Dynamically linking influenza virus infection kinetics, lung injury, inflammation, and disease severity

Development of a general anti-viral therapeutic using cholestosome technology to exploit inhibition of intracellular viral production

The recent events of the worldwide Covid-19 pandemic showed the need for a general anti-viral therapeutic, independent of the specific characteristics of the virus, that targets intracellular mechanisms of viral production to prevent the rapid, overwhelming spread of infection and its devastating consequences. The development of the Cholestosome technology, a drug delivery system made exclusively of cholesteryl esters, is a solution for intracellular targeting of viral replication. It is well known that Zn2+ is capable of inhibiting viral replication but the control of intracellular Zn2+ concentration is tightly regulated. Cholestosome technology can encapsulate Zn2+ and deliver it to cells to inhibit viral replication. The human betacoronavirus OC43 (OC43) model system was used to infect cells and infected cells were treated with Zn2+ encapsulated in Cholestosomes as well as appropriate controls. Viral production was measured using CPE as well as PCR methods to determine inhibition of infection. Experimental results indicated a 55 % reduction in viral load for those cells treated with Zn2+ encapsulated in cholestosomes versus Zn2+ alone.

TLR7 Promotes Acute Inflammatory-Driven Lung Dysfunction in Influenza-Infected Mice but Prevents Late Airway Hyperresponsiveness

Severe lower respiratory tract disease following influenza A virus (IAV) infection is characterized by excessive inflammation and lung tissue damage, and this can impair lung function. The effect of toll-like receptor 7 (TLR7), which detects viral RNA to initiate antiviral and proinflammatory responses to IAV, on lung function during peak infection and in the resolution phase is not fully understood. Using wild-type (WT) C57BL/6 and TLR7 knockout (TLR7 KO) mice, we found that IAV infection induced airway dysfunction in both genotypes, although in TLR7 KO mice, this dysfunction manifested later, did not affect lung tissue elastance and damping, and was associated with a different immune phenotype. A positive correlation was found between lung dysfunction and the infiltration of neutrophils and Ly6Clo patrolling monocytes at day 7 post-infection. Conversely, in TLR7 KO mice, eosinophil and CD8+ cytotoxic T cells were associated with airway hyperactivity at day 14. IL-5 expression was higher in the airways of IAV-infected TLR7 KO mice, suggesting an enhanced Th2 response due to TLR7 deficiency. This study highlights an underappreciated duality of TLR7 in IAV disease: promoting inflammation-driven lung dysfunction during the acute infection but suppressing eosinophilic and CD8+ T cell-dependent hyperresponsiveness during disease resolution.

Estimating the distribution of parameters in differential equations with repeated cross-sectional data

Differential equations are pivotal in modeling and understanding the dynamics of various systems, as they offer insights into their future states through parameter estimation fitted to time series data. In fields such as economy, politics, and biology, the observation data points in the time series are often independently obtained (i.e., Repeated Cross-Sectional (RCS) data). RCS data showed that traditional methods for parameter estimation in differential equations, such as using mean values of RCS data over time, Gaussian Process-based trajectory generation, and Bayesian-based methods, have limitations in estimating the shape of parameter distributions, leading to a significant loss of data information. To address this issue, this study proposes a novel method called Estimation of Parameter Distribution (EPD) that provides accurate distribution of parameters without loss of data information. EPD operates in three main steps: generating synthetic time trajectories by randomly selecting observed values at each time point, estimating parameters of a differential equation that minimizes the discrepancy between these trajectories and the true solution of the equation, and selecting the parameters depending on the scale of discrepancy. We then evaluated the performance of EPD across several models, including exponential growth, logistic population models, and target cell-limited models with delayed virus production, thereby demonstrating the ability of the proposed method in capturing the shape of parameter distributions. Furthermore, we applied EPD to real-world datasets, capturing various shapes of parameter distributions over a normal distribution. These results address the heterogeneity within systems, marking a substantial progression in accurately modeling systems using RCS data. Therefore, EPD marks a significant advancement in accurately modeling systems with RCS data, realizing a deeper understanding of system dynamics and parameter variability.

Identifiability investigation of within-host models of acute virus infection

Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, we used four mathematical models of influenza A infection with increased degrees of biological realism. We tested the ability of each model to reveal its parameters in the presence of unlimited data by performing structural identifiability analyses. We then refined the results by predicting practical identifiability of parameters under daily influenza A virus titers alone or together with daily adaptive immune cell data. Using these approaches, we presented insight into the sources of uncertainty in parameter estimation and provided guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions.

Saturated lysing efficiency of CD8+ cells induced monostable, bistable and oscillatory HIV kinetics

Effector CD8+ cells lyse human immunodeficiency viruses (HIV)-infected CD4+ cells by recognizing a viral peptide presented by human leukocyte antigens (HLA) on the CD4+ cell surface, which plays an irreplaceable role in within-host HIV clearance. Using a semi-saturated lysing efficiency of a CD8+ cell, we discuss a model that captures HIV dynamics with different magnitudes of lysing rate induced by different HLA alleles. With the aid of local stability analysis and bifurcation plots, exponential interactions among CD4+ cells, HIV, and CD8+ cells were investigated. The system exhibited unexpectedly complex behaviors that were both mathematically and biologically interesting, for example monostability, periodic oscillations, and bistability. The CD8+ cell lysing rate, the CD8+ cell count, and the saturation effect were combined to determine the HIV kinetics. For a given CD8+ cell count, a low CD8+ cell lysing rate and a high saturation effect led to monostability to a high viral titre, and a low CD8+ cell lysing rate and a low saturation effect triggered periodic oscillations; this explained why patients with a non-protective HLA allele were always associated with a high viral titer and exhibited bad infection control. On the other hand, a high CD8+ cell lysing rate led to bistability and monostability to a low viral titer; this explained why protective HLA alleles are not always associated with good HIV infection outcomes. These mathematical results explain how differences in HLA alleles determine the variability in viral infection.

Modeling the CD8＋ T cell immune response to influenza infection in adult and aged mice

The CD8＋ T cell response is the main determinant of viral clearance during influenza infection. However, influenza viral dynamics and the respective immune responses are affected by the host's age. To investigate age-related differences in the CD8＋ T cell immune response dynamics, we propose 16 ordinary differential equation models of existing experimental data. These data consist of viral titer and CD8＋ T cell counts collected periodically over a period of 19 days from adult and aged mice infected with influenza A/Puerto Rico/8/34 (H1N1). We use the corrected Akaike Information Criterion to identify the models which best represent the considered data. Our model selection process indicates differences in mechanisms which reduce the CD8＋ T cell response: linear downregulation is favored for adult mice, while baseline exponential decay is favored for aged mice. Parameter fitting of the top ranked models suggests that the aged population has reduced CD8＋ T cell proliferation compared to the adult population. More experimental work is needed to determine the specific immunological features through which age might cause these differences. A better understanding of the immunological mechanisms by which aging leads to discrepant CD8＋ T cell dynamics may inform future treatment strategies.

A Prime-Boost Vaccination Approach Induces Lung Resident Memory CD8+ T Cells Derived from Central Memory T Cells That Prevent Tumor Lung Metastasis

Memory T cells play a key role in immune protection against cancer. Vaccine-induced tissue-resident memory T (TRM) cells in the lung have been shown to protect against lung metastasis. Identifying the source of lung TRM cells can help to improve strategies, preventing tumor metastasis. Here, we found that a prime-boost vaccination approach using intramuscular DNA vaccine priming, followed by intranasal live-attenuated influenza-vectored vaccine (LAIV) boosting induced higher frequencies of lung CD8+ TRM cells compared with other vaccination regimens. Vaccine-induced lung CD8+ TRM cells, but not circulating memory T cells, conferred significant protection against metastatic melanoma and mesothelioma. Central memory T (TCM) cells induced by the DNA vaccination were major precursors of lung TRM cells established after the intranasal LAIV boost. Single-cell RNA sequencing analysis indicated that transcriptional reprogramming of TCM cells for differentiation into TRM cells in the lungs started as early as day 2 post the LAIV boost. Intranasal LAIV altered the mucosal microenvironment to recruit TCM cells via CXCR3-dependent chemotaxis and induced CD8+ TRM-associated transcriptional programs. These results identified TCM cells as the source of vaccine-induced CD8+ TRM cells that protect against lung metastasis. Significance: Prime-boost vaccination shapes the mucosal microenvironment and reprograms central memory T cells to generate lung resident memory T cells that protect against lung metastasis, providing insights for the optimization of vaccine strategies.

Diet switch pre-vaccination improves immune response and metabolic status in formerly obese mice

Metabolic disease is epidemiologically linked to severe complications upon influenza virus infection, thus vaccination is a priority in this high-risk population. Yet, vaccine responses are less effective in these same hosts. Here we examined how the timing of diet switching from a high-fat diet to a control diet affected influenza vaccine efficacy in diet-induced obese mice. Our results demonstrate that the systemic meta-inflammation generated by high-fat diet exposure limited T cell maturation to the memory compartment at the time of vaccination, impacting the recall of effector memory T cells upon viral challenge. This was not improved with a diet switch post-vaccination. However, the metabolic dysfunction of T cells was reversed if weight loss occurred 4 weeks before vaccination, restoring a functional recall response. This corresponded with changes in the systemic obesity-related biomarkers leptin and adiponectin, highlighting the systemic and specific effects of diet on influenza vaccine immunogenicity.

Mathematical modeling of viral infection and the immune response controlled by the circadian clock

Time of day affects how well the immune system responds to viral or bacterial infections. While it is well known that the immune system is regulated by the circadian clock, the dynamic origin of time-of-day-dependent immunity remains unclear. In this paper, we studied the circadian control of immune response upon infection of influenza A virus through mathematical modeling. Dynamic simulation analyses revealed that the time-of-day-dependent immunity was rooted in the relative phase between the circadian clock and the pulse of viral infection. The relative phase, which depends on the time the infection occurs, plays a crucial role in the immune response. It can drive the immune system to one of two distinct bistable states, a high inflammatory state with a higher mortality rate or a safe state characterized by low inflammation. The mechanism we found here also explained why the same species infected by different viruses has different time-of-day-dependent immunities. Further, the time-of-day-dependent immunity was found to be abolished when the immune system was regulated by an impaired circadian clock with decreased oscillation amplitude or without oscillations.

Quantitatively Mapping Immune Control during Influenza

Host immune responses play a pivotal role in defending against influenza viruses. The activation of various immune components, such as interferon, macrophages, and CD8+ T cells, works to limit viral spread while maintaining lung integrity. Recent mathematical modeling studies have investigated these responses, describing their regulation, efficacy, and movement within the lung. Here, we discuss these studies and their emphasis on identifying nonlinearities and multifaceted roles of different cell phenotypes that could be responsible for spatially heterogeneous infection patterns.

Identifiability investigation of within-host models of acute virus infection

Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, we use four mathematical models of influenza A infection with increased degrees of biological realism. We test the ability of each model to reveal its parameters in the presence of unlimited data by performing structural identifiability analyses. We then refine the results by predicting practical identifiability of parameters under daily influenza A virus titers alone or together with daily adaptive immune cell data. Using these approaches, we present insight into the sources of uncertainty in parameter estimation and provide guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions.

Decoding immune kinetics: unveiling secrets using custom-built mathematical models

Custom-built mathematical models make the immune response more predictable and offer mechanistic insights into fundamental immunology.

Dynamical Modeling and Qualitative Analysis of a Delayed Model for CD8 T Cells in Response to Viral Antigens

Although the immune effector CD8 T cells play a crucial role in clearance of viruses, the mechanisms underlying the dynamics of how CD8 T cells respond to viral infection remain largely unexplored. Here, we develop a delayed model that incorporates CD8 T cells and infected cells to investigate the functional role of CD8 T cells in persistent virus infection. Bifurcation analysis reveals that the model has four steady states that can finely divide the progressions of viral infection into four states, and endows the model with bistability that has ability to achieve the switch from one state to another. Furthermore, analytical and numerical methods find that the time delay resulting from incubation period of virus can induce a stable low-infection steady state to be oscillatory, coexisting with a stable high-infection steady state in phase space. In particular, a novel mechanism to achieve the switch between two stable steady states, time-delay-based switch, is proposed, where the initial conditions and other parameters of the model remain unchanged. Moreover, our model predicts that, for a certain range of initial antigen load: 1) under a longer incubation period, the lower the initial antigen load, the easier the virus infection will evolve into severe state; while the higher the initial antigen load, the easier it is for the virus infection to be effectively controlled and 2) only when the incubation period is small, the lower the initial antigen load, the easier it is to effectively control the infection progression. Our results are consistent with multiple experimental observations, which may facilitate the understanding of the dynamical and physiological mechanisms of CD8 T cells in response to viral infections.

NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases

On July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives.

Toward mechanistic medical digital twins: some use cases in immunology

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

The effect of model structure and data availability on Usutu virus dynamics at three biological scales

Understanding the epidemiology of emerging pathogens, such as Usutu virus (USUV) infections, requires systems investigation at each scale involved in the host-virus transmission cycle, from individual bird infections, to bird-to-vector transmissions, and to USUV incidence in bird and vector populations. For new pathogens field data are sparse, and predictions can be aided by the use of laboratory-type inoculation and transmission experiments combined with dynamical mathematical modelling. In this study, we investigated the dynamics of two strains of USUV by constructing mathematical models for the within-host scale, bird-to-vector transmission scale and vector-borne epidemiological scale. We used individual within-host infectious virus data and per cent mosquito infection data to predict USUV incidence in birds and mosquitoes. We addressed the dependence of predictions on model structure, data uncertainty and experimental design. We found that uncertainty in predictions at one scale change predicted results at another scale. We proposed in silico experiments that showed that sampling every 12 hours ensures practical identifiability of the within-host scale model. At the same time, we showed that practical identifiability of the transmission scale functions can only be improved under unrealistically high sampling regimes. Instead, we proposed optimal experimental designs and suggested the types of experiments that can ensure identifiability at the transmission scale and, hence, induce robustness in predictions at the epidemiological scale.

The chemokine receptor CXCR3 promotes CD8+ T cell-dependent lung pathology during influenza pathogenesis

The dual role of CD8+ T cells in influenza control and lung pathology is increasingly appreciated. To explore whether protective and pathological functions can be linked to specific subsets, we dissected CD8+ T responses in influenza-infected murine lungs. Our single-cell RNA-sequencing (scRNA-seq) analysis revealed notable diversity in CD8+ T subpopulations during peak viral load and infection-resolved state. While enrichment of a Cxcr3hi CD8+ T effector subset was associated with a more robust cytotoxic response, both CD8+ T effector and central memory exhibited equally potent effector potential. The scRNA-seq analysis identified unique regulons regulating the cytotoxic response in CD8+ T cells. The late-stage CD8+ T blockade in influenza-cleared lungs or continuous CXCR3 blockade mitigated lung injury without affecting viral clearance. Furthermore, adoptive transfer of wild-type CD8+ T cells exacerbated influenza lung pathology in Cxcr3-/- mice. Collectively, our data imply that CXCR3 interception could have a therapeutic effect in preventing influenza-linked lung injury.

The effect of chemotaxis on T-cell regulatory dynamics

Autoimmune diseases, such as Multiple Sclerosis, are often modelled through the dynamics of T-cell interactions. However, the spatial aspect of such diseases, and how dynamics may result in spatially heterogeneous outcomes, is often overlooked. We consider the effects of diffusion and chemotaxis on T-cell regulatory dynamics using a three-species model of effector and regulator T-cell populations, along with a chemical signalling agent. While diffusion alone cannot lead to instability and spatial patterning, the inclusion of chemotaxis can result in multiple forms of instability that produce highly complicated spatiotemporal behaviour. The parameter regimes in which different instabilities occur are determined through linear stability analysis and the full dynamics is explored through numerical simulation.

Exploring the global immune landscape of peripheral blood mononuclear cells in H5N6-infected patient with single-cell transcriptomics

BACKGROUND

Avian influenza viruses (AIV), particularly H5N6, have risen in infection frequency, prompting major concerns. Single-cell RNA sequencing (scRNA-seq) can illustrate the immune cell landscape present in the peripheral circulation of influenza H5N6-infected individuals at the single-cell level. This study attempted to employ scRNA-seq technology to map the potentially hidden single cell landscape of influenza H5N6.

METHODS

High-quality transcriptomes were generated from scRNA-seq data of peripheral blood mononuclear cells (PBMCs), which were taken from a critically-ill child diagnosed with H5N6 avian influenza infection and one healthy control donor. Cluster analysis was then performed on the scRNA-seq data to identify the different cell types. The pathways, pseudotime developmental trajectories and gene regulatory networks involved in different cell subpopulations were also explored.

RESULTS

In total, 3,248 single cell transcriptomes were captured by scRNA-seq from PBMC of the child infected with H5N6 avian influenza and the healthy control donor and further identified seven immune microenvironment cell types. In addition, a subsequent subpopulation analysis of innate lymphoid cells (ILC) and CD4+ T cells revealed that subpopulations of ILC and CD4+ T cells were involved in cytokine and inflammation-related pathways and had significant involvement in the biological processes of oxidative stress and cell death.

CONCLUSION

In conclusion, characterizing the overall immune cell composition of H5N6-infected individuals by assessing the immune cell landscape in the peripheral circulation of H5N6 avian influenza-infected and healthy control donors at single-cell resolution provides key information for understanding H5N6 pathogenesis.

The effect of random virus failure following cell entry on infection outcome and the success of antiviral therapy

A virus infection can be initiated with very few or even a single infectious virion, and as such can become extinct, i.e. stochastically fail to take hold or spread significantly. There are many ways that a fully competent infectious virion, having successfully entered a cell, can fail to cause a productive infection, i.e. one that yields infectious virus progeny. Though many stochastic models (SMs) have been developed and used to estimate a virus infection's establishment probability, these typically neglect infection failure post virus entry. The SM presented herein introduces parameter [Formula: see text] which corresponds to the probability that a virion's entry into a cell will result in a productive cell infection. We derive an expression for the likelihood of infection establishment in this new SM, and find that prophylactic therapy with an antiviral reducing [Formula: see text] is at least as good or better at decreasing the establishment probability, compared to antivirals reducing the rates of virus production or virus entry into cells, irrespective of the SM parameters. We investigate the difference in the fraction of cells consumed by so-called extinct versus established virus infections, and find that this distinction becomes biologically meaningless as the probability of establishment approaches zero. We explain why the release of virions continuously over an infectious cell's lifespan, rather than as a single burst at the end of the cell's lifespan, does not result in an increased risk of infection extinction. We show, instead, that the number of virus released, not the timing of the release, affects infection establishment and associated critical antiviral efficacy.

Tissue-resident memory T cells and lung immunopathology

Rapid reaction to microbes invading mucosal tissues is key to protect the host against disease. Respiratory tissue-resident memory T (TRM ) cells provide superior immunity against pathogen infection and/or re-infection, due to their presence at the site of pathogen entry. However, there has been emerging evidence that exuberant TRM -cell responses contribute to the development of various chronic respiratory conditions including pulmonary sequelae post-acute viral infections. In this review, we have described the characteristics of respiratory TRM cells and processes underlying their development and maintenance. We have reviewed TRM -cell protective functions against various respiratory pathogens as well as their pathological activities in chronic lung conditions including post-viral pulmonary sequelae. Furthermore, we have discussed potential mechanisms regulating the pathological activity of TRM cells and proposed therapeutic strategies to alleviate TRM -cell-mediated lung immunopathology. We hope that this review provides insights toward the development of future vaccines or interventions that can harness the superior protective abilities of TRM cells, while minimizing the potential for immunopathology, a particularly important topic in the era of coronavirus disease 2019 (COVID-19) pandemic.

Neutrophil-to-lymphocyte ratio trend at admission predicts adverse outcome in hospitalized respiratory syncytial virus patients

Background and aims

Severe cases of respiratory syncytial virus (RSV) infection are relatively rare but may lead to serious clinical outcomes, including respiratory failure and death. These infections were shown to be accompanied by immune dysregulation. We aimed to test whether the admission neutrophil-to-leukocyte ratio, a marker of an aberrant immune response, can predict adverse outcome.

Methods

We retrospectively analyzed a cohort of RSV patients admitted to the Tel Aviv Medical Center from January 2010 to October 2020d. Laboratory, demographic and clinical parameters were collected. Two-way analysis of variance was used to test the association between neutrophil-lymphocyte ratio (NLR) values and poor outcomes. Receiver operating characteristic (ROC) curve analysis was applied to test the discrimination ability of NLR.

Results

In total, 482 RSV patients (median age 79 years, 248 [51%] females) were enrolled. There was a significant interaction between a poor clinical outcome and a sequential rise in NLR levels (positive delta NLR). The ROC curve analysis revealed an area under curve (AUC) of poor outcomes for delta NLR of (0.58). Using a cut-off of delta = 0 (the second NLR is equal to the first NLR value), multivariate logistic regression identified a rise in NLR (delta NLR>0) as being a prognostic factor for poor clinical outcome, after adjusting for age, sex and Charlson comorbidity score, with an odds ratio of 1.914 (P = 0.014) and a total AUC of 0.63.

Conclusions

A rise in NLR levels within the first 48 h of hospital admission can serve as a prognostic marker for adverse outcome.

How Interactions during Viral-Viral Coinfection Can Shape Infection Kinetics

Respiratory viral infections are a leading global cause of disease with multiple viruses detected in 20-30% of cases, and several viruses simultaneously circulating. Some infections with unique viral copathogens result in reduced pathogenicity, while other viral pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral-viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV), followed by influenza A virus (IAV) after 3 days. The results suggest that IAV reduced the rate of RSV production, while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios that had not been examined experimentally, including a different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight-loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis shows that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to the slower clearance of IAV-infected cells by the other viruses. The improved outcome when IAV followed RV, on the other hand, could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral-viral coinfections in this way provides new insights about how viral-viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

Maternal exposure to ultrafine particles enhances influenza infection during pregnancy

BACKGROUND

Interactions between air pollution and infectious agents are increasingly recognized and critical to identify, especially to protect vulnerable populations. Pregnancy represents a vulnerable period for influenza infection and air pollution exposure, yet interactions during pregnancy remain unclear. Maternal exposure to ultrafine particles (UFPs, [Formula: see text] 100 nm diameter), a class of particulate matter ubiquitous in urban environments, elicits unique pulmonary immune responses. We hypothesized that UFP exposure during pregnancy would lead to aberrant immune responses to influenza enhancing infection severity.

RESULTS

Building from our well-characterized C57Bl/6N mouse model employing daily gestational UFP exposure from gestational day (GD) 0.5-13.5, we carried out a pilot study wherein pregnant dams were subsequently infected with Influenza A/Puerto Rico/8/1934 (PR8) on GD14.5. Findings indicate that PR8 infection caused decreased weight gain in filtered air (FA) and UFP-exposed groups. Co-exposure to UFPs and viral infection led to pronounced elevation in PR8 viral titer and reduced pulmonary inflammation, signifying potential suppression of innate and adaptive immune defenses. Pulmonary expression of the pro-viral factor sphingosine kinase 1 (Sphk1) and pro-inflammatory cytokine interleukin-1β (IL-1 [Formula: see text]) was significantly increased in pregnant mice exposed to UFPs and infected with PR8; expression correlated with higher viral titer.

CONCLUSIONS

Results from our model provide initial insight into how maternal UFP exposure during pregnancy enhances respiratory viral infection risk. This model is an important first step in establishing future regulatory and clinical strategies for protecting pregnant women exposed to UFPs.

How Interactions During Viral-Viral Coinfection Can Shape Infection Kinetics

Respiratory virus infections are a leading cause of disease worldwide with multiple viruses detected in 20-30% of cases and several viruses simultaneously circulating. Some infections with viral copathogens have been shown to result in reduced pathogenicity while other virus pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral-viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV) followed by influenza A virus (IAV) after 3 days. The results suggested that IAV reduced the rate of RSV production while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios not examined experimentally, including different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis showed that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to slower clearance of IAV infected cells by the other viruses. On the contrary, the improved outcome when IAV followed RV could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral-viral coinfections in this way provides new insights about how viral-viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Isolation and characterization of an orthoreovirus from Indonesian fruit bats

Nelson Bay orthoreovirus (NBV) is an emerging bat-borne virus and causes respiratory tract infections in humans sporadically. Over the last two decades, several strains genetically related to NBV were isolated from humans and various bat species, predominantly in Southeast Asia (SEA), suggesting a high prevalence of the NBV species in this region. In this study, an orthoreovirus (ORV) belonging to the NBV species was isolated from Indonesian fruit bats' feces, tentatively named Paguyaman orthoreovirus (PgORV). Serological studies revealed that 81.2% (108/133) of Indonesian fruit bats sera had neutralizing antibodies against PgORV. Whole-genome sequencing and phylogenetic analysis of PgORV suggested the occurrence of past reassortments with other NBV strains isolated in SEA, indicating the dispersal and circulation of NBV species among bats in this region. Intranasal PgORV inoculation of laboratory mice caused severe pneumonia. Our study characterized PgORV's unique genetic background and highlighted the potential risk of PgORV-related diseases in Indonesia.

Increased virus dissemination leads to enhanced lung injury but not inflammation during influenza-associated secondary bacterial infection

Secondary bacterial infections increase influenza-related morbidity and mortality, particularly if acquired after 5-7 d from the viral onset. Synergistic host responses and direct pathogen-pathogen interactions are thought to lead to a state of hyperinflammation, but the kinetics of the lung pathology have not yet been detailed, and identifying the contribution of different mechanisms to disease is difficult because these may change over time. To address this gap, we examined host-pathogen and lung pathology dynamics following a secondary bacterial infection initiated at different time points after influenza within a murine model. We then used a mathematical approach to quantify the increased virus dissemination in the lung, coinfection time-dependent bacterial kinetics, and virus-mediated and postbacterial depletion of alveolar macrophages. The data showed that viral loads increase regardless of coinfection timing, which our mathematical model predicted and histomorphometry data confirmed was due to a robust increase in the number of infected cells. Bacterial loads were dependent on the time of coinfection and corresponded to the level of IAV-induced alveolar macrophage depletion. Our mathematical model suggested that the additional depletion of these cells following the bacterial invasion was mediated primarily by the virus. Contrary to current belief, inflammation was not enhanced and did not correlate with neutrophilia. The enhanced disease severity was correlated to inflammation, but this was due to a nonlinearity in this correlation. This study highlights the importance of dissecting nonlinearities during complex infections and demonstrated the increased dissemination of virus within the lung during bacterial coinfection and simultaneous modulation of immune responses during influenza-associated bacterial pneumonia.

Identification of cytochrome c oxidase subunit 4 isoform 1 as a positive regulator of influenza virus replication

Human infection with highly pathogenic H5N1 influenza virus causes severe respiratory diseases. Currently, the drugs against H5N1 are limited to virus-targeted inhibitors. However, drug resistance caused by these inhibitors is becoming a serious threat to global public health. An alternative strategy to reduce the resistance risk is to develop antiviral drugs targeting host cell proteins. In this study, we demonstrated that cytochrome c oxidase subunit 4 isoform 1 (COX41) of host cell plays an important role in H5N1 infection. Overexpression of COX41 promoted viral replication, which was inhibited by silencing or knockout the expression of COX41 in the host cell. The ribonucleoproteins (RNPs) of H5N1 were retained in the cell nucleus after knockout cellular COX41. Strikingly, inhibition of cellular COX41 by lycorine, a small-molecule compound isolated from Amaryllidaceae plants, reduced the levels of COX41-induced ROS and phosphorylation of extracellular signal-regulated kinase (ERK) in cells, thus resulting in the blockage of nuclear export of vRNP and inhibition of viral replication. In H5N1-infected mice that were treated with lycorine, we observed a reduction of viral titers and inhibition of pathological changes in the lung and trachea tissues. Importantly, no resistant virus was generated after culturing the virus with the continuous treatment of lycorine. Collectively, these findings suggest that COX41 is a positive regulator of H5N1 replication and might serve as an alternative target for anti-influenza drug development.

Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection

The definition of correlates of protection is critical for the development of next-generation SARS-CoV-2 vaccine platforms. Here, we propose a model-based approach for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication.

Mechanistic Models of COVID-19: Insights into Disease Progression, Vaccines, and Therapeutics

The COVID-19 pandemic has severely impacted health systems and economies worldwide. Significant global efforts are therefore ongoing to improve vaccine efficacies, optimize vaccine deployment, and develop new antiviral therapies to combat the pandemic. Mechanistic viral dynamics and quantitative systems pharmacology models of SARS-CoV-2 infection, vaccines, immunomodulatory agents, and antiviral therapeutics have played a key role in advancing our understanding of SARS-CoV-2 pathogenesis and transmission, the interplay between innate and adaptive immunity to influence the outcomes of infection, effectiveness of treatments, mechanisms and performance of COVID-19 vaccines, and the impact of emerging SARS-CoV-2 variants. Here, we review some of the critical insights provided by these models and discuss the challenges ahead.

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clinic.

Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infections During SARS-CoV-2

Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate infection with the SARS-CoV-2 USA-WA1/2020 strain increased the risk of pneumococcal (type 2 strain D39) coinfection in a time-dependent, but sex-independent, manner in the transgenic K18-hACE2 mouse model of COVID-19. Bacterial coinfection increased lethality when the bacteria was initiated at 5 or 7 d post-virus infection (pvi) but not at 3 d pvi. Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.

The timing of differentiation and potency of CD8 effector function is set by RNA binding proteins

CD8+ T cell differentiation into effector cells is initiated early after antigen encounter by signals from the T cell antigen receptor and costimulatory molecules. The molecular mechanisms that establish the timing and rate of differentiation however are not defined. Here we show that the RNA binding proteins (RBP) ZFP36 and ZFP36L1 limit the rate of differentiation of activated naïve CD8+ T cells and the potency of the resulting cytotoxic lymphocytes. The RBP function in an early and short temporal window to enforce dependency on costimulation via CD28 for full T cell activation and effector differentiation by directly binding mRNA of NF-κB, Irf8 and Notch1 transcription factors and cytokines, including Il2. Their absence in T cells, or the adoptive transfer of small numbers of CD8+ T cells lacking the RBP, promotes resilience to influenza A virus infection without immunopathology. These findings highlight ZFP36 and ZFP36L1 as nodes for the integration of the early T cell activation signals controlling the speed and quality of the CD8+ T cell response.

Human placental hematopoietic stem cell derived natural killer cells (CYNK-001) mediate protection against influenza a viral infection

Influenza A virus (IAV) infections are associated with a high healthcare burden around the world and there is an urgent need to develop more effective therapies. Natural killer (NK) cells have been shown to play a pivotal role in reducing IAV-induced pulmonary infections in preclinical models; however, little is known about the therapeutic potential of adoptively transferred NK cells for IAV infections. Here, we investigated the effects of CYNK-001, human placental hematopoietic stem cell derived NK cells that exhibited strong cytolytic activity against a range of malignant cells and expressed high levels of activating receptors, against IAV infections. In a severe IAV-induced acute lung injury model, mice treated with CYNK-001 showed a milder body weight loss and clinical symptoms, which led to a delayed onset of mortality, thus demonstrating their antiviral protection in vivo. Analysis of bronchoalveolar lavage fluid (BALF) revealed that CYNK-001 reduced proinflammatory cytokines and chemokines highlighting CYNK-001’s anti-inflammatory actions in viral induced-lung injury. Furthermore, CYNK-001-treated mice had altered immune responses to IAV with reduced number of neutrophils in BALF yet increased number of CD8+ T cells in the BALF and lung compared to vehicle-treated mice. Our results demonstrate that CYNK-001 displays protective functions against IAV via its anti-inflammatory and immunomodulating activities, which leads to alleviation of disease burden and progression in a severe IAV-infected mice model. The potential of adoptive NK therapy for IAV infections warrants clinical investigation.

Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infection during SARS-CoV-2 Infection

Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate SARS-CoV-2 infection increased the risk of pneumococcal coinfection in a time-dependent, but sexindependent, manner in the transgenic K18-hACE mouse model of COVID-19. Bacterial coinfection was not established at 3 d post-virus, but increased lethality was observed when the bacteria was initiated at 5 or 7 d post-virus infection (pvi). Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.

Interferon-γ promotes monocyte-mediated lung injury during influenza infection

Influenza A virus (IAV) infection triggers an exuberant host response that promotes acute lung injury. However, the host response factors that promote the development of a pathologic inflammatory response to IAV remain incompletely understood. In this study, we identify an interferon-γ (IFN-γ)-regulated subset of monocytes, CCR2⁺ monocytes, as a driver of lung damage during IAV infection. IFN-γ regulates the recruitment and inflammatory phenotype of CCR2⁺ monocytes, and mice deficient in CCR2 (CCR2^-/-) or IFN-γ (IFN-γ^-/-) exhibit reduced lung inflammation, pathology, and disease severity. Adoptive transfer of wild-type (WT) (IFN-γR1^+/+) but not IFN-γR1^-/- CCR2⁺ monocytes restore the WT-like pathological phenotype of lung damage in IAV-infected CCR2^-/- mice. CD8⁺ T cells are the main source of IFN-γ in IAV-infected lungs. Collectively, our data highlight the requirement of IFN-γ signaling in the regulation of CCR2⁺ monocyte-mediated lung pathology during IAV infection.

Protective and pathogenic role of humoral responses in COVID-19

Since the advent of SARS-CoV-2 in Dec. 2019, the global endeavor to identify the pathogenic mechanism of COVID-19 has been ongoing. Although humoral immunity including neutralizing activity play an important role in protection from the viral pathogen, dysregulated antibody responses may be associated with the pathogenic progression of COVID-19, especially in high-risk individuals. In addition, SARS-CoV-2 spike-specific antibodies acquired by prior infection or vaccination act as immune pressure, driving continuous population turnover by selecting for antibody-escaping mutations. Here, we review accumulating knowledge on the potential role of humoral immune responses in COVID-19, primarily focusing on their beneficial and pathogenic properties. Understanding the multifaceted regulatory mechanisms of humoral responses during SARS-CoV-2 infection can help us to develop more effective therapeutics, as well as protective measures against the ongoing pandemic.

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

Deciphering the triad of infection, immunity and pathology

The factors which drive and control disease progression can be inferred from mathematical models that integrate measures of immune responses, data from tissue sampling and markers of infection dynamics.