The lack of technology to rapidly, accurately and cost-effectively assess mutagenic outcomes in humans has essentially constrained a more complete understanding of individual susceptibility to environmental mutagens and their relationship to cancer, genetic disease and aging. An assay to measure mutation frequency and spectrum directly in human blood or tissue samples would provide information about previous exposures to environmental mutagens in relation to the status of an individual's diverse array of genome maintenance pathways. This would enable human population-based studies, clinical studies and routine biomonitoring for risk assessment. Here we propose to develop a highly sensitive, robust and scalable assay for directly measuring DNA mutations in human cells or tissues by sequence capture and massively parallel sequencing (SC-MPS).

Public Health Relevance

Humans are exposed to a large variety of environmental mutagens and carcinogens. We propose to develop a method that allows measuring DNA mutation load in human blood or tissue samples directly, without selection. This should provide a robust and cost-effective assay to assess individual human risk, for example, after possible exposure to mutagens.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21ES019520-01
Application #
8013181
Study Section
Special Emphasis Panel (ZRG1-GGG-B (51))
Program Officer
Mcallister, Kimberly A
Project Start
2011-01-01
Project End
2012-12-31
Budget Start
2011-01-01
Budget End
2011-12-31
Support Year
1
Fiscal Year
2011
Total Cost
$207,500
Indirect Cost
Name
Albert Einstein College of Medicine
Department
Genetics
Type
Schools of Medicine
DUNS #
110521739
City
Bronx
State
NY
Country
United States
Zip Code
10461
Dong, Xiao; Zhang, Lei; Milholland, Brandon et al. (2017) Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat Methods 14:491-493
Li, Wenge; Calder, R Brent; Mar, Jessica C et al. (2015) Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles. Mutat Res 772:55-62
Gundry, Michael; Li, Wenge; Maqbool, Shahina Bano et al. (2012) Direct, genome-wide assessment of DNA mutations in single cells. Nucleic Acids Res 40:2032-40
Gundry, Michael; Vijg, Jan (2012) Direct mutation analysis by high-throughput sequencing: from germline to low-abundant, somatic variants. Mutat Res 729:1-15
Li, Wenge; Vijg, Jan (2012) Measuring genome instability in aging - a mini-review. Gerontology 58:129-38