Method for identification of condition-associated public antigen receptor sequences

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Adaptive immunity relies on diverse T-cell receptors (TCRs) and B-cell receptors (BCRs) for antigen recognition.
• Receptor repertoire sequencing enables large-scale studies of antigen receptor sequences in relation to diseases.
• Existing methods often require large cohorts and control groups for disease association studies.

Purpose of the Study:

• To develop a novel statistical framework for identifying disease-associated immune receptor sequences.
• To enable repertoire-wide disease association studies with limited patient cohorts and without a control group.
• To validate the framework's efficacy in identifying known disease-responsive receptor sequences.

Main Methods:

• Development of a statistical framework for associating immune receptor sequences with disease states.
• Application of the framework to analyze TCR and BCR repertoires from small patient cohorts.
• Validation of identified sequences against known disease associations.

Main Results:

• The developed statistical framework successfully identified disease-associated TCR sequences.
• Previously validated TCR sequences responsive to Cytomegalovirus infection were identified.
• TCR sequences associated with type one diabetes were also successfully identified by the method.

Conclusions:

• The novel statistical framework facilitates the identification of disease-associated immune receptor sequences, even with small sample sizes.
• This method reduces the need for extensive patient cohorts and control groups in immunoinformatics studies.
• The successful identification of known disease-responsive TCR sequences highlights the framework's potential for future immunologic research.