# THE SEARCH FOR COVID-19 PREVENTION AND CURE: ADDRESSING THE CRITICAL ROLE OF INNATE/ADAPTIVE IMMUNITY BY INTEGRATING NOVEL INFORMATICS, TRANSLATIONAL TECHNOLOGIES, AND ONGOING CLINICAL TRIAL RESEARCH

> **NIH NIH UL1** · UNIVERSITY OF CALIFORNIA-IRVINE · 2020 · $1,088,735

## Abstract

Individual CTSA hubs are leading the national clinical and translational research efforts in developing new
approaches to address the COVID-19 pandemic. This crucial role was natural. Long before the current crisis,
CTSA hubs were committed to translation, building multidisciplinary teams of investigators and community
partners, overcoming regulatory burdens, ensuring quality in clinical and human research, developing
transformative informatics, and disruptive technologies for diagnostics and therapeutics. In this proposal, we
build on our center’s active participation in meaningful clinical trials (e.g., the NIH Remdesivir RCT), the early
creation of a biospecimen repository from COVID-19 patients, institutional commitment and fundraising that led
to a $3.5 million pilot fund distribution, a robust and accessible clinical database repository, and the ongoing
work of an NCATS-supported CTSA Collaboration Innovation Award (a coalition of the J. Craig Venter Institute,
UCSD, UCI, and Stanford) focused on artificial intelligence approaches for the analysis of flow cytometry
data. Using the emerging informatics framework of supervised generalized canonical correlation for
integrative data analysis, we will link clinical data from COVID-19 patients enrolled in a variety of trials and
at various stages of disease with innovative in vitro evaluation of innate and adaptive immunity, an area
still poorly understood in SARS-CoV-2 pathology, obtained from patient biospecimens to obtain mechanistic
insights of COVID-19 pathogenesis at a systems level. Innate and adaptive immunity are particularly
relevant to COVID-19 disease pathogenesis because they play key, but distinct, roles at all phases of the
illness (initial tissue-virus interaction; systemic responses; the cell-mediated cytokine storm leading to multi-
organ failure and death, likely long after levels of viremia have fallen; and, ultimately, protective immunity). The
current CCIA novel flow cytometry informatics research permits elucidation of dynamic cellular immune
responses related to the COVID-19 pandemic that were heretofore unobservable. Using Hi-DAFi for mass
cytometry analysis, validated informatics pipelines for single cell transcriptomics analysis, and cutting-edge
statistical data integration and machine learning strategies tied back to the available clinical data we will be
able to discover novel associations between cellular biomarkers and disease state, a particular therapy, and
disease mediating factors such as age, health disparities, and the presence of other diseases or conditions like
obesity. This information will aid in critical efforts to target new therapies and possibly identify idiosyncratic
individual physiologic variables that render certain patients who seem to have no known comorbidities more
vulnerable to severe COVID-19 disease. Finally, the robust connection between the UCI hub and both regional
and national networks (e.g., BRAID, the coalition of the 5 UC CTSAs, and...

## Key facts

- **NIH application ID:** 10158982
- **Project number:** 3UL1TR001414-06S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** DAN M COOPER
- **Activity code:** UL1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,088,735
- **Award type:** 3
- **Project period:** 2020-09-03 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10158982

## Citation

> US National Institutes of Health, RePORTER application 10158982, THE SEARCH FOR COVID-19 PREVENTION AND CURE: ADDRESSING THE CRITICAL ROLE OF INNATE/ADAPTIVE IMMUNITY BY INTEGRATING NOVEL INFORMATICS, TRANSLATIONAL TECHNOLOGIES, AND ONGOING CLINICAL TRIAL RESEARCH (3UL1TR001414-06S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10158982. Licensed CC0.

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