Gastrointestinal helminths increase Bordetella bronchiseptica shedding and host variation in supershedding

  1. Nhat TD Nguyen
  2. Ashutosh K Pathak
  3. Isabella M Cattadori

  • Curated by eLife

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    Evaluation Summary:

    Nguyen et al. examine how helminth co-infection alters shedding from a respiratory bacterial infection (Bordetella bronchiseptica), fitting a model to data from experimentally infected rabbits to link the presence/absence of two helminth species with immune responses (neutrophil and two antibody classes) and bacterial shedding. The authors find a larger frequency of intense bacterial shedding-supershedding events-among helminth-infected rabbits, and model results suggest that triple infection may be associated with faster bacterial replication in the respiratory tract and more rapid shedding of bacteria. Linking immune responses with infection outcomes is of enormous practical interest, as is identifying why certain hosts are superspreaders, but there are some limits to what can be gained from this data set and model framework.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

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Abstract

Co-infected hosts, individuals that carry more than one infectious agent at any one time, have been suggested to facilitate pathogen transmission, including the emergence of supershedding events. However, how the host immune response mediates the interactions between co-infecting pathogens and how these affect the dynamics of shedding remains largely unclear. We used laboratory experiments and a modeling approach to examine temporal changes in the shedding of the respiratory bacterium Bordetella bronchiseptica in rabbits with one or two gastrointestinal helminth species. Experimental data showed that rabbits co-infected with one or both helminths shed significantly more B. bronchiseptica , by direct contact with an agar petri dish, than rabbits with bacteria alone. Co-infected hosts generated supershedding events of higher intensity and more frequently than hosts with no helminths. To explain this variation in shedding an infection-immune model was developed and fitted to rabbits of each group. Simulations suggested that differences in the magnitude and duration of shedding could be explained by the effect of the two helminths on the relative contribution of neutrophils and specific IgA and IgG to B. bronchiseptica neutralization in the respiratory tract. However, the interactions between infection and immune response at the scale of analysis that we used could not capture the rapid variation in the intensity of shedding of every rabbit. We suggest that fast and local changes at the level of respiratory tissue probably played a more important role. This study indicates that co-infected hosts are important source of variation in shedding, and provides a quantitative explanation into the role of helminths to the dynamics of respiratory bacterial infections.

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  1. Version published to 10.7554/elife.70347 on eLife
  2. eLife

    Author Response

    Joint Public Review:

    Strengths: The study represents a step forward in relating immune responses to infection outcomes that of urgent interest to public health, especially the timing of shedding and frequency of supershedding events. Nguyen et al.'s model provides a useful framework for understanding the links between immune effectors and infection outcomes, and it can be expanded to encompass further biological complexity. The study system is a good choice, given the ubiquity of both helminth and bacterial infections, and experimental infections of rabbits provide a useful point of comparison for past work in mice.

    We appreciated these general comments.

    Limitations: The present study does not explicitly account for differences in helminth infection dynamics across the two species represented in the data nor does it include feedbacks between the bacterial and helminth infections. Nguyen et a. therefore show the limits of what can be learned from focusing on the bacterial and immune dynamics alone, and this study should serve to motivate further work that can build on this modeling approach to produce a more comprehensive view of the interactions among species infecting the same host. Future studies examining the impact of helminth infection intensity would be tremendously useful for assessing the potential of anthelminthics to reduce the prevalence of bacterial respiratory diseases. Finally, subsequent studies may need to look beyond the factors examined here to understand why shedding varies so much through time for individual hosts.

    We agree that focusing only on the bacterial infection is a limitation in this study. We followed a parsimonious approach and decided to concentrate on B. bronchiseptica shedding in the four types of infection. While we do have data on the dynamics of infection of the two helminth species, adding these data would have been an enormous amount of work and too much to present in a single paper. Yet, we have already investigated some of these bi-directional effects using the BT group (Thakar et al. 2012 Plos Comp. Biol.) and plan to keep working on these rich datasets in the future.

    We also agree that it is important to understand the rapid variation in Bordetella shedding observed, which appears to be a common feature in many other host-pathogen systems. This requires a completely new set of experiments on infection and shedding at the local tissue level.

    Specific comments

    Definition of supershedding: A major stated goal of the MS is to investigate the effect of coinfection by helminths on supershedding. In order to compare animals with different coinfections, it is therefore necessary to have a common definition of supershedding. At present, the authors use a definition that depends on which arm of the experiment the animals belong to. This complicates the analysis and clouds its interpretation.

    We value this comment and see the implication of using different datasets to quantify supershedding. To overcome this problem, we now propose a slightly different approach where we pull the four infections together and calculate a common 99th or 95th percentile threshold. This common threshold is then used to calculate the number of hosts with at least one supershedding event above this cut-off, for every type of infection. Therefore, while the threshold is the same the percentage of hosts with supershedding events varies among infection groups.

    Inconsistent approach: Within each experimental treatment, the data display variability on at least three levels: (i) within animals, day-to-day shedding displays variability on a fast timescale; (ii) within animals, infection status varies more slowly over the course of infection; (iii) between animals, there is variation in both (i) and (ii). The authors' model seems well-designed to handle this variability, but the authors are strangely inconsistent in their use of it. To be specific, to account for level (i), the authors very sensibly adopt a zero-inflated model for the shedding data, whereby the rate of shedding (colony-forming units per second, CFU/s) is assumed to arise from a mixture of a quantitative process (which we might think of as intensity of potential shedding) and an all-or-nothing process (which might arise, for example, if some discrete behavior of the animal is necessary for shedding to occur at all). The inclusion of the all-or-nothing process necessitates an additional parameter, but it allows the non-zero shedding data to inform the model. To account for level (ii), the authors use a four-dimensional deterministic dynamical system. Three of the four variables are related to the measured components of the immune response. The fourth is related to the aforementioned potential shedding. Level (iii) is accounted for using a hierarchical Bayesian approach, whereby the individual animals have parameters drawn from a common prior distribution. This approach seems very well designed to address the authors' questions using the data at hand. However, they fail to exploit this, in at least three ways. First, even though the model appears designed specifically to allow for non-shedding animals, the authors exclude animals on an ad hoc basis. Second, rather than display the shedding data in the form recommended by the model, they display log(1+CFU/sec), which is arbitrary and problematic. Its arbitrariness stems from the fact that this quantity is sensitive to the units used for shedding rate. Third, despite the fact that the model appears specifically designed to account for variability at each of the three levels, they do not give enough information to allow the reader to judge whether the model does in fact do a good job of partitioning this variability.

    Please see comments to each specific matter below.

    Exclusion of animals: In view of the fact that the model the authors describe can account for variability on all three levels, it is strange that they exclude animals that shed too little or not at all. It would be preferable were the authors to base their conclusions on all the data they collected rather than on a subset chosen a posteriori. It is true that the non-shedders will have no information about the time-course of shedding; on the other hand, including them does not complicate the analysis, and it does allow for estimation of the all-or-nothing probability in a coherent fashion. In particular, the fact that coinfection appears to have an impact on whether animals shed at all is itself directly related to the authors' central questions. More generally, ad hoc exclusion of data raises concerns about the repeatability of the experiments that, in this case, appear entirely avoidable.

    Rabbits that were infected but never shed were excluded from all our original analysis and continue to be excluded in our updated version. Our focus is on the dynamics of shedding and including animals that do not shed is not informative to our objective. Moreover, these animals do not provide meaningful information on rabbits that are infected but do not shed, since this is a very small number (n=7) to draw meaningful conclusions across four types of infection. Rabbits with three or less shedding events larger than zero (i.e. CFU/s>0) were originally excluded from the modeling and continue to be excluded. This decision was motivated by technical reasons of model convergence and our commitment to generate meaningful results; in other words, it is difficult to fit a model, and provide robust results, on a time series with only three points larger than zero, irrespective of the number of zero points in the time series.
    In summary our subset of animals was not chosen a posteriori but based on clear objectives (i.e. pattern of shedding between and within types of infections), a rigorous approach and reliable results. We have further clarified our approach in the Results and Material and Methods.

    Incomplete description of the analysis: The description of the statistical analysis will not be complete until sufficient information is provided to allow the interested reader to decide for him- or herself whether the conclusions are warranted and for the motivated reader to reproduce the analysis. In particular, it is necessary to specify all priors fully. At present, these are not described at all, except in vague, and even incoherent, ways. Also, it is necessary to provide details of the MCMC performed. Specifically, the authors should describe the MCMC sampler and show their MCMC convergence diagnostics. Finally, it is good practice to display both the priors and the posteriors: it is impossible to assess the posteriors without an understanding of the priors.

    We have carefully revised our approach and results and now provide a complete description of our analysis with additional/new details on Parameter calibration, Model fitting, Model validation and Model selection in Material and Methods, and Appendix (Appendix-3 and 4). Specifically, we have included all priors, along with all posteriors, for the four types of infection in Table 2. We have also explained how the MCMC simulations were performed and how model convergence diagnosis was assessed (section ‘Parameter calibration and Model fitting’). In Appendix-3 we also show the parameter MCMC trace plots for the four types of infection.

    Second, rather than display the shedding data in the form recommended by the model, they display log(1+CFU/sec), which is arbitrary and problematic. Its arbitrariness stems from the fact that this quantity is sensitive to the units used for shedding rate.

    A clear feature of our shedding data is that there is large variation in the level of shedding both within and between hosts. Because of this, data were presented as log(1+CFU/s) to reduce the skewness of the datasets, and thus the variance, and facilitate the visualization of the experimental and simulated results. The use of data in the form of CFU/s would have made the visualization much harder, especially at low shedding where a large fraction of the data come from.

    The practice of displaying the data on a log-scale is appropriate when the underlying process is exponential or when the amount of relative variation is large, including when representing rates. This practice is widely used when modeling infectious diseases and describing biomedical results. A typical example is the overdispersion of macroparasite infections in host populations, or the large variation in the size of outbreaks by microparasite infections, these data are often described on a log-scale. An example closer to our case is the study on influenza-bacteria coinfection by Smith et al. 2013 Plos Pathogens. Given the nature of our data we found that plotting the level of shedding on a log-scale was the most effective way to represent our results.

    Model adequacy: The authors' argument rests on the model's ability to adequately account for the data. The authors need to provide some evidence of this, in one form or another. Ultimately, the question is whether the data are a plausible realization of the model. The authors should show simulations from the model (including the measurement error and not merely the deterministic trajectories) and compare these simulations to the data. In particular, it seems worryingly possible that the fitted model is capable of capturing certain averages in the data while, at the same time, failing to describe the infection progression for any of the actual infected animals.

    As previously reported, we have now provided full details on model fitting and model convergence in the section ’Parameter calibration and Model fitting’ and ‘Model validation’ in Material and Methods, and ‘Model validation’ and ‘Model convergence’ in Appendix (Appendix3 and 4).

    Regarding the evidence that the data are a plausible realization of the model, we have moved the original figure S1 in the main text (now figure 5). This figure shows the good fit of the model to neutrophil, IgA and IgG, both using individual and group data from every infection. We have also revised the quality of the plot to highlight individual simulations. To avoid too much crowding the 95% CIs for every individual are not reported, however, in Appendix-1 we provide the posterior parameter estimations and their 95% CIs, for every individual and as a group average, for the three co-infections (simulations for B rabbits were performed at the group level only).

    In the new figure 6 (original figure 5), we have now included the individual trajectories (without 95% CIs to avoid overcrowding), alongside the group trends, for the neutralization rates of neutrophils, IgA and IgG which are the important parameter regulating infection and where the CIs are large enough to show the individual data. The other rates have too narrow CIs to single out individual trajectories and, thus, we only reported the group trends.

    In the revised figure 7 (original figure 6) we have revised the quality of the plots to highlight individual trajectories, in addition to the median trend, but have not included the individual 95% CIs, again to avoid overcrowding.

    Finally, the main text associated to these figures has been updated accordingly.

    Confusion of correlation and causation: At various points, the authors succumb to the temptation to interpret their model literally and to interpret the correlations they observe as evidence for a causal linkage between the three immune components they measure, bacterial shedding, and coinfection. They should be more careful and circumspect in the description of their results.

    We have thoroughly revised the presentation and discussion of the results to avoid the overinterpretation of the findings.

    Additional Issues:

    Eqs 1-4. These equations are not mechanistic in any meaningful sense. Essentially, they posit the existence of exponential time-lags between the three immunity variables, and a simple linear killing relationship between each of the variables and pathogen load. To interpret the equations literally risks making unwarranted conclusions. For example, any physiological variable correlated with any of the three variables in the model might equally well be credited with the influence on shedding attributed to IgA, IgG, or neutrophils.

    This work tests the hypothesis that neutrophils, IgA and IgG affect the dynamics of B. bronchispetica infection and, in turn, bacterial shedding. Of course, there are many other immunological mechanisms that could contribute to the pattern observed and that can be tested, as there are many other variables correlated with these dynamics that do not play any role in these patterns, as noted by the reviewer. We follow a parsimonious approach by focusing on three immune variables previously identified as important in regulating Bordetella infection. To avoid excessive complexity and allow model tractability, our informed decision was to simplify the relationship between immunity and infection, without losing the important role of the immune variables selected. Finally, by referring to previous work by others and us we do note that the immune mechanisms described can be much more complex.

    l 456. Do the authors account for the variability in time spent with plates? Implicitly, the assumption is made that the amount of time a rabbit spends with a plate, i.e., the decision as to whether to engage in a behavior that will terminate the plate interaction, is independent of everything else. This raises the question: Does the time spent per plate correlate with anything?

    We always recorded the amount of time spent with the plate, and every rabbit had a maximum interaction time of 10 minutes. Rabbits are very inquisitive and rarely we had animals that did not interact or had to remove the plate because they were chewing the media; usually animals used the entire 10 minutes. Analyses do account for the interaction time and are presented as Colony Forming Unit/second (CFU/s). As noted in the Material and Methods section ‘Observation model’: ‘The probability of having a shedding event is independent of time since inoculation, in that shedding can occur anytime during the experiment and anytime during the interaction with the petri dish”. This assumption is based on our observations of rabbit behavior during the trials.

  3. eLife

    Evaluation Summary:

    Nguyen et al. examine how helminth co-infection alters shedding from a respiratory bacterial infection (Bordetella bronchiseptica), fitting a model to data from experimentally infected rabbits to link the presence/absence of two helminth species with immune responses (neutrophil and two antibody classes) and bacterial shedding. The authors find a larger frequency of intense bacterial shedding-supershedding events-among helminth-infected rabbits, and model results suggest that triple infection may be associated with faster bacterial replication in the respiratory tract and more rapid shedding of bacteria. Linking immune responses with infection outcomes is of enormous practical interest, as is identifying why certain hosts are superspreaders, but there are some limits to what can be gained from this data set and model framework.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

  4. eLife

    Joint Public Review:

    Strengths: The study represents a step forward in relating immune responses to infection outcomes that of urgent interest to public health, especially the timing of shedding and frequency of supershedding events. Nguyen et al.'s model provides a useful framework for understanding the links between immune effectors and infection outcomes, and it can be expanded to encompass further biological complexity. The study system is a good choice, given the ubiquity of both helminth and bacterial infections, and experimental infections of rabbits provide a useful point of comparison for past work in mice.

    Limitations: The present study does not explicitly account for differences in helminth infection dynamics across the two species represented in the data nor does it include feedbacks between the bacterial and helminth infections. Nguyen et a. therefore show the limits of what can be learned from focusing on the bacterial and immune dynamics alone, and this study should serve to motivate further work that can build on this modeling approach to produce a more comprehensive view of the interactions among species infecting the same host. Future studies examining the impact of helminth infection intensity would be tremendously useful for assessing the potential of anthelminthics to reduce the prevalence of bacterial respiratory diseases. Finally, subsequent studies may need to look beyond the factors examined here to understand why shedding varies so much through time for individual hosts.

    Specific comments:

    Definition of supershedding: A major stated goal of the MS is to investigate the effect of coinfection by helminths on supershedding. In order to compare animals with different coinfections, it is therefore necessary to have a common definition of supershedding. At present, the authors use a definition that depends on which arm of the experiment the animals belong to. This complicates the analysis and clouds its interpretation.

    Inconsistent approach: Within each experimental treatment, the data display variability on at least three levels: (i) within animals, day-to-day shedding displays variability on a fast timescale; (ii) within animals, infection status varies more slowly over the course of infection; (iii) between animals, there is variation in both (i) and (ii). The authors' model seems well-designed to handle this variability, but the authors are strangely inconsistent in their use of it. To be specific, to account for level (i), the authors very sensibly adopt a zero-inflated model for the shedding data, whereby the rate of shedding (colony-forming units per second, CFU/s) is assumed to arise from a mixture of a quantitative process (which we might think of as intensity of potential shedding) and an all-or-nothing process (which might arise, for example, if some discrete behavior of the animal is necessary for shedding to occur at all). The inclusion of the all-or-nothing process necessitates an additional parameter, but it allows the non-zero shedding data to inform the model. To account for level (ii), the authors use a four-dimensional deterministic dynamical system. Three of the four variables are related to the measured components of the immune response. The fourth is related to the aforementioned potential shedding. Level (iii) is accounted for using a hierarchical Bayesian approach, whereby the individual animals have parameters drawn from a common prior distribution. This approach seems very well designed to address the authors' questions using the data at hand. However, they fail to exploit this, in at least three ways. First, even though the model appears designed specifically to allow for non-shedding animals, the authors exclude animals on an ad hoc basis. Second, rather than display the shedding data in the form recommended by the model, they display log(1+CFU/sec), which is arbitrary and problematic. Its arbitrariness stems from the fact that this quantity is sensitive to the units used for shedding rate. Third, despite the fact that the model appears specifically designed to account for variability at each of the three levels, they do not give enough information to allow the reader to judge whether the model does in fact do a good job of partitioning this variability.

    Exclusion of animals: In view of the fact that the model the authors describe can account for variability on all three levels, it is strange that they exclude animals that shed too little or not at all. It would be preferable were the authors to base their conclusions on all the data they collected rather than on a subset chosen a posteriori. It is true that the non-shedders will have no information about the time-course of shedding; on the other hand, including them does not complicate the analysis, and it does allow for estimation of the all-or-nothing probability in a coherent fashion. In particular, the fact that coinfection appears to have an impact on whether animals shed at all is itself directly related to the authors' central questions. More generally, ad hoc exclusion of data raises concerns about the repeatability of the experiments that, in this case, appear entirely avoidable.

    Incomplete description of the analysis: The description of the statistical analysis will not be complete until sufficient information is provided to allow the interested reader to decide for him- or herself whether the conclusions are warranted and for the motivated reader to reproduce the analysis. In particular, it is necessary to specify all priors fully. At present, these are not described at all, except in vague, and even incoherent, ways. Also, it is necessary to provide details of the MCMC performed. Specifically, the authors should describe the MCMC sampler and show their MCMC convergence diagnostics. Finally, it is good practice to display both the priors and the posteriors: it is impossible to assess the posteriors without an understanding of the priors.

    Model adequacy: The authors' argument rests on the model's ability to adequately account for the data. The authors need to provide some evidence of this, in one form or another. Ultimately, the question is whether the data are a plausible realization of the model. The authors should show simulations from the model (including the measurement error and not merely the deterministic trajectories) and compare these simulations to the data. In particular, it seems worryingly possible that the fitted model is capable of capturing certain averages in the data while, at the same time, failing to describe the infection progression for any of the actual infected animals.

    Confusion of correlation and causation: At various points, the authors succumb to the temptation to interpret their model literally and to interpret the correlations they observe as evidence for a causal linkage between the three immune components they measure, bacterial shedding, and co-infection. They should be more careful and circumspect in the description of their results.

    Additional Issues:

    Eqs 1-4. These equations are not mechanistic in any meaningful sense. Essentially, they posit the existence of exponential time-lags between the three immunity variables, and a simple linear killing relationship between each of the variables and pathogen load. To interpret the equations literally risks making unwarranted conclusions. For example, any physiological variable correlated with any of the three variables in the model might equally well be credited with the influence on shedding attributed to IgA, IgG, or neutrophils.

    l 456. Do the authors account for the variability in time spent with plates? Implicitly, the assumption is made that the amount of time a rabbit spends with a plate, i.e., the decision as to whether to engage in a behavior that will terminate the plate interaction, is independent of everything else. This raises the question: Does the time spent per plate correlate with anything?

  5. Version published to 10.1101/2021.05.06.442912v1 on bioRxiv