Project Details
Description
PROJECT SUMMARY
The genomic difference between individuals is estimated to be approximately 0.5% of base pairs, but this small
diversity can drastically affect the interpretation of both genome- and downstream proteome-level data. While
most germline variants between individuals are neutral, there are thousands of known genetic disorders in
humans linked to specific germline variants. Similarly, somatic variations are a central hallmark of cancer,
where mutations may cause marked phenotypic consequences in tumors as so-called “driver mutations”, but
most genetic variants in tumors have little or no observable effect. Our lab is interested in understanding the
effects of germline and somatic variants at the proteome level and studying changes in variant abundance over
time. We believe a greater understanding of which genetic variants have molecular phenotypes will lead to
improved understanding of clinically actionable features of diseases. Recent advances in proteomics have
yielded a data independent acquisition mass spectrometry (DIA-MS) as a powerful new tool for quantitative
studies. We have demonstrated that it is possible to accurately and consistently measure genetic
variant-containing peptides (GVPs) with DIA-MS. In this proposal we will develop new bioinformatics tools to
interpret GVPs using DIA-MS both to improve our sensitivity and specificity. By working with clinical
collaborators to study genetic variants in amyloidosis, we hope to improve both typing and outcomes in
patients carrying specific genetic variants. Furthermore, focusing on Transthyretin, the most common
amyloidogenic protein, we will use mutagenized libraries to study variants in high-throughput. With this data,
we will class the massive number of observed Transthyretin variants of unknown significance as either likely
benign or likely pathogenic. This protein is an ideal candidate for demonstration because several Transthyretin
variants have known clinical outcomes, allowing us to validate our approach. Taken together, these
bioinformatic and analytical tools, coupled with validation in a disease with both known and unknown biology
driven by genetic variants, will help us build powerful tools to study genetic diseases from a new proteomic
viewpoint.
Status | Active |
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Effective start/end date | 09/1/23 → 07/31/24 |
Funding
- National Institute of General Medical Sciences: $393,750.00
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