# Deciphering Functional Consequences of Specific and Combinatorial Mutations in Protein Interaction Networks

> **NIH NIH R35** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2020 · $405,000

## Abstract

PROJECT SUMMARY/ABSTRACT
In the recent past, genome and exome sequencing projects have identified millions of genetic mutations across
the human populations. Considerable amount of efforts have been made to identify functional or driver mutations
by computational predictions or by high-throughput experimental approaches. However, most of these
approaches have focused on single mutations and have overlooked the diploid genome structure and the
context-specific nature of gene regulation. Studies of disease mutations should take the genotypic composition
and inheritance mode into account. In human disease, patients can carry one (monoallelic) or two (biallelic)
different mutations on the two alleles, both of which are often expressed. Our recent systematic studies indicate
that while a small fraction of disease mutations affect gene expression and protein folding/stability, the majority
of these mutations influence protein interaction networks. There is, therefore, a critical need to determine the
regulatory mechanisms that underlie biallelic genetic heterogeneity and potentiate functional diversification
across patient populations. To address this challenge, we recently developed and pioneered the technology of
functional variomics. In characterizing genotype-to-phenotype relationships via interactome networks, a single
genotypic variation can lead to either a complete gene knockout-like behavior, or alternatively as interaction-
specific changes or “edgetic” perturbations. The mutations on the two alleles of the chromosomes could exhibit
allele-specific and allele-combinatorial effect. However, it remains largely unknown how two allelic mutations
coordinate together to generate their ultimate functional consequence. In this proposal, we will develop
innovative technologies, build a ‘biallelic functionality continuum’ model, and assess the functional effect of
monoallelic and biallelic mutations at large scale. We will bridge the current gaps in our knowledge, including:
determining the functional impact of large numbers of monoallelic and biallelic mutations of unknown
significance, deciphering the extent to which they perturb interactome networks, and interrogating if these
perturbations depends on specific contexts. Our long-term goal is to contribute toward a systems-level
understanding of the interplay between genetic variations, external stimuli, and functional consequences in
cellular networks that can be used for developing improved diagnostic and therapeutic strategies in disease.

## Key facts

- **NIH application ID:** 10026526
- **Project number:** 1R35GM137836-01
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Nidhi Sahni
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $405,000
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10026526

## Citation

> US National Institutes of Health, RePORTER application 10026526, Deciphering Functional Consequences of Specific and Combinatorial Mutations in Protein Interaction Networks (1R35GM137836-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10026526. Licensed CC0.

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