Dynamically linking influenza virus infection kinetics, lung injury, inflammation, and disease severity
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Curated by eLife
Evaluation Summary:
This is an interesting synthesis of iterative model development and experimental work in an influenza model of infection in mice. The work is quite groundbreaking in the field as it is the first to use models to link dynamics of lung viral load, infected cells, inflammation, virus-specific CD8+ T cells, bystander CD8+ T cells, and disease status. The paper suggests that CD8+ T cells are vital for elimination of infected cells but can also contribute directly to lung damage and disease severity.
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
Influenza viruses cause a significant amount of morbidity and mortality. Understanding host immune control efficacy and how different factors influence lung injury and disease severity are critical. We established and validated dynamical connections between viral loads, infected cells, CD8 + T cells, lung injury, inflammation, and disease severity using an integrative mathematical model-experiment exchange. Our results showed that the dynamics of inflammation and virus-inflicted lung injury are distinct and nonlinearly related to disease severity, and that these two pathologic measurements can be independently predicted using the model-derived infected cell dynamics. Our findings further indicated that the relative CD8 + T cell dynamics paralleled the percent of the lung that had resolved with the rate of CD8 + T cell-mediated clearance rapidly accelerating by over 48,000 times in 2 days. This complimented our analyses showing a negative correlation between the efficacy of innate and adaptive immune-mediated infected cell clearance, and that infection duration was driven by CD8 + T cell magnitude rather than efficacy and could be significantly prolonged if the ratio of CD8 + T cells to infected cells was sufficiently low. These links between important pathogen kinetics and host pathology enhance our ability to forecast disease progression, potential complications, and therapeutic efficacy.
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Author Response:
Reviewer #1 (Public Review):
The experimental data and modeling are highly robust. The conclusions of the paper are clearly supported by the results. The sensitivity analysis is particularly impressive and suggests a system that is highly conserved across a wide parameter space. Model validation with CD8+ depletion is a nice addition that leads to interesting and surprising conclusions.The figures are highly instructive and easy to read.
An area where the paper could be improved is conveying the actual scientific conclusions more clearly and precisely with more focused review of existing literature. The relevance of the paper's conclusions for human influenza could be discussed with more careful language.
Thank you for the suggestions. As mentioned above, we updated the abstract and the text to better highlight …
Author Response:
Reviewer #1 (Public Review):
The experimental data and modeling are highly robust. The conclusions of the paper are clearly supported by the results. The sensitivity analysis is particularly impressive and suggests a system that is highly conserved across a wide parameter space. Model validation with CD8+ depletion is a nice addition that leads to interesting and surprising conclusions.The figures are highly instructive and easy to read.
An area where the paper could be improved is conveying the actual scientific conclusions more clearly and precisely with more focused review of existing literature. The relevance of the paper's conclusions for human influenza could be discussed with more careful language.
Thank you for the suggestions. As mentioned above, we updated the abstract and the text to better highlight the biological conclusions in addition to the mathematical conclusions. We also included some additional explanation on the specific points below.
First, the mechanistic conclusions of the work could be emphasized along with the methodology of the work. At present, these are completely lacking from the abstract which somewhat blandly just says that the paper describes a model which fits to data. From my perspective, currently underemphasized and novel / interesting conclusions are that:
- CD8+ mediated killing becomes much more rapid on a per capita basis (40000 fold increase) when infected cells dip below several hundred cells approximately 7 days post infection.
- There is a negative correlation between infected cell clearance by innate versus CD8+ mediated mechanisms, implying that poorer initial clearance of virus may result in more effective later killing by acquired immune mechanisms.
- Even ~80% reduction in maximal CD8E+ levels could prolong infection by 10 days though delay in attaining these threshold CD8E+ levels due to experimental or in silico CD8+ depletion only delays viral elimination by a day.
In our CD8 depletion data, the entire infection is altered (see Lines 270-303). That is, our results suggest that there are fewer infected cells initially infected, which leads to lower viral loads at d2 and will automatically result in fewer CD8s. However, the depletion antibody itself is known to directly alter CD8s (see Lines 282-284), so one cannot make direct, quantified conclusions about the precise reduction percentage under this experimental condition.
- Most interesting and counterintuitively, CD8+ depletion allows for considerable reductions in the size of lung lesions as well as inflammation scores and degree of weight loss during primary influenza infection. This result suggests that CD8+ T cells have the potential to create significant bystander damage in the lung.
While bystander damage may be present, our CD8 depletion data do not directly show bystander damage. As we mention above, one important aspect of depleting CD8s prior to infection was that it reduced the viral loads early on. We believe this is likely a result of immune activation from the initial CD8 kill-off (noted in Lines 284-285). A decrease in target/infected cells will automatically reduce the number of total cells that become infected and, thus, reduce the lesioned area of the lung. This was verified by the reduced weight loss, and no modifications were made to the predictions (Fig 4) or correlation to weight loss (Fig 5).
Second, the introduction and discussion continue to not differentiate whether past experimental results are from humans or mice. It is somewhat misleading to cite mouse studies without acknowledging that these are from a model that in no way captures the totality of human infection conditions. For all animal models of human infection, the strengths of the model (ability to control experimental inputs and obtain frequent measurements) are counter-balanced by lack of realism. Humans have a complex background of immunity based on past vaccination and infection, different modes of exposure and other innumerable differences. In most human infections, the degree of lung involvement is minimal. Please stipulate in the review of existing literature which papers were done in mice versus humans. Please also frame conclusions of this paper in the discussion in terms of how it may or may not be relevant to human infection.
As mentioned above, we updated the text to better highlight the human relevance and distinguish results between different host species in Lines 43, 61, 69, 74, 77, 88, 99, 101, 111, 116, 118, 120, 400-406, 410-411, 413, 435, 437, 446-450, 452, 463, 471, 485, and 846. We use the words ‘human’, ‘clinical’, and ‘patient’ to denote humans and the words ‘animal’, ‘murine’, and ‘experimental’ to denote animal models. Further distinguishment in some areas comes from the methods noted (e.g., CT scans/imaging is used/relevant for humans but not animals (this would be a microCT)). Because many features of the infection are observed in both humans and animals (see, for example, 10.1016/j.jim.2014.03.023 and 10.3390/pathogens3040845), underscoring the strong relevance of animal models to study influenza (noted in Lines 453-454), we limited specifying this in every sentence as we feel it would reduce the readability. In addition, we feel that splitting up the references mid-sentence would also reduce readability given the journal’s reference style, and have left most at the end of a sentence. The references themselves should provide a reader to easily distinguish.
Third, this is a primary infection model, and this point also should be emphasized. The greatest relevance of the mouse model in the paper may be for pediatric infection in humans, rather than adults who have had multiple prior influenza exposures and possibly vaccinations. Presumably CD8+ responses can be expected to be more rapid with availability of a pre-existing population of tissue resident CD8+ T cells as would occur with re-infection. The results of CD8+ depletion prior to re-infection would potentially be very different (likely harmful) in a re-infection model and this should be discussed. This is mentioned in Line 467 but is given short attention elsewhere.
We added text to highlight that we are studying a primary infection in Lines 46, 130, and 176-177. In general, primary infections may not necessarily always equate to children as any novel strain or strain novel to that individual may act as a primary infection. It is also feasible that waning immunity would appear similar to the dynamics of a primary infection.
Because CD8 depletion would also deplete out resident and other T cells (>99% efficiency as noted in Line 273), one would expect the exact same results as we showed here. Thus, it would not be the appropriate experimental design to study recall responses. As we mentioned in our prior response, we might expect some adjustments to account primed responses and added text that highlights this in Lines 424-427 and 498.
Line 60: stating that other studies have had limited success is rather insulting. Please rephrase and be more specific about why this study breaks new ground.
We did not mean to be insulting and reworded Lines 66-67 to “…but have had not yet found the appropriate mathematical relation with the available data.” Of note, even the authors of Price et al. noted their inability to capture the dynamics: “Some trajectories, notably activated macrophages and epithelial damage are not well captured by the model, suggesting that the immunophenotype we selected for active macrophages may not be accurate, and that using animals weight as a proxy for epithelial damage may not be appropriate”. The novelty of our study and the gaps in the field are stated throughout the introduction and discussion.
Line 81:: "viral loads in the upper respiratory tract do not reflect the lower respiratory tract environment. " Please include a citation, remove or clarify that this is a possible confounding variable in the analysis.
We’ve added several citations from both human and animals studies to Lines 90-91.
Line 91: define lung histomorphometry. This is a fairly novel approach for most readers.
This was defined in our prior revision and remains in Lines 102-104.
Line 101: This is a strong statement about viral load. Unless formal correlate studies have been done in humans (which they have not), I would day "may not be correlated" or remove altogether.
We updated Line 110 to “…may not be directly correlated…”.
Line 201: involved with what? I am not sure what this sentence means.
We were referencing effector-mediated killing and memory generation as noted earlier in the sentence. We updated Lines 210-212 to clarify: “One benefit of using the total CD8+ T cells is that the model automatically deduces the dynamics of effector-mediated killing and memory generation without needing to specify which phenotypes might be involved in these processes as they may be dynamically changing”
Line 209: I would suggest denoting a separate section to the sensitivity analysis versus the parameter fitting as the fitted correlation between delta and delta_e appears separate mechanistically from the relationship between delta and viral clearance / total # of CD8E
We appreciate the suggestion but have chosen to leave this part of the text unaltered.
Line 251: Please cite the clinical correlate oof this in the discussion. Immuncompromised humans often shed influenza (and SARS CoV-2) for months. See work from Jesse Bloom's group published in Elife on this subject.
The suggested article has been added to Line 474.
Line 321 should this read "clear infected cells from the lung?" I am confused about what this sentence means.
Line 332 has been updated to “…of the infected areas within the lung”.
Fig 5D: why are the dots yellow? Is the magenta line CD8 depleted?
We inadvertently left off the explanation, but added clarification to the caption and text (Lines 371- 374 and 380-384). The yellow markers are interstitial inflammation while the white markers are alveolar inflammation. The magenta markers/lines are the CD8 depletion prediction.
Line 386: Has antiviral therapy been linked with extent of radiologic lung lesions in clinical trials. This would be a very atypical clinical trial endpoint so please be more precise with language. It is possible as previously mentioned in the paper that viral load may not predict lesion size or disease severity in humans.
To our knowledge, CT images are not typically taken in many contexts for a variety of clinical reasons (e.g., cost, exposure to the patient, etc.), but antivirals have been linked to reductions in disease severity (e.g., see 10.1056/NEJMoa1716197). In that particular line, we mention that minor reductions in viral load are paired with more significant reductions in disease/symptom, which is reported in the referenced clinical and experimental data.
Line 477: add degree of immunity from prior infections as a critical variable
This has been added to Line 478.
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Evaluation Summary:
This is an interesting synthesis of iterative model development and experimental work in an influenza model of infection in mice. The work is quite groundbreaking in the field as it is the first to use models to link dynamics of lung viral load, infected cells, inflammation, virus-specific CD8+ T cells, bystander CD8+ T cells, and disease status. The paper suggests that CD8+ T cells are vital for elimination of infected cells but can also contribute directly to lung damage and disease severity.
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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Reviewer #1 (Public Review):
The experimental data and modeling are highly robust. The conclusions of the paper are clearly supported by the results. The sensitivity analysis is particularly impressive and suggests a system that is highly conserved across a wide parameter space. Model validation with CD8+ depletion is a nice addition that leads to interesting and surprising conclusions.The figures are highly instructive and easy to read.
An area where the paper could be improved is conveying the actual scientific conclusions more clearly and precisely with more focused review of existing literature. The relevance of the paper's conclusions for human influenza could be discussed with more careful language.
First, the mechanistic conclusions of the work could be emphasized along with the methodology of the work. At present, these are …
Reviewer #1 (Public Review):
The experimental data and modeling are highly robust. The conclusions of the paper are clearly supported by the results. The sensitivity analysis is particularly impressive and suggests a system that is highly conserved across a wide parameter space. Model validation with CD8+ depletion is a nice addition that leads to interesting and surprising conclusions.The figures are highly instructive and easy to read.
An area where the paper could be improved is conveying the actual scientific conclusions more clearly and precisely with more focused review of existing literature. The relevance of the paper's conclusions for human influenza could be discussed with more careful language.
First, the mechanistic conclusions of the work could be emphasized along with the methodology of the work. At present, these are completely lacking from the abstract which somewhat blandly just says that the paper describes a model which fits to data. From my perspective, currently underemphasized and novel / interesting conclusions are that:
CD8+ mediated killing becomes much more rapid on a per capita basis (40000 fold increase) when infected cells dip below several hundred cells approximately 7 days post infection.
There is a negative correlation between infected cell clearance by innate versus CD8+ mediated mechanisms, implying that poorer initial clearance of virus may result in more effective later killing by acquired immune mechanisms.
Even ~80% reduction in maximal CD8E+ levels could prolong infection by 10 days though delay in attaining these threshold CD8E+ levels due to experimental or in silico CD8+ depletion only delays viral elimination by a day.
Most interesting and counterintuitively, CD8+ depletion allows for considerable reductions in the size of lung lesions as well as inflammation scores and degree of weight loss during primary influenza infection. This result suggests that CD8+ T cells have the potential to create significant bystander damage in the lung.
Second, the introduction and discussion continue to not differentiate whether past experimental results are from humans or mice. It is somewhat misleading to cite mouse studies without acknowledging that these are from a model that in no way captures the totality of human infection conditions. For all animal models of human infection, the strengths of the model (ability to control experimental inputs and obtain frequent measurements) are counter-balanced by lack of realism. Humans have a complex background of immunity based on past vaccination and infection, different modes of exposure and other innumerable differences. In most human infections, the degree of lung involvement is minimal. Please stipulate in the review of existing literature which papers were done in mice versus humans. Please also frame conclusions of this paper in the discussion in terms of how it may or may not be relevant to human infection.
Third, this is a primary infection model, and this point also should be emphasized. The greatest relevance of the mouse model in the paper may be for pediatric infection in humans, rather than adults who have had multiple prior influenza exposures and possibly vaccinations. Presumably CD8+ responses can be expected to be more rapid with availability of a pre-existing population of tissue resident CD8+ T cells as would occur with re-infection. The results of CD8+ depletion prior to re-infection would potentially be very different (likely harmful) in a re-infection model and this should be discussed. This is mentioned in Line 467 but is given short attention elsewhere.
Line 60: stating that other studies have had limited success is rather insulting. Please rephrase and be more specific about why this study breaks new ground.
Line 81:: "viral loads in the upper respiratory tract do not reflect the lower respiratory tract environment. " Please include a citation, remove or clarify that this is a possible confounding variable in the analysis.
Line 91: define lung histomorphometry. This is a fairly novel approach for most readers.
Line 101: This is a strong statement about viral load. Unless formal correlate studies have been done in humans (which they have not), I would day "may not be correlated" or remove altogether.
Line 201: involved with what? I am not sure what this sentence means.
Line 209: I would suggest denoting a separate section to the sensitivity analysis versus the parameter fitting as the fitted correlation between delta and delta_e appears separate mechanistically from the relationship between delta and viral clearance / total # of CD8E
Line 251: Please cite the clinical correlate oof this in the discussion. Immuncompromised humans often shed influenza (and SARS CoV-2) for months. See work from Jesse Bloom's group published in Elife on this subject.
Line 321 should this read "clear infected cells from the lung?" I am confused about what this sentence means.
Fig 5D: why are the dots yellow? Is the magenta line CD8 depleted?
Line 386: Has antiviral therapy been linked with extent of radiologic lung lesions in clinical trials. This would be a very atypical clinical trial endpoint so please be more precise with language. It is possible as previously mentioned in the paper that viral load may not predict lesion size or disease severity in humans.
Line 477: add degree of immunity from prior infections as a critical variable
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Reviewer #2 (Public Review):
The authors have undertaken an elaborated and extensive analysis on the question how viral and inflammation dynamics correlate with disease pathology during influenza virus infection in mice. Combining a rich set of data, comprising the time course of viral load and CD8+ T cell dynamics for mice across two weeks of influenza infection and detailed histomorphometry on lung tissue sections, with mathematical models on their interaction, they provide a mechanistic relationship between inflammation dynamics and tissue pathology. Based on their analysis, they predict that infected cells are cleared by CD8+ T cells in a density-dependent manner.
The study is well written and thoroughly presented although some aspects on the analysis would benefit from more detailed explanations. It represents an innovative …
Reviewer #2 (Public Review):
The authors have undertaken an elaborated and extensive analysis on the question how viral and inflammation dynamics correlate with disease pathology during influenza virus infection in mice. Combining a rich set of data, comprising the time course of viral load and CD8+ T cell dynamics for mice across two weeks of influenza infection and detailed histomorphometry on lung tissue sections, with mathematical models on their interaction, they provide a mechanistic relationship between inflammation dynamics and tissue pathology. Based on their analysis, they predict that infected cells are cleared by CD8+ T cells in a density-dependent manner.
The study is well written and thoroughly presented although some aspects on the analysis would benefit from more detailed explanations. It represents an innovative approach combining an extensive set of data with mathematical modeling. The mathematical model shows a remarkable ability in following even abrupt changes in the dynamics of the different components, leading to some questions towards the interpretability of the obtained parameterizations. Especially the fact that the CD8+ T cell mediated clearance rate delta_E seems to reach the boundary of the imposed prior range might need some additional investigation and discussion. Alternative approaches reducing model complexity could be potentially considered. In general, the authors performed a thorough analysis to address these issues, and also added additional data to address previous concerns.
In summary, I consider this an interesting study that could provide additional insights into the relationship between viral/inflammation dynamics and induced pathology during influenza virus infection.
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