Stochastic variation in the initial phase of bacterial infection predicts the probability of survival in D. melanogaster
- Cornell University, United States
- UMR5174 EDB, CNRS, ENSFEA, Université Toulouse 3 Paul Sabatier, France
Figures
Figure 1 with 2 supplements
The outcome of infection ranges from 100% to 0% survival.
(A) Canton S flies were injected with the same inoculum (OD600 = 1, ca. 30,000 bacteria) of different bacteria species. Providencia alcalifaciens (n = 29) and Serratia marcescens (n = 30) are lethal …
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Figure 1—source data 1
Data set for Figure 1.
- https://doi.org/10.7554/eLife.28298.006
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Figure 1—source data 2
Data set for Figure 1—figure supplement 1.
- https://doi.org/10.7554/eLife.28298.007
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Figure 1—source data 3
Data set for Figure 1—figure supplement 2.
- https://doi.org/10.7554/eLife.28298.008
Figure 1—figure supplement 1
The variability of outcome in infection is not due to variation in the time of exposure to CO2 before and after injection.
We monitored the survival after infection of groups either exposed to CO2 for 2 min during the injection, exposed to 15 min of CO2 before an injection time frame of 2 min, or injected during a time …
Figure 1—figure supplement 2
The chronic phase of infection is characterised by the Set Point Bacterial Load (SPBL).
(A) The Set-Point Bacterial Load (SPBL) is stable up to 10 days post-injection (linear regression: Time: df = 1, F = 0.11, p=0.73). The grey dot represents the dose initiating the infection and the …
Figure 2 with 1 supplement
Hosts die at a set bacterial burden, the BLUD, which varies with host and pathogen.
(A) The bacterial load in flies within 30 min of death from P. rettgeri infection is a constant and is higher than that of flies that did not die from the infection. This is true when comparing dead …
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Figure 2—source data 1
Data set for Figure 2.
- https://doi.org/10.7554/eLife.28298.011
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Figure 2—source data 2
Data set for Figure 2—figure supplement 1.
- https://doi.org/10.7554/eLife.28298.012
Figure 2—figure supplement 1
The BLUD is constant over time and does not depend on the initial dose.
(A) BLUD several days post-injection. The BLUD is consistent even over 10 days post-injection. (B) Effect of initial dose on BLUD. For both Canton S and Oregon R hosts, the BLUD for P. rettgeri was …
Figure 3 with 1 supplement
The infection outcome depends on inter-individual variation in within-host bacterial proliferation.
(A) For bacteria that do not kill any infected hosts, bacterial loads decreased soon after the beginning of the infection. Grey dots on the x-axis represent individuals without detectable bacteria. …
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Figure 3—source data 1
Data set for Figure 3.
- https://doi.org/10.7554/eLife.28298.015
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Figure 3—source data 2
Data set for Figure 3—figure supplement 1.
- https://doi.org/10.7554/eLife.28298.016
Figure 3—figure supplement 1
In vivo proliferation in the early phase of infection is equivalent to the rate of in vitro growth in LB medium.
During the eight first hours, bacterial proliferation in LB medium (Culture 1 to 4) did not differ from the bacterial proliferation within the host Canton S. This was confirmed by the …
Figure 4
Early intra-host bacterial evolution does not explain the dual outcome of infection.
Bacterial evolution inside the fly does not explain the binary outcomes of infection. (A) Quantification of bacterial load to determine bacterial populations killing the host (bacterial load close …
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Figure 4—source data 1
Data set for Figure 4.
- https://doi.org/10.7554/eLife.28298.018
Figure 5
Role of the immune system in the infection dynamic.
(A) Within-host P. rettgeri loads at different times post-injection for wild-type (Canton S) flies and flies deficient in phagocytosis (Hml-Gal4 >UAS GFP; UAS-Bax, Gal80ts). Distinct groups of …
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Figure 5—source data 1
Data set for Figure 5.
- https://doi.org/10.7554/eLife.28298.020
Figure 6 with 1 supplement
A generalized model of infection.
(A) A schematic representation of a conceptual model for within-host bacterial growth dynamics that, upon three phases, lead either to host survival or death. In a first early phase, the bacterial …
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Figure 6—source data 1
Data set for Figure 6.
- https://doi.org/10.7554/eLife.28298.023
Figure 6—figure supplement 1
A simulation of the WHD model.
(A) Red curves correspond to the predicted average log2-transformed bacterial load in the Baranyi model (the sigmoid curve) and in the bacterial decrease model. Shades of grey indicate the …
Figure 7 with 1 supplement
The time to effective control by the immune response determines the outcome of infection.
(A) Bacterial load over time in hosts with pre-activated immune systems (Gal80TS; daughterless-Gal4 > UAS Imd) and in the corresponding control flies (Gal80TS; daughterless-Gal4 > Canton s). Early …
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Figure 7—source data 1
Data set for Figure 7.
- https://doi.org/10.7554/eLife.28298.026
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Figure 7—source data 2
Data set for Figure 7—figure supplement 1.
- https://doi.org/10.7554/eLife.28298.027
Figure 7—figure supplement 1
Correlation between the number of P. rettgeri in head and abdomen of the same fly (Spearman correlation test, S = 633.31, r = 0.84, p=8.8e-09).
https://doi.org/10.7554/eLife.28298.025Tables
Table 2
Effect of early growth (tlag, µ and σb) and control (t̄c, Vc and ntip) parameters on the capacity of the model to predict survival.
Log-likelihood is computed either on bacterial load data (i.e. on the data set we used to fit the model) or on survival data. In this latter case, we used the probability of control predicted by the …
| Complete data set | Intermediate survival | ||||||
|---|---|---|---|---|---|---|---|
| Bacterial load | Survival | Bacterial load | Survival | ||||
| df | logLik | logLik | p-value | logLik | logLik | p-value | |
| Full | −1243.119 | −17.85860 | −800.0603 | −11.48770 | |||
| Control | 24 | −1629.965 | −21.68408 | 0.999 | −839.5523 | −17.18319 | 0.496 |
| t̄c | 8 | −1660.396 | −19.18941 | 0.954 | −839.5791 | −16.82534 | 0.030 |
| Vc | 8 | −1643.985 | −17.37663 | 1 | −826.4296 | −14.67760 | 0.173 |
| ntip | 8 | −1634.382 | −25.23397 | 0.064 | −832.8896 | −12.85596 | 0.603 |
| Growth | 24 | −1798.630 | −81.13355 | 6.512e-16 | −854.1100 | −11.37739 | 1 |
| tlag | 8 | −1692.743 | −16.49076 | 1 | −835.3833 | −11.30397 | 1 |
| µ | 8 | −1705.175 | −61.56015 | 1.564e-15 | −836.3794 | −11.75406 | 0.970 |
| σb | 8 | −1704.156 | −21.95270 | 0.415 | −848.2682 | −12.28487 | 0.810 |
Table 1
List of parameters of the mixture model with their signification.
https://doi.org/10.7554/eLife.28298.029| Baranyi model | |
|---|---|
| n0 | Bacterial load upon injection |
| nmax | Maximum bacterial load |
| tlag | Lag time |
| µ | Early bacterial growth rate |
| σb | Standard deviation of loads in the absence of control |
| Exponential model | |
| nc | Intercept of the exponential decrease model |
| δ | Decrease rate in bacterial load when infection is controlled |
| σc | Standard deviation of loads in controlled infections |
| Control | |
| t̄c | Average time to control |
| Vc | Variance in time to control |
| ntip | Bacterial load above which the host cannot control infection |
Additional files
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Supplementary file 1
Primer sequences used in the qPCR analysis.
- https://doi.org/10.7554/eLife.28298.030
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Transparent reporting form
- https://doi.org/10.7554/eLife.28298.031
