SBIR/STTR Research Award

Targeting endogenous retrovirus Gag genes for biomarker development in type 1 diabetes

Award identifier 99b785de4c5da477b565157934a87a3f7cad9650fc47e36281322421664215c7Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$291,949

Program and phase

SBIR · Phase I

Award year

2025

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Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
BIOSETTIA INC
Proposal award date
2025-09-15
Contract end date
2026-08-31
UEI
GZM5S2UYEGD2
Solicitation number
PA23-230
Topic code
200

Research abstract

Project Summary: Approximately 5-8% of the genome consists of remnants of ancient retroviruses that were integrated into the host genome through germ line infection. These retroviruses are known as endogenous retroviruses (ERVs). Some ERV proviruses have a complete or partial genome structure and can produce viral antigens and pseudo-viruses that interact with the immune system. Human endogenous retroviruses (HERVs) have been linked to autoimmune diseases such as type 1 diabetes (T1D). In our study, which focuses on the relationship between ERVs and autoimmunity using deep sequencing, we unexpectedly discovered a connection between the number of sequence variants encoding an open reading frame (ORF) of the ERV gene Gag and disease progression in both a non-obese diabetic (NOD) mouse model and human T1D patients. Abnormal sequence variants of ERV Gag genes could potentially be associated with increased autoimmune activation. In this study, we will use deep sequencing to measure the Gag gene variants with ORFs as biomarkers. The experiments aim to further investigate NOD mice and various T1D-susceptible and -resistant mouse strains in order to understand the mechanism that regulates the expression of these gene variants. Furthermore, we will expand the study on T1D patients by including autoantibody-negative and prediabetic controls to validate whether this sequencing-based biomarker could be used to screen high-risk children, stage pre-diabetic patients, and predict the onset of diabetes.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

2 SBIR/STTR records share this UEI (2010–2025). Reported award amounts total $391,903. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: Department of Health and Human Services

Agency intelligence

Department of Health and Human Services

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