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EAGER: Private Blockchain-Enabled Federated Learning Framework for Distributed Manufacturing Networks

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University of South Carolina at Columbia

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In recent years, global manufacturing networks experienced a variety of shocks and disturbances including COVID-19. Thus, improving network resiliency, transparency, and cybersecurity have emerged as a national priority. Smart Manufacturing technologies such as Artificial Intelligence and Machine Learning show promise in achieving these objectives, yet struggle to materialize at the manufacturing network level. Particularly small and medium-sized manufacturers struggle in their adoption of these data-driven, value added technologies due to a lack of resources and incentives. Consequently, they cannot participate in many high-value manufacturing networks that often require certain technologies and data sharing. This EArly-concept Grant for Exploratory Research (EAGER) project supports research that intends to address this challenge through a Blockchain-enabled framework that leverages secure and private Federated Learning which meets the unique requirements of defense manufacturing networks. This framework enhances the availability and integrity of critical supplies, as well as strengthens and diversifies the defense industrial base. The project’s secure and privacy-preserving data sharing and collaboration mechanisms can be applied in various domains beyond manufacturing, such as healthcare, finance, and supply chain, empowering individuals and organizations to share data securely and collaborate effectively. The results have potential to transform industry, drive economic growth, foster innovation, and enhance societal well-being.     The project’s research problem stems from manufacturing networks’ inability to securely and efficiently exchange data and leverage network level Federated Learning. The project aims to increase the resiliency of distributed and dynamic manufacturing networks, specifically including small and medium-sized manufacturers, by providing access to a secure private Blockchain platform that enables decentralized, secure, and transparent communication channels. This enables manufacturing network level learning through Federated Learning while respecting data ownership and ensuring retention of competitive or controlled (raw) data and machine learning models. To achieve these goals, the project utilizes Federated Learning by integrating a private Blockchain to manage metadata, access controls, and model updates. Unlike existing approaches, the framework focuses on specific challenges and requirements of manufacturing networks. This means ensuring confidential data remains local under full control of the individual nodes while leveraging Blockchain for efficient coordination of the Federated Learning process as well as reducing overhead cost for smaller network participants that are resource constraint. The project advances the state-of-the-art in Federated Learning and Blockchain technology through efficient algorithms for model aggregation and coordination in the presence of heterogeneous data for manufacturing networks.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Collaborative Research: RI: Medium: Transparent Fair Division of Indivisible Items

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Fair division deals with the distribution of resources and tasks among different parties, e.g., individuals, firms, nations, or autonomous agents, with the goal of achieving fairness and economic efficiency. Fairness has increasingly become crucial in distributing precious and scarce medical equipment, and its absence has exacerbated healthcare issues during the COVID-19 global pandemic. A wide variety of real-world applications such as scheduling, dispute resolution, healthcare management, and refugee settlement assume complete knowledge about allocation decisions, which gives rise to negative computational and impossibility results. The existing approaches to mitigate these challenges, in turn, impose a high cost on transparency. The broad goal of this project is to provide theoretical and algorithmic solutions for fair allocation of indivisible items in practical, large-scale settings, as a broad contribution to the grand scheme of artificial intelligence (AI) and economics for social good. This research will offer a novel and promising perspective for developing practical and transparent fair solutions while providing a systematic investigation on the perceived fairness of allocation mechanisms that are applicable to societies at large. This project will integrate and develop algorithmic solutions for transparent fair division in a publicly available software system with the goal of extending its reach--and in general promoting fairness and transparency--to a broad national and international audience.     This project will develop a new framework for achieving fairness and efficiency in the allocation of indivisible resources with minimum cost on transparency. Specifically, it will make progress in four interconnected dimensions: 1) Tradeoffs between transparency, fairness, and efficiency, that aim at analyzing the compatibility of the properties and devising algorithmic solutions when allocating indivisible items, 2) Strategic aspects of fair division, that investigates agents' behavior and strategies under transparency requirements, 3) Domain restriction, that focuses on developing tractable solutions by circumventing the impossibility results in achieving compatible solutions, and 4) Bads and mixtures, that extend the transparency and fairness framework to include desirable (goods) and undesirable items (bads). Furthermore, this research plans to close the current gap between theoretical foundations of fairness and the perception of fairness through a series of comprehensive empirical evaluations.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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BII: Predicting the global host-virus network from molecular foundations

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The Viral Emergence Research Initiative Biology Integration Institute (VERENA BII) will integrate data and biological theory across the fields of microbiology, immunology, ecology, evolution, and global change biology, working towards a unified understanding that improves our ability to predict viral emergence. The COVID-19 pandemic highlights a pressing need to understand the ecology and evolution of emerging viruses. These global dynamics are determined first and foremost by the genetic code of both viruses and their hosts, and by microscopic interactions between the two at the level of proteins and cells. However, biologists frequently struggle to connect theory across these scales. At the heart of this research effort is an open clearinghouse of big data, creating new opportunities to apply artificial intelligence to real-world problems. To foster a core set of data fluency and interdisciplinary research skills, the Lighthouse Learning Community will train participants at every career stage in the boundary-spanning science of the host-virus network, including more than 100 early career scientists. Undergraduates will be introduced to both biology and data science through a Course-based Undergraduate Research Experience in “The Fundamentals of Disease Surveillance,” while graduate students and postdoctoral fellows will explore these methods deeper through a biology integration workshop series, including a new Summer in the Capitol program in Washington, D.C. This cohort of emerging scholars will use open source materials, K-12 outreach, and digital media to harness public interest in emerging diseases like COVID-19, raising awareness about key issues while sharing the importance of basic biological research to save lives and protect ecosystems.    To identify the mechanistic and molecular Rules of Life that govern host-virus dynamics at planetary scales, the VERENA BII will leverage a unique mix of data synthesis, computational innovation, field sampling, and laboratory experiments to identify the molecular underpinnings of host-virus interactions. An unprecedented comparative study of the chiropteran within-host environment will generate and test hypotheses about the immunological adaptations that allow bats to tolerate deadly viruses. In parallel, model-guided experiments will measure the features of the invertebrate immune system that play the greatest role in mosquitoes’ competence as arboviral vectors. Together, these model systems will illuminate the hard-coded basis of host-virus compatibility, supporting new machine learning methods to predict ecological and evolutionary networks and anticipate global risks of viral emergence in a changing climate. More broadly, the VERENA BII will expand an existing role as a hub of open data, software, and cyberinfrastructure for host-virus interactions, experimental virology, and wildlife disease surveillance.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Targeting Angiopoietin-like 4 (ANGPTL4) in Severe Community Acquired Pneumonia

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National Heart Lung and Blood Institute (NHLBI)

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Community-acquired pneumonia (CAP) is a common cause of morbidity and mortality in hospitalized patients but therapeutics are limited. In response, identification of modifiable pathways to alter host response and improve outcomes in patients with severe CAP has been highlighted as a NHLBI research priority. Our research group has identified angiopoietin-like 4 (ANGPTL4) as a potential mediator in adverse outcomes in CAP from viral and bacterial pathogens. We have generated preliminary data in a discovery proteomic analysis of 5000 different plasma proteins. We found that ANGPTL4 was one of the top proteins associated with fewer ventilator free days and worse hospital mortality in severe CAP due to COVID-19. Next, in a multi-center cohort, we replicated these findings in COVID-19 that higher ANGPTL4 concentrations were associated with worse clinical outcomes, and obtained preliminary evidence that ANGPTL4 is also associated with outcomes in severe CAP due to bacteria3. We also have generated data that genetically targeting Angptl4 is protective in mice with severe influenza, a finding that is supported by pre-clinical data that inhibition of ANGPTL4 signaling through a monoclonal antibody is protective in viral pneumonia. In addition, independent research groups have also found that ANGPTL4 is associated with clinical outcomes in severe CAP. Together, these findings support our hypothesis that ANGPTL4 expression is a significant determinant of outcomes from CAP, independent of pathogen type, and that modulation can lead to improved clinical outcomes. To further examine this hypothesis, we will use complementary clinical and pre-clinical studies in the following aims. In Aim 1, we will determine the relationship between plasma ANGPTL4 levels and outcomes in a hospitalized population with varying severity at enrollment (acute care and ICU) and pathogen type (viral and bacterial). In Aim 2, we will infer causal relationships between ANGPTL4 concentrations and risk for pulmonary and extra- pulmonary organ dysfunction using a non-overlapping 2-sample Mendelian randomization genetic approach. In Aim 3, we will evaluate the role of ANGPTL4 in pre-clinical models of viral and bacterial pneumonia and determine the relative contributions of the proteolytically processed cANGPTL4 and nANGPTL4 peptides. The outstanding qualifications of our team in the fields of sepsis, community acquired pneumonia, molecular epidemiology, and pre-clinical models uniquely position us to deliver an integrated molecular view of host response in CAP that is not only responsive to the challenges in severe CAP care identified by global leaders, but could fundamentally alter paradigms of patient care in severe CAP. The long-term goals are to delineate the role of ANGPTL4 in severe CAP through understanding which clinical outcomes are most closely linked with ANGPTL4 levels through epidemiological and genetic causal inference analyses and to understand the cell of origin and relative contributions of different cleavage products of ANGPTL4 through pre-clinical studies.

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Acute Renal Failure with Renal Papillary Necrosis

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National Heart  Lung  and Blood Institute

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Background and Innovation: Mortality from infectious diseases remains the second leading cause of death worldwide, a fact highlighted by 4 years of a global pandemic, making the understanding of host responses, development of new vaccines, and improving existing vaccines important priorities of biomedical research. Memory T cells mediate protection from reinfection with previously encountered pathogens, and a large number of these cells, termed tissue-resident memory cells (TRM), do not recirculate throughout the body, reside within tissues, and provide essential sentinel protection at body surfaces. Using a murine infection model that is well established in the field for studying CD8 TRM, this application will investigate the context- and tissue-specific roles of the transcription factor Foxo1. Conceptual innovations include the study of Foxo1, the role of which is poorly understood in TRM biology. Technical innovations include the use of numerous cutting- edge approaches, including Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) which enables proteomic and transcriptomic (scRNA-seq) analyses in the same single-cells, to identify consequences of Foxo1-deficiency in TRM. Bioinformatic innovations include the application of Taiji, a state-of-the-art bioinformatic analysis algorithm integrating transcriptomic and epigenomic data, to reveal Foxo1-transcription factor networks and identify putative regulatory factors and pathways controlled by Foxo1. Significance and Impact to Veterans Healthcare: Infectious diseases have a substantial public health and economic burden on the Veteran population. These include diseases for which we have vaccines, such as SARS-CoV-2 and influenza, as well as diseases that we do not yet have vaccines for, such as Hepatitis C (HCV) and HIV. A major gap in knowledge is that current vaccines generate neutralizing antibodies but do not generate a robust memory T cell response, including TRM, which are crucial for optimal protection at barrier surfaces. Another gap in knowledge is a comprehensive molecular understanding of the tissue-specific requirements for the generation and persistence of TRM, which will be addressed by this project. This research will address the VHA/ORD research priority of exploring fundamental biologic principles in pre-clinical models with the ultimate goal of improving the well-being of the nation’s Veterans specifically in the area of infectious diseases. Path to translation/implementation: Current vaccination regimens aim to generate protective antibodies but do not generate TRM. A detailed understanding of regulatory programs and transcriptional networks that govern T cell adaptation to tissues and barrier sites must be gained in order to provide the foundation and rational scientific basis to develop “tissue-tailored” immune responses. In this way, immune cells that promote or regulate inflammation can be transcriptionally engineered for trafficking to, retention in, and function within a particular tissue. Next steps to move this research along the translational pathway will involve testing whether modulation of Foxo1-mediated pathways enhances vaccine-generated immune memory.

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Cellular Indexing of Transcriptomes and Epitopes by Sequencing

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Point-of-care DNA diagnostics from raw samples

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National Institute of Biomedical Imaging and Bioengineering (NIBIB)

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The proposed project will develop living biosensors for detecting and analyzing DNA at the single- base level, without requiring sample purification or any equipment. DNA is the prime information carrier for life, and DNA analysis provides valuable information for, e.g., diagnosing microbial infections or tracking disease outbreaks. Many techniques exist for detecting and analyzing DNA, but these generally require processing steps to extract and purify samples, and most require expensive equipment and significant training and expertise. This proposal will transfer that complexity into the biosensor itself, harnessing functions that evolved into living bacteria over billions of years to pull DNA out of raw samples, analyze it, and produce easily read output. The biosensors will pull in DNA using natural competence, and analyze it with single-base precision using their endogenous CRISPR-Cas system. Upon detecting a target sequence, the living biosensors will release thousands of signal molecules that can be detected using a lateral flow assay, similar to a consumer pregnancy or Covid-19 test. Several target DNA sequences will be used for demonstrations: urinary tract pathogens, E. coli, and Salmonella. The target uropathogens are difficult to diagnose with standard culture tests. Using single-base sequence analysis, the biosensors will subtype E. coli as likely pathogenic or likely commensal. A similar strategy will be employed to detect single-base mutations responsible for the majority of fluoroquinolone-resistant Salmonella isolates. DNA biosensing will be demonstrated in clinically relevant human samples, without the extensive purification required by other methods. The result will be a hybrid living biosensor / lateral flow assay that requires minimal sample preparation, produces rapid results, and can achieve single-base resolution. The biosensors developed in this project could find applications any time DNA monitoring is needed that is inexpensive, requires minimal sample preparation, equipment, and expertise, or takes place at the point of care. Examples include clinical diagnostics, monitoring disease outbreaks for public health, or environmental monitoring, with particular benefits where resources are limited.

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Clustered Regularly Interspaced Short Palindromic Repeats

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Developing a Biomimetic Lactating Mammary Lobe for Therapeutic Safety

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Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

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PROJECT SUMMARY. Breast milk is rich with bioactive components that are critical to an infant’s development. It is highly recommended that infants ingest breast milk; but, fluctuating maternal hormones and substandard post-parturition health directly mediate breast milk production. Maternal ingestion of small molecule drugs further compounds decreased breast milk synthesis and secretion, and adversely compromises breast milk quality. Although the majority of actively breastfeeding women consume medication or receive therapeutics, small drug molecule transport from maternal plasma to synthesized breast milk remains largely unknown. Important strides in understanding pharmacokinetics in milk-producing mammary glands have yet to occur because of the lack of engineered bioinspired mammary lobe systems that mimic complex in vivo signatures— topographical lobule microcurves, spiked levels of lactogenic hormones, cellular landscapes, and mechanically-driven lobe expansion and contraction. The objective of this proposal is to determine if our established microengineered mammary lobe system, which integrates key physiological characteristics, i.) faithfully mirrors multifactorial breast milk synthesis processes and ii.) could be employed as a versatile screening testbed for evaluating drug and therapeutic safety during lactation. The project is based on the central hypothesis that exogenous stimuli that reflect in vivo mechanisms, such as hormone levels, dynamic mechanical lobe stimulation, and passive transport of small drug molecules, will potentiate differential cellular landscape phenotypes and lead to unique content differences in engineered breast milk. This could develop a new in vitro preclinical model that promotes the cognizance of drugs or therapeutics that are safe to ingest or receive during lactation. We believe this contributes to improving important women’s health issues. Our hypothesis will be tested through the following two aims. Aim 1 will develop a 3D mammary lobe model and determine how in vivo relevant parameters alter physical and molecular mammary cell phenotypes, and regulate the secretion of important breast milk components. Aim 2 will investigate the pharmacokinetics of small molecule drugs or therapeutics that passively diffuse into the engineered breast milk. Nicotine or mRNA encoding for SARS-CoV-2 will serve as a model drug or therapeutic, respectively. We will pursue these aims using an innovative combination of analytical and adaptable techniques from engineering and biological sciences. These include the development of a scalable lobe model, by which the application of physiologically relevant stimuli and compartments can mimic breast milk synthesis and drug distribution. The engineering approaches that we leverage will develop foundational resources for the ongoing efforts and research revolving lactation and post-parturition health equity. The expected outcome of this work will highlight the importance of engineering new microsystems for in vivo mimicry. These platforms can facilitate clinical translation of rapid drug and therapeutic safety screening. The results will have a significant positive impact to women and will encourage the ongoing efforts to support women during their breastfeeding journey.

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United States National Institutes of Health

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Development of 12-Lipoxygenase Inhibitors for diabetes treatment.

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Approximately 25% of US Veterans have diabetes and the annual mortality rate due to diabetes in Veterans is almost double of the rate in Veterans without diabetes. The presence of diabetes in the Veteran population is an important cause of disability due to complications such as cardiovascular disease, neuropathy, retinopathy, and kidney disease. Recent studies have indicated higher rates of new onset diabetes in Veterans who have had COVID-19 and analysis of Veterans using the Million Veteran Database indicate a growing identification of new onset type 1 diabetes (T1D) in adults. Almost half of the newly diagnosed cases of T1D are adults and there is an unmet medical need to identify treatments to prevent or halt progression of the disease. 12-lipoxygenase (12-LOX) is a lipid-generating enzyme produced by various sites including pancreatic islet beta cells. 12- LOX expression is elevated in obesity, Type 2 diabetes ( T2D) and T1D. We showed that 12-LOX protein is increased in islets of auto-antibody-positive and Type 1 and Type 2 individuals. 12-LOX products are highly pro-inﬂammatory and reduce pancreatic beta cell function and viability. 12-LOX lipids lead to oxidative and endoplasmic reticulum stress and macrophage activation. Genetic deletion of 12-LOX in mouse models improves insulin signaling and prevents T1D development. We earlier discovered a highly selective 12-LOX inhibitor, ML-355, through a comprehensive screening, followed by iterative medicinal chemistry optimization. ML-355 has been licensed and pre-clinical and phase 1 safety testing has been successfully completed and found to be safe. ML-355 is in a phase 2 clinical trial for intravenous administration for a hematologic indication as the inhibitor is not suﬃciently orally bio-available for chronic diabetes use. We recently discovered a new 12-LOX inhibitor, Slug001, which manifests increased potency both in vitro and in rescuing inﬂamed human islet cells, relative to ML355. We propose to explore this novel structural space with a suite of modiﬁcations of Slug001, guided by our computational modeling of ML355 docking. The goal in Aim 1 is to derivatize Slug001 and ﬁll the active site cavity more eﬃciently so that the inhibitor potency can be increased more than its current 7-fold improvement relative to ML355 and have improved oral bioavailability. The docking model will greatly enhance the identiﬁcation of promising new inhibitors. Of the six Slug001 lead derivatives, we have already synthesized several and shown that Slug002 is as potent as Slug001 but has better solubility. The completed studies will generate novel inhibitors and a new VA patent. In Aim 1 of the proposal, we will synthesize the new molecules and measure their potency and selectivity. Established methods, comparing potency with docking scores, will allow us to quickly design these derivatives, increase their potency and then subject them to our human diabetic cellular assays established in Aim 2. The derivatives of Slug001 will be screened by the Holman laboratory against the LOX isozymes (5-LOX, 12-LOX, 15-LOX-1, 15-LOX-2) to establish their selectivity. In Aim 2 we will test the efficacy of the inhibitors in a reliable and novel human functional Islet beta cell line and then move to testing in primary cadaveric human pancreatic islets. The testing will utilize using our established and validated assays to test effects on insulin secretion and protection from cytokine mediated damage. Our expectation at the completion of this pilot, is to move the lead compound (s) into a novel in humanized 12-LOX mouse model of T1D that will facilitate rapid translation to clinical testing. The proposed project will provide a new way to address the high rates of adult-onset Type 1 diabetes and complications in the Veteran population.

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insulin dependent diabetes mellitus onset

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NCOA7 deficiency worsens brain damage after stroke

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Background and Innovation: Lysosomes (with an acidic milieu of pH around 4.5) contain numerous acidic hydrolases for maintaining cellular homeostasis via the degradation of unwanted cellular components. NCOA7 (nuclear receptor coactivator 7), a member of the Tre2/Bub2/Cdc16 (TBC), lysin motif (LysM), domain catalytic (TLDc) protein family, was initially implicated in the oxidative stress as oxidation resistance proteins. However, new research demonstrates that NCOA7 directly binds and modulates vacuolar H+- ATPase (V-ATPase) assembly and activity to control endolysosomal acidification. Our pilot data from SNP- edited, human stem cell-derived endothelial cells have demonstrated that allele-specific binding of the inflammatory transcription factor NF-κB to a common intronic variant SNP rs11154337 in NCOA7 controls gene expression. Namely, we found that a C/C genotype, present in ~25% of the population, promoted lower NCOA7 expression and less lysosomal acidification (Prelim Data). Global Ncoa7 transgenic knockout mice (Ncoa7-/-, KO) are viable up to 18 months of age, with comparable gross brain structure to wild-type littermate (WT). However, upon ischemic stroke, compared to WT mice, we found that Ncoa7 KO mice exhibited worsened ischemic stroke outcomes (higher mortality, worsened blood-brain barrier impairment, increased astrogliosis and microglial activation, and abnormal accumulation of myelin basic protein, MBP) (Prelim Data). Whether the above worsened ischemic stroke outcomes resulted from V-ATPase dysfunction and lysosomal de-acidification remains unknown. In this proposal, we will test our hypotheses: 1). NCOA7 plays an important role in V-ATPase activity and lysosomal function in stroke brain in a cell-specific manner; 2). NCOA7 deficient in neurons and in oligodendrocytes drives lysosomal dysfunction and oxysterol/bile acid- specific inflammation, as well as abnormal cholesterol and MBP accumulation; 3). Post-stroke administration of NCOA7 activator Compound 958 will stimulate NCOA7 activity and reduce stroke brain damage. Significance and Impact to Veterans Healthcare: Cardiovascular diseases such as hypertension and pre- hypertension are common in active US military personnel. Moreover, post-traumatic stress disorder is associated with different cardiovascular and cerebrovascular diseases in older veterans. Collectively, these are well-established risk factors for stroke. The goal of this proposal is to study cellular mechanisms underlying the worsened ischemic stroke outcomes in NCOA7 deficient conditions and to determine whether pharmacological stimulation of NCOA7 is a novel therapeutic strategy for improving acute ischemic stroke outcomes. Therefore, our proposal is closely relevant to Veterans and the VA mission. Path to translation/implementation: completion of this study will directly address our knowledge gap about role of NCOA7 in regulating lysosome function and cholesterol metabolism in the stroke brains. To explore pharmacological tools, utilizing computational modeling, we developed a novel small molecule activator of NCOA7, Compound 958, which reduced pulmonary endothelial immunoactivation and robustly improved survival of a mouse model of acute COVID-19 (Prelim Data). Our findings from this study will reveal potentials of NCOA7 as a therapeutic target for attenuating V-ATPase dysfunction in stroke brain, and efficacy of NCOA7 activator Compound 958 in stroke therapy.

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Pathogenic Mechanism and Therapeutic Approaches for Exercise Intolerance in Post-Acute Sequelae of COVID-19

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National Heart Lung and Blood Institute (NHLBI)

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Post-acute sequelae of COVID-19 (PASC) is an emerging public health priority with up to 18% prevalence. Noteably, almost 30% patients diagnosed with PASC experence exercise intolerance. This activity limitation continues to negatively impact our workforce, and poses a persistent socialeconimic burden on our society. Our Post-Covid Recovery Clinic, a RECOVERY Vital site, has evaluated exercise intolerant PASC for nearly 4 years. We recently discovered pathophysiologic endotypes that contribute to exercise intolerance in PASC via invasive cardiopulmonary exercise testing (iCPET). Yet, the molecular drivers for this population remain elusive. Four- years after the onset of the pandemic we are left without PASC-defining biomarkers, or targeted therapeutics. Thus, it is crucial to investigate the interconnected molecular and pathophysiologic links in exercise intolerant PASC, a task uniquely within our team’s expertise. Angiotensin-converting enzyme 2 (ACE2) is not just an entry receptor for SARS-CoV-2 but also an enzyme with a protective function through regulation of the renin- angiotensin system. Studies have shown that a high level of plasma ACE2 is associated with an increased risk of SARS-CoV-2-related mortality. Our preliminary data showed that the catalytic activity of increased plasma ACE2 was significantly impaired in the exercise intolerant PASC patients, and closely correlated with reduced exercise capacity as measured by peak oxygen consumption evaluated during iCPET. Furthermore, to study the pathogenic mechanism of exercise intolerance in PASC, we established a novel PASC mouse model. In this model, we observed the persistence of the SARS-CoV-2 RNAs in lung microvascular ECs, impaired ACE2 activity, chronic pulmonary inflammation, along with a significant reduction in exercise capacity. Thus, we hypothesize that dysfunctional ACE2 shed from pulmonary ECs is a major driver for exercise intolerance in PASC and an engineered solube ACE2 with enhanced ACE2 activity will improve exercise capacity of PASC. To test our hypotheses, we will investigate the predictive value of ACE2 activity as a clinical biomarker and assess its association with exercise capacity over 12 months in PASC patients in Aim 1. We will define an engineered soluble ACE2 with enhanced ACE2 activity as an innovative therapeutic intervention to improve exercise capacity and vascular function in the PASC mouse model in Aim 2. Furthermore, we will explore the mechanism of ACE2 dysfunction shed from the pulmonary vasculature in Aim 3. If successful, we will identify a diagnostic and therapeutic paradigm urgently needed for PASC patients experiencing exercise intolerance, and remediate the deficient response to this global public health threat.

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Post-Acute Sequelae of SARS-CoV-2 Infection