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Temperate bacterial viruses (phages) may enter a symbiosis with their host cell, forming a unit called a lysogen. Infection and viral replication are disassociated in lysogens until an induction event such as DNA damage occurs, triggering viral-mediated lysis. The lysogen–lytic viral reproduction switch is central to viral ecology, with diverse ecosystem impacts. It has been argued that lysogeny is favoured in phages at low host densities. This paradigm is based on the fraction of chemically inducible cells (FCIC) lysogeny proxy determined using DNA-damaging mitomycin C inductions. Contrary to the established paradigm, a survey of 39 inductions publications found FCIC to be highly variable and pervasively insensitive to bacterial host density at global, within-environment and within-study levels. Attempts to determine the source(s) of variability highlighted the inherent complications in using the FCIC proxy in mixed communities, including dissociation between rates of lysogeny and FCIC values. Ultimately, FCIC studies do not provide robust measures of lysogeny or consistent evidence of either positive or negative host density dependence to the lytic–lysogenic switch. Other metrics are therefore needed to understand the drivers of the lytic–lysogenic decision in viral communities and to test models of the host density-dependent viral lytic–lysogenic switch.
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Variability and host density independence in
inductions-based estimates of
environmental lysogeny
Ben Knowles1*,BarbaraBailey
2, Lance Boling1, Mya Breitbart3, Ana Cobián-Güemes1,
Javier del Campo4,RobEdwards
5,6,7,BenFelts
2, Juris Grasis1, Andreas F. Haas1, Parag Katira8,
Linda Wegley Kelly1, Antoni Luque2,5,7, Jim Nulton2, Lauren Paul1, Gregory Peters1, Nate Robinett1,
Stuart Sandin9, Anca Segall1,7, Cynthia Silveira1, Merry Youle10 and Forest Rohwer1,7*
Temperate bacterial viruses (phages) may enter a symbiosis with their host cell, forming a unit called a lysogen. Infection
and viral replication are disassociated in lysogens until an induction event such as DNA damage occurs, triggering viral-
mediated lysis. The lysogenlytic viral reproduction switch is central to viral ecology, with diverse ecosystem impacts. It
has been argued that lysogeny is favoured in phages at low host densities. This paradigm is based on the fraction of
chemically inducible cells (FCIC) lysogeny proxy determined using DNA-damaging mitomycin C inductions. Contrary to the
established paradigm, a survey of 39 inductions publications found FCIC to be highly variable and pervasively insensitive
to bacterial host density at global, within-environment and within-study levels. Attempts to determine the source(s) of
variability highlighted the inherent complications in using the FCIC proxy in mixed communities, including dissociation
between rates of lysogeny and FCIC values. Ultimately, FCIC studies do not provide robust measures of lysogeny or
consistent evidence of either positive or negative host density dependence to the lyticlysogenic switch. Other metrics are
therefore needed to understand the drivers of the lyticlysogenic decision in viral communities and to test models of the
host density-dependent viral lyticlysogenic switch.
Lysogenic dynamics can disassociate viral infection and
production, leading to virushost predatorprey feedbacks, den-
sities and ecosystem impacts divergent from those predicted
under lytic dynamics13. Although the majority of cultured laboratory
and environmental bacterial strains are known lysogens4,5,quantify-
ing the fraction of lysogens in natural mixed communities remains
challenging. The prevalence of lysogeny is most commonly estimated
by using the DNA-damaging agent mitomycin C to induce
prophages (viruses that have established sustained intra- or extra-
chromosomal residence in their hosts) to enter the lytic cycle and
produce quantiable viral progeny6,7. Lysogeny has been diagnosed
using this technique in laboratory strains for half a century6,8.
However, treatment of lysogens can yield induction, unsuccessful
induction, or inhibition of host and viral production under different
mitomycin C concentrations (Table 1)6that vary on a strain-specic
basis9. Inductions are difcult to interpret, even under single-strain
laboratory conditions. Despite these challenges, in the 1990s Jiang
and Paul4,10 extended this induction method from laboratory strains
to bacterial isolates from mixed natural communities, showing that
lysogeny is a common viral strategy in the environment (2562.5%
of strains were lysogens). Higher percentages were commonly
observed under oligotrophic conditions than in eutrophic systems,
suggesting links between the rate of lysogeny, nutrient regime and
host density4,10.
Concurrent studies directly probed natural communities by
adding mitomycin C into samples of sea water11,12. In those
studies, the fraction of lysogenic cells (FLC, hereafter referred to
as the fraction of chemically inducible cells, FCIC, due to disassocia-
tion of lysogeny and the induced fraction, as a percent of total
cellular density) was estimated as
% FCIC =
(ViVc)/B
C× 100 (1)
using viral densities in the induced (V
i
) and control (V
c
) treatments,
burst size Band host density C(cells per ml or g of sample) before
incubation1113. In contrast to earlier isolate-based studies, this mixed-
community approach showed that either FCIC was insensitive to
ecosystem nutrient status12 or was higher undereutrophic conditions11.
Subsequent research led to the consensus view that the frequency
of lysogeny was inversely related to host density and nutrient avail-
ability1417. This suggested that lysogeny provides a temporary
refuge for viruses when hosts are starving and scarce14, seemingly
providing a low-density lysogenic dynamic complementary to the
1Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA. 2Department of Mathematics and Statistics,
San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA. 3College of Marine Science, University of South Florida, 140 Seventh
Avenue South, St Petersburg, Florida 33701, USA. 4Department of Botany, University of British Columbia, 3529-6270 University Boulevard, Vancouver,
British Columbia V6T 1Z4, Canada. 5Computational Science Research Center, San Diego State University, 5500 Campanile Drive, San Diego, California
92182, USA. 6Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA. 7Viral Information
Institute, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA. 8Department of Mechanical Engineering, San Diego State
University, 5500 Campanile Drive, San Diego, California 92182, USA. 9Scripps Institution of Oceanography, University of California San Diego, 950 Gilman
Drive, California 92903, USA. 10Rainbow Rock, Ocean View, Hawaii 96737, USA. Present address: Department of Marine and Coastal Sciences, Rutgers
University, 71 Dudley Road, New Brunswick, New Jersey 08901, USA. *e-mail: benjaminwilliamknowles@gmail.com;frohwer@gmail.com
ARTICLES
PUBLISHED: 28 APRIL 2017 | VOLUME: 2 | ARTICLE NUMBER: 17064
NATURE MICROBIOLOGY 2, 17064 (2017) | DOI: 10.1038/nmicrobiol.2017.64 |www.nature.com/naturemicrobiology 1
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
modelled high-density lytic kill-the-winnerdynamic18. By contrast,
recent work using extensive eld direct counts, experiments and
viromes (c.f. chemical induction) suggests that lysogeny is favoured
when hosts are highly abundant and rapidly growing1.Wecallthis
model piggyback-the-winner. The association of high host densities
with high rates of lysogeny reects the benets of lysogeny to hosts,
including prophage-mediated immunity against further infec-
tion19,20, protection from protist predation via virulence factors,
and gain of metabolic functions2123.
Approximately 40 studies measuring rates of lysogeny in mixed
natural communities using mitomycin C induction have been pub-
lished in the twenty years since the pioneering work of Jiang and
Paul4,10 (Supplementary Table 1). We have compiled data from pub-
lished environmental induction studies and found that FCIC is highly
variable and seldom correlated with host density. Attempts to identify
the source(s) of FCIC variability revealed issues with estimating the
rates of lysogeny using mitomycin C induction. We propose that lyso-
geny and FCIC are not equivalent and that FCIC measurements
obscure relationships between host density and lysogeny.
Results
Global distribution of FCIC studies. Clusters of saltwater studies
in the Gulf of Mexico (18% of all studies) and predominantly
freshwater studies in France (26%) account for 44% of all FCIC
studies (Fig. 1a). Of the 39 studies, most (90%, n= 35) were from
aquatic environments. Of the 352 published data points compiled,
91% (320 points) came from aquatic environments (compared to
32 points from sediment and soil combined).
Global host density versus published FCIC values. The full 352-
point global data set was analysed for a correlation between host
density and FCIC (Fig. 1b). This analysis showed high variability
and no signicant support for the established paradigm that
FCIC is inversely related to host density (P= 0.07, n= 352,
m(slope) = 0.08, R
2
= 0.01, 39 studies; linear regression with
FCIC and host density log-transformed). Type II regression
yielded similar results (95% condence interval (CI) [0.17, 0.01]
for the slope, m=0.08, n= 352, R
2
= 0.01).
Owing to the high variability between FCIC and host density,
468 further data points (that is, site averages) would be required
to nd any (positive or negative) signicant global-level relation-
ship between host density and FCIC (two-tailed power analysis,
power = 0.8, P< 0.05; Fig. 1b). This represents a 2.3-fold increase
in data over that generated in the two-decade history of the eld.
Host density versus published FCIC values by environment. A
high degree of variability and lack of signicant relationships was
also observed when FCIC was plotted against host density for
specic environments (Fig. 2 and Supplementary Fig. 1).
Regressions in freshwater, saltwater and sediment data sets
showed no signicant effect of host density on FCIC (Fig. 2;
freshwater: P= 0.16, n= 131, m=0.18, R
2
= 0.02, 15 studies;
saltwater: P= 0.76, n= 189, m=0.02, R
2
< 0.01, 22 studies;
sediment: P= 0.32, n= 19, m=0.10, R
2
= 0.06, 3 studies; linear
regressions with FCIC and host density log-transformed). The
only environment with a slope signicantly different from zero
was soil, where a positive relationship was observed (P< 0.01,
n= 13, m= 0.20, R
2
= 0.58, 2 studies; linear regressions with FCIC
and host density log-transformed). Global regression lines
explained 58, 6, 2 and <1% of the variability between FCIC and
host density in soil (2 studies), sediment (3 studies), freshwater
(15 studies) and saltwater (22 studies), respectively (Fig. 2). Type
II regressions conrm these results (freshwater: 95% CI [0.42,
0.07] for the slope, m=0.17, n= 131, R
2
= 0.02, 15 studies;
saltwater: 95% CI [0.14, 0.11] for the slope, m=0.02, n= 189,
R
2
< 0.01, 22 studies; sediment: 95% CI [0.29, 0.10] for the slope,
m=0.10, n= 19, R
2
= 0.06, 3 studies; soil: 95% CI [0.09, 0.32]
for the slope, m= 0.20, n= 13, R
2
= 0.58, 2 studies). Evaluation of
within-study variability expressed as median within-study ranges
in FCIC from each environment ranked sediment as least variable
(3.9% median range), followed by freshwater (12.60% median
range), seawater (26.62% median range) and soil (most variable,
36.55% median range) (Fig. 2).
Due to the high variability between FCIC and host density, 386,
15,450 and 110 further values (that is, site averages) are required to
nd any (positive or negative) signicant relationships between
FCIC and host density in freshwater, saltwater and sediment
environments, respectively (two-tailed power analysis, power = 0.8,
P< 0.05; no further values are required in soil) (Fig. 2). This rep-
resents approximately 4-fold, 83-fold and 7-fold increases in FCIC
sampling over extant data accumulated in the past two decades in
these environments.
Host density versus FCIC values within studies. Non-signicant
relationships (P> 0.05) were also the norm within studies (Fig. 3a,
Supplementary Table 1 and Supplementary Fig. 1). Of the
42 analyses (39 studies, three of which include samples from two
environments; Supplementary Table 1 and Fig. 3a), only ve
studies showed signicant relationships (11.90% of studies,
reported in refs 16, 17, 24 and 25 and the sediment subset of
ref. 26). While negative relationships (slopes) between FCIC and
host density are most common, 40% of freshwater, 32% of
saltwater, 33% of sediment and both soil studies showed positive
trends (linear regressions with FCIC and host density log-
transformed; Figs 2 and 3a and Supplementary Fig. 1). Aquatic
environments with more intensive sampling, either as individual
data points or as number of studies, have lower R
2
values than the
less-sampled sediment and soil environments (Supplementary
Table 1 and Fig. 2).
Distribution of published FCIC values. The most common
published FCIC values for global and freshwater, saltwater and
sediment environments are 05% FCIC. Pooled published FCIC
data show a truncated normal distribution centred approximately
around 0% FCIC. Commonly excluded FCIC values 0, possibly
generated by host inhibition via mitomycin C treatment (Table 1),
probably ll out the lower range (Fig. 3b).
Frequency of FCIC values 0. To assess the frequency and
distribution of FCIC values 0, FCIC was estimated in technical
Table 1 | Phenotypes observed when mitomycin C is added to a lysogen lineage in correct, under- and over-dose concentrations.
Dose
Change in host densities (relative to
control)
Change in viral densities (relative to
control)
FCIC
range Conclusion
Error
type
Correct
dose
Decline (induction) Increase (induction) >0% Lysogeny found at correct
rate
None
Under-dose No change (unsuccessful induction) No change (unsuccessful induction) 0% Lysogeny underestimated Type II
Over-dose Decline (inhibition) Decline (inhibition) <0% Lysogeny underestimated Type II
Under- and over-dosing leadsto unsuccessful and inhibitedinduction, respectively. Although thesedifferent dosage categories yield distinctive FCIC rangeswhen lysogens are probed, incorrectdosage of either
form consistently yields Type II error.
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replicates from three saltwater and three freshwater sites. Highly
variable FCIC values were observed within and between sites
(Fig. 4a). The within-site average FCIC values varied from 2.17
to 9.61% and the within-site ranges were up to 14.51%
(Supplementary Table 2 and Fig. 4a). The site with the highest
variability, Spanish Landing, showed both negative and positive
values (4.17 to 10.34%; coefcient of variation of 1.44), despite a
markedly consistent host density across replicates (5.09 × 10
6
to
6.55 × 10
6
cells per ml, coefcient of variation of 0.11; Fig. 4a).
Four sites showed higher variability between technical replicates
than was observed in the 25th percentile of published freshwater
and saltwater environmental studies (Fig. 5c; comparison of
ranges in each study or site). FCIC values 0 were observed in
four of six sites and spanned the full range of host densities,
indicating that unsuccessfulinduction events or host inhibition
are independent of host density and site (Fig. 4a).
Effects of excluding FCIC values 0. When all data points were
considered, half of all sites showed no signicant evidence of
lysogeny (that is, FCIC values with bootstrapped 95% CIs that
included zero: Famosa Slough, Lake Murray and Spanish Landing;
Fig. 4b). When values 0 were excluded, site means increased and
CIs constricted so as to give the appearance of signicant levels of
lysogeny at all sites (Fig. 4b). Similar to the published FCIC data
sets, most FCIC values from our survey fell between 0 and 5% in
a normal distribution centred around zero (Fig. 4c), indicating the
true distribution and extent of the excluded data in Fig. 3b.
Experimental manipulation and FCIC. To probe the potential
effects of host density and host growth rate on FCIC, we
estimated FCIC in communities in technical replicates diluted
either with nutrient-free buffers (low growth) or ltered site water
(high growth). It was expected that higher dilutions would lead to
a
6
5
3
21
8
13
7
Southern Ocean 12
914
39
37
3635
32
29
27
26
23
25
19
18
Antarctica 15 16 17
38
37
4
21
34
2220
1110 2824
31 32
30
Fraction of chemically inducible cells (%)
Cells per ml or g of sample
m = −0.08
P = 0.07
R2 = 0.01
n = 352
Studies = 39
100
50
10
5
1
0.5
0.1
104105106107108109
Environment
Freshwater
Saltwater
Sediment
Soil
Global regression summary statistics
bGlobal
Figure 1 | Locations and global linear regression of data from meta-analysis of 39 published studies based on chemical induction of lysogens.
a, Schematic map showing the global distribution of studies included in the meta-analysis, numbered by reference and coloured by environment
(Supplementary Table 1). Note that samples for study 1 were from both freshwater and sediment, for studies 17 and 21 were from freshwater and saltwater
(white circles), and for study 37 were from two distant marine environments. Blue circle with no number: freshwater and saltwater sampling sites for the
current study around San Diego, California, USA. b, Global synthesis of FCIC data across host density across environments (both axes log-transformed).
Black: global line of best t. Grey shading: global 95% prediction interval. Summary statistics (m,P,R
2
and n) are from linear regression across all data.
Map adapted from https://commons.wikimedia.org/wiki/File:BlankMap-World6.svg.
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NATURE MICROBIOLOGY 2, 17064 (2017) | DOI: 10.1038/nmicrobiol.2017.64 |www.nature.com/naturemicrobiology 3
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
articially increased FCIC estimates due to a greater sensitivity to
mitomycin C (ref. 16) and increased growth rates27, especially in the
higher-nutrient-site water dilutions compared to buffer dilutions.
Neither dilution series yielded monotonic changes in FCIC;
instead, there was a dramatic and inconsistent increase in FCIC
variability (Fig. 5a,b) compared to the undiluted technical replicates
(Fig. 5c). Buffer dilutions of samples from Chollas Reservoir,
Famosa Slough, Lake Murray, Old Mission Dam, Spanish Landing
and Vacation Road sites showed ranges of 39.19, 16.17, 22.70,
19.58, 38.39 and 15.39% FCIC, respectively, and the corresponding
site water dilutions were 32.73, 24.03, 13.19, 16.88, 17.86 and 56.36%
FCIC (Fig. 5c). Variability was not consistent with either dilution or
diluent at most sites (Fig. 5a,b). Furthermore, variation (range)
within the diluted technical replicates from four of six sites
(buffer dilutions: Chollas Reservoir and Spanish Landing; site
water dilutions: Chollas Reservoir and Vacation Road) exceeded
that observed across sites in half of the published freshwater and
saltwater studies (Fig. 5c), suggesting that variability in local
dynamics equals the impact of broader ecological drivers on
FCIC. Compared to the correctundiluted sample, dilution can
drive up to 39.19% overestimates or 38.39% underestimates of lyso-
geny, with bias measured as deviation from the undiluted samples
(Fig. 5a,b; median change from undiluted samples ± 12.85% FCIC
with dilution). As above, FCIC values showed a normal distribution
during dilutions (Fig. 5d). A third of the FCIC values measured with
(Fig. 5a,b) and without (Fig. 4a) experimental manipulation were
0 (25 of 74 values, 33.78%) and thus would normally have been
excluded (Fig. 3b).
Discussion
Host density as a driver of lysogeny in published data sets. The
relationship between FCIC and host density at the global
(Fig. 1b), within-environment (Fig. 2) and within-study (Fig. 3a)
levels was almost universally highly variable and non-signicant.
Only a few studies show increasing FCIC at low host
densities16,17,2426. In soils, the relationship between FCIC and host
density was constrained (R
2
= 0.58), signicant (P< 0.01) and
positive (m= 0.20), contrary to the current paradigm. Because
analysis of aggregated data sets is predisposed towards nding
signicant relationships28 and because we conducted regressions
without alpha values corrected for our legion analyses (for
example, 50 linear regressions), the lack of signicance here is
robust evidence against a global, environmental or within-study
level dependence of FCIC on host density. Increased sampling
was associated with increased variability at both the within-study
and across-study levels, even though the vast majority of studies
Fraction of chemically inducible cells (%) Fraction of chemically inducible cells (%)
Cells per ml or g of sample Cells per ml or g of sample
Freshwater
m = −0.18
P = 0.16
R2 = 0.02
n = 131
Studies = 15
m = −0.02
P = 0.76
R2 < 0.01
n = 189
Studies = 22
Saltwater
100
50
10
5
1
0.5
0.1
100
50
10
5
1
0.5
0.1
100
50
10
5
1
0.5
0.1
100
50
10
5
1
0.5
0.1
104105106107108109
104105106107108109
104105106107108109
104105106107108109
m = 0.20
P < 0.01
R2 = 0.58
n = 13
Studies = 2
Soil
m = −0.10
P = 0.32
R2 = 0.06
n = 19
Studies = 3
Sediment
Figure 2 | Reported fractions of chemically inducible cells (FCIC, %) in freshwater, saltwater and sediment environments, showing a lack of negative host
density dependence, and soils, showing positive host density dependence (cells per ml or g of sample). Host density (cells per ml or g of sample) and
FCIC are both log-transformed. Black lines: across-study lines of best t (black). Grey shading: 95% prediction intervals. Summary statistics (m,P,R
2
and n)
are from linear regression across all data within each environment. Data points and associated linear regression lines of best t from individual studies are
colour-coded and do not show prediction intervals (see Supplementary Table 1 for details of each study). Boxplots show the distribution of ranges from
individual studies within each environment (stars colour-coded by study). Boxplots are shown in sediment and soil environments for consistency, despite the
low sample numbers. Boxes show the 25th, 50th (median) and 75th percentiles and whiskers range from minimum to maximum values. Data points from
each reference are plotted individually in Supplementary Fig. 1.
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focused on two geographic areas (Supplementary Table 1 and
Fig. 1). Ultimately, multiple decades to centuries of further
research would be required to obtain a reliable host density
dependence in FCIC (positive or negative), based on current rates
of data accumulation in the twenty-year history of the eld (as
shown by power analysis). This lack of support for the current
density-dependence paradigm invites the search for unidentied
drivers of environmental lysogeny.
The silent thirdof FCIC values 0. FCIC values 0 made up
one-third of the FCIC values (the silent third) we observed and
were (1) remarkably inconsistently distributed, with some
technical replicates showing inhibition while others did not, and
(2) ubiquitous across a range of host densities and sites (Figs 4a
and 5a). Because FCIC cannot logically be less than zero, these
frequent negative FCIC values at the community level show an
abiding disassociation of lysogeny and FCIC. Combined with the
capacity for excluding these values to skew estimates of lysogeny
(Fig. 4b), the distribution of FCIC values 0 suggests that there
may be pervasive bias in the characterization of lysogeny and its
drivers so far.
Origins of the silent thirdof FCIC values 0. Mitomycin C
induction is a highly dose-dependent approach. FCIC values 0
arise when induction treatment yields unchanged or lowered viral
densities (V
i
V
c
in equation (1), Table 1). While an unchanged
viral density (FCIC = 0) may accurately reect the absence of
lysogens if mitomycin C is correctly dosed, it may also indicate
unsuccessful induction (that is, an absence of induction whether
lysogeny is present or not) if mitomycin C is underdosed, yielding
a false negative (Type II error, Table 1). FCIC values <0 (V
i
<V
c
)
are more consistent with mitomycin overdose causing inhibition
than unsuccessful induction6(Table 1), although these silent
thirdof values are commonly treated similarly to FCIC = 0 values
and excluded as unsuccessful induction13. Although mitomycin C
dosage may be tailored in single-strain systems, when dosing
mixed communities an unknown proportion of organisms or
lineages may be underdosed, overdosed or correctly dosed at any
given dose11. Although it is unclear what any FCIC value really
means, the observed variability in FCIC may be indicative of the
taxonomic or physiological state of organisms in a given sample29,30.
Induction in mixed communities versus isolates. Although
lysogeny appears to be a variable phenomenon, the majority of
sequenced bacterial genomes contain prophages5, and previous
investigations reported 43% (ref. 10), 2562.5% (ref. 4) and 71%
(ref. 27) of bacterial isolates to be inducible with mitomycin C.
Two-thirds of bacterial viruses with known lifestyles and
sequenced genomes are temperate31. In comparison with these
gures, the 05% mode of FCIC values (Fig. 3b) suggests that
mitomycin C induction underestimates community-level lysogeny
relative to strain-level analyses, an effect probably masked by
exclusion of the silent third(Fig. 4b). Inclusion of the silent
third, combined with the use of more diverse inducers10,32,33 and
further investigation of the linkages between lysogeny and
mitomycin C induction, as well as host density13, identity30 and
growth rate27, may reconcile FCIC and lysogeny.
Stochastic effects of dilution on FCIC. We induced lysogens in
diluted samples to probe the potential systematic impacts of host
density on FCIC estimation in slow- (buffer diluent) and
fast- (site water diluent) growing communities. Mitomycin C was
added simultaneously with diluent, precluding changes in
lysogeny during induction. Regardless of diluent, the most
marked effect of dilution was a large and inconsistent increase in
the variability in FCIC between replicates compared to undiluted
replicates or published studies (Fig. 5a,b versus Fig. 4a). Dilution
can result in lowered taxonomic diversity, but with variable
changes in functional capability3437, making stochastic functional
changes in microbial communities with dilution a candidate for
further investigation.
Dilution stochasticity and viral production assays. When dilution
is used to derive estimates of lytic and lysogenic viral production,
rates of lysogeny are assumed to remain unchanged (for example,
ref. 38). However, if the variation in FCIC with dilution observed
here (Fig. 5) is real, changing rates of lysogeny during dilution
will methodologically bias dilution-based estimates of viral
Freshwater
Saltwater
Sediment
Soil
a
*
Soil
*
Sediment
Positive
Negative
None
Insu.
Positive
Negative
None
Insu.
0 5 10 20150 5 10 2015
0 5 10 2015 0 5 10 2015
**
SaltwaterFreshwater
Positive
Negative
None
Insu.
Positive
Negative
None
Insu.
Studies showing relationship
Studies showing relationship
*
Relationship (slope)
Relationship (P < 0.1) Total studies in environment
Relationship in global analysis
b
Fraction of chemically inducible cells (%)
1007550250
Observations
0
50
100
150
Figure 3 | Relationships between FCIC and host density at the within-study level and the truncated distribution of published FCIC values. a, Summary of
the number of positive, negative and at relationships observed in individual studies within freshwater (n= 15), saltwater (n= 22), sediment (n=3)and soil
(n= 2) environments. Black bars: signicant relationships. Insuff: insufcient data, studies with n< 3. Grey bars: slopes. Stars: 95% condence level drawn
from the environment-level linear regressions shown in Fig. 2. Dashed vertical lines: total number of studies in that environment. b, Stacked histogram of
FCIC values (in 5% bins) with the number of observations for each bin, colour-coded by environment (n= 352). Data points from each reference are plotted
individually in Supplementary Fig. 1.
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production. By contrast, if rates of lysogeny remain constant during
dilution, then the observed variability reveals a divergence of actual
and estimated lysogeny (FCIC). Pairing FCIC and dilution-based
viral production estimates in future studies may characterize the
conditions that drive disassociation of FCIC from lysogeny.
The lurking variablethat drives FCIC. Although FCIC was not
driven by host density, the FCIC distribution was constrained and
consistent across published studies (Fig. 3b), undiluted samples
(Fig. 4c) and diluted samples (Fig. 5c). FCIC thus appears to be a
non-random parameter possibly driven by environmental
condition(s). Observations in this study suggest that the lurking
variable(or variables) (1) varies signicantly between sites,
unrelated to abiotic conditions such as temperature and salinity;
(2) does not vary in soil but is variable in particulate sediment
and aquatic environments; (3) varies inconsistently with dilution;
(4) is conserved across a range of microbial densities; and
(5) does not correlate with host density or growth rate. Many
variables t this description, including community taxonomic,
metabolic and functional composition (for example, refs 29, 30),
possibly varying by environment. While techniques such as
metagenomics provide both community proling and the
identication of prophage elements39,40, observational approaches
alone cannot identify which variables determine the prevalence of
lysogeny. Here, we have combined observational and experimental
approaches to capture the variability of FCIC and prole its driver(s),
while eliminating canonical determinants such as host density, to
inform future studies using mitomycin C inductions.
Conclusions
We have examined the evidence of host density dependence in
inductions-based studies to determine whether the paradigm that
lysogeny is promoted at low host density is generally supported,
or whether the recently proposed piggyback-the-winnermodel of
high-density lysogeny1is actually consistent with inductions data
sets. Rather than supporting either model, the analysis showed
high variability and a lack of density dependence (with the excep-
tion of soils, where piggyback-the-winnerwas supported) in
FCIC estimates.
No transitionas suggested by earlier FCIC studies and lytic
modelling effortsfrom low-density lysogenic to high-density
lytic dominance is supported. The observed issues hinder the use
of FCIC in describing the lyticlysogenic switch and may have
impeded earlier attempts to understand patterns of environmental
lysogeny (for example, ref. 43) while aiming to resolve the broadly
observed sublinear power-law relationship between viral and host
densities1,4143. Despite high variability, FCIC values showed mark-
edly constrained distributions when plotted against host density,
Saltwater
Freshwater
Lake Murray
Chollas Reservoir
Old Mission Dam
Famosa Slough
Vacation Road
Spanish Landing
−5
0
5
15
10
Cells per ml (×106)Cells per ml (×106)
5 7.5 10 25 505 7.5 10 25 50
−10
Fraction of chemically inducible cells (%)
a
c
Observations
Fraction of chemically
inducible cells (%)
9
6
3
0
−10 0 10 20
Means ± 95% CIs with or
without FCIC values 0
b
−5
0
5
15
10
Fraction of chemically inducible cells (%)
All sites pooled
Values > 0
All values
***
Ranges
Ranges
Figure 4 | FCIC variability between technical replicates, within sites and between sites, with signicant impacts of excluding values 0inmostsites.
a, FCIC from technical replicates (n= 4) across three freshwater and three saltwater sites (colour-coded by site; host density log-transformed). Grey shaded
areas: stars show ranges of FCIC values in each environment. b, Comparison of FCIC means and condence levels with (unshaded area; n=4for all sites) or
without (shaded area; n= 2 in Lake Murray samples; n= 3 in Chollas Reservoir, Spanish Landing and Famosa Slough sites; n=4 in Old Mission Dam and
Vacation Road sites) FCIC values 0. Squares: means. Whiskers: bootstrapped 95% CIs. Colour-coded by site, with global mean ± 95% CIs also shown
(black). Asterisks: sites where exclusion of FCIC values 0signicantly alters evidence of the presence or absence of lysogeny (P< 0.05; 95% CIs do or do
not cross zero). c, Histogram (5% bins) of the distribution of replicate FCIC values from these sites, showing an approximately normal distribution centred
around the 05% FCIC bin (n=24).
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© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
indicative of a Simpson paradox in which unexamined variables are
driving FCIC (ref. 44). Probing of this variability revealed profound
artefacts resulting from exclusion of FCIC values 0 in published
studies and also allowed proling, but not identication, of the
possible driver. Although it is currently unclear what FCIC values
mean, the variability in FCIC reported here may guide future
studies elucidating what now appears to be a nascent driver of
viral dynamics.
Saltwater
Freshwater
Fraction of chemically inducible cells (%)
−20
0
20
30
10
−10
Buer diluent
Estimated cells per ml (×106) Estimated cells per ml (×106)
SaltwaterFreshwater
151057.52.51151057.52.51
Estimated cells per ml (×106) Estimated cells per ml (×106)
151057.52.51151057.52.51
Fraction of chemically inducible cells (%)
−20
−10
0
10
20
30
Famosa Slough
Lake Murray
Chollas Reservoir
Vacation Road
Spanish Landing
Old Mission Dam
Diluent
a
b
−20 0 4020
0
3
6
9
Fraction of chemically inducible cells (%)
Observations
c
Published (across sites)
Range in fraction of chemically
inducible cells (%)
0
25
50
75
100
Buer site water
Within site (dilutions)
Within site (no treatment)
d
Site water diluent
Buer
Site water
Figure 5 | Effect of dilution on variability in FCIC. a,b, FCIC plotted against cell density following dilution with 0.02-µm-ltered buffer (a)or
0.02-µm-ltered site water (b). Dashed heuristic arrows: lines of best t across technical replicates, colour-coded by site, pointing towards more diluted
samples. Black rings: correctundiluted samples, n= 1 for each data point shown. c, FCIC ranges from published studies and both undiluted (no treatment)
and diluted samples from this study (n= 4 for each range from this study; sample numbers for each published range are provided in Supplementary Table 1).
Boxplot: FCIC ranges from individual published studies (grey stars) from freshwater and saltwater environments. Dashed lines: extensions of published median
value and 25th and 75th percentiles across the undiluted and diluted samples analysed here. The majority (66%) of undiluted technical replicates showranges
in the 25th to 50th percentiles of published studies using biological replicates and addressing FCIC across sites. d, Histogram (5% bins) of the distribution of
replicate FCIC values from the dilution experiments, showing an approximately normal distribution centred around zero (buffers, white; site water,black).
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Methods
Mining published values. Published values of the fraction of chemically inducible
cells (FCIC, %) were taken from 39 studies mined from Google Scholar that reported
host densities and corresponding FCIC values estimated using induction by the
addition of mitomycin C (Supplementary Table 2, Supplementary Fig. 1 and
Supplementary Source Data)11,12,1417,2426,29,30,32,4570. Google Scholar hits to the
search mitomycin C induction lysogenywere mined to the tenth page of the search
engine results. Supplementary probing of papers citing Jiang and Paul10 showed a
high degree of redundancy to the Google Scholar approach and was considered
evidence that the majority of papers satisfying the SYBR and mitomycin C
methodological criteria had been retrieved. Where necessary, values were extracted
from gures using the webplotdigitizer Chrome extension tool (http://arohatgi.info/
WebPlotDigitizer/app/). Most papers did not report FCIC if induced viral densities
did not exceed untreated viral densities, as this would yield FCIC estimates 0,
which are interpreted as unsuccessful induction13. Published FCIC data sets were
therefore analysed with values 0 excluded to ensure consistent analysis. All
within-study and global across-study linear regressions were conducted using data
at the greatest resolution possible. Individual measures were used whenever
accessible, and mean values otherwise (Supplementary Table 1). Where FCIC
estimates were provided using minimum and maximum burst sizes (for example,
refs 12,30), minimum values were used.
FCIC estimation. Mitomycin C from a stock less than 1 month old, suspended in
Sigma water (Sigma-Aldrich) was added to environmental samples to a nal
concentration of 1 µg ml
1
, consistent with most environmental induction studies
(Supplementary Source Data) and as prescribed for high-density near-shore
samples13. Sigma water was substituted for control samples to ensure a similar
dilution to induced samples. Samples, with corresponding mitomycin-C-negative
controls, were then incubated for 1824 h at room temperature in the dark, and viral
densities were then compared between mitomycin-C-positive treatments and
mitomycin-C-negative controls. FCIC values were then calculated from equation
(1)1113 using a burst size of 25 to approximate the median value of burst sizes used
in published studies that did (median burst size 22.5) and did not (median burst size
30) vary burst sizes for each sample, respectively (Supplementary Source Data).
In situ studies. The following freshwater and saltwater sites around San Diego,
California, USA (Supplementary Table 2) were sampled with sterile 50 ml
polypropylene tubes: Famosa Slough, Spanish Landing, Vacation Road, Chollas
Reservoir, Old Mission Dam and Lake Murray. Samples were stored in the dark at
room temperature and processed within 2 h of collection. Subsamples were
aliquoted into 2 ml technical replicates in 24-well plates (technical replicates are
ecological pseudoreplicates as they are intentionally not independent samples;
Corning). The three technical replicate fractions required to estimate FCIC (initial
cell counts, nal mitomycin-C-positive viral counts and nal mitomycin-C-negative
control viral counts) for each technical replicate were transferred within one syringe-
draw to keep technical replicate fractions as coupled as possible. Initial counts were
then conducted, and mitomycin-C-positive and mitomycin-C-negative samples
treated. Mitomycin-C-positive and mitomycin-C-negative samples were segregated
in different plates to preclude airborne antibiotic impacts on controls. After
incubation for 1820 h (ref. 13), 1 ml samples were drawn from each 2 ml well, xed
with paraformaldehyde (2% nal concentration) for 30 min, and then ash-frozen
in liquid nitrogen1,71. Samples were thawed at room temperature immediately before
staining with 2× SYBR Gold nucleic acid stain (Life Technologies) for 30 min and
ltered onto 0.02 µm Anodisc lters (Whatman)1,71. Filters were mounted on slides
and imaged on an Olympus ×100 object magnication oil-immersion microscope,
and counts were conducted using Image Pro software (Media Cybernetics), with
observers blind to the sample identity until statistical analysis.
Dilution experiments. Sites sampled in the observational study were resampled for
manipulative dilution experiments. All sampling and aliquoting procedures were the
same as above. However, rather than aliquoting 2 ml of subsampled site water into
each well of the 24-well plates, technical replicates were diluted to produce
undiluted, 25, 50 and 75% dilutions of unltered water samples (undiluted, 75, 50
and 25% unltered site water, respectively). This was done by adding either
0.02-µm-ltered buffer (buffer dilutions, Fig. 5a; articial seawater buffer (Tropic
Marin, Germany) at saltwater sites or Hydra Media buffer72 at freshwater sites) or
0.02-µm-ltered site water (site water dilutions, Fig. 5b). Host densities in each
dilution were estimated as host density in undiluted samples when mitomycin C was
added multiplied by the dilution factor. Both articial seawater and freshwater
diluents maintain chemical buffering of site water, but do not contain nutrients to
sustain or enhance host growth. As a result of this, host density was diluted, with
probable concomitant simplication of community composition and elevation of the
mass action dose of inducing agent per cell present, while the rates of lysogeny and
host growth rate were presumably unchanged. Dilution with site water dilutes host
densities but does not change nutrient availability, allowing host growth to increase in
proportion to dilution. Undiluted samples (0% dilutions) were considered correct
estimates of FCIC for comparison, because they are equivalent to samples typically
processed for published FCIC studies. Mitomycin C addition, incubation, sample
xation, storage and processing were thesame as above, but slides were imaged using
an Olympus ×60 objective magnication oil-immersion microscope.
Statistical analysis. All statistical analyses were conducted with a conventional
a priori alpha of 0.05 (95% condence). Despite the large number of analyses
conducted herein (for example, 50 linear regressions), no correction was applied to
the alpha to facilitate identifying signicant relationships between FCIC and host
density, even at risk of incurring Type I error. Linear regressions were conducted
using the lm() function in R (reported in the plots in Figs 13). These were
complemented by ordinary least squares (OLS) Type II linear regressions using the
lmodel2() function of the lmodel2 R package across all data and across freshwater,
saltwater, sediment and soil environments, to ensure variability in host density
measurements was not skewing our analyses. Published data sets showed a negative
relationship between nand R
2
, suggesting that the analysis of means, as the majority
of data available for this study, facilitated nding relationships between FCIC and
host density (Supplementary Table 1). Power analysis using a test for correlation
were conducted using published data sets (Supplementary Source Data) with the
pwr.r.test() function in R to estimate the sample sizes needed to obtain a power of
0.8 with a signicance level of 0.05 in a two-tailed test. As the FCIC values generated
here were intentionally not independent (that is, they were technical, not biological,
replicates), a conservative non-parametric bootstrapping approach was used to
generate means and 95% CIs (Fig. 4). These parameters were estimated using the
boot_out() function in R with 10,000 iterations on data with all FCIC values
included as well as with FCIC values 0 excluded, consistent with the literature.
Data availability. Published data subjected to meta-analysis are provided in the
Supplementary Source Data. These data and experimental data sets are also available
from the corresponding author upon request.
Received 15 November 2016; accepted 22 March 2017;
published 28 April 2017
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Acknowledgements
The authors thank R. Young for microbiological insight and guidance. Canadian Institute
for Advanced Research Integrated Microbial Biodiversity Program Fellowship Award
141679, National Science Foundation grants OISE-1243541 and DEB-1046413, a Gordon
and Betty Moore Foundation Investigator Award GBMF-3781 (to F.R.) and National
Science Foundation grants OCE-1538567 (to L.W.K.), IOS-1456301 and DEB-1555854 (to
M.B.) funded this work. The authors thank G. Gueiros and K. Furby for critiquing
the manuscript.
Author contributions
B.K. and F.R. designed, conducted and wrote up the study. B.B., L.B., M.B., A.C.-G., J.d.C.,
R.E., B.F., J.G., A.H., P.K., L.W.K., A.L., J.N., G.P., L.P., N.R., S.S., A.S., C.S. and M.Y.
contributed data, analysis and manuscript preparation.
Additional information
Supplementary information is available for this paper.
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How to cite this article: Knowles, B. et al. Variability and host density independence
in induct ions-based estimates of e nvironmental lysogeny. Nat. Microbiol. 2,
17064 (2017).
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NATURE MICROBIOLOGY ARTICLES
NATURE MICROBIOLOGY 2, 17064 (2017) | DOI: 10.1038/nmicrobiol.2017.64 |www.nature.com/naturemicrobiology 9
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... A lytic or productive infection is characterized by an immediate replication and expression of the viral genome within the host cell, subsequently releasing new viral particles by cell lysis (5). In contrast, a lysogenic infection is characterized by integrating the viral genome into the host chromosome or as an episomal element, forming a new biological entity known as lysogen (5)(6)(7). This cycle does not produce viral particles immediately after infection; however, it can switch to a productive cycle depending on multiple factors, such as virus or host genetics, virus-host ratio, host physiological state, host density (quorum sensing), and environmental conditions (5)(6)(7)(8). ...
... In contrast, a lysogenic infection is characterized by integrating the viral genome into the host chromosome or as an episomal element, forming a new biological entity known as lysogen (5)(6)(7). This cycle does not produce viral particles immediately after infection; however, it can switch to a productive cycle depending on multiple factors, such as virus or host genetics, virus-host ratio, host physiological state, host density (quorum sensing), and environmental conditions (5)(6)(7)(8). ...
... Lytic viruses directly influence microbial community composition through predatorprey dynamics, in which the dominant or active taxa in the microbial community are selectively lysed, as described in the "kill-the-winner" ecological model (2,6,7,(9)(10)(11). Conversely, it has been proposed that in ecosystems with high nutrients and high microbial abundances, viruses will lysogenize the most metabolically active and sometimes dominant taxa in the microbial community following the "piggyback-the-winner" ecological model (6,7). ...
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... For example, Ribbon-Helix-Helix (RHH) domains are common in MetJ/Arc-family transcription factors as well as in TAs, suggesting that the bacterial transcription factors of this family were originally derived from antitoxins (Aravind et al., 2005). Given that TAs and other MGEs are activated by various signals, such as DNA damage (Knowles et al., 2017) or the presence of other MGE (McKitterick and Seed, 2018;LeGault et al., 2021), recruitment of MGE-encoded transcription factors could be favorable for the host, enabling it to respond to the same stressors (Benler and Koonin, 2020). ...
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... Additionally, the relative abundance of hallmark genes encoded by temperate viruses increased with microbial density in a coral reef (Knowles et al. 2016). These findings suggest that lysogenic infection may become dominant at high-cell densities because proviruses can replicate quickly in a way that will keep pace with their fast-growing host and provirus-mediated superinfection resistance might become increasingly important at high cell densities (called "Piggyback-the-Winner" model 4 ) (Knowles et al. 2016(Knowles et al. , 2017Coutinho et al. 2017) (Fig. 1.2). ...
... Virus-mediated prokaryotic mortality was calculated by dividing the lytic VP by burst sizes of 25 determined from a previous global investigation (Knowles et al., 2017). The percentage of lysed cells (PLC; expressed as % L − 1 ) was then estimated from the ratio of the virusmediated prokaryotic mortality to the in situ prokaryotic abundance of the macrocosm. ...
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... Whereas this long-established concept is well supported by theoretical and experimental studies 31,32 , data from environmental studies are more ambiguous. Indeed, some studies have reported a positive (or negative) correlation between the frequency of lytic (or lysogenic) phages and bacterial density 33,34 , whereas other studies reported weak or no correlations 35 . Also contradicting this view, the nutrient-rich, bacterially dense ecosystem of the mammalian gut is dominated by lysogenic bacteria 36,37 , and copiotroph species tend to have more prophages 38 . ...
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