# COVID-19: Understanding The Role of Corona Virus InducedDisruption Of Alveolar Type 2 Cell Function And SurfactantHomeostasis In The Pathogenesis Of COVID-19 AcuteRespiratory Distress Syndrome

> **NIH VA I01** · PHILADELPHIA VA MEDICAL CENTER · 2022 · —

## Abstract

ABSTRACT
The Severe Acute Respiratory Syndrome (SARS)-associated coronavirus 2 (SCoV2) is the cause of COVID-19
syndrome which is marked by a refractory acute lung injury that results in dramatic hypoxic respiratory failure
and high mortality. Despite the recurrent health and economic devastation produced by novel coronaviruses
(nCoV) over the past 20 years including SCoV2 as well as its predecessors SCoV1, and MERS-CoV, there
remains a significant unmet need both for a clearer understanding of virus-host cell interactions as well as
identification of new therapeutic targets. To address these issues with the ultimate goal of improving the health
and outcomes of veterans with COVID-19, we have assembled a team of internationally recognized scientists
with expertise in coronavirus virology and in lung biology coupled with a strong foundation of our own prior work
on surfactant biology, lung injury, and fibrotic repair funded, in part, by the VA Merit Review program. Utilizing
this expertise, this proposal is directed at filling in large knowledge gaps that exist in the pathogenesis of nCoV
induced respiratory failure. The motivation for this investigation has been fueled by the recent recognition that
the alveolar type 2 (AT2) epithelial cell of the distal lung has emerged as an important portal of entry for SCoV-
2. The central hypothesis of this application is that AT2 cells infected with nCoV acquire defects in surfactant
biosynthesis/metabolism, activate cellular stress pathways, and develop alterations in progenitor cell function all
of which promote hypoxic respiratory failure, persistent lung inflammation and injury, and impact recovery
through effects on epithelial repair capacity. To test this, we will leverage an established murine model of CoV
infection (MHV-1) with a pulmonary phenotype combined with reductionist studies supported by ex vivo infection
of primary human AT2 cells obtained from a robust human lung pipeline with an already in hand SARS-CoV-2
isolate. Our experimental approach will interrogate these preclinical models using tools and reagents available
in our program, to map the effect of CoV infection on distal lung cell populations with a focus on identifying and
translating molecular mechanisms linking the disrupted AT2 function with altered surfactant biology and
proinflammatory/profibrotic cell cross talk in the alveoilar niche. In Specific Aim 1, we will first define temporal
changes in distal lung epithelial endophenotypes focusing on the ontogeny of the disruption of AT2 homeostasis
by viral infection using a well characterized mouse model of MHV-1 lung infection. Using both unbiased
approaches such as transcriptomic profiling as well as classical cell biology and biochemistry this aim will
investigate CoV induced changes in surfactant metabolism/biophysical activity, AT2 cell stress (i.e. ER stress,
proteasome dysfunction, autophagy malfunction, changes in mitochondrial dynamics/ / bioenergetics) and AT2
progenitor ...

## Key facts

- **NIH application ID:** 10367948
- **Project number:** 5I01BX005411-02
- **Recipient organization:** PHILADELPHIA VA MEDICAL CENTER
- **Principal Investigator:** MICHAEL FRANCIS BEERS
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2021-01-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10367948

## Citation

> US National Institutes of Health, RePORTER application 10367948, COVID-19: Understanding The Role of Corona Virus InducedDisruption Of Alveolar Type 2 Cell Function And SurfactantHomeostasis In The Pathogenesis Of COVID-19 AcuteRespiratory Distress Syndrome (5I01BX005411-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10367948. Licensed CC0.

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