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| Funder | NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE |
|---|---|
| Recipient Organization | New York University School of Medicine |
| Country | United States |
| Start Date | Apr 10, 2023 |
| End Date | Mar 31, 2025 |
| Duration | 721 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | NIH (US) |
| Grant ID | 10657882 |
PROJECT SUMMARY/ABSTRACT Somatic mosaic mutations accumulate over time in every healthy cell but detecting them requires specialized sequencing technologies with extremely low error rates. However, all current technologies for profiling mosaic mutations require amplification of DNA, which introduces single-strand DNA artifacts. Therefore, even the
highest fidelity technologies can only detect mosaic mutations when they are present in both strands of the original DNA, but they cannot detect the single-strand mutations and damage from which they originate. Here, we develop a technology that can directly sequence DNA molecules without any amplification at ultra-high
fidelity, such that mutations and damage present in only one of the two strands of a DNA molecule can be detected for the first time. It achieves this by significantly increasing the accuracy of single-molecule DNA sequencing, and furthermore, it utilizes long reads that can be used to study regions of the genome that are
not accessible to all prior high-fidelity mosaic mutation technologies that utilize short reads. Our technology, called Hairpin Duplex Enhanced Fidelity Sequencing (HiDEF-seq), will be developed as part of the SMaHT Network, and we will work in close coordination with the Network at all stages of the project to ensure it
contributes significantly to the Network’s goals of creating a comprehensive catalogue of somatic mosaicism in human tissues. In the first UG3 phase of the project, we will develop our technology to cost-effectively and reliably profile any bulk human tissue. In Aim 1 of UG3, we will develop the technology to profile all classes of
single- and double-strand mosaic mutations at ultra-high fidelity (substitutions, insertions, deletions, structural variants, and retroelements). In Aim 2 of UG3, we will use machine-learning models of single-molecule polymerase kinetics to detect diverse types of single-strand DNA damage and modifications. Importantly,
HiDEF-seq will achieve detection of all these events simultaneously in one assay. In the second UH3 phase of the project, we will work closely and integrally with the SMaHT Network to validate and scale the throughput of the technology so that it can profile the entire collection of SMaHT tissue samples. In Aim 1 of UH3, we will
fully automate the laboratory component of HiDEF-seq to enable creation of sequencing libraries for hundreds of samples per day. In Aim 2 of UH3, we will scale the computational pipeline of our technology for rapid analysis of thousands of samples. Throughout this project, we will work with the SMaHT Network to validate,
standardize, and disseminate the technology. HiDEF-seq’s achievement of ultra-high fidelity sequencing of single-strand DNA mutations and damage will enable fundamentally new types of mosaic mutation studies that will disentangle the interrelated processes of DNA mutation, repair and replication. It will also enable
systematic dissection of sources of artifacts stemming from laboratory processing of DNA. Furthermore, it will reveal the instantaneous effects and temporal dynamics of exogenous mutagens, with broad implications for environmental health and discovery of factors that reduce or increase the rate at which our genomes mutate.
New York University School of Medicine
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