# Quantitative Analysis of Serum Autoantibody Repertories in Systemic Lupus Erythematosus

> **NIH NIH R01** · UNIVERSITY OF OKLAHOMA · 2020 · $402,042

## Abstract

SUMMARY
Systemic Lupus Erythematosus (SLE) is a multi-organ, systemic autoimmune disorder, estimated to affect at
least 1.5 million Americans. The hallmark of SLE is the production of serum autoantibodies, a unifying feature
present in over 99% of untreated patients. Such autoantibodies are directly pathogenic, eventually causing the
symptoms of SLE including debilitating joint pain and rashes, followed by organ damage and early mortality.
Previous work has shown that autoantibodies begin to accrue months to years before the symptoms of SLE
appear which may allow a window for detecting them and starting medications to prevent or at least delay the
onset of SLE. These serum autoantibodies, however, consist of a complex mixture in the blood including
pathogenic, non-pathogenic, and beneficial antibodies which may number in the millions. While current
diagnostic platforms can screen for total autoantibodies during autoimmune disease, finding specific
monoclonal autoantibodies linked to the development of SLE is currently impossible. Thus, the lack of
capability to directly detect monoclonal, pathogenic, autoantibodies presents a significant barrier in
understanding how autoantibodies arise, and there is a critical need to develop advanced analytical tools to
characterize these antibodies. Our long-term goal is to understand SLE autoantibody development at the
monoclonal level and to develop high diagnostic value autoantibody biomarkers. The overall objective of this
proposal to establish a novel integrated proteomics platform that employs two complementary scientific
approaches, a quantitative top-down MS approach for autoantibody biomarker discovery, and a top-down
proteogenomics sequencing approach for autoantibody biomarker validation and functional characterization.
Our proposed top-down autoantibody proteomics platform will be applied to identify intact autoantibody Fab
signatures in longitudinal SLE serum samples. As a result, we will provide a first top-down proteomics platform
for characterizing SLE autoantibodies at the monoclonal level. Applying it to the analysis of SLE
autoantibodies will provide foundations for new strategies in SLE prognosis, intervention, and prevention, and
may lead to novel high diagnostic value biomarkers. After development, our top-down autoantibody
characterization platform can be easily adapted to other autoimmune diseases such as Sjogren’s Syndrome.

## Key facts

- **NIH application ID:** 9822946
- **Project number:** 5R01AI141625-02
- **Recipient organization:** UNIVERSITY OF OKLAHOMA
- **Principal Investigator:** Kenneth Michael Smith
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $402,042
- **Award type:** 5
- **Project period:** 2018-11-15 → 2023-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9822946

## Citation

> US National Institutes of Health, RePORTER application 9822946, Quantitative Analysis of Serum Autoantibody Repertories in Systemic Lupus Erythematosus (5R01AI141625-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9822946. Licensed CC0.

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