, 18, 22, 1397, 85, 71, 18, 35, and 46 independent measures for Animal-associated, Coastal/estuarine, Coral reef, Deep ocean, Drinking water, Open ocean, Polar lakes, Sediment, Soil, Soil pore water, and Temperate lake/river environments, respectively; pooled n = 1,881. Dashed lines depict 10:1 linear relationships; blue lines of best fit and pink 99% prediction intervals from linear regression are shown. All slopes (m) and R2 values describe linear regressions, and P values are derived from a two-sided t-test against a slope ≠ 1; details, including false-detection rate corrected values in Extended Data Table 1.

  3. Density dependence does not drive viral predation.
    Figure 3: Density dependence does not drive viral predation.

    a, c, Viral and host densities (individual counts shown) follow an m < 1 distribution despite high host densities (a; Mission Bay (stars) and Palmyra (circles) incubations; n = 12 and 25, respectively, from repeated measures), compared to putatively ‘lytic’ (slope= 1) and ‘non-lytic’ (m < 1) published data (c; Hennes et al. (1995)31 (triangles) and Wilcox and Fuhrman (1994)3 (squares), respectively; mean values shown; n = 6 and 28, respectively, from repeated measures). b, d, Microbe density and VMR over time in Mission Bay and Palmyra (b; individual values; n = 3 and 5 per time-point, respectively, except for time zero, when n = 1), and published putative lytic and non-lytic incubations (d; mean values; n = 1 and 4 per time-point, respectively) plotted over a thin plate spline. a, c, Dashed 10:1 lines, solid lines of best fit, with 99% prediction intervals in grey; all slopes (m) and R2 values describe linear regressions, and P values are derived from a two-sided t-test against a slope ≠ 1. Individual incubation data are shown in Extended Data Fig. 3a. Mission Bay and Palmyra incubation experiments were each conducted once.

  4. Temperate features in viromes increase with host density.
    Figure 4: Temperate features in viromes increase with host density.

    ad, The relationship between log-transformed microbial density and the percent abundance of integrase (a), excisionase (b), and virulence reads in viromes (d), normalized by total sequences in each sample, and Shannon (H′) viral functional diversity (c) (n = 24 independent measures for all analyses). The linear equations and lines of best fit from robust regression and bootstrapped 95% and 90% confidence intervals (CIs) for the slopes are shown. Goodness of fit metrics are inappropriate for robust regression and are omitted.

  5. 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.
    Extended Data Fig. 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.

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

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

  8. Temperateness of viral communities increases with host density and viral functional composition change.
    Extended Data Fig. 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.

Tables

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

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Author information

  1. These authors contributed equally to this work.

    • B. Knowles &
    • C. B. Silveira

Affiliations

  1. Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, 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, Rio de Janeiro 21941-599, 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, California 92182, 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, Hawaii 96744, USA

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

    • V. A. Cantu,
    • 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, California 92037, USA

    • E. Sala
  9. Marine Sciences Department, Sao Paulo Federal University - Baixada Santista, Av. Alm. Saldanha da Gama, 89, Santos, São Paulo 11030-400, 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, California 92093, 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, 1098XH Amsterdam, The Netherlands

    • C. Young &
    • R. Brainard
  14. Rainbow Rock, Ocean View, Hawaii 96737, USA

    • F. H. Coutinho,
    • E. A. Dinsdale &
    • R. A. Edwards
  15. Coral Reef Ecosystem Division-PIFSC-NOAA, 1845 Wasp Blvd, Honolulu, Hawaii 96818, USA

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

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

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.

Author details

Extended data figures and tables

Extended Data Figures

  1. 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. (79 KB)

    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.

  2. Extended Data Figure 2: Meta-analysis of the frequency of lysogenic cells (FLC) from mitomycin C induction experiments yields ambiguous results. (87 KB)

    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).

  3. 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. (264 KB)

    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).

  4. Extended Data Figure 4: Temperateness of viral communities increases with host density and viral functional composition change. (189 KB)

    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.

Extended Data Tables

  1. Extended Data Table 1: Summary of linear regression analyses of published microbial and viral counts (89 KB)
  2. Extended Data Table 2: Summary information on the post-quality control viromes analysed (334 KB)
  3. Extended Data Table 3: Summary of model II OLS, MA, and SMA regression analyses (736 KB)

Additional data