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Improved methods for detecting selection by mutation analysis of Ig V region sequences.

Uri Hershberg1, Mohamed Uduman, Mark J Shlomchik

  • 1Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.

International Immunology
|April 10, 2008
PubMed
Summary

A new statistical test improves detection of selection forces shaping B cell repertoires. This method offers higher specificity and sensitivity, overcoming limitations of previous approaches for analyzing immunoglobulin gene sequences.

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • Statistical methods analyzing B lymphocyte immunoglobulin (Ig) V region sequences are crucial for understanding B cell repertoire selection.
  • Existing methods often yield high false positive rates and are susceptible to somatic hypermutation biases, mimicking selection.
  • The interplay between positive and negative selection can reduce the specificity of current statistical tests.

Purpose of the Study:

  • To develop a novel statistical test for detecting selection forces in B cell repertoire development with improved sensitivity and specificity.
  • To address limitations of existing methods, particularly their susceptibility to somatic hypermutation biases and false positive detection of selection.

Main Methods:

  • Development and validation of a new statistical test for analyzing Ig V region sequences.

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  • In silico simulations to assess the interaction between positive and negative selection effects.
  • In vivo validation using Ig transgenic mouse models and re-analysis of published diffuse large B cell lymphoma data.
  • Enhancement of mutation models to account for microsequence effects and transition bias in somatic hypermutation.

    • Main Results:

    • The proposed statistical test demonstrates a superior balance between sensitivity and specificity compared to existing methods.
    • In silico analysis revealed that the interaction of selection forces caused false positives in prior methods.
    • In vivo validation confirmed the method's sensitivity in detecting positive selection and its high specificity using non-productively rearranged Ig sequences.

    Conclusions:

    • The new statistical method accurately detects antigen-driven selection in vivo with high specificity.
    • This approach offers enhanced insights into the forces shaping the B cell repertoire, overcoming limitations of previous techniques.
    • Re-analysis of existing studies using this improved method may be necessary to correct previous findings.