Deconvolution of B cell receptor repertoire in multiple sclerosis patients revealed a delay in tBreg maturation.

Background: B lymphocytes play a pivotal regulatory role in the development of the immune response. It was previously shown that deficiency in B regulatory cells (Bregs) or a decrease in their anti-inflammatory activity can lead to immunological dysfunctions. However, the exact mechanisms of Bregs development and functioning are only partially resolved. For instance, only a little is known about the structure of their B cell receptor (BCR) repertoires in autoimmune disorders, including multiple sclerosis (MS), a severe neuroinflammatory disease with a yet unknown etiology. Here, we elucidate specific properties of B regulatory cells in MS. Methods: We performed a prospective study of the transitional Breg (tBreg) subpopulations with the CD19+CD24highCD38high phenotype from MS patients and healthy donors by (i) measuring their content during two diverging courses of relapsing-remitting MS: benign multiple sclerosis (BMS) and highly active multiple sclerosis (HAMS); (ii) analyzing BCR repertoires of circulating B cells by high-throughput sequencing; and (iii) measuring the percentage of CD27+ cells in tBregs. Results: The tBregs from HAMS patients carry the heavy chain with a lower amount of hypermutations than tBregs from healthy donors. The percentage of transitional CD24highCD38high B cells is elevated, whereas the frequency of differentiated CD27+ cells in this transitional B cell subset was decreased in the MS patients as compared with healthy donors. Conclusions: Impaired maturation of regulatory B cells is associated with MS progression.

Heavy-light chain interrelations of MS-associated immunoglobulins probed by deep sequencing and rational variation.

The mechanisms triggering most of autoimmune diseases are still obscure. Autoreactive B cells play a crucial role in the development of such pathologies and, in particular, production of autoantibodies of different specificities. The combination of deep-sequencing technology with functional studies of antibodies selected from highly representative immunoglobulin combinatorial libraries may provide unique information on specific features in the repertoires of autoreactive B cells. Here, we have analyzed cross-combinations of the variable regions of human immunoglobulins against the myelin basic protein (MBP) previously selected from a multiple sclerosis (MS)-related scFv phage-display library. On the other hand, we have performed deep sequencing of the sublibraries of scFvs against MBP, Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1), and myelin oligodendrocyte glycoprotein (MOG). Bioinformatics analysis of sequencing data and surface plasmon resonance (SPR) studies have shown that it is the variable fragments of antibody heavy chains that mainly determine both the affinity of antibodies to the parent autoantigen and their cross-reactivity. It is suggested that LMP1-cross-reactive anti-myelin autoantibodies contain heavy chains encoded by certain germline gene segments, which may be a hallmark of the EBV-specific B cell subpopulation involved in MS triggering.

Hidden Markov models for evolution and comparative genomics analysis.

The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb.