# Network-based Framework to Decode Novel 'Gain-of-Function' Mutations and their Mechanistic Roles in General Human Disease

> **NIH NIH R35** · UNIVERSITY OF TEXAS AT AUSTIN · 2022 · $199,990

## Abstract

PROJECT SUMMARY
Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more and
more clear that many deleterious mutations could exhibit a ‘gain-of-function’ behavior. Systematic investigation
of such mutations has been lacking and largely overlooked. In the last few years it has become more clear that
the efficacy and specificity of signal transduction in a cell is, at heart, a problem of molecular recognition and
protein interaction. In distinct cell types (with varying genotypes), precise signal transduction controls cell
decision, including gene regulation and phenotypic output. When signal transduction goes awry due to gain-of-
function mutations, it would give rise to various disease types. Research in my laboratory is focused on
developing and utilizing quantitative and molecular technologies to understand protein interaction networks and
their perturbations by genomic mutations, bridging genotype and phenotype in health and disease. Our overall
goal is to contribute to the understanding of disease mechanisms and of more open ended questions about
explanations for ‘missing heritability’ in genome-wide association studies. We envision that It will be
instrumental to push current human genetics research paradigm towards a thorough functional and quantitative
modeling of all genomic mutations and their mechanistic molecular interaction events involved in disease
development and progression. Therefore, gaining a systems-level understanding of gain-of-function mutations
requires to resolve the plastic nature of molecular interactions, and to integrate experimental and
computational strategies at the genome scale. Many fundamental questions pertaining to genotype-phenotype
relationships remain unresolved. For example, how do interaction networks undergo rewiring upon gain-of-
function mutations? Which mutations are key for gene regulation and cellular decisions? Do mutagtions exhit
allel-specific behaviors or how do the allelic combinations work to coordinate cellular phenotypes? Is it possible
to leverage molecular interaction networks to engineer signal transduction in cells, aiming to cure disease? To
begin to address these questions, in this proposal, we will systematically interrogate of gain-of-function disease
mutations using a novel network-based systems biology framework. We will then decipher condition-dependent
protein-protein interaction perturbations induced by gain-of-function mutations in disorder regions and
phosphorylation sites. Finally, we will determine allele-specific and allele-combinatorial effect of gain-of-
function mutations on protein interaction network rewiring. Together, this integrative proposal is innovative
because it will provide insights in prioritizing driver functional gain-of-function disease mutations, and
uncovering individualized molecular mechanisms at a base resolution. Furthermore, it is significant because it
will greatly facilitate the functional annotation...

## Key facts

- **NIH application ID:** 10582371
- **Project number:** 3R35GM133658-03S1
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** S. Stephen Yi
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $199,990
- **Award type:** 3
- **Project period:** 2019-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10582371

## Citation

> US National Institutes of Health, RePORTER application 10582371, Network-based Framework to Decode Novel 'Gain-of-Function' Mutations and their Mechanistic Roles in General Human Disease (3R35GM133658-03S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10582371. Licensed CC0.

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