Literature DB >> 16162266

Problems in using statistical analysis of replacement and silent mutations in antibody genes for determining antigen-driven affinity selection.

Biplab Bose1, Subrata Sinha.   

Abstract

The analysis of molecular signatures of antigen-driven affinity selection of B cells is of immense use in studies on normal and abnormal B cell development. Most of the published literature compares the expected and observed frequencies of replacement (R) and silent (S) mutations in the complementarity-determining regions (CDRs) and the framework regions (FRs) of antibody genes to identify the signature of antigenic selection. The basic assumption of this statistical method is that antigenic selection creates a bias for R mutations in the CDRs and for S mutations in the FRs. However, it has been argued that the differences in intrinsic mutability among different regions of an antibody gene can generate a statistically significant bias even in the absence of any antigenic selection. We have modified the existing statistical method to include the effects of intrinsic mutability of different regions of an antibody gene. We used this method to analyse sequences of several B cell-derived monoclonals against T-dependent antigens, T-independent antigens, clones derived from lymphoma and amyloidogenic clones. Our sequence analysis indicates that even after correcting for the intrinsic mutability of antibody genes, statistical parameters fail to reflect the role of antigen-driven affinity selection in maturation of many clones. We suggest that, contrary to the basic assumption of such statistical methods, selection can act both for and against R mutations in the CDR as well as in the FR regions. In addition we have identified different methodological difficulties in the current uses of such statistical analysis of antibody genes.

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Year:  2005        PMID: 16162266      PMCID: PMC1817819          DOI: 10.1111/j.1365-2567.2005.02208.x

Source DB:  PubMed          Journal:  Immunology        ISSN: 0019-2805            Impact factor:   7.397


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