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

Abstract

Summary 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.

Ectopic GC in the thymus of myasthenia gravis patients show characteristics of normal GC

“…Selection has been assumed to be indicated when the R:S ratio of the observed mutations is higher than the R:S ratio expected under random conditions in the CDR, or lower in the FR [26,27]. However, these methods have been shown to yield false indications of selection [28,29]. In this study, we have implemented a recent correction to the conventional method [30] to analyze mutations from MG and normal GC clones.…”

“…R:S analysis methods measure the extent of selection by R and S mutation analysis, which compares the frequency of R mutations found in different regions in mutated IgV gene sequences to their expected frequency based on codon usage of the GL sequence. The conventional method [26,27] was shown to give false indications of selection [28,29]. Therefore, we have used a correction to this method, the focused binomial test, by Hershberg et al [30].…”

Constitutive Production of Catalytic Antibodies to a Staphylococcus aureus Virulence Factor and Effect of Infection

“…an average of 15.1 replacements per antibody for the panel of antibodies to microbial antigens in Ref. 54. Similarly, the average number of replacements per scFv clone in the library was 13.3 (n ϭ 20 clones sequenced).…”

E2A-positive gastric MALT lymphoma has weaker plasmacytoid infiltrates and stronger expression of the memory B-cell-associated miR-223: possible correlation with stage and treatment response

“…As gastric MALT lymphoma is driven by the stimulus of Helicobacter infection, we evaluated the ratios of replacement to silent mutations to see whether the ratios were correlated with the expression of E2A. 26,27 The ratios in the framework regions (2.0 vs 1.9, P ¼ 0.85; Figure 2c) and in the complementarity determining regions (2.9 vs 2.9 at P ¼ 0.88; Figure 2d) were similar. Without significant differences in somatic hypermutations, intraclonal variations, or the ratios replacement to silent mutations between E2A…”

“…Total numbers of somatic hypermutations and the ratios of replacement to silent mutations in the framework regions and the complementarity determining regions were calculated. 26,27 Deviations from the consensus were regarded as intraclonal variations. The numbers of deviations divided by the number of sequences were calculated.…”