Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets

Area of Science:

• Immunology
• Genetics
• Biotechnology

Background:

• Type 1 Diabetes (T1D) is an autoimmune disease primarily affecting children and adolescents worldwide.
• Current prediction methods rely on genetic risk and autoantibodies, which can lead to diagnostic errors.
• T cells are crucial in T1D pathogenesis, but T cell sequencing data complexity has limited its use in prognostics.

Purpose of the Study:

• To investigate T cell receptor (TCR) sequencing data as a source of predictive features for T1D onset.
• To assess the utility of TCR gene usage and immunogenetic background in predicting clinical disease risk.
• To explore the feasibility of extracting predictive features from bulk RNA-sequencing data.

Main Methods:

• Analysis of over 730 TCR repertoires from 321 patients with T1D.
• Investigated TCR gene usage patterns as potential predictive markers.
• Compared feature extraction from TCR sequencing versus bulk RNA-sequencing data.

Main Results:

• TCR gene usage was found to be highly predictive of clinical T1D risk.
• Immunogenetic background significantly influenced feature selection, highlighting personalized medicine potential.
• Identical predictive features were successfully extracted from more economical bulk RNA-seq data.

Conclusions:

• TCR sequencing data offers valuable predictive features for T1D onset.
• Bulk RNA-seq provides a cost-effective alternative for extracting these prognostic markers.
• The identified features can advance the development of accessible pre-clinical T1D diagnostic tests.