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246 records · Page 14

Next Generation Respiratory Viral Vaccine System: Advanced and Emerging Bioengineered Human Lung Epithelia Model (HLEM) Organoid Technology

Acute respiratory infections, including pneumonia and influenza, are the S t" leading cause of United States and worldwide deaths. Newly emerging pathogens signaled the need for an advanced generation of vaccine technology.. Human bronchial-tracheal epithelial tissue was bioengineered to detect, identify, host and study the pathogenesis of acute respiratory viral disease. The 3-dimensional (3D) human lung epithelio-mesechymal tissue-like assemblies (HLEM TLAs) share characteristics with human respiratory epithelium: tight junctions, desmosomes, microvilli, functional markers villin, keratins and production of tissue mucin. Respiratory Syntial Virus (RSV) studies demonstrate viral growth kinetics and membrane bound glycoproteins up to day 20 post infection in the human lung-orgainoid infected cell system. Peak replication of RSV occurred on day 10 at 7 log10 particles forming units per ml/day. HLEM is an advanced virus vaccine model and biosentinel system for emergent viral infectious diseases to support DoD global surveillance and military readiness.

Goodwin, Thomas J.↗

Using Earth Observations to Understand and Predict Infectious Diseases

This presentation discusses the processes from data collection and processing to analysis involved in unraveling patterns between disease outbreaks and the surrounding environment and meteorological conditions. We used these patterns to estimate when and where disease outbreaks will occur. As a case study, we will present our work on assessing the relationship between meteorological conditions and influenza in Central America. Our work represents the discovery, prescriptive and predictive aspects of data analytics.

public health↗

Moving With Parasites: Relationships Between Animal Movement and Infectious Disease Across Systems and Scales

Animal movement patterns are important for ecological processes such as population dynamics and infectious disease transmission. Because animals move in response to environmental cues like temperature and precipitation, changes to the environment – whether they are seasonal, interannual, or long-term – can change these processes, leading to changes in population sizes or infectious disease prevalence across space and time. My work uses a combination of simulation modeling and data analysis at multiple spatial and temporal scales to understand the environment-movement-disease connection in diverse animal taxa and environments. In this seminar, I will highlight the diverse ways that the environment can influence infectious disease dynamics via changes to animal movement patterns, including through a global study of migration in ungulate populations, a study of American White Ibis adapting to urban environments in South Florida, and examples of avian influenza prevalence in wild waterfowl. Together, these examples highlight that understanding environment-movement interactions can help predict how infectious disease dynamics might change in the future, as changes to climate, land use, and habitats alter animal movement patterns.

Animal movement↗

Linking Individual Variation in Movement Behavior to Infection Dynamics in Highly Mobile Species

Animal movements are linked to infectious disease dynamics in wildlife through multiple mechanisms across various spatial and temporal scales. For example, infections can impede the movement of infected individuals, but infected hosts can also disperse infectious agents as they move. Individuals inevitably vary in their responses to infections and their roles in determining infection dynamics, which means that considering variation among individuals – whether across species, across time, or within populations – is important for understanding movement-disease relationships and their implications for epidemiology. Here, I draw on studies from multiple systems to examine how variation in movement behavior links to infectious disease epidemiology. For example, a comparative study of habitat selection across multiple waterfowl species highlights that diel behavioral patterns, rather than individual or seasonal variation, is the most important driver of the risk of overlap between waterfowl and poultry farms, and therefore for the risk of avian influenza spillover. Together, these studies show that individual traits interact with the environment to influence the roles of individuals for infectious disease ecology.

Claire Stewart Teitelbaum↗

A compendium of multi-omics data illuminating host responses to lethal human virus infections

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

60 APPLIED LIFE SCIENCES↗

Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response

Motivation: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems such as protein complexes, metabolic reactions, and signal transduction pathways. Hypergraphs are generalizations of graphs that naturally model multi-way interactions in data, and we therefore seek to understand how they can more faithfully identify, and potentially predict, complex relationships in genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges are differentially expressed genes and vertices represent conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Our results demonstrate the utility of using hypergraphs to represent complex biological systems, and highlight potentially interesting biological results about host response to highly pathogenic viruses.

systems biology, hypergraph, viral infection, biol↗

Quantification of Ebola virus replication kinetics in vitro

Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola virus modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola virus infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.

59 BASIC BIOLOGICAL SCIENCES↗

COVID-19-associated school closures and related efforts to sustain education and subsidized meal programs, United States, February 18-June 30, 2020

Pre-emptive school closures are frontline community mitigation measures recommended by the US Centers for Disease Control and Prevention (CDC) for implementation during severe pandemics. This study describes the spatiotemporal patterns of publicly announced school closures implemented in response to the coronavirus disease 2019 (COVID-19) pandemic and assesses how public K-12 districts adjusted their methods of education delivery and provision of subsidized meals. During February 18–June 30, 2020, we used daily systematic media searches to identify publicly announced COVID-19–related school closures lasting ≥1 day in the United States (US). We also collected statewide school closure policies from state government websites. Data on distance learning and subsidized meal programs were collected from a stratified sample of 600 school districts. The first COVID-19–associated school closure occurred on February 27, 2020 in Washington state. By March 30, 2020, all but one US public school districts were closed, representing the first-ever nearly synchronous nationwide closure of public K-12 schools in the US. Approximately 100,000 public schools were closed for ≥8 weeks because of COVID-19, affecting >50 million K-12 students. Of 600 districts sampled, the vast majority offered distance learning (91.0%) and continued provision of subsidized meal programs (78.8%) during the closures. Despite the sudden and prolonged nature of COVID-19–associated school closures, schools demonstrated flexibility by implementing distance learning and alternate methods to continue subsidized meal programs.

60 APPLIED LIFE SCIENCES↗

Ventilation in Residential Care Environments

The purpose of this study was to evaluate the effect of ventilation rate, directional airflow (e.g. pressure relationships) and airflow barriers on bioaerosol concentration and movement within assisted-living and residential care environments. Included within this report is a comprehensive literature review, field data collection test plan and, an evaluation of commercially available mitigation technologies related to the transmission of COVID-19 in LTC environments. Additionally, this report includes aerosol testing in an actual LTC facility using the test plan developed herein. Nearly 1.5 million people live in 16,000 nursing homes in the U.S. As of December 20, 2020 the Centers for Medicare and Medicaid Services (CMS) identified a total of 662,549 confirmed and suspected COVID-19 cases in these and other long-term care (LTC) facilities resulting in 92,373 deaths. At this time, COVID-19 cases in LTC facilities accounted for only 3% of the U.S. total of 21M confirmed cases yet nearly a third of the 350,000 total U.S. deaths. Preliminary studies have found little correlation between the quality of care and transmission of COVID-19 in these facilities, suggesting that even the best infection control practices may not be effective in containing the spread of this potentially airborne disease. As a result, many LTC facilities have implemented quarantine procedures and other measures to isolate infectious residents from the general population. Unfortunately, most LTC facilities were not designed for airborne infection control and guidance for retrofitting existing LTC spaces for airborne isolation is limited.

42 ENGINEERING↗

PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection

The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.

59 BASIC BIOLOGICAL SCIENCES↗