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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dose Coefficient Calculation for Use in Dosimetry Assessment of a Fission-Based Weapon

In the event of a fission-based weapon or improvised nuclear device (IND) detonation, dose coefficients can be harnessed to provide dose assessments for defense, emergency preparedness, and consequence management, as well as to prospectively inform the assessment of radiation biomarkers and development of medical prophylaxis countermeasures for defense and homeland security stakeholders and decision-makers. Although dose coefficients have previously been calculated for this group, they would apply specifically to the studied population, the 1945 Japanese cohort, after which their anthropomorphic computational phantoms were modeled. For this reason, applications to other populations may be limited, and instead, an assessment of a more standardized population is desired. We employed a series of computational human phantoms representing international reference individuals: UF/NCI voxel phantom series containing newborn, 1-, 5-, 10-, 15-, and 35-year-old males and females. Irradiation of the phantoms was simulated using the Monte Carlo N-Particle transport code to determine organ dose coefficients under four idealized irradiation geometries at three distances from the detonation hypocenter at Hiroshima and Nagasaki using DS02 free-in-air prompt neutron and photon fluence spectra. Through these simulations, age-specific dose coefficients were determined for individual organs. Various articulated PIMAL stylized phantoms were simulated as well to estimate the effect of body posture on dose coefficients and determine the effect of posture on dosimetric estimation and reconstruction. Results additionally demonstrate that 137 Cs and the Watt fission spectra are not ideal general surrogate sources for fission weapons, which may be considered for experimental testing of medical countermeasures. Supplementary data provided tabulates the compilation of organ dose-rate coefficients in this study.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

19-LW-045 Full Length Final Report. Molecular Mechanisms of Bacterial Pathogenesis: Waging the Arms Race with Superbugs

As the current global pandemic makes abundantly clear, we need a better understanding of infectious disease to safeguard human health, the economy and global security. Modern omics techniques hold the promise of providing a comprehensive understanding of the molecular mechanisms of life, including causes of pathogenesis from infectious disease at the molecular level, but we there is a serious gap in annotation of gene function. For as much as half of the genes and gene products encoded in genomes the molecular and/or cellular function is unknown or only partially understood. Recent innovations in fluorescence microscopy for live cell imaging and genetic engineering make it possible to determine the temporal correlation between molecular events, such as a gene being expressed due to host-pathogen interaction, and cellular events, such as bacterial invasion of immune cells. This is turn allows us to gain new insight as to the molecular and cellular role of individual genes and will enable the discovery and validation of new molecular mechanisms essential for infectious disease. Knowing the molecular mechanisms of disease processes will provide new therapeutic targets or novel countermeasure strategies. We aimed to develop a lattice light sheet fluorescence microscope as a unique resource at LLNL for long time course live cell imaging experiments; to develop the reagents and cell lines needed to monitor molecular events during the course pathogenic bacteria infecting mammalian immune cells; and to demonstrate that we could capture molecular events during an infection. We fully commissioned the LLNL lattice light sheet microscope and conducted initial proof of principle imaging experiments on mammalian immune cells and pathogenic bacteria. It is clear from the experience gained that long time course live cell imaging has tremendous potential to help elucidate molecular mechanisms of host-pathogen interactions and to help annotate gene function, which would establish a basis for new countermeasures. It is also clear that if live cell imaging is to realize its full potential new data processing and analysis tools will need to be developed to facilitate analysis of molecular events within cells; new sample chambers and stages could facilitate studies with a wider range of cell and tissue types; and alternative molecular tagging methods need to be explored to enable more facile engineering of cells labeled with molecular specificity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rapid Computational Identification of Therapeutic Targets for Pathogens

Biological threats continue to persist and evolve as an important challenge to national security. There are multiple ways in which novel viral pathogens could emerge to pose a serious threat to human health. This project developed a pathogen target identification tool that can rapidly respond to a novel or emerging viral biological threat. A set of computational tools were developed that provide detailed information on the newly sequenced genes, their protein products and the drug target sites for the proteins that are best suited for biological countermeasure development. Three key innovations were developed in the project. 1) Development of a new extensive database of protein pocket structures with structure-based search algorithms to rapidly link novel protein targets with the complete collection of previously experimentally solved protein structures. 2) A novel clustering pipeline was introduced to group matching structures and associated small-molecule binding ligands into a consensus protein pocket with the associated small-molecule chemotypes predicted to fit in the pocket site. The matching experimentally solved structures were used to inform the value of different target sites. 3) Where there are viral protein targets with pockets structurally matched to similar human proteins, a biological knowledge graph, which links molecular interactions with human disease, was used to further assess the potential negative impact of a viral protein target with similarities to human proteins that could have important off target side effects. In total, the project produced a new resource for rapid and detailed assessment of promising targets for countermeasures, reflecting the ongoing wet lab, clinical, and computational data being collected. These capabilities will improve the ability to respond to a biological threat in multiple domains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Defining Lipidomic Responses to Coronavirus Infection

Highly pathogenic human coronavirus infection can cause a severe atypical, rapid onset pneumonia with a mortality rate of up to 10% for severe acute respiratory syndrome coronavirus 1 (SARS-CoV 1), 35% for Middle East respiratory syndrome coronavirus (MERS-CoV), or 1% for severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2 causative agent of COVID 19). While medical countermeasures successfully controlled the worldwide SARS-CoV epidemic, the MERS-CoV epidemic is still ongoing and continues to be a concern for travelers in the Middle East and the multi-year SARS-CoV 2 pandemic underscore the importance of defining biomarkers that are diagnostic and/or predictive of severe disease outcomes for respiratory viruses. Systems biology approaches provide global snapshots of infection induced changes in host cells/tissues and provide extremely rich datasets for understanding host pathogen interactions. Metabolites, especially lipids, are critical for viral replication but less is understood about infection induced changes to lipids due to limits in lipid species detection and identification. To characterize how individual lipid species and proteins with lipid associated functions contribute to highly pathogenic human coronavirus replication and disease severity, existing datasets were probed to determine cell type specific lipid responses to MERS-CoV infection and verification studies were performed to determine if modification of the host lipid signature (by inhibiting enzymatic functions that produce specific lipid species) can perturb CoV replication in human lung cells. MERS-CoV infects human lung epithelial, endothelial and fibroblast cells. All three cell types were infected with MERS-CoV and samples collected to analyze lipids, proteins, metabolites, and transcripts from 12 to 48 hours post infection. Time matched mock-infected cells were collected in parallel for each cell type. Following sample and statistical analysis, functional enrichment and bioinformatic analysis was performed to identify differentially expressed lipids and proteins. Two lipid species were found to be significantly upregulated following MERS-CoV infection, ceramides, and triacylglycerol both of which are indicative of cells undergoing apoptotic cell death. In contrast, sphingomyelins (lipid molecules that can serve as a precursor for one pathway for ceramide synthesis) had significantly decreased expression in MERS-CoV infected cells. An inhibitor of acid sphingomyelinase (that converts sphingomyelin to ceramides and phosphorylcholine) reduced MERS-CoV replication suggesting that production of ceramides is key for successful viral replication and transmission. Acyl-CoA-synthetase 3 (ACSL3), the only protein whose function is lipid associated and had increased differential expression in our dataset, regulates the synthesis of triacylglycerol (increased expression). Inhibitors that directly block ACSL3 (Triacsin C) but not steps later in the triacylglycerol synthesis pathway (Etomoxir) inhibit MERS-CoV replication suggesting that triacylglycerol production and/or ACSL3 activity is also key for viral replication. ACSL3 expression is also upregulated in lung cancer cells and is predicted to promote continued cell viability which would also enhance viral replication. The differentially expressed lipid and lipid-associated protein species identified in our studies suggest that MERS-CoV infection is activating cellular death pathways to limit the number of cells that become infected but also stimulating the production of lipid-associated enzymes that can prolong host cell viability and the amount of time progeny virions can be produced and released. As the inhibitors that worked against MERS-CoV infection were also efficacious against SARS-CoV 2 infection, countermeasures that target these host pathways may provide novel ways to block highly pathogenic human coronavirus infection and/or prevent severe disease outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Simulated Microgravity Alters Gene Regulation Linked to Immunity and Cardiovascular Disease

Microgravity exposure induces a cephalad fluid shift and an overall reduction in physical activity levels which can lead to cardiovascular deconditioning in the absence of countermeasures. Future spaceflight missions will expose crew to extended periods of microgravity among other stressors, the effects of which on cardiovascular health are not fully known. In this study, we determined cardiac responses to extended microgravity exposure using the rat hindlimb unloading (HU) model. We hypothesized that exposure to prolonged simulated microgravity and subsequent recovery would lead to increased oxidative damage and altered expression of genes involved in the oxidative response. To test this hypothesis, we examined hearts of male (three and nine months of age) and female (3 months of age) Long–Evans rats that underwent HU for various durations up to 90 days and reambulated up to 90 days post-HU. Results indicate sex-dependent changes in oxidative damage marker 8-hydroxydeoxyguanosine (8-OHdG) and antioxidant gene expression in left ventricular tissue. Three-month-old females displayed elevated 8-OHdG levels after 14 days of HU while age-matched males did not. In nine-month-old males, there were no differences in 8-OHdG levels between HU and normally loaded control males at any of the timepoints tested following HU. RNAseq analysis of left ventricular tissue from nine-month-old males after 14 days of HU revealed upregulation of pathways involved in pro-inflammatory signaling, immune cell activation and differential expression of genes associated with cardiovascular disease progression. Taken together, these findings provide a rationale for targeting antioxidant and immune pathways and that sex differences should be taken into account in the development of countermeasures to maintain cardiovascular health in space.

Genetics & Heredity↗
DOE OSTI2021

Identifying Candidate Biomarkers of Ionizing Radiation in Human Pulmonary Microvascular Lumens Using Microfluidics—A Pilot Study

The microvasculature system is critical for the delivery and removal of key nutrients and waste products and is significantly damaged by ionizing radiation. Single-cell capillaries and microvasculature structures are the primary cause of circulatory dysfunction, one that results in morbidities leading to progressive tissue and organ failure and premature death. Identifying tissue-specific biomarkers that are predictive of the extent of tissue and organ damage will aid in developing medical countermeasures for treating individuals exposed to ionizing radiation. In this pilot study, we developed and tested a 17 µL human-derived microvascular microfluidic lumen for identifying candidate biomarkers of ionizing radiation exposure. Through mass-spectrometry-based proteomics, we detected 35 proteins that may be candidate early biomarkers of ionizing radiation exposure. This pilot study demonstrates the feasibility of using humanized microfluidic and organ-on-a-chip systems for biomarker discovery studies. A more elaborate study of sufficient statistical power is needed to identify candidate biomarkers and test medical countermeasures of ionizing radiation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Immediate effects of acute Mars mission equivalent doses of SEP and GCR radiation on the murine gastrointestinal system-protective effects of curcumin-loaded nanolipoprotein particles (cNLPs)

Introduction: Missions beyond low Earth orbit (LEO) will expose astronauts to ionizing radiation (IR) in the form of solar energetic particles (SEP) and galactic cosmic rays (GCR) including high atomic number and energy (HZE) nuclei. The gastrointestinal (GI) system is documented to be highly radiosensitive with even relatively low dose IR exposures capable of inducing mucosal lesions and disrupting epithelial barrier function. IR is also an established risk factor for colorectal cancer (CRC) with several studies examining long-term GI effects of SEP/GCR exposure using tumor-prone APC mouse models. Studies of acute short-term effects of modeled space radiation exposures in wildtype mouse models are more limited and necessary to better define charged particle- induced GI pathologies and test novel medical countermeasures (MCMs) to promote astronaut safety. Methods: In this study, we performed ground-based studies where male and female C57BL/6J mice were exposed to γ-rays, 50 MeV protons, or 1 GeV/n Fe-56 ions at the NASA Space Radiation Laboratory (NSRL) with histology and immunohistochemistry endpoints measured in the first 24 h post-irradiation to define immediate SEP/GCR-induced GI alterations. Results: Our data show that unlike matched γ-ray controls, acute exposures to protons and iron ions disrupts intestinal function and induces mucosal lesions, vascular congestion, epithelial barrier breakdown, and marked enlargement of mucosa-associated lymphoid tissue. We also measured kinetics of DNA double-strand break (DSB) repair using gamma-H2AX- specific antibodies and apoptosis via TUNEL labeling, noting the induction and disappearance of extranuclear cytoplasmic DNA marked by gamma-H2AX only in the charged particle-irradiated samples. We show that 18 h pre-treatment with curcumin-loaded nanolipoprotein particles (cNLPs) delivered via IV injection reduces DSB-associated foci levels and apoptosis and restore crypt villi lengths. Discussion: These data improve our understanding of physiological alterations in the GI tract immediately following exposures to modeled space radiations and demonstrates effectiveness of a promising space radiation MCM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Whole‐Cell MALDI‐ToF MS Coupled with Untargeted Metabolomics Facilitates Investigations of Microbial Chemical Interactions

Abstract The emergence of drug‐resistant pathogens necessitates the development of new countermeasures. In this regard, the introduction of probiotics to directly attack or competitively exclude pathogens presents a useful strategy. Application of this approach requires an understanding of how a probiotic and its target pathogen interact. A key means of probiotic‐pathogen interaction involves the production of small molecules called natural products (NPs). Here, we report the use of whole‐cell matrix‐assisted laser desorption/ionization time‐of‐flight (MALDI‐ToF) mass spectrometry to characterize NP production by candidate probiotics (mouse airway microbiome isolates) when co‐cultured with the respiratory pathogen Burkholderia . We found that a Bacillus velezensis strain inhibits growth of and elicits NP production by Burkholderia thailandensis . Dereplication of known NPs detected in the metabolome of this B. velezensis strain suggests that a previously unannotated bioactive compound is involved. Thus, we present the use of whole‐cell MALDI as a broadly applicable method for screening the NP composition of microbial co‐cultures; this can be combined with other ‐omics methods to characterize probiotic‐pathogen and other microbe‐microbe interactions.

Aiosa, Nicole↗
DOE OSTI2025

Towards Automated Assessment of Vulnerability Exposures in Security Operations

Current approaches for risk analysis of software vulnerabilities using manual assessment and numeric scoring do not complete fast enough to keep pace with the maintenance work rate to patch and mitigate the vulnerabilities. This paper proposes a new approach to modeling software vulnerability risk in the context of the network environment and firewall configuration. In the approach, vulnerability features are automatically matched up with networking, target asset, and adversary features to determine whether adversaries can exploit a vulnerability. The ability of adversaries to reach a vulnerability is modeled by automatically identifying the network services associated with vulnerabilities through a pipeline of machine learning and natural language processing and automatically analyzing network reachability. Our results show that the pipeline can identify network services accurately. We also find that only a small number of vulnerabilities pose real risks to a system. However, if left unmitigated, adversarial reach to vulnerabilities may extend to nullify the effect of firewall countermeasures.

Huff, Philip↗
DOE OSTI2023

Usage Pattern Analysis for the Summit Login Nodes

High performance computing (HPC) users interact with Summit through dedicated gateways, also known as login nodes. The performance and stability of these login nodes can have a significant impact on the user experience. In this study, the performance and stability of Summit’s five login nodes are evaluated by analyzing the log data from 2020 and 2021. The analysis focuses on the computing capability (CPU average load, users and tasks) and the storage performance, along with the associated job scheduler activity. The outcome of this study can serve as the foundation of a predictive modeling framework that enables the system admin of an HPC system to preemptively deploy countermeasures before the onset of a system failure.

Eiffert, Brett↗
DOE OSTI2020

A Novel Simulation-Based Framework for Sensor Error Impact Analysis in Smart Building Systems: A Case Study for a Demand-Controlled Ventilation System

Sensors are one of the fundamental components for sensor-rich controls in buildings but are prone to different errors. Existing studies show that sensor errors hold a place among top-priority faults in building systems. Before we take countermeasures to mitigate the sensor errors, it is vital to prioritize key sensors and quantify the collective impacts of concurrent sensor errors. In response to this, a simulation-based methodology is introduced to conduct a comprehensive sensor error impact analysis in building systems, which adds a stochastic sensor prioritization through a sensitivity analysis on top of a commonly used deterministic sensor error quantification. The synergies of these two parts help better interpret the sensor error impacts on building energy consumption, ventilation performance, thermal comfort, etc. A sensor-rich CO2-based Demand-Controlled Ventilation system is used as a case study to demonstrate the viability of the methodology as a proof-of-the-concept. The results show that the energy savings potential and ventilation performance are mostly influenced by the accuracy of the AHU outdoor airflow sensors. The accuracy of zone level airflow sensors has a negligible impact on both energy savings and ventilation performance. The accuracy of zone CO2 sensors has more influence on the ventilation performance compared with the accuracy of zone airflow sensors. Compared with the baseline case with zero errors, the largest deviation percentages could reach 16.90% and 94.32%, respectively, in terms of the Heating, Ventilation, and Air-Conditioning (HVAC) annual energy consumption and the Outdoor Air Ratio (OAR) when multiple key sensors suffer from normal error intensities simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Application of machine learning to evaluating and remediating models for energy and environmental engineering

Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO 2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. Further, to the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Crimean-Congo hemorrhagic fever survivors elicit protective non-neutralizing antibodies that target 11 overlapping regions on glycoprotein GP38

Crimean-Congo hemorrhagic fever virus can cause lethal disease in humans yet there are no approved medical countermeasures. Viral glycoprotein GP38, exclusive to Nairoviridae , is a target of protective antibodies and is a key antigen in preclinical vaccine candidates. Here, we isolate 188 GP38-specific antibodies from human survivors of infection. Competition experiments show that these antibodies bind across 5 distinct antigenic sites, encompassing 11 overlapping regions. Additionally, we show structures of GP38 bound with 9 of these antibodies targeting different antigenic sites. Although these GP38-specific antibodies are non-neutralizing, several display protective efficacy equal to or better than murine antibody 13G8 in two highly stringent rodent models of infection. Together, these data expand our understanding regarding this important viral protein and may inform the development of broadly effective CCHFV antibody therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Unlocking the self-supported thermal runaway of high-energy lithium-ion batteries

Layered Ni-rich LiNi x Mn y Co 1-x-y O 2 (NMC) materials are the most promising cathode materials for Li-ion batteries due to their favorable energy densities. However, the low thermal stability typically caused by detrimental oxygen release leads to significant safety concerns. Determining the pathways of oxygen evolution reaction is essential, as the ideal safety countermeasure is to break the reaction chain of thermal runaway. In this study, we demonstrate that two endogenous pathways of oxygen involved in strong exothermic reactions lead the NMC811|graphite pouch cell to an uncontrollable state, and we quantify the individual contribution of the pathways to thermal runaway. Approximately 41% of thermal-induced oxygen reacts aggressively with ethylene carbonate (EC) at the cathode/electrolyte interface with 16% heat generation, accelerating the self-heating rate and thereby further triggering thermal runaway. The residual oxygen that survives the reaction with carbonate spreads to the lithiated anode with major heat generation (65%), bringing the battery to the maximum destructive temperature during thermal runaway. By confirming the significant roles of EC and anode, a deeper understanding on battery fire was achieved. Finally, the revealed mechanism can help guide studies on stopping the two reaction pathways, allowing for the safer use of high-energy lithium-ion batteries in the future.

25 ENERGY STORAGE↗
DOE OSTI2022

Public Health Response and Medical Management of Internal Contamination in Past Radiological or Nuclear Incidents: A Review

Following a radiological or nuclear emergency, workers, responders and the public may be internally contaminated with radionuclides. Screening, monitoring and assessing any internal contamination and providing necessary medical treatment, especially when a large number of individuals are involved, is challenging. Experience gained and lessons learned from the management of previous incidents would help to identify gaps in knowledge and capabilities on preparedness for and response to radiation emergencies. In this paper, eight largescale and five workplace radiological and nuclear incidents are reviewed cross 14 technical areas, under the broader topics of emergency preparedness, emergency response and recovery processes. The review findings suggest that 1) new strategies, algorithms and technologies are explored for rapid screening of large populations; 2) exposure assessment and dose estimation in emergency response and dose reconstruction in recovery process are supported by complementary sources of information, including ‘citizen science’; 3) surge capacity for monitoring and dose assessment is coordinated through national and international laboratory networks; 4) evidence-based guidelines for medical management and follow-up of internal contamination are urgently needed; 5) mechanisms for international and regional access to medical countermeasures are investigated and implemented; 6) long-term health and medical follow up programs are designed and justified; and 7) capabilities and capacity developed for emergency response are sustained through adequate resource allocation, routine nonemergency use of technical skills in regular exercises, training, and continuous improvement.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Risk-Averse Bi-Level Stochastic Network Interdiction Model for Cyber-Security Risk Management

Security of cyber networks is crucial; recent severe cyber-attacks have had a devastating effect on many large organizations. The attack graph, which maps the potential attack paths of a cyber network, is a popular tool for analyzing cyber system vulnerability. In this study, we propose a bi-level stochastic network interdiction model on an attack graph to enable a risk-averse, resource constrained cyber network defender to optimally deploy security countermeasures that protect against attackers with an uncertain budget. This risk- averse conditional-value-at-risk (CVaR) model minimizes a weighted sum of the expected maximum loss over all scenarios and the expected maximum loss from the most damaging attack scenarios. We develop a customized constraint and column generation algorithm to solve our model as well as several acceleration techniques to improve the computational efficiency. Numerical experiments demonstrate that the acceleration techniques enable the solution of relatively large problems within a reasonable amount of time: applying all the acceleration techniques also reduces the average computation time of the basic algorithm by 71% for 100-node graphs. Using metrics called mean-risk value of stochastic solution and value of risk-aversion, computational results suggest that our stochastic risk-averse model significantly outperforms deterministic and risk-neutral models when 1) the distribution of attacker budget is heavy-right-tailed and 2) the defender is highly risk-averse.

Bhuiyan, Tanveer H.↗
DOE OSTI2026

Agnostic capture of pathogens for the detection and diagnostics of emerging threats

The continued emergence of pathogens, whether novel, re-emerging, or engineered, poses a persistent global biosecurity and public health challenge. Recent outbreaks, including COVID-19, Lassa fever, Marburg virus, mpox, and avian influenza, underscore the urgent need for robust systems that enable rapid surveillance, early diagnosis, and timely countermeasures before widespread human transmission occurs. In this article, we focus on early detection technologies and systematically evaluate current diagnostic and sensing modalities. We highlight sequencing and spectroscopy as two complementary approaches capable of providing broad, agnostic detection and rich biological insight. Our analysis emphasizes that scientific innovation alone is insufficient: effective preparedness also requires improved data curation, integration, and sharing to build AI-ready resources that accelerate future responses. We argue for coordinated advances in both technological capabilities and supporting infrastructure to enable the rapid identification and characterization of emerging pathogens and to fully leverage modern science against evolving infectious threats.

Environmental health↗
DOE OSTI2022

Identification of a pharmacological approach to reduce ACE2 expression and development of an in vitro COVID-19 viral entry model

Because of rapid emergence and circulation of the SARS-CoV-2 variants, especially Omicron which shows increased transmissibility and resistant to antibodies, there is an urgent need to develop novel therapeutic drugs to treat COVID-19. In this study we developed an in vitro cellular model to explore the regulation of ACE2 expression and its correlation with ACE2-mediated viral entry. We examined ACE2 expression in a variety of human cell lines, some of which are commonly used to study SARS-CoV-2. Using the developed model, we identified a number of inhibitors which reduced ACE2 protein expression. The greatest reduction of ACE2 expression was observed when CK869, an inhibitor of the actin-related protein 2/3 (ARP2/3) complex, was combined with 5-(N-ethyl-N-isopropyl)-amiloride (EIPA), an inhibitor of sodium-hydrogen exchangers (NHEs), after treatment for 24 h. Using pseudotyped lentivirus expressing the SARS-CoV-2 full-length spike protein, we found that ACE2-dependent viral entry was inhibited in CK869 + EIPA-treated Calu-3 and MDA-MB-468 cells. This study provides an in vitro model that can be used for the screening of novel therapeutic candidates that may be warranted for further pre-clinical and clinical studies on COVID-19 countermeasures.

59 BASIC BIOLOGICAL SCIENCES↗