R01AI173314
Project Grant
Overview
Grant Description
Gene regulatory network modeling of disease-associated DNA methylation perturbations - project summary.
Somatic mutations in DNMT3A and TET2 are common in the hematopoietic lineages of elderly individuals, estimated to affect more than 10% of adults over the age of 65. These mutations increase the risk for age-related comorbidities, including severe infection, atherosclerotic cardiovascular disease, osteoporosis, chronic kidney disease, and hematologic malignancies, nearly doubling the mortality rate of affected individuals.
DNMT3A and TET2 encode enzymes essential for remodeling DNA methylation during cellular differentiation. Animal studies suggest that mutations in these genes drive aberrant activation of immune cells, such as macrophages, which may underlie the disease associations.
We recently developed a human pluripotent stem cell (HPSC)-derived macrophage model, where the differentiation-dependent effects of DNMT3A or TET2 perturbation can be precisely delineated. We discovered that DNMT3A- and TET2-perturbations impaired DNA methylation remodeling at thousands of regulatory loci, altering enhancer activities and expression of genes important for macrophage function.
Our study highlighted the need for engineering approaches, and mathematical modeling in particular, to unravel the complex effects of DNMT3A and TET2 perturbations on cellular function and disease risk. Here, we pair novel computational modeling approaches with unique experimental resources to mechanistically connect site-specific changes in DNA methylation to aberrant immune responses and disease risk.
Aim 1 builds deep neural network models (and requisite training data resources) to predict the effects of DNA methylation on chromatin binding of 100+ transcription factors (TFs), the "readers" of DNA methylation patterns that ultimately recruit RNA polymerase and co-activators to drive gene transcription.
In Aim 2, we predict genome-scale TF-binding patterns from chromatin accessibility, transcriptional activity, and DNA methylation data in our contexts of interest: DNMT3A- or TET2-perturbed human macrophages in response to viral and bacterial infection-induced immune activation. To discover links between existing and novel disease associations, we will intersect the TFBS predictions with curated sets of age-related disease risk variants, to nominate TFs and contexts where DNMT3A- or TET2-perturbation and downstream alterations in TF binding might mediate disease risk.
In Aim 3, we will construct gene regulatory network (GRN) models of DNMT3A- and TET2-perturbed human macrophages to identify TFs driving differential gene expression responses to infection, hypotheses that (1) we will experimentally test and (2) could eventually lead to therapies that mitigate the negative, pathogenic consequences of common DNMT3A and TET2 mutations.
Furthermore, we build significant generalizable resources (models, modeling methodologies, and training data) that will enable future discoveries in new cell types and disease contexts where alterations in DNA methylation drive phenotypes.
Somatic mutations in DNMT3A and TET2 are common in the hematopoietic lineages of elderly individuals, estimated to affect more than 10% of adults over the age of 65. These mutations increase the risk for age-related comorbidities, including severe infection, atherosclerotic cardiovascular disease, osteoporosis, chronic kidney disease, and hematologic malignancies, nearly doubling the mortality rate of affected individuals.
DNMT3A and TET2 encode enzymes essential for remodeling DNA methylation during cellular differentiation. Animal studies suggest that mutations in these genes drive aberrant activation of immune cells, such as macrophages, which may underlie the disease associations.
We recently developed a human pluripotent stem cell (HPSC)-derived macrophage model, where the differentiation-dependent effects of DNMT3A or TET2 perturbation can be precisely delineated. We discovered that DNMT3A- and TET2-perturbations impaired DNA methylation remodeling at thousands of regulatory loci, altering enhancer activities and expression of genes important for macrophage function.
Our study highlighted the need for engineering approaches, and mathematical modeling in particular, to unravel the complex effects of DNMT3A and TET2 perturbations on cellular function and disease risk. Here, we pair novel computational modeling approaches with unique experimental resources to mechanistically connect site-specific changes in DNA methylation to aberrant immune responses and disease risk.
Aim 1 builds deep neural network models (and requisite training data resources) to predict the effects of DNA methylation on chromatin binding of 100+ transcription factors (TFs), the "readers" of DNA methylation patterns that ultimately recruit RNA polymerase and co-activators to drive gene transcription.
In Aim 2, we predict genome-scale TF-binding patterns from chromatin accessibility, transcriptional activity, and DNA methylation data in our contexts of interest: DNMT3A- or TET2-perturbed human macrophages in response to viral and bacterial infection-induced immune activation. To discover links between existing and novel disease associations, we will intersect the TFBS predictions with curated sets of age-related disease risk variants, to nominate TFs and contexts where DNMT3A- or TET2-perturbation and downstream alterations in TF binding might mediate disease risk.
In Aim 3, we will construct gene regulatory network (GRN) models of DNMT3A- and TET2-perturbed human macrophages to identify TFs driving differential gene expression responses to infection, hypotheses that (1) we will experimentally test and (2) could eventually lead to therapies that mitigate the negative, pathogenic consequences of common DNMT3A and TET2 mutations.
Furthermore, we build significant generalizable resources (models, modeling methodologies, and training data) that will enable future discoveries in new cell types and disease contexts where alterations in DNA methylation drive phenotypes.
Funding Goals
NOT APPLICABLE
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Cincinnati,
Ohio
45229
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 281% from $810,398 to $3,090,694.
Childrens Hospital Medical Center was awarded
DNA Methylation Modeling for Disease Risk Prediction
Project Grant R01AI173314
worth $3,090,694
from the National Institute of Allergy and Infectious Diseases in June 2023 with work to be completed primarily in Cincinnati Ohio United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.855 Allergy and Infectious Diseases Research.
The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 6/22/26
Period of Performance
6/1/23
Start Date
5/31/28
End Date
Funding Split
$3.1M
Federal Obligation
$0.0
Non-Federal Obligation
$3.1M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01AI173314
Transaction History
Modifications to R01AI173314
Additional Detail
Award ID FAIN
R01AI173314
SAI Number
R01AI173314-1160230374
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Nonprofit With 501(c)(3) IRS Status (Other Than An Institution Of Higher Education)
Awarding Office
75NM00 NIH National Institute of Allergy and Infectious Diseases
Funding Office
75NM00 NIH National Institute of Allergy and Infectious Diseases
Awardee UEI
JZD1HLM2ZU83
Awardee CAGE
01SC8
Performance District
OH-01
Senators
Sherrod Brown
J.D. (James) Vance
J.D. (James) Vance
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| National Institute of Allergy and Infectious Diseases, National Institutes of Health, Health and Human Services (075-0885) | Health research and training | Grants, subsidies, and contributions (41.0) | $810,398 | 100% |
Modified: 6/22/26