Polyreactive monoclonal autoantibodies in multiple sclerosis: functional selection from phage display library and characterization by deep sequencing analysis

Abstract

Multiple Sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system that primarily affects young and middle-aged people. It is widely accepted that B lymphocyte activation is required for MS progression. Despite the fact that the exact triggering mechanisms of MS remain enigmatic, one may suggest that MS can be induced by viral or bacterial infection in combination with specific genetic and environmental factors. Using deep sequencing and functional selection methodologies we characterized clones of poly- and cross-reactive antibodies that are capable of simultaneous recognition of viral proteins and autoantigens. The latter, in turn, possibly may trigger MS progression through molecular mimicry. It was identified that two cross-reactive antigens are probably recognized by light or heavy chains individually. According to the high structural homology between selected autoantibodies and a number of various antiviral IgGs, we suggest that a wide range of pathogens, instead of a single virus, be regarded as possible triggers of MS.

Keywords: Multiple sclerosis; autoreactive B cells; cross-reactivity; deep sequencing; myelin basic protein; viral triggers.

Fig. 1

Monoclonal phage ELISA of MBP (green bars) and lmp1 (violet bars) binding by the antibodies under study. The bacteriophage M13K07 (m13) and bacteriophage exposing on its surface scFv towards thyroglobulin (TH-scFv) were used as negative controls

Fig. 2

Homology of the selected heavy chains with antiviral antibodies as indicated. The scFvs selected in the current study are shown in bold

Fig. 3

Homology of the selected light chains with antiviral antibodies as indicated. The scFvs selected in the current study are shown in bold

Fig. 4

Occurrence of the CDR3 of heavy chain in enriched sublibraries as compared to the initial MS library. Each circle indicates unique CDR3 with the number of reads for this CDR3 in MS library (X axis) and in respective enriched sublibrary (Y axis). For each pair of libraries, the regression and the outlier analysis were done using ‘car’ R package (outliers are colored in red). The functionally selected monoclones are shown in green with indication of their code according to the Table. Sequences of the H-CDR3 for the most interesting clones are indicated

Fig. 5

Occurrence of the CDR3 of light chain in enriched sublibraries as compared to the initial MS library. Each circle indicates a unique CDR3 with the number of reads for this CDR3 in MS library (X axis) and in the respective enriched sublibrary (Y axis). For each pair of libraries, the regression and the outlier analysis were done using the ‘car’ R package (outliers are colored in red). The functionally selected monoclones are shown in green with indication of their code according to the Table. Sequences of the L-CDR3 for the most interesting clones are indicated

Fig. 6

Distribution of the CDR3 net charge in sublibraries enriched for different antigens for heavy (A) and light (B) chains