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DOE OSTI2024

Mechanism of allosteric inhibition of human p97/VCP ATPase and its disease mutant by triazole inhibitors

Human p97 ATPase is crucial in various cellular processes, making it a target for inhibitors to treat cancers, neurological, and infectious diseases. Triazole allosteric p97 inhibitors have been demonstrated to match the efficacy of CB-5083, an ATP-competitive inhibitor, in cellular models. However, the mechanism is not well understood. This study systematically investigates the structures of new triazole inhibitors bound to both wild-type and disease mutant forms of p97 and measures their effects on function. These inhibitors bind at the interface of the D1 and D2 domains of each p97 subunit, shifting surrounding helices and altering the loop structures near the C-terminal α2 G helix to modulate domain-domain communications. A key structural moiety of the inhibitor affects the rotameric conformations of interacting side chains, indirectly modulating the N-terminal domain conformation in p97 R155H mutant. The differential effects of inhibitor binding to wild-type and mutant p97 provide insights into drug design with enhanced specificity, particularly for oncology applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2012

Paramyxovirus Infection Mimics In Vivo Cellular Dynamics in Three-Demensional Human Bronchio-Epithelial Tissue-Like Assemblies

Respiratory syncytial virus and parainfluenza virus cause severe respiratory disease, especially in infants, children and the elderly. An in vitro model that accurately mimics infection of the human respiratory epithelium (HRE) would facilitate vaccine development greatly. Monolayer cultures traditionally used to study these viruses do not accurately and precisely differentiate the replication efficiencies of wild type and attenuated viruses. Therefore, we engineered novel three-dimensional (3D) tissue-like assemblies (TLAs) of human broncho-epithelial (HBE) cells to produce a more physiologically relevant in vitro model of the HRE. TLAs resemble HRE structurally and by expression of differentiated epithelial cell markers. Most significantly, wild type viruses exhibited a clear growth advantage over attenuated strains in TLAs unlike monolayer cultures. In addition, the TLAs responded to virus infection by secreting pro-inflammatory mediators similar to the respiratory epithelia of infected children. These characteristics make the TLA model a valuable platform technology to develop and evaluate live, attenuated respiratory virus vaccine candidates for human use. Respiratory virus diseases, the most frequent and least preventable of all infectious diseases, range in severity from the common cold to severe bronchiolitis and pneumonia . Two paramyxoviruses, respiratory syncytial virus (RSV) and parainfluenza virus type 3 (PIV3), are responsible for a majority of the most severe respiratory diseases of infants and young children. RSV causes 70% of all bronchiolitis cases and is a major cause of morbidity and mortality worldwide, especially in infants. PIV3 causes 10-15% of bronchiolitis and pneumonia during infancy, second only to RSV, and 40% of croup in infants To date, licensed vaccines are not available to prevent these respiratory diseases. At present, traditional monkey kidney (Vero and LLC-MK2) and human (HEp-2) tissue culture cells and small animal models (mouse, cotton rat, guinea pig, ferret, and hamster) fail to accurately imitate viral replication and human disease states (8). Lacking an authentic model has impeded the development and evaluation of live, attenuated vaccine candidates. Development of a physiologically relevant in vitro tissue culture model that reproduces characteristics of the HRE, the primary target of RSV and PIV3, would aid in predicting clinical attenuation and safety of vaccine candidates. Successful tissue engineering of a 3D human intestinal model using novel NASA technology inspired the development of a tri-culture 3D model for the HRE. Sequential layering of primary mesenchymal cells (comprised of normal human fibroblasts and endothelial cells) followed by BEAS-2B epithelial cells derived from human bronchi and tracheae were recapitulated on Cultisphere and/or cytodex3 microcarriers in cylindrical vessels that rotate horizontally creating an organized epithelial structure. Horizontal rotation randomizes the gravity vector modeling aspects of microgravity. Mesenchymal and epithelial cells grown under these conditions reproduce the structural organization, multi-cellular complexity, and differentiation state of the HRE. The opportunity to study respiratory viruses in a nasal epithelium model is invaluable because the most promising respiratory virus vaccine candidates are live attenuated viruses for intranasal administration. Here we characterize the interactions of respiratory viruses and epithelial cells grown under modeled microgravity in comparison to gravity-ladened monolayers. 3D HBE TLAs and traditional monolayers (2D) are infected at 35 C, the upper temperature of the upper HRE, to simulate in vivo infection conditions. Growth kinetics of wild type (wt) RSV and PIV3 viruses were compared in 2D and 3D cells to that of strains attenuated in humans or rhesus macaques. This novel 3D HBE model also offers an opportunity to study whether the epithelial cell function, especially in host defenses recapitulated by mimicking the structural organization of the HRE. In vivo, airway epithelial cells play a significant and dynamic role in host defense by blocking paracellular permeability and modulating airway function through cellular interactions or tight junctions. As regulators of the innate immune response, epithelial cells constitutively express cytokines, chemokines, and colony stimulating factors including RANTES, IL-8, IL-6, GM-CSF, and G-CSF for proactive host defense. In response to viral infection, epithelial cells induce potent immuno-modulatory and pro-inflammatory cytokines that recruit phagocytic and inflammatory cells to clear the virus and enhance protection. Although disease pathogenesis is classically attributed to the cytopathic effects of the pathogen, severe disease states associated with RSV and PIV3 are attributed to the inflammatory response, especially in infants. RSV is a potent inducer of cytokines and pro-inflammatory mediators in epithelial cells in vivo. A differentiated human epithelial model independent of the complete functional immune system will help elucidate the role of epithelial cells in respiratory disease. We reported here, virus and host cell interactions in 3D HBE TLAs are similar to that in vivo. Because the epithelial cell organization of the TLAs impacts not only the expression of airway epithelial characteristics, but also cellular communication, the TLAs represent a more physiologically relevant model of the HRE than BEAS-2B or other non-tumour monolayer models of respiratory disease. As a result, wild type respiratory viruses have a clear growth advantage over attenuated viruses in TLAs unlike traditional monolayers. In addition, the TLAs respond to wild type virus infection by secreting pro-inflammatory mediators characteristic of infected HRE. TLAs expressing microbial defense mechanisms provide an excellent model to study the interactions of respiratory pathogens with their host and to identify the innate immunity mediators. Therefore, 3D HBE TLAs offer advantages for the study of respiratory viruses and the development of viral vaccine candidates.

Deatly, Anne M.↗

NASA NTRS2000

Microgravity as a novel environmental signal affecting Salmonella enterica serovar Typhimurium virulence

The effects of spaceflight on the infectious disease process have only been studied at the level of the host immune response and indicate a blunting of the immune mechanism in humans and animals. Accordingly, it is necessary to assess potential changes in microbial virulence associated with spaceflight which may impact the probability of in-flight infectious disease. In this study, we investigated the effect of altered gravitational vectors on Salmonella virulence in mice. Salmonella enterica serovar Typhimurium grown under modeled microgravity (MMG) were more virulent and were recovered in higher numbers from the murine spleen and liver following oral infection compared to organisms grown under normal gravity. Furthermore, MMG-grown salmonellae were more resistant to acid stress and macrophage killing and exhibited significant differences in protein synthesis than did normal-gravity-grown cells. Our results indicate that the environment created by simulated microgravity represents a novel environmental regulatory factor of Salmonella virulence.

NASA Discipline Environmental Health↗

DOE OSTI2021

Cross-scale dynamics and the evolutionary emergence of infectious diseases

When emerging pathogens encounter new host species for which they are poorly adapted, they must evolve to escape extinction. Pathogens experience selection on traits at multiple scales, including replication rates within host individuals and transmissibility between hosts. We analyze a stochastic model linking pathogen growth and competition within individuals to transmission between individuals. Our analysis reveals a new factor, the cross-scale reproductive number of a mutant virion, that quantifies how quickly mutant strains increase in frequency when they initially appear in the infected host population. This cross-scale reproductive number combines with viral mutation rates, single-strain reproductive numbers, and transmission bottleneck width to determine the likelihood of evolutionary emergence, and whether evolution occurs swiftly or gradually within chains of transmission. We find that wider transmission bottlenecks facilitate emergence of pathogens with short-term infections, but hinder emergence of pathogens exhibiting cross-scale selective conflict and long-term infections. Furthermore, our results provide a framework to advance the integration of laboratory, clinical, and field data in the context of evolutionary theory, laying the foundation for a new generation of evidence-based risk assessment of emergence threats.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Changing temperature profiles and the risk of dengue outbreaks

As temperatures change worldwide, the pattern and competency of disease vectors will change, altering the global distribution of both the burden of infectious disease and the risk of the emergence of those diseases into new regions. To evaluate the risk of potential summer dengue outbreaks triggered by infected travelers under various climate scenarios, we develop an SEIR-type model, run numerical simulations, and conduct sensitivity analyses under a range of temperature profiles. Our model extends existing theoretical frameworks for studying dengue dynamics by introducing temperature dependence of two key parameters: the mosquito extrinsic incubation period and the lifespan of mosquitoes, which empirical data suggests are both highly temperature dependent. We find that changing temperature significantly alters dengue risk in an inverted U-shape, with temperatures in the range 27-31°C producing the highest risk. As temperatures increase beyond 31°C, the determinants of dengue risk begin to shift from mosquito biting rate and carrying capacity to the duration of the human infectious period, suggesting that changing temperatures not only alter dengue risk but also the potential efficacy of control measures. To illustrate the role of spatial and temporal temperature heterogeneity, we select five US cities where the primary dengue vector, the mosquito Aedes aegypti , has been observed, and which have had dengue cases in the past: Los Angeles, Houston, Miami, Brownsville, and Phoenix. Our analysis suggests that an increase of 3°C leads to an approximate doubling of the risk of dengue in Los Angeles and Houston, but a reduction of risk in Miami, Brownsville, and Phoenix due to extreme heat.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

NASA Earth Observation Systems and Applications for Public Health and Air Quality Models and Decisions Support

Health and Air Quality providers and researchers need environmental data to study and understand the geographic, environmental, and meteorological differences in disease. Satellite remote sensing of the environment offers a unique vantage point that can fill in the gaps of environmental, spatial, and temporal data for tracking disease. This presentation will demonstrate the need for collaborations between multi-disciplinary research groups to develop the full potential of utilizing Earth Observations in studying health. Satellite earth observations present a unique vantage point of the earth's environment from space, which offers a wealth of health applications for the imaginative investigator. The presentation is directly related to Earth Observing systems and Global Health Surveillance and will present research results of the remote sensing environmental observations of earth and health applications, which can contribute to the public health and air quality research. As part of NASA approach and methodology they have used Earth Observation Systems and Applications for Public Health and Air Quality Models to provide a method for bridging gaps of environmental, spatial, and temporal data for tracking disease. This presentation will provide an overview of projects dealing with infectious diseases, water borne diseases and air quality and how many environmental variables effect human health. This presentation will provide a venue where the results of both research and practice using satellite earth observations to study weather and it's role in public health research.

Estes, Sue↗

NASA NTRS2013

NASA Earth Observation Systems and Applications for Public Health and Air Quality Models and Decisions Support

Estes, Sue↗

DOE OSTI2023

Assessment of a reconfiguration of the InterSpread Plus US national FMD model as a potential tool to analyze a foot-and-mouth disease outbreak on a single large cattle feedlot in the United States

An incursion of foot-and-mouth disease (FMD) into the United States remains a concern of high importance and would have devastating socioeconomic impacts to the livestock and associated industries. This highly transmissible and infectious disease poses continual risk for introduction into the United States (US), due to the legal and illegal global movement of people, animals, and animal products. While stamping out has been shown to effectively control FMD, depopulation of large cattle feedlots (>50,000 head) presents a number of challenges for responders due to the resources required to depopulate and dispose of large numbers of animals in a timely and effective manner. However, evaluating alternative strategies for FMD control on large feedlots requires a detailed within-farm modeling approach, which can account for the unique structure of these operations. To address this, we developed a single feedlot, within-farm spread model using a novel configuration within the InterSpread Plus (ISP) framework. As proof of concept we designed six scenarios: (i) depopulation - the complete depopulation of the feedlot, (ii) burn-through – a managed “burn-through” where the virus is allowed to spread through the feedlot and only movement restriction and biosecurity are implemented, (iii) firebreak-NV – targeted depopulation of infected pens and adjacent pens without vaccination; (iv) firebreak - targeted depopulation of infected pens and adjacent pens with vaccination of remaining pens; (v) harvest-NV - selective harvest of pens where a 100% movement restriction is applied for 28-30 days, then pens are set for selection to be sent to slaughter, while allowing a controlled “burn-through” without vaccination; and (vi) harvest - selective harvest of pens with vaccination. Overall, the burn-through scenario (ii) had the shortest epidemic duration (31d (30, 33)) median (25th, 75th percentiles), while the firebreak scenario (iv) had the longest (47d (38,55)). Additionally, we found that scenarios implementing depopulation delayed the peak day of infection and reduced the total number of pens infected compared to non-depopulation scenarios.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Cooperative Research and Infectious Disease Surveillance: A 2021 Epilogue

As the world looks forward to turning a corner in the face of the COVID-19 pandemic, it becomes increasingly evident that international research cooperation and dialogue is necessary to end this global catastrophe. Last year, we initiated a research topic: “Infectious Disease Surveillance: Cooperative Research in Response to Recent Outbreaks, Including COVID-19,” which aimed at featuring manuscripts focused on the essential link between surveillance and cooperative research for emerging and endemic diseases, and highlighting scientific partnerships in countries under-represented in the scientific literature. Here we recognize the body of work published from our manuscript call that resulted in over 50 published papers. This current analysis describes articles and authors from a variety of funded and unfunded international sources. The work exemplifies successful research and publications which are frequently cooperative, and may serve as a basis to model further global scientific engagements.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Simulating nationwide coupled disease and fear spread in an agent-based model

Human cognitive responses, behavioral responses, and disease dynamics co-evolve over the course of any disease outbreak, and can result in complex feedbacks. We present a dynamic agent-based model that explicitly couples the spread of disease with the spread of fear surrounding the disease, implemented within the EpiCast simulation framework. EpiCast models transmission within a realistic synthetic population, capturing individual-level interactions. In our model, fear propagates through both in-person contact and broadcast media, prompting individuals to adopt protective behaviors that reduce disease spread. In order to better understand these coupled dynamics, we create and compare a range of compartmental models to ensure that introducing additional disease states does not prevent the emergence of multiple waves in these simpler models. Additionally, we compare a range of behavioral scenarios within EpiCast, varying the level and intensity of fear and behavior change. Our results show that the addition of asymptomatic, exposed, and pre-symptomatic disease states can impact both the rate at which an outbreak progresses and its overall trajectory in compartmental models. In EpiCast, the combination of non-local fear spread via broadcasters and strong behavioral responses by fearful individuals generally leads to multiple epidemic waves, an outcome that occurs only within a narrow parameter range when fear spreads purely through local contact. Accounting for the coupled spread of fear and disease is critical for understanding disease dynamics and designing timely, targeted responses to emerging infectious threats.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Small Particle Aerosol Exposure of African Green Monkeys to MERS-CoV as a Model for Highly Pathogenic Coronavirus Infection

Emerging coronaviruses are a global public health threat because of the potential for person-to-person transmission and high mortality rates. Middle East respiratory syndrome coronavirus (MERS-CoV) emerged in 2012, causing lethal respiratory disease in $\gg$35% of cases. Primate models of coronavirus disease are needed to support development of therapeutics, but few models exist that recapitulate severe disease. For initial development of a MERS-CoV primate model, 12 African green monkeys were exposed to 10 3 , 10 4 , or 10 5 PFU target doses of aerosolized MERS-CoV. We observed a dose-dependent increase of respiratory disease signs, although all 12 monkeys survived for the 28-day duration of the study. This study describes dosedependent effects of MERS-CoV infection of primates and uses a route of infection with potential relevance to MERSCoV transmission. Aerosol exposure of African green monkeys might provide a platform approach for the development of primate models of novel coronavirus diseases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States

Although many persons in the United States have acquired immunity to COVID-19, either through vaccination or infection with SARS-CoV-2, COVID-19 will pose an ongoing threat to non-immune persons so long as disease transmission continues. We can estimate when sustained disease transmission will end in a population by calculating the population-specific basic reproduction number ℛ0, the expected number of secondary cases generated by an infected person in the absence of any interventions. The value of ℛ0 relates to a herd immunity threshold (HIT), which is given by 1−1/ℛ0. When the immune fraction of a population exceeds this threshold, sustained disease transmission becomes exponentially unlikely (barring mutations allowing SARS-CoV-2 to escape immunity). Here, we report state-level ℛ0 estimates obtained using Bayesian inference. Maximum a posteriori estimates range from 7.1 for New Jersey to 2.3 for Wyoming, indicating that disease transmission varies considerably across states and that reaching herd immunity will be more difficult in some states than others. ℛ0 estimates were obtained from compartmental models via the next-generation matrix approach after each model was parameterized using regional daily confirmed case reports of COVID-19 from 21 January 2020 to 21 June 2020. Our ℛ0 estimates characterize the infectiousness of ancestral strains, but they can be used to determine HITs for a distinct, currently dominant circulating strain, such as SARS-CoV-2 variant Delta (lineage B.1.617.2), if the relative infectiousness of the strain can be ascertained. On the basis of Delta-adjusted HITs, vaccination data, and seroprevalence survey data, we found that no state had achieved herd immunity as of 20 September 2021.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Predicting the effects of climate change on the cross-scale epidemiological dynamics of a fungal plant pathogen

The potential for climate change to exacerbate the burden of human infectious diseases is increasingly recognized, but its effects on infectious diseases of plants have received less attention. Understanding the impacts of climate on the epidemiological dynamics of plant pathogens is imperative, as these organisms play central roles in natural ecosystems and also pose a serious threat to agricultural production and food security. We use the fungal ‘flax rust’ pathogen (Melampsora lini) and its subalpine wildflower host Lewis flax (Linum lewisii) to investigate how climate change might affect the dynamics of fungal plant pathogen epidemics using a combination of empirical and modeling approaches. Our results suggest that climate change will initially slow transmission at both the within- and between-host scales. However, moderate resurgences in disease spread are predicted as warming progresses, especially if the rate of greenhouse gas emissions continues to increase at its current pace. These findings represent an important step towards building a holistic understanding of climate effects on plant infectious disease that encompasses demographic, epidemiological, and evolutionary processes. A core result is that neglecting processes at any one scale of plant pathogen transmission may bias projections of climate effects, as climate drivers have variable and cascading impacts on processes underlying transmission that occur at different scales.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2013

Improving Decision-Making Activities for Meningitis and Malaria

Public health professionals are increasingly concerned about the potential impact that climate variability and change can have on infectious disease. The International Research Institute for Climate and Society (IRI) is developing new products to increase the public health community's capacity to understand, use and demand the appropriate climate data and climate information to mitigate the public health impacts of climate on infectious disease, in particular meningitis and malaria. In this paper, we present the new and improved products that have been developed for: (i) estimating dust aerosol for forecasting risks of meningitis and (ii) for monitoring temperature and rainfall and integrating them into a vectorial capacity model for forecasting risks of malaria epidemics. We also present how the products have been integrated into a knowledge system (IRI Data Library Map Room, SERVIR) to support the use of climate and environmental information in climate-sensitive health decision-making.

parasitic diseases↗

NASA NTRSDate not supplied

Spaceflight-induced Changes in Microbial Virulence and the Impact to the Host Immune Response

INTRODUCTION Over the past 50 years, many microorganisms have displayed unexpected responses relevant to infectious disease when grown in microgravity and microgravity analogue environments, including changes in stress resistance, biofilm production, antibiotic sensitivity, final cell concentration, gene expression, enhanced host-pathogen interaction, and virulence. In parallel, astronaut studies have characterized a persistent spaceflight-induced dysregulation of the human immune system; consisting of altered leukocyte distribution, reductions in T and NK cell function, altered cytokine profiles, and reactivation of latent herpesviruses. Further, astronauts have some degree of clinical incidence, primarily infectious disease episodes and atopic dermatitis. The impact of the microgravity environment on host-pathogen interactions and potential for clinical disease remains understudied and poorly characterized. SPECIFIC AIMS In this study, the following Specific Aims are being investigated, using the microbial pathogens, Salmonella enterica Enteritidis, Pseudomonas aeruginosa, Burkholderia cepacia, Streptococcus pneumoniae, and enterohemorrhagic Escherichia coli (EHEC). Aim 1: Characterize the effect of spaceflight analogue culture on microbial pathogenesis-related stress responses and in vitro host-pathogen interactions. Analyses include microbial stress responses as well as colonization and viability following pathogen challenge of three-dimensional (3-D) tissue co-culture models containing immune cells. We specifically selected obligate and opportunistic pathogens that are medically important and have been or are likely to be found aboard spacecraft. Aim 2: Characterize the effect of spaceflight analogue culture on the virulence potential of pathogenic microorganisms. Virulence will be assessed using a mouse model of infection. SIGNIFICANCE The goal of this study is to use spaceflight analogue conditions to gain insight into the breadth of medically-significant International Space Station microorganisms that have altered virulence and the impact of those changes on the immune response of the host. This information will provide critical understanding into the impact of microgravity on potential alterations in microbial virulence and associated infectious disease risk to crew health during spaceflight missions.

C M Ott↗

DOE OSTI2022

A simple model of COVID-19 explains disease severity and the effect of treatments

Abstract Considerable effort has been made to better understand why some people suffer from severe COVID-19 while others remain asymptomatic. This has led to important clinical findings; people with severe COVID-19 generally experience persistently high levels of inflammation, slower viral load decay, display a dysregulated type-I interferon response, have less active natural killer cells and increased levels of neutrophil extracellular traps. How these findings are connected to the pathogenesis of COVID-19 remains unclear. We propose a mathematical model that sheds light on this issue by focusing on cells that trigger inflammation through molecular patterns: infected cells carrying pathogen-associated molecular patterns (PAMPs) and damaged cells producing damage-associated molecular patterns (DAMPs). The former signals the presence of pathogens while the latter signals danger such as hypoxia or lack of nutrients. Analyses show that SARS-CoV-2 infections can lead to a self-perpetuating feedback loop between DAMP expressing cells and inflammation, identifying the inability to quickly clear PAMPs and DAMPs as the main contributor to hyperinflammation. The model explains clinical findings and reveal conditions that can increase the likelihood of desired clinical outcome from treatment administration. In particular, the analysis suggest that antivirals need to be administered early during infection to have an impact on disease severity. The simplicity of the model and its high level of consistency with clinical findings motivate its use for the formulation of new treatment strategies.

60 APPLIED LIFE SCIENCES↗