# Biomarkers and causal key drivers of phenotypic heterogeneity in peanut allergy

> **NIH NIH U19** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $963,872

## Abstract

Biomarkers and causal key drivers of phenotypic heterogeneity in peanut allergy
PROJECT 3 SUMMARY / ABSTRACT
Unexpected allergic reactions to peanut are the most common cause of fatal food-related anaphylaxis. There is
currently no method to predict reaction thresholds for subjects with peanut allergy. Given two peanut allergic
subjects with similar clinical profiles, one may react with anaphylaxis to minute exposure while the other may
react with hives to larger amounts. The same individuals may have disparate responses to desensitization that
cannot be predicted. This heterogeneity in reaction threshold, reaction severity, and desensitization success is
crippling to peanut allergic individuals, whose lives are impaired by anxiety that small exposures could lead to
anaphylaxis at any time. Additionally, providers cannot offer early guidance whether resource-intensive
desensitization efforts will succeed. With peanut allergy now affecting 2-5% of US schoolchildren, these areas
of uncertainty stress the need to identify biomarkers of reaction threshold, reaction severity, and
desensitization potential. Based on our demonstrated work in biomarker development and integrative
genomics, we hypothesize that biomarkers and causal key drivers of phenotypic heterogeneity in peanut
allergy can be identified through integrated network-based examination of peripheral blood transcriptomes,
epitope-binding, and clinical parameters from peanut allergic subjects. We will study peanut allergic subjects
undergoing oral food challenges and desensitization to pursue three specific aims that address unmet needs in
peanut allergy care and knowledge. In Aim 1, we will identify a resting-state peripheral blood biomarker of
exquisitely sensitive (low threshold) peanut allergy by RNA-sequence profiling baseline peripheral blood from
low and high threshold peanut allergic children, differential gene expression analysis, machine learning, and
weighted gene coexpression network analysis. The predictive biomarker of reaction threshold identified will be
prospectively validated. In Aim 2, we will identify causal key drivers of peanut allergy severity. We will use
baseline and post-challenge RNAseq profiles, expression quantitative trait loci, epitope-binding scores, and
clinical variables to build the first probabilistic causal network specific to food allergy. We will apply key driver
analysis to this network to identify genes, epitopes, and clinical variables that causally modulate peanut
reaction severity. This information-rich, data-driven network will be shared with other investigators seeking to
elucidate mechanisms underlying peanut allergy. In Aim 3, we will identify an early-appearing peripheral blood
biomarker of peanut desensitization potential using samples obtained from subjects during desensitization,
leukocyte deconvolution, machine learning, and weighted gene coexpression network analysis. The results
from this project will directly address unmet needs in the m...

## Key facts

- **NIH application ID:** 9934156
- **Project number:** 5U19AI136053-03
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Supinda Bunyavanich
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $963,872
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9934156

## Citation

> US National Institutes of Health, RePORTER application 9934156, Biomarkers and causal key drivers of phenotypic heterogeneity in peanut allergy (5U19AI136053-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9934156. Licensed CC0.

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