Immunoglobulin variable-region gene mutational lineage tree analysis: application to autoimmune diseases

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Immunoglobulin variable-region (IGV) gene diversification via somatic hypermutation (SHM) is often illustrated using lineage trees.
• Existing methods for analyzing IGV sequences have limitations in quantifying SHM and selection dynamics.
• Autoimmune diseases like Myasthenia Gravis (MG), Rheumatoid Arthritis (RA), and Sjögren's Syndrome (SS) involve B cell clone diversification.

Purpose of the Study:

• To develop and apply a novel mathematical method for analyzing the graphical properties of IGV gene lineage trees.
• To quantify differences in SHM and antigen-driven selection dynamics across various contexts (tissues, species, diseases).
• To investigate IGV gene lineage trees from B cell clones in patients with MG, RA, and SS.

Main Methods:

• Development of a novel mathematical method to analyze graphical properties of IGV gene lineage trees.
• Application of the method to published IGV sequence data from B cell clones in autoimmune disease patients.
• Comparison of lineage trees across different patients, tissues, and immunoglobulin isotypes (IgG, IgA).

Main Results:

• The novel method allows quantification of SHM and selection dynamics.
• Lineage trees from different patients, tissues within the same patient, and different B cell clones (IgG/IgA) often exhibit similarities.
• Lineage trees generally reflect the chronic nature of the investigated autoimmune diseases.

Conclusions:

• The developed mathematical method provides a robust tool for analyzing IGV gene lineage trees.
• Despite variations in data acquisition, IGV gene lineage trees offer consistent insights into B cell dynamics in autoimmune diseases.
• The findings highlight the utility of lineage tree analysis for understanding B cell clonal evolution in chronic autoimmune conditions.