Invariant VD and DJ Motifs Define a Novel Class of Human Antibodies and TCRs Prototyped by antigen receptors of Dual-Expresser Lymphocytes.

INTRODUCTION: Dual-expressing lymphocytes (DEs) are unique immune cells that express both B cell receptors (BCRs, surface antibody) and T cell receptors (TCRs). In type 1 diabetes, DE antibodies are predominated by one antibody (x-mAb), an IgM monoclonal antibody with a germline-encoded CDR3 that recognizes self-reactive TCRs. We explored if x-mAb and its interacting TCRs have distinct structural features. METHODS: Using bioinformatics, we compared x-mAb and its most common interacting TCRαß to billions of antigen receptor sequences to determine if they were unique or randomly generated. RESULTS: X-mAb represents a unique class of human antibodies with a conserved CDR3 sequence (CARx1-4DTAMVYYFYDW), consisting of a fixed DJH motif (DTAMVYYFDYW) paired with various VH genes. A public TCRß clonotype (CASSPGTEAFF) associated with x-mAb on DEs features two invariant segments, VßD (CASSPGT) and DJß (PGTEAFF), key to two large families of public TCRß clonotypes-CASSPGT-Jßx and CASSPGT-Jßx-formed by recombining the VßD motif with Jß genes and the DJß motif with Vß genes. B cells also use CASSPGT as a VHD motif for public IGH clonotypes (CASSPGT-Jßx). DISCUSSION: DEs, unlike conventional T and B cells, use invariant motifs to create public antibodies and TCRs, a trait previously seen only in cartilaginous fish.

Systematic evaluation of B-cell clonal family inference approaches.

The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.

Optimized Threshold Inference for Partitioning of Clones From High-Throughput B Cell Repertoire Sequencing Data.

During adaptive immune responses, activated B cells expand and undergo somatic hypermutation of their B cell receptor (BCR), forming a clone of diversified cells that can be related back to a common ancestor. Identification of B cell clones from high-throughput Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data relies on computational analysis. Recently, we proposed an automated method to partition sequences into clonal groups based on single-linkage hierarchical clustering of the BCR junction region with length-normalized Hamming distance metric. This method could identify clonal sequences with high confidence on several benchmark experimental and simulated data sets. However, determining the threshold to cut the hierarchy, a key step in the method, is computationally expensive for large-scale repertoire sequencing data sets. Moreover, the methodology was unable to provide estimates of accuracy for new data. Here, a new method is presented that addresses this computational bottleneck and also provides a study-specific estimation of performance, including sensitivity and specificity. The method uses a finite mixture model fitting procedure for learning the parameters of two univariate curves which fit the bimodal distribution of the distance vector between pairs of sequences. These distributions are used to estimate the performance of different threshold choices for partitioning sequences into clones. These performance estimates are validated using simulated and experimental data sets. With this method, clones can be identified from AIRR-seq data with sensitivity and specificity profiles that are user-defined based on the overall goals of the study.