Follicular lymphoma subgroups with and without t(14;18) differ in their N-glycosylation pattern and IGHV usage

We previously reported that t(14;18)-negative follicular lymphomas (FL) show a clear reduction of newly acquired N-glycosylation sites (NANGS) in immunoglobulin genes. We therefore aimed to investigate in-depth the occurrence of NANGS in a larger cohort of t(14;18)-positive and t(14;18)-negative FL, including early (I/II) and advanced (III/IV) stage treatment naïve and relapsed tumors. We determined the clonotype using a next generation sequencing approach in a series of 68 FL with fresh frozen material (36 t(14;18)-positive and 32 t(14;18)-negative). The frequency of NANGS differed considerably between t(14;18)-positive and t(14;18)-negative FL III/IV, but no difference was observed among t(14;18)-positive and t(14;18)-negative FL I/II. The introduction of NANGS in all t(14;18)-negative clinical subgroups occurred significantly more often in the FR3 region. Moreover, t(14;18)-negative treatment naïve FL, specifically those with NANGS, showed a strong bias for IGHV4-34 usage compared to t(14;18)-positive treatment naïve cases with NANGS, while IGHV4-34 usage was never found in relapsed FL. In conclusion, subgroups of t(14;18)-negative FL might use different mechanisms of BCR stimulation compared to the lectin-mediated binding described in t(14;18)-positive FL, including responsiveness to autoantigens as indicated by biased IGHV4-34 usage and strong NANGS enrichment in FR3.

A Bayesian model based computational analysis of the relationship between bisulfite accessible single-stranded DNA in chromatin and somatic hypermutation of immunoglobulin genes

The B cells in our body generate protective antibodies by introducing somatic hypermutations (SHM) into the variable region of immunoglobulin genes (IgVs). The mutations are generated by activation induced deaminase (AID) that converts cytosine to uracil in single stranded DNA (ssDNA) generated during transcription. Attempts have been made to correlate SHM with ssDNA using bisulfite to chemically convert cytosines that are accessible in the intact chromatin of mutating B cells. These studies have been complicated by using different definitions of “bisulfite accessible regions” (BARs). Recently, deep-sequencing has provided much larger datasets of such regions but computational methods are needed to enable this analysis. Here we leveraged the deep-sequencing approach with unique molecular identifiers and developed a novel Hidden Markov Model based Bayesian Segmentation algorithm to characterize the ssDNA regions in the IGHV4-34 gene of the human Ramos B cell line. Combining hierarchical clustering and our new Bayesian model, we identified recurrent BARs in certain subregions of both top and bottom strands of this gene. Using this new system, the average size of BARs is about 15 bp. We also identified potential G-quadruplex DNA structures in this gene and found that the BARs co-locate with G-quadruplex structures in the opposite strand. Using various correlation analyses, there is not a direct site-to-site relationship between the bisulfite accessible ssDNA and all sites of SHM but most of the highly AID mutated sites are within 15 bp of a BAR. In summary, we developed a novel platform to study single stranded DNA in chromatin at a base pair resolution that reveals potential relationships among BARs, SHM and G-quadruplexes. This platform could be applied to genome wide studies in the future.

Revealing how variations in antibody repertoires correlate with vaccine responses

An important challenge in vaccine development is to figure out why a vaccine succeeds in some individuals and fails in others. Although antibody repertoires hold a key to answering this question, there have been very few personalized immunogenomics studies so far aimed at revealing how variations in immunoglobulin genes affect a vaccine response. We conducted an immunosequencing study of 204 calves vaccinated against bovine respiratory disease (BRD) with the goal to reveal variations in immunoglobulin genes and somatic hypermutations that impact the efficacy of vaccine response. Our study represents the largest longitudinal personalized immunogenomics study reported to date across all species, including humans. To analyze the generated dataset, we developed an algorithm for identifying variations of the immunoglobulin genes (as well as frequent somatic hypermutations) that affect various features of the antibody repertoire and titers of neutralizing antibodies. In contrast to relatively short human antibodies, cattle have a large fraction of ultralong antibodies that have opened new therapeutic opportunities. Our study revealed that ultralong antibodies are a key component of the immune response against the costliest disease of beef cattle in North America. The detected variants of the cattle immunoglobulin genes, which are implicated in the success/failure of the BRD vaccine, have the potential to direct the selection of individual cattle for ongoing breeding programs.

DNA Methylation, Deamination, and Translesion Synthesis Combine to Generate Footprint Mutations in Cancer Driver Genes in B-Cell Derived Lymphomas and Other Cancers

Cancer genomes harbor numerous genomic alterations and many cancers accumulate thousands of nucleotide sequence variations. A prominent fraction of these mutations arises as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases followed by the replication/repair of edited sites by DNA polymerases (pol), as deduced from the analysis of the DNA sequence context of mutations in different tumor tissues. We have used the weight matrix (sequence profile) approach to analyze mutagenesis due to Activation Induced Deaminase (AID) and two error-prone DNA polymerases. Control experiments using shuffled weight matrices and somatic mutations in immunoglobulin genes confirmed the power of this method. Analysis of somatic mutations in various cancers suggested that AID and DNA polymerases η and θ contribute to mutagenesis in contexts that almost universally correlate with the context of mutations in A:T and G:C sites during the affinity maturation of immunoglobulin genes. Previously, we demonstrated that AID contributes to mutagenesis in (de)methylated genomic DNA in various cancers. Our current analysis of methylation data from malignant lymphomas suggests that driver genes are subject to different (de)methylation processes than non-driver genes and, in addition to AID, the activity of pols η and θ contributes to the establishment of methylation-dependent mutation profiles. This may reflect the functional importance of interplay between mutagenesis in cancer and (de)methylation processes in different groups of genes. The resulting changes in CpG methylation levels and chromatin modifications are likely to cause changes in the expression levels of driver genes that may affect cancer initiation and/or progression.

Deconvolving multiplexed histone modifications in single cells

Recent advances have enabled mapping of histone modifications in single cells, but current methods are constrained to profile only one histone modification per cell. Here we present an integrated experimental and computational framework, scChIX (single-cell chromatin immunocleavage and unmixing), to map multiple histone modifications in single cells. We first validate this method using purified blood cells and show that although the two repressive marks, H3K27me3 and H3K9me3, are generally mutually exclusive, the transitions between the two regions can vary between cell types. Next we apply scChIX to a heterogenous cell population from mouse bone marrow to generate linked maps of active (H3K4me1) and repressive (H3K27me3) chromatin landscapes in single cells, where coordinates in the active modification map correspond to coordinates in the repressive map. Linked analysis reveals that immunoglobulin genes in the region are in a repressed chromatin state in pro-B cells, but become activated in B cells. Overall, scChIX unlocks systematic interrogation of the interplay between histone modifications in single cells.