# Discovery of early immunologic biomarkers for risk of PTLDS through machine learning-assisted broad temporal profiling of humoral immune response

> **NIH NIH R01** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2024 · $624,294

## Abstract

Project Summary/Abstract
Lyme Disease (LD) is a tickborne illness with markedly increasing prevalence in the United States. While more
than 80% of the LD cases can be effectively treated using established antibiotic treatments, 10-15% of patients
develop post-treatment, long-term sequalae manifested as fatigue, cognitive impairment, joint pain, and other
symptoms and termed Post Treatment Lyme Disease Syndrome (PTLDS) causing patients to experience
substantial loss in quality of life and resulting in a marked financial burden on both patient and health care
system. The absence of approved molecular diagnostic tests leads many physicians to dismiss the notion of
PTLDS entirely, leaving patients with few or poorly defined treatment options. While currently no approved
treatment for PTLDS exists, emerging evidence suggests substantially better clinical outcomes from early
intervention with targeted therapies in a number of chronic and autoimmune disorders. Early risk assessment
of developing PTLDS offers a window of opportunity by alerting both the patient and the physician to anticipate
a long-term symptomatic result and adjust symptom-based treatment. The proposed study focuses on the
urgent need to identify immunologic biomarkers for predicting the risk of a patient developing PTLDS early
during the acute phase of the disease and has the potential to markedly improve clinical outcomes through
early intervention. The proposed approach derives disease-specific antigen information from a comprehensive
binding profile of the patient’s circulating antibody repertoire. The novelty of the approach is in representing an
entire binding space of a donor’s circulating antibody repertoire, instead of simply focusing on a priori known
antigenic targets. The approach relies on using machine learning models trained on the antibody binding
profile to a diverse, random library of 126,050 unique peptides with an average length of 9-10 amino acids as a
sparse representation of all possible combinatorial 9-mer sequences. Predictive models are then used to
identify disease-associated pathogen epitopes with high predictive power that can be combined into a potential
panel for PTLDS risk assessment. It is hypothesized that B. burg. antigens and/or self-antibodies from the
human proteome are involved and that there is a set that can be used as biomarkers to predict progression to
PTLDS early in the disease. The antibody response over time will be profiled in a set of longitudinally collected
patient samples as they progress from confirmed acute LD, through treatment to disease outcome. This
approach applied will enable the breadth of the antibody response, including a potential response/cross-
reactivity to human proteins to be examined along with the heterogeneity of antibody responses across a
cohort of patients. In silico predictions of protein antigenicity will be confirmed using solution-based
immunoassays. The proposed work is expected to identify a set of B. burg...

## Key facts

- **NIH application ID:** 10884335
- **Project number:** 5R01AI178727-02
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** NEAL Walter WOODBURY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $624,294
- **Award type:** 5
- **Project period:** 2023-07-07 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10884335

## Citation

> US National Institutes of Health, RePORTER application 10884335, Discovery of early immunologic biomarkers for risk of PTLDS through machine learning-assisted broad temporal profiling of humoral immune response (5R01AI178727-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10884335. Licensed CC0.

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