A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction...

Area of Science:

• Immunology and Computational Biology
• Focuses on B cell receptor repertoire analysis and evolutionary modeling.

Background:

• B cell affinity maturation (AM) via somatic hypermutations (SHMs) is key for pathogen recognition.
• High-throughput sequencing allows B cell receptor repertoire analysis and clonal tree reconstruction.
• Accuracy of traditional phylogenetic methods for reconstructing B cell clonal trees remains unclear.

Purpose of the Study:

• To develop a realistic model for B cell clonal lineage evolution.
• To benchmark existing clonal tree reconstruction methods using the developed model.
• To propose metrics for comparing clonal trees and evaluating their properties.

Main Methods:

• Developed an extensible model simulating B cell clonal lineage evolution and sequence changes.
• Incorporated features like simultaneous evolution of clonal trees and sequences, modeling selective pressure, scalable simulations, and multiple infection rounds.
• Benchmarked phylogenetic reconstruction methods, including maximum likelihood, against alternative clonal tree reconstruction approaches.

Main Results:

• Naive application of maximum likelihood phylogenetic methods can miss key clonal expansion features.
• A simple post-processing step (contracting short branches) significantly improves phylogenetic inference accuracy.
• The proposed model provides a realistic framework for evaluating clonal tree reconstruction methods.

Conclusions:

• The developed B cell evolution model is valuable for benchmarking and improving clonal tree reconstruction.
• Optimized phylogenetic methods, with post-processing, offer accurate reconstruction of B cell clonal trees.
• This work advances computational immunology by providing tools to accurately study immune system microevolution.