plospbioplbiPLoS BiolplosbiolPLoS Biology1544-91731545-7885Public Library of ScienceSan Francisco, USA10.1371/journal.pbio.006007407-PLBI-RA-3594R2plbi-06-04-07Research ArticleEcologyGenetics and GenomicsImmunologyInfectious DiseasesMicrobiologyModelling within-Host Spatiotemporal Dynamics of Invasive Bacterial DiseaseDynamics inS. enterica
InfectionsGrantAndrew J12*RestifOlivier12McKinleyTrevelyan J12SheppardMark1MaskellDuncan J1MastroeniPietro1 Department of Veterinary Medicine, University of Cambridge, Cambridge, United
Kingdom Cambridge Infectious Diseases Consortium, University of Cambridge, Cambridge,
United Kingdom RelmanDavid AAcademic EditorStanford University, United States of America* To whom correspondence should be addressed. E-mail: ajg60@cam.ac.uk
AJG conceived the project, performed experimental work, analysed and interpreted the
data, wrote the manuscript, and led the project. OR analysed and interpreted the data,
performed the model development, and prepared the manuscript. TJM analysed and
interpreted the data, performed the statistical analysis, and prepared the manuscript.
MS contributed to the experimental work. DJM and PM conceived the project, obtained the
funding, interpreted the data, and prepared the manuscript.
The authors have declared that no competing interests exist.
4200884200864e743010200713220082008 Grant et alThis is an open-access article distributed under the terms
of the Creative Commons Attribution License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original author and source are credited.
Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts
cannot be fully resolved studying a single bacterial population. They are also currently
poorly understood. Here, we report on the application of sophisticated experimental
approaches to map spatiotemporal population dynamics of bacteria during an infection. We
analyzed heterogeneous traits of simultaneous infections with tagged Salmonella
enterica populations (wild-type isogenic tagged strains
[WITS]) in wild-type and gene-targeted mice. WITS are phenotypically
identical but can be distinguished and enumerated by quantitative PCR, making it possible,
using probabilistic models, to estimate bacterial death rate based on the disappearance of
strains through time. This multidisciplinary approach allowed us to establish the timing,
relative occurrence, and immune control of key infection parameters in a true
host–pathogen combination. Our analyses support a model in which shortly after
infection, concomitant death and rapid bacterial replication lead to the establishment of
independent bacterial subpopulations in different organs, a process controlled by host
antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential
increase in the number of bacteria that spread locally, with subsequent mixing of bacteria
between organs via bacteraemia and further stochastic selection. This approach provides us
with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the
complex spatial and stochastic effects that drive an infectious disease. The application
of the novel method that we present in appropriate and diverse host–pathogen
combinations, together with modelling of the data that result, will facilitate a
comprehensive view of the spatial and stochastic nature of within-host dynamics.
Author Summary
Global patterns and mechanistic determinants of bacterial spread in mammalian organisms
are difficult to obtain through numerical and topographical mapping of a single
bacterial population. Appreciation of the true pathogenetic events during infections
needs to be based on the understanding of the fine interactions that control the
infection dynamics of individual subpopulations in the same host. We have used molecular
techniques to tag individually otherwise identical subpopulations of bacteria. We have
used these bacteria, called wild-type isogenic tagged strains (WITS), in simultaneous
infections in the same animal to gather insights into the patterns of spread of
individual subpopulations of bacteria in the tissues and interactions between bacteria
and phagocytes. Combining numerical fluctuation in the WITS populations with
mathematical modelling and statistical analysis, we have gathered data on the relative
occurrence of bacterial growth and death in different phases of the disease process. Our
analyses support a model in which shortly after infection, concomitant death and rapid
bacterial replication lead to the establishment of independent bacterial subpopulations
in different organs. Later, decreased microbial mortality leads to an exponential
increase in the number of bacteria that spread locally, with subsequent mixing of
bacteria between organs. The work illustrates the importance of unravelling
heterogeneous traits of infections to reconstruct and understand the true nature of the
global disease process.
Genetically identical bacterial strains reveal the population dynamics and interactions
of subpopulations of bacteria with the host's immune system in vivo during infection.
This work was supported by Biotechnology and Biological Sciences Research Council:
(BBSRC) grant BBS/B/02266 awarded to PM and DJM.citationGrant AJ, Restif O, McKinley TJ, Sheppard M, Maskell DJ, et al. (2008)
Modelling within-host spatiotemporal dynamics of invasive bacterial disease. PLoS Biol
6(4): e74. doi:10.1371/journal.pbio.0060074Introduction
Central to a complete understanding of any disease is the ability to integrate information
from different scales into a coherent model that fully explains the disease process
[1]. One challenge
that remains is how to move from our ever more detailed understanding of cellular and
molecular microbiology in artificial laboratory systems, towards an explanation of the
dynamics of pathogen survival and growth in a whole-animal infection model [2]. A promising approach relies on
concepts and mathematical models from population ecology to derive quantitative information
on within-host bacterial dynamics from experimental data [3,4]. However, development of an appreciation of within-host dynamics has
been hampered by the difficulty of identifying and observing directly, within tissues, the
multiple key variables that underlie the infection process. Numerical variations in total
microbial load in bacterial infections and the effects of host factors or therapeutic
intervention on these variations are usually followed in vivo by counting the number of
colony-forming units (CFUs) present in infected organs and plotting this through time. This
provides an overarching idea of the global numerical variation in the bacterial population
in the organ as a whole, but gives no information about the fine structure of the population
in vivo. At one extreme, all bacteria might display identical growth in all infected cells.
At the other extreme, only one clone of bacteria might be growing very rapidly, with all
others not growing or growing very slowly. Both these scenarios could lead to the same
overall growth dynamics at the organ level. However, the biology underlying them would be
very different, having major consequences for understanding the most appropriate way to
manipulate the immune system and/or antimicrobial drug regimes to combat these infections.
Attempts have been made to measure the actual division rate of bacteria in vivo (reviewed by
[2]): they
typically rely on introducing nonreplicating elements (e.g., phages, plasmids, or
temperature-sensitive mutants) into the bacteria. However, their widespread use has been
hindered by the sensitivity of foreign elements to experimental conditions. In addition, the
extent to which those methods disrupt the phenotype of bacteria and the immune response has
not been assessed.
The aim of this study was to obtain a comprehensive picture of in vivo bacterial population
dynamics by unravelling the variations in basic demographic processes that drive
spatiotemporal events. To this aim, it was necessary to use (1) a host–pathogen
system already established as a model for bacterial dynamics, (2) a reliable marker for
bacterial division or death that does not modify the infection, and (3) an analytical
platform that allowed us to elicit information about unobserved processes from observed
data. In this study, we used Salmonella
enterica serovar Typhimurium infections in mice, a well-documented,
genetically tractable system and a natural host–pathogen interaction with defined
infection dynamics amenable to robust statistical analysis. S. enterica infections of mice are a good
model for human typhoid fever, for other invasive salmonelloses, and for infections with
intracellular pathogens in general.
We report an integrated approach to investigate bacterial growth dynamics over the first
few days of an acute murine Salmonella infection, which relies on tracing
tagged subpopulations of otherwise identical bacteria through infected animals and their
organs. Previous studies [5–7] have
exploited coinfections of single hosts with several strains of a given pathogen as a
powerful tool to explore population dynamics during infection; however, such studies are
limited by three main factors: the number of strains that can be distinguished inside the
host, the limit of detection/discrimination of strains, and the potential phenotypic
variations associated with genetic diversity. To overcome these limitations, we generated
eight wild-type isogenic tagged strains (WITS); phenotypically identical bacterial strains
each carrying a different 40-bp DNA signature tag in the same noncoding region of the
chromosome (Figure 1A) with no
interstrain difference in in vitro or in vivo growth rates. Using inocula consisting of
mixtures of low numbers of each tagged strain, we monitored the dynamics of each
subpopulation at different body sites throughout the course of a systemic infection.
10.1371/journal.pbio.0060074.g001
A Cartoon Detailing the Construction of the WITS and the Experimental Design
(A) To investigate the in vivo population dynamics of S. enterica during systemic infection, we
generated a series of WITS. These are S.
enterica strains each carrying a different 40-bp
([NK]20 : N = A, C, G, or T; K
= G or T) signature tag [29] in precisely the same noncoding region of
the chromosome (between two pseudogenes malX and
malY). Each mutant differs from the others only in that it carries a
different tag, thus there should be no selective advantage between the different
mutants.
(B) Mice were inoculated i.v. with approximately 80 to 100 CFU of S.
Typhimurium, being approximately equal CFU of each of eight WITS. At 0.5, 6, 24, 48, and
72 h p.i., groups of mice were killed, and the livers, spleens, “other
organs” (kidneys, heart, lungs, and mesenteric lymph nodes), and blood were
cultured for the presence of bacteria. All bacterial colonies were scraped from the agar
plates, and chromosomal DNA was purified using standard commercial kits. The
S. enterica population
structure in individual organs at different times after i.v. inoculation was detected
using quantitative PCR. Each biological sample was assessed by qPCR using each of the
eight primer pair combinations, the number of copies of each WITS chromosomal DNA per
reaction (biological sample) was calculated from standard curves for each of the
WITS.
We address three specific questions about the dynamics of infection: how does an acute
bacterial infection replicate and grow in infected organs? Does the spread of infection
within and between organs result from homogeneous bacterial growth, or from expansion of
subpopulations? How do components of the immune response affect spatiotemporal bacterial
population dynamics? We used depletion of tagged diversity as a proxy for bacterial death,
and spatiotemporal heterogeneity as a proxy for bacterial transfer between organs as a
function of time postinoculation. This allowed the development of mathematical models to
dissect the determinants of net growth at different infection stages, and the mapping of
local (intraorgan) versus systemic spread of each bacterial subpopulation. This work
illustrates that to understand the basic idiosyncrasies of infection requires a
disaggregation of the global disease dynamics and awareness of the separate infection
dynamics of subpopulations within the same host. This approach is suitable for many animal
infection models; evaluating infection dynamics in these terms will provide a paradigm shift
in our understanding of the whole animal infection process.
ResultsUsing WITS to Determine the Within-Host Growth/Death Rate of S. enterica during an Infection
As a first step in our study, we monitored the growth kinetics of the WITS in the
systemic compartment of mice after intravenous (i.v.) injection (Figure 1B). Total bacterial loads in the different organs
are shown in Figure 2A and 2B. Between 0.5 and 6 h postinfection
(p.i.). we observed blood clearance, a slight decrease in bacterial numbers in the liver,
and a slight increase in bacterial numbers in the spleen. This was followed by exponential
bacterial growth at the expected rate of approximately 10-fold increase per day. Low
bacterial numbers persisted in the “other organs” throughout, and a
resurgence of bacteraemia occurred from 48 h p.i. onwards. To determine the true variation
in net bacterial growth rates in the tissues, we monitored bacterial division and death
rates in infected tissues to assess whether bacteria started dividing in the spleen and
liver within the first 6 h p.i. Using quantitative PCR (qPCR), which in this application
provides a discrimination range of at least 104-fold, we analysed the
S. enterica WITS population
structure in individual organs (Figure
2C). Shortly after inoculation (0.5 h), the qualitative population structure of the
WITS between spleen and liver in each animal was not homogeneous (Figures 2C and 3), reflecting low bacterial numbers present in each
organ. At 6 h p.i., the WITS population structure remained heterogeneous between the two
organs (Figures 2C and 3); however, the frequencies of individual
WITS recoverable from each organ were lower than those observed at the earlier time point
of 0.5 h p.i. (Figures 3 and 4). Observed WITS distributions enabled us
to reject the hypothesis that bacteria have not started dividing by 6 h p.i.; the
combinations of bacterial counts and WITS frequencies in the spleens and livers of
individual mice at 6 h p.i. were incompatible with random sampling from the initial
inoculum (Figures
S1 and S2;
Protocol S1,
Section 1). This indicates that in the first 6 h p.i., heterogeneous bacterial division
and death occur in parallel, leading to the disappearance of some bacterial subpopulations
and the expansion of others (Figure
5). Thus, the prevalence of individual bacterial subpopulations in an animal
originally infected with genetically identical bacteria starts from the very early stages
of the infection process.
10.1371/journal.pbio.0060074.g002
CFU and WITS Frequencies in the Organs of Infected Mice
(A) Log10 CFU in “other organs” (purple triangle) and
blood (green circle) at 0.5, 6, 24, 48 h (n = 5), and 72 h
p.i. (n = 4); the arrow indicates inoculum size.
(B) Log10 CFU in liver (red) and spleen (blue) (n
= 20 per time point), results expressed as mean log10 viable
count ± standard deviation. Lines (grey) indicate deterministic models;
(grey) error bars indicate stochastic simulations (described in Protocol S1)
expressed as mean ± standard deviation.
(C) Frequency of WITS recovered from liver (red) and spleen (blue) compared to
distributions of WITS as predicted from 1,000 stochastic simulations (grey).
10.1371/journal.pbio.0060074.g003
Presence and Absence of WITS in Individual Mice
WITS presence (grey box), or absence (white box) in the liver (L) or spleen (S) of
individual mice (n = 20 per time point) at 0.5, 6, 24, and
48 h p.i. The individual WITS are indicated by coloured boxes (dark blue =
WITS 1, olive green = WITS 2, light blue = WITS 11, pink
= WITS 13, yellow = WITS 17, bright green = WITS 19,
brown = WITS 20, and lavender = WITS 21). In addition, the
number of WITS (No) and CFU (CFU) in each organ are indicated.
10.1371/journal.pbio.0060074.g004
Comparison of the Observed and Predicted (Model) Frequency and Colocalisation of
WITS in the Organs
Showing the mean number of WITS present in the spleen and liver (dark grey), one
organ only (light grey), and neither organ (white). Experimental data are presented
above the horizontal time line. The WITS frequencies as predicted by the modelling are
presented below the horizontal time line (described in detail in Protocol S1,
Models Section 3).
10.1371/journal.pbio.0060074.g005
A Schematic Diagram Summarizing the Initial Dynamics of Systemic S. enterica Infections
During the early stages of infection, bacteria move from the blood into the spleen
and the liver (CFU in each organ denoted by nS and
nL, respectively), representing a frequency of WITS in
the spleen (wS) and liver
(wL). WITS frequency at 0.5 h p.i. is consistent with
stochastic removal of bacteria from the blood by the liver and spleen. Between 0.5 and
6 h p.i., bacterial growth and death occur in parallel, leading to the disappearance
of individual subpopulations and the expansion of others. If bacterial death did not
occur, the frequency of WITS per organ at 0.5 and 6 h would be comparable (dashed line
and faded shading).
The WITS Indicate Bacterial Growth and Death in Early Systemic Infection
We fitted a first mathematical model to the observed number of CFUs in the organs, to
estimate (1) initial rates of transfer from the blood into the organs and (2) net growth
rates in the first 6 h of infection (Equation 1 in Materials and Methods; Figure S3; Protocol S1, Section
2a). The model suggests that the average 93 bacteria of the inoculum lodge in the spleen
and liver within approximately 4 h, and that densities subsequently decrease at rates
corresponding to a 50% drop every 5 h in the spleen and every 3 h in the liver.
To distinguish between bacterial divisions and death within the first 6 h p.i., we
generated another model, which uses a branching process to track the number of copies of
an individual WITS population in a given organ (Equation 2; Protocol S1, Section 3). To summarize, at 6 h p.i., we
matched the probability of a WITS being absent from an organ in the model with the
proportion of WITS found missing across all mice. It is worth pointing out here that the
observed decrease in WITS diversity between 0.5 and 6 h p.i. could in theory be due to
either bacterial death or migration to other organs or tissues. Actually, we were able to
dismiss the latter hypothesis by comparing the WITS found in the liver and spleen within
each animal; the high heterogeneity between the two organs at 6 and 24 h p.i. (Figure 4) could not be maintained if there
was any bacterial migration between them during that time (Protocol S1, Section
3). We discovered that the slow decline in bacterial numbers during the first 6 h p.i., is
caused by the combined occurrence of high division rates (doubling times around 1.7 h in
both organs) and higher death rates (half-lives around 1.1 h). The model also allows us to
determine the likely maximum duration of this process and predicts that it must cease
within 7 h p.i. In fact, if both high division and death rates extended beyond 7 h, the
numbers of WITS in the organs would keep decreasing, at odds with the steady WITS
distributions observed experimentally at 24 h. Therefore, it is possible to predict that a
“switch” from the early phase of active bacterial killing to a phase
of low bacterial killing and exponential growth must occur in the spleen and liver within
7 h p.i. (Figures
S4–S6, Table
S4; Protocol
S1, Models 2b). The bacterial dynamics arising from this combination of migration
from the blood to the organs, rapid divisions and killing, can be simulated using our
mathematical model (grey curve on Figure
2B). The peak of bacterial density in the organs around 2 h p.i. occurs when
killing balances immigration and division.
The WITS Indicate Independent Infections in Systemic Organs during the First 24 h
Postinfection
During this second phase of the infection (6 to 24 h p.i.), bacterial numbers increased
exponentially; we have previously shown that this is paralleled by a similar increase in
the number of infected host cells [4,5]. The
frequency of individual WITS in the spleen and liver of each animal remained almost
constant (Figures 2C and 3). Our model shows that bacterial
mortality in the organs must be very low during this period, so that observed division
rates (doubling times around 8 h in both organs) must decrease to reduce the overall
observed bacterial growth (3- to 4-fold decrease compared to phase one, 0.5 to 6 h p.i.)
(Protocol S1,
Section 3). The analysis of WITS frequency between organs (Figure 3) reveals that the observed differences between
the liver and the spleen remain different between 6 and 24 h p.i. (Figures 2C and 4). Consequently, the hepatic and splenic subpopulations
must grow independently without mixing for at least 24 h, with negligible mortality after
6 h. In summary, the models capture the observed data and predict an initial phase that
lasts for at most 6–7 h and is characterised by high division rates and even
higher death rates of bacteria in both the spleen and liver, and a second phase that is
characterised by very low bacterial death rate, (possibly zero), and moderate division
rates in both organs. Thus, we can conclude that net growth rate in the tissues is
governed by the bacteria growing at a controlled rate as the host restrains bacterial
division, and not by killing of the bacteria. The data also indicate that during the early
stages of the exponential growth phase, bacterial subpopulations remain spatially
separated, growing and spreading from cell to cell locally [4,5]; they do not escape to other organs.
The WITS Indicate Local and Then Systemic Haematogenous Spread during Infection
At 48 h p.i., the frequency of individual WITS recovered from the spleen and liver
increased and became more homogeneous between the two organs within each mouse (Figures 2C and 3), indicating that the infection had entered a third
phase, characterised by systemic bacterial spread and mixing of WITS populations between
distant body sites. The high frequency of WITS recovered at 48 h p.i. suggests that
bacterial mortality had not increased from 24 h p.i. Systemic dissemination of WITS
correlated with the appearance of WITS in the blood. A simple model for global bacterial
dynamics (ignoring the WITS structure) allowed us to estimate the relative rate of
transfer of bacteria from the organs to the blood, around 1% of the bacterial
transfer rate from the blood to the organs (Protocol S1, Section 4a). Collectively, the data
indicate local spread within each organ early in infection, followed by later systemic
haematogenous spread. The systemic spread can be reasonably explained in the light of
bacteraemia; however, the exclusive within-organ spread early in infection is intriguing,
especially when considering that S.
enterica grows and spreads from cell to cell via the extracellular
compartment at a constant rate throughout infection [4].
Stochastic Model of Systemic S.
enterica Infection
Analysis of the global, proportional population structure of WITS within individual
animals demonstrates a skewed subpopulation structure, with increased predominance of
typically a couple of WITS within each animal at 48 and 72 h p.i. (Figure 6). The data therefore indicate preferential
amplification of subpopulations during the infection process despite the fact that the
WITS exhibit no systematic bias in terms of relative “fitness” or
ability to colonise the host. The lack of bias is an assumption built into the models and
is based on experimental data showing qualitatively, (Figure 6) and quantitatively (p-value
> 0.97) (Table
S5), no evidence of colonisation bias in any of the individual WITS across mice. We
now complete our analysis with a stochastic model that encompasses all the processes
previously analysed over the first 48 h p.i. and that allows for random variations in the
initial conditions (Protocol S1, Section 4.b; Table S6). More precisely, we assumed that the number of
copies of each WITS in the inoculum followed a Poisson distribution with mean 11.65, in
line with the experimental protocol in which an even mix of the eight WITS was diluted
down to an estimated density of 93.22 bacteria per inoculum (mean value from nine
independent samples that was determined by plating aliquots of the input inocula onto LB
agar plates). We tracked the numbers of WITS in the “virtual” blood,
liver, and spleen in a series of 1,000 simulations, and compared the output to
experimental observations (Figures 2B,
2C, 4, and S7). The simulations, which rely on demographic
stochasticity, reproduced levels of variability compatible with those observed
experimentally, except at the earliest time point (0.5 h p.i.), when empirical data were
slightly more variable. This could be due to heterogeneities among mice or among
experiments or, possibly, to mechanistic detail not included in the model. Although most
of our analyses have focused on patterns of presence/absence of WITS, our stochastic model
actually keeps track of the number of copies of each WITS in the blood, liver, and spleen.
This enabled us to compare, at least qualitatively, the patterns of WITS proportions in
the simulations and in the experiments (Figure S7), providing further support for the validity
of our approach. The only notable discrepancy related to distributions in the blood at 0.5
and 48 h p.i. where simulations appear to underestimate the level of heterogeneity. This
is likely to be due in large part to the small blood sample sizes in the experiments,
which may have resulted in the less frequent WITS being systematically underreported.
10.1371/journal.pbio.0060074.g006
Proportions of WITS Distributions in Mice
Results for the predicted proportions of each WITS within individual mice from one
experiment, at 0.5, 6, 24, and 48 h p.i. (n = 5) and 72 h
p.i. (n = 4). These were generated from an initial
Bayesian linear regression model fitted to log10(conc.) against CT value
(the point at which the fluorescence crosses the threshold) from the qPCR analysis.
Each panel represents a different mouse, with boxes giving the median and quartiles,
and the whiskers representing 95% credible intervals centred around the
median. Within any panel, the left-hand box plot represents the most frequent WITS,
and right-hand box plot the least frequent WITS. Below each panel, the four-line grid
indicates the presence (shaded) or absence (white) of each individual WITS (as ordered
in the panel directly above) in the different organs: “other
organs” (purple), blood (green), spleen (blue), and liver (red). The
individual WITS are indicated by coloured boxes (see Figure 3 for a detailed description). Asterisks
indicate absence of WITS.
Using WITS to Investigate How Immunological Bottlenecks Affect the Distribution of
S. enterica during an
Infection
The approach combining the use of WITS and mathematical analysis of fluctuations in
individually tagged bacterial populations within an animal can also be used as a powerful
tool to analyse the effects of immunological mechanisms on the population dynamics of
infection. As an example, we used WITS infections in
phox−/− mice to investigate the role
of NADPH oxidase in controlling S.
enterica during systemic infection. Reactive oxygen intermediates
(generated via the phagocyte NADPH oxidase, encoded by phox) are produced
by phagocytes at infection foci to control the growth of intracellular bacteria
[8,9]. We analysed fluctuations in total bacterial
numbers and WITS population structure throughout the infection. CFU and frequency of WITS
in the organs of phox−/− mice at 0.5 h
were comparable to wild-type C57BL/6 mice (Figure 7A and 7B). However,
by 6 h, when bacterial load in spleens and livers of wild-type mice had decreased by
roughly one quarter, bacterial load in
phox−/− mice had increased over 12-fold.
This observation combined with the fact that at 6 h, no WITS were lost in the majority of
the phox−/− mice, indicates
little or no killing in the absence of a functional NADPH oxidase and demonstrates the
bactericidal action of NADPH oxidase in these early phases of the disease process. The
lack of disappearance of WITS in the
phox−/− mice also indicates that the early
in vivo selection of bacterial subpopulations observed here in S. enterica infections is actively mediated
by innate immunity mechanisms. The calculated combined mean generation time (spleen and
liver) between 6–48 h gives 7.46 h and 3.38 h for wild-type and
phox−/− mice, respectively. The
difference in generation time, which occurs in a phase of the infection when bacterial
killing is negligible, indicates a bacteriostatic role for NADPH oxidase from 6 h p.i. The
data provide an increasing level of resolution in understanding the dynamics of
antibacterial functions of phagocytes. In fact, contrary to what was previously postulated
[8,9], the reactive molecules generated by NADPH
oxidase are highly bactericidal only in the very early stages of infection, becoming
bacteriostatic as infection progresses. This analysis illustrates, therefore, that an
individual immunological mechanism can have different sequential effects on bacterial
population dynamics. This may reflect transmission from resident macrophages to
polymorphonuclear neutrophils (PMNs) [10], and consequent differences in intracellular
control mechanisms.
10.1371/journal.pbio.0060074.g007
CFU and WITS Frequency in Infected C57BL/6 and
phox−/− Mice
phox−/− mice and congenic wild-type
control mice on a C57BL/6 background were infected i.v. with approximately 80 to 100
CFU of S. Typhimurium WITS. (A) shows bacterial numbers and (B) the
frequency of WITS recovered, from the C57BL/6 liver (red square), C57BL/6 spleen (blue
square), phox−/− liver (golden
triangle), and phox−/− spleen (cyan
triangle) at 0.5, 6, 24, and 48 h p.i. Results are expressed as log10
viable count ± standard deviation; arrow indicates inoculum.
Using Model Frameworks to Make Predictions about the Infection Process
We can use the model frameworks that we have generated, not only as a resolving tool to
increase the power of the biological data, but also to make additional predictions about
the infection process. For example, we can consider the probability of clearance of the
infection for various inoculum sizes. Our analysis predicts that the 50%
infectious dose (ID50) for the S. Typhimurium WITS is
approximately 5 CFU (Figure 8), in
line with the 50% lethal dose (LD50) of the parent strain, SL1344
for BALB/c mice, fewer than 20 CFU [11]. Figure 8 also
suggests that if the initial bacterial death rate in the early phase of infection is
increased from 0.64 to 1.0 h−1 in the liver and from 0.59 to 0.95
h−1 in the spleen, the ID50 increases to approximately
25 CFU, thus demonstrating the ability of the models to shed light on the within-host
dynamics of bacterial control.
10.1371/journal.pbio.0060074.g008
Estimated Probability of Clearance of Infection for Various Inoculum Sizes and
Initial Death Rates
Probability values shown along the isoclines were estimated as the proportion of
stochastic simulations resulting in bacterial clearance. For each combination of
inoculum size (horizontal axis) and death rate (vertical axis), we ran 1,000
simulations. Early death rate in the spleen was varied in line with that in the liver
(i.e., the difference between the two parameters remained constant). The horizontal
dashed line represents the baseline death rates as estimated from the experiments. The
thick isocline represents the predicted ID50 (i.e., intravenous inoculum
size giving a 50% risk of successful infection) read on the horizontal axis
as a function of death rate read on the vertical axis. Variation in death rates might
be achieved with different mouse strains or following treatment or immunisation. Other
parameter values were as estimated from the experimental values.
Discussion
The course and outcome of a S.
enterica infection depends upon many host and bacterial factors, and a
number of models have been proposed to describe the infection process: the hypothesis of
independent action of pathogens [12], the birth–death model [13], and the two-stage model of microbial
infection [14].
Results from numerous studies over the last 50 y indicate that the progression of murine
salmonellosis can be divided into two different phases, an early phase of exponential
Salmonella growth in the spleen and liver, and a later septic phase that
precedes death [15,16]. The onset of these phases
differs according to variations in experimental design and differences in bacterial and host
strain.
Bacterial growth, death, and spread in the body are still undetermined aspects of the
infection process despite their exquisite relevance to vaccination and treatment. Studies
often focus on observation of the disease course at the whole-animal level or in whole
organs, and interpret the gross, cumulative effects of many individual interactions and use
the mean to represent what is a complex phenomenon. However, an appreciation of population
dynamics at the gross level provides almost no information on where individual bacteria
locate and how they spread and interact during infection. Simply assessing bacterial load in
each animal by plate count is not sufficient or sophisticated enough to allow us to fully
understand the infection process. Spatial and functional independence between individual
bacterial populations in the same host has been conclusively demonstrated by several studies
from our group. Previously, we have shown that during a systemic S. enterica infection, each infectious focus
is a separate unit resulting from clonal growth of an individual bacterium, with spatial
segregation of bacterial populations and continuous distribution to new phagocytes
throughout the infection process [4,5,17]. Thus, understanding the overall dynamics of
an infectious disease hinges on the elucidation of the complexity and dimensionality of
functional independence and heterogeneity at the subpopulation level in the animal.
In this study, we have used an infection model based on simultaneous administration of
tagged subpopulations of otherwise identical bacteria (WITS) in the same animal, to resolve
the difficult question of how components of the host immune responses counteract the net
growth of the bacterial load in the tissues and to determine the relevance of bactericidal
and bacteriostatic mechanisms in these processes. We have shown that shortly after
infection, concomitant NADPH oxidase-dependent death and rapid bacterial replication lead to
the establishment of independent bacterial subpopulations in different organs. Subsequently,
a reduction in bacterial mortality leads to an exponential increase in the number of
bacteria that spread locally and independently within each organ (e.g., spleen and liver).
During the later stages of infection, different bacterial populations mix between organs via
the blood with a further stochastic selection of bacterial subpopulations.
Populations of genetically identical bacterial strains are a very powerful tool to address
novel questions on the nature of individual events that determine the global profile of
population dynamics during infection. Having developed and validated the use of WITS in the
study of the in vivo population biology of S. enterica, we are now in the
position to exploit these systems to great effect to understand the impact of key host and
bacterial factors on the pathogenesis of S.
enterica at the level of individual bacterial subpopulations. This also
allows us to address the extent to which we can rely on data generated in in vitro cell
biology systems to describe what is actually happening in vivo. Understanding the effect of
anatomical bottlenecks and individual components of the immune system on local and
bacteraemic spread is fundamental in order to tailor appropriate prevention and treatment
strategies to different stages and forms of S.
enterica diseases in animals and man.
The specific model we have chosen has focused on S. enterica, but it is an approach that can
be applied to the study of the population dynamics of many infectious animal diseases. The
use of tagged wild-type strains has the potential to transform our understanding of
within-host dynamics of pathogen interactions with host cells, to provide the information
needed to build mechanistic mathematical models, generate new research hypotheses, and make
quantitative predictions. For example, by using combinations of WITS in animals from
different genetic backgrounds, in gene-targeted immunodeficient animals, or in previously
vaccinated animals, it will be possible to investigate the way in which different components
of the innate and acquired immune system control the expansion (net growth rate) or
determine the contraction (clearance) of the bacterial load in the tissues. Using
combinations of defined bacterial mutants and gene-targeted mice, it will be possible to
determine precisely how individual bacterial virulence genes counteract the antimicrobial
functions of the host. Moreover, understanding the impact of the immune response and
different classes of vaccines on the distribution and population dynamics of pathogens will
be instrumental in furthering the development of preventive measures and drug therapies.
Materials and MethodsBacterial strains, media, and growth conditions.
All bacterial strains used in this study are listed in Table S1. The
Escherichia coli strain
DH5α was used for gene cloning, unless otherwise indicated. S. enterica serovar Typhimurium strain
JH3010 [18] is a
virulent wild-type strain, derived from SL1344, which has an LD50 of fewer than
20 CFU for BALB/c mice [11]. E. coli
strains were grown for 16 h in Luria-Bertani (LB) broth at 37 °C; cultures were
shaken at 220 rpm. S. enterica
strains were grown for 16 h as a stationary culture at 37 °C in LB broth. Where
necessary, media were supplemented with the appropriate antibiotic for selection
(ampicillin, 100 μg/ml, kanamycin 50 μg/ml, and chloramphenicol 10
μg/ml). S. enterica
were diluted in phosphate-buffered saline (PBS) prior to i.v. inoculation. Long-term
storage of bacteria was at −80 °C in Microbank vials (Prolab
Diagnostics). Preparation of electrocompetent E. coli and S.
enterica cells and transformations were performed as previously described
[19]. In vitro
growth rates of WITS in LB broth were determined by optical density reading of LB broth
cultures, serially diluted at each time point, and also plated to obtain the number of CFU
per millilitre.
Animals.
All aspects of animal procedures were approved by the local ethical committee and
performed according to UK law. phox−/−
mice were bred in the Cambridge animal unit from breeding pairs generously provided by
Prof. Jennie Blackwell [20]. Female C57BL/6 mice were purchased from Harlan Olac Ltd.,
Blackthorn, Bicester, UK, and used when over 8 wk of age.
Enumeration of viable Salmonella in tissues and blood.
Approximately 0.4 ml of blood was obtained from the tail vein. Mice were killed by
cervical dislocation; the liver and spleen were removed and individually homogenised in a
Seward Stomacher 80 Biomaster (Seward) in 5 ml of distilled water. “Other
organs” (lungs, heart, kidneys, and mesenteric lymph nodes) were removed and
homogenized together, using an Ultra-Turrax T25 blender in 5 ml of distilled water. Viable
bacterial counts from the whole organ(s) or blood were assayed on plates of LB agar
supplemented with antibiotics where necessary for selection. To obtain the estimated total
number of CFU in the blood of an animal, the number of CFU per volume in the sample
obtained was corrected by assuming a total circulating blood volume of 2 ml.
Recombinant DNA techniques.
Standard methods were used for molecular cloning [21]. Chromosomal and plasmid DNA purifications
were performed using commercial kits following the manufacturers' instructions (QIAGEN).
Routine DNA modifications, including restriction endonuclease digestion of DNA,
modifications of DNA, and ligations, were carried out as per manufacturers' instructions
(Promega, Invitrogen, Roche, and New England Bioloabs). DNA concentration and purity were
measured using a Nanodrop ND-1000 spectrophotometer.
Oligonucleotides and PCR.
The sequences of primers used in this study are listed in Table S2 and were
purchased from Sigma (Sigma-Genosys). Q-PCR primers were designed using Primer3, freely
available at http://frodo.wi.mit.edu/, employing, where possible, the parameters of
Inglis and Kalischuk [22] (product of 105–125 bases, length of primer 18–25
bases, melting temp 58–60 °C, GC% 50–60, no
3′ T, no more than two G or C residues in the last five bases at the
3′ end). PCRs were carried out as described previously [16]. All PCRs were performed in
25-μl reaction volumes in 0.2-ml Eppendorf tubes in a PerkinElmer Gene Amp 2400
thermal cycler. Reactions contained 200 μM dNTPs, 2 mM Mg2+,
0.01 volumes of Proof Start DNA polymerase (QIAGEN; 2.5 U
μl−1), 0.1 volumes polymerase buffer (10×), 1
μM forward and reverse primers, and template DNA (∼50-ng plasmid DNA or
∼100-ng chromosomal DNA). Typical thermal cycler conditions were 94 °C for
5 min, 30 cycles of 94 °C for 1 min, 55 °C for 1 min, and 72 °C
for 1 min, followed by a final extension of 72 °C for 10 min.
Quantitative PCR.
All qPCRs were performed in 25-μl reaction volumes in 0.1-ml tubes in a Corbett
Research RG3000. Reactions contained 12.5 μl of QuantiTect SYBR Green PCR Kit
reagent (QIAGEN), 7 μl of RNase-free water, 25 μM forward and reverse
primers, and 5 μl of template DNA (∼1–10 ng per reaction
= ∼105–106 copies of DNA/reaction).
Reaction conditions were 95 °C for 15 min, 40 cycles of 94 °C for 15 sec,
61 °C for 30 sec, and 72 °C for 20 sec. It was not possible to perform a
full standard curve for each primer pair on every rotor; however, individual standards
were included on each rotor run to ensure that the values obtained were in the range
expected. (For detailed description of qPCR method, see Protocol S1, Materials and Methods section). For scoring WITS as
present/absent, a threshold value was determined with reference to the background values
obtained with the standards for a given batch of QuantiTect SYBR Green PCR Kit reagent.
Typically, WITS were scored as present if the value was above 102 copies of
DNA/reaction. However, for one experiment when the typical amount of DNA used in each
reaction was greater than 107 copies, the cutoff value was taken as
103 copies of DNA/reaction due to the increased background.
Plasmids.
All of the plasmids used in this study are listed in Table S3.
pBADλred [23] was induced with 0.2% l-arabinose. pAJG300 was
generated by PCR amplification of part of ydgA and malX
with primers ajg350 and ajg351 using SL1344 chromosomal DNA as template. The product was
cloned into the EcoRI and BamHI sites of pUC19 as an EcoRI-BamHI fragment. The
rpsM promoter was cloned into the BamHI and XbaI sites of pAJG300 as a
BamHI-XbaI fragment generated by PCR amplification from SL1344 chromosomal DNA using the
primers ajg352 and ajg353 (BamHI and XbaI sites incorporated into primers) to generate
pAJG301. The gfp+ gene was cloned into the NheI and
PstI sites of pAJG301 as an NheI-PstI fragment generated by PCR amplification from
pWH1012gfp+ [24] using the primers ajg354 and ajg355 (NheI and PstI sites
incorporated into primers) to generate pAJG303. PCR amplification of part of
malY and part of add was performed with primers ajg356
and ajg357 using SL1344 chromosomal DNA as template, and cloned into the PstI and SphI
sites of pAJG303 as a PstI-SphI fragment, generating pAJG309. After XbaI digestion of
pAJG309, an approximately 2.9-kbp fragment, containing
ydgA,malX,rpsM,gfp+,malY,add, was cloned into
XbaI-digested pDS132 [25] to generate pAJG315. The tags were generated by PCR amplification
from DNA generously donated by Prof. David Holden [26]. DNA sequencing (conducted by GeneService,
Cambridge, UK) was used to confirm that the plasmids had individually identifiable
(NK)20 tag sequences. Individually tagged constructs were constructed as
follows: a kanamycin resistance cassette was PCR amplified from pACYC184 using primers
ajg400–420 (each forward primer contained a unique 40-bp tag) and ajg425. The
resulting products were digested with BglII and SpeI, and cloned into
BglII-SpeI–digested pAJG315, generating the individually tagged constructs.
Chromosomal integration of tags in Salmonella strains.
Individually tagged kanamycin constructs were integrated onto the chromosome of
S. Typhimurium strain JH3016 [18] using a modification of the ET cloning
procedure [27,28] as previously described
[23]. A fragment
containing an individual 40-bp tag and the kanamycin resistance cassette was amplified
from the individually tagged constructs, using the primers ajg474, 475, 481, 482, 485,
487, 488, 489, and ajg465. Approx 1 μg of each linear PCR product was used for
integration onto the chromosome using a modification of the Lambda Red method
[29], as
previously detailed [23]. Transformants were verified by plating onto selective media. Loss of
the pBADλred helper plasmid was essentially as previously described
[18], using MAST
ID Intralactam circles (MAST Diagnostics) to screen for the absence of beta-lactamase in
bacterial colonies. Loss of the helper plasmid was also confirmed by a negative PCR result
using primers to the bla gene (ajg516 and ajg517). The resultant mutants,
WITS with a chromosomally located individually tagged kanamycin cassette were confirmed by
PCR using a primer designed to the respective unique tag and primer ajg469 designed to
ydgA, away from the integration site (unpublished data). Additionally,
candidates were verified by Southern hybridization (unpublished data).
Mathematical models for bacterial dynamics.
We designed two sets of mathematical models in order to describe the dynamics of the
system and estimate six key parameters: bacterial division rates in the liver and the
spleen, bacterial death rates in the liver and the spleen, and transfer rates from the
blood to the liver and the spleen. In all the analyses, unless specified otherwise, we
assume that the inoculum follows a Poisson distribution with mean 93.22 bacteria, and that
the number of CFU of each WITS follows a Poisson distribution with mean 11.65 (i.e.,
93.22/8). This was guided by the experimental protocol, in which an initial mix with
(approximately) equal CFU of each WITS was diluted to obtain an expected number of 80 to
100 bacteria. The actual bacterial numbers inoculated into each mouse could not be
measured before the infection, but in nine independent aliquots from the input inocula, we
obtained a mean of 93.22 CFU and a standard deviation of 11.31 CFU. We ignored bacteria
demography in the blood, and as explained in the Results section, we dismissed transfer
from the organs back into the blood until after 24 h p.i. after analysing experimental
evidence. Based on the observation that most bacteria can be found at any time point in
either blood, the spleen, or the liver, we only included these three compartments in our
models. Because the data pertaining to the “other organs” were scarce
and composed of very low numbers, their inclusion in our models would have added
unnecessary complexity and would have almost certainly led to very similar results.
Bacterial dynamics were described using simple differential equations classically used for
population dynamics. Importantly, we assumed that the parameters of the model could vary
during the course of infection (leading us to identify three main phases, see Results and Protocol S1), but at any given time, they did not differ
among bacteria within each organ.
Here, we describe overall bacterial dynamics (regardless of WITS) as three interconnected
populations: nB, nL, and
nS represent the expected bacterial numbers of bacteria in
the blood, liver, and spleen, respectively. This leads to the following set of equations:
where rL and rS are
the net growth rates of bacteria in the liver and the spleen (we cannot distinguish
divisions from deaths until we take the WITS into account),
θL and θS
are the transfer rates from the blood to the liver and the spleen, and
n0 is the inoculum size. So, in the equations above,
represents the expected number of bacteria left in the blood at time t.
As explained in Protocol
S1, Section 2, we then fitted this model to the data to estimate the four
parameters over different time periods.
In order to estimate the underlying division and death rates, we used a second model that
describes the probability distributions of the numbers of copies of a single WITS in the
liver, spleen, and blood. Indeed, since the eight WITS are identical and independent,
there is no need to track them all simultaneously. We denoted by
qLm,n(t) the probability that
m and n copies of any given WITS are present in the
blood and in the liver, respectively, at time t, and by
qSm,n(t) the probability that
m and n copies are present in the blood and in the
spleen, respectively, at time t. The division rates are named
λL and
λS, and the death rates
μL and μS (in the
liver and the spleen, respectively). The dynamics for the liver in the early phase of
infection are governed by the following equations:
The last equation represents the initial condition, assuming that the inoculum size
follows a Poisson distribution of mean 11.65 for each WITS. The equations for the spleen
can be obtained by swapping L and S. Because we had
previously estimated the transfer rates and the net growth rates
rL = λL − μL and rS = λS − μS, we estimated the death rates in each organ by fitting this second model to the
WITS data. In brief, we used the model to assess the probability of extinction of a single
WITS at 6 and 24 h p.i., from which we derived the probability distribution of the number
of WITS present in each organ at those two time points. We then fitted the expected mean
to the observed mean number of WITS averaged across all mice. (See Protocol S1 for more
details).
Supporting Information
Schematic to Illustrate the Sampling Model
(A) Two random samples (spleen and liver) are taken from the initial inoculum: given
the sample sizes nL and nS, the
model returns the frequency of WITS wL and
wS.
(B) If divisions occur in the organs before the observation is made, then the number of
bacteria increases from n to n′, while the
number of WITS w remains constant; in other words, the observed number
of WITS should be lower than expected under the null hypothesis given the observed
sample size n.
(1.2 MB TIF)
Model to Test for Bacterial Growth and Death during the Early Stages of Infection
The figure indicates the probability of obtaining, at most, the observed frequency of
WITS wL and wS given the
bacterial counts nL and nS under
the null hypothesis (no bacterial divisions) in individual mice, at 0.5 and 6 h p.i. A
single asterisk (*) denotes significance at p ≤ 0.05.
(2.7 MB TIF)
Bacterial Growth in the Liver and Spleen—Settling in the Organs
Numerical estimates of transfer rates from the blood
(θL [red dashed line] into the
liver and θS [blue dashed line]
into the spleen) and initial net growth rates rL1
(red line) and rS1 (blue line) based on average
bacterial counts at 0.5 and 6 h p.i., plotted against the (unknown) inoculum size
n0. The horizontal black dashed line shows the expected
average inoculum size in the experiments (93.22 CFU)
(1.0 MB TIF)
Bacterial Growth in the Liver and Spleen
Numerical estimates of parameters (A) rL1,
(liver, red) and rS1 (spleen, blue), and (B)
θL (red) and
θS (blue) plotted against time of switch
χ and inoculum size n0.
(16.6 MB TIF)
Bacterial Growth in the Liver and Spleen
Numerical estimates of the growth rates in the second phase of infection
(6–24 h p.i.) of infection, rL2 (liver, red) and
(spleen, blue) rS2, plotted against time of switch
χ and inoculum size n0.
(10.7 MB TIF)
Probability of Absence of a Single WITS, as Estimated from the Model up to 7 h p.i.
Probability is shown (A) in the liver and (B) in the spleen. The arrows indicate the
95% confidence intervals on the observed proportions of missing WITS in each
organ at (a) 0.5, (b) 6, and (c) 24 h p.i. If the early dynamics (with high division and
death rates) extend for more than 7 h, the model predicts that the proportion of WITS
lost will exceed that observed at 24 h p.i (arrow c).
(2.4 MB TIF)
Comparison of WITS Distributions, between Experimental and Stochastic Simulation
Data
Distributions of WITS proportions per compartment (columns) and time point (rows).
(A) From one experiment (Figure 6),
the mean proportions of the eight WITS in each organ were ranked in decreasing order in
each of the five mice available at each time point. Box plots represent the
distributions of these frequencies across five mice. Within any panel, the left-hand box
plot represents the most frequent WITS and right-hand box plot the least frequent WITS.
(B) We performed a similar analysis with the results of 1,000 stochastic simulations.
WITS were ranked in each compartment in each simulation according to their proportions.
(3.2 MB TIF)
Details of Model Constructions and Statistical Analyses
(105 KB DOC)
Bacterial Strains Used in the Study
aS. enterica
strain SL1344 was derived from strain 4/74, which was isolated from a calf bowel.
bThe gfp gene fusion was inserted at the
putPA locus at positions 1,210,040 to 1,211,657 in the LT2 genome.
Φ indicates a transcriptional gene fusion.
cThe individual tag::kan cassette was inserted at the
malXY locus at base 1,678,843 in the SL1344 genome (http://www.sanger.ac.uk/Projects/Salmonella/).
(15 KB XLS)
Oligonucleotides Used in the Study
(18 KB XLS)
Plasmids Used in the Study
(16 KB XLS)
Parameters Used in the Models
Numerical values (expressed in h−1) estimated from the
experimental data, assuming transitions at 6, 24, and 36 h p.i. The values that change
at each transition are indicated by an up or down arrow.
(15 KB XLS)
There Is No Apparent Difference in Relative Fitness between the WITS
In order to detect any systematic bias in terms of relative fitness of the WITS, two
formal tests were conducted using the data presented in Figure 3. (A) The first measured uniformality in the
distribution of the WITS regardless of location, which returned a χ2
value of 1.8068 on 7 df (p = 0.9698). (B)
The second tested association between the WITS and organ location by constructing an 8
× 2 contingency table comparing the total counts of each individual WITS
present by organ (liver and spleen) across all mice (0.5, 6, 24, 48, and 72 h). The
χ2 value is 2.7795, again on 7 df with
p = 0.9046. The data are consistent with being uniformly
spread in each organ; with no systematic differences in the probabilities of
presence/absence of any particular WITS. So we conclude that the probability of
colonisation of the WITS is independent of the organ.
(9 KB XLS)
Events Included in the Stochastic Model
The variables “liveri,” “spleeni,” and “bloodi” represent the number of WITS i (1 ≤
i ≤ 8) in each compartment.
(15 KB XLS)
We would like to thank Isabelle Hautefort and Jay Hinton for JH3010, Dominique Schneider
for pDS132, Mikael Niederweis for pWH1012gfp+, David Holden for the
signature tags and Jennie Blackwell for the
phox−/− mice breeding pairs. We thank
Christopher Coward for useful discussions regarding qPCR, Julia Gog for helpful comments
regarding the mathematical models, and Andrea Kells, David Holden, and Bryan Grenfell for
reading the manuscript prior to publication.
AbbreviationsCFU
colony-forming units
i.v.
intravenous
p.i.
postinfection
qPCR
quantitative polymerase chain reaction
WITS
wild-type isogenic tagged strains
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