Two Mutually Exclusive Local Chromatin States Drive Efficient V(D)J Recombination

Abstract

SummaryVariable (V), diversity (D), and joining (J) (V(D)J) recombination is the first determinant of antigen receptor diversity. Understanding how recombination is regulated requires a comprehensive, unbiased readout of V gene usage. We have developed VDJ sequencing (VDJ-seq), a DNA-based next-generation-sequencing technique that quantitatively profiles recombination products. We reveal a 200-fold range of recombination efficiency among recombining V genes in the primary mouse Igh repertoire. We used machine learning to integrate these data with local chromatin profiles to identify combinatorial patterns of epigenetic features that associate with active VH gene recombination. These features localize downstream of VH genes and are excised by recombination, revealing a class of cis-regulatory element that governs recombination, distinct from expression. We detect two mutually exclusive chromatin signatures at these elements, characterized by CTCF/RAD21 and PAX5/IRF4, which segregate with the evolutionary history of associated VH genes. Thus, local chromatin signatures downstream of VH genes provide an essential layer of regulation that determines recombination efficiency.

Sequence-Dependent Dynamics of Synthetic and Endogenous RSSs in V(D)J Recombination

“…We find that the RSSs of frequently-used gene segments typically do not support more efficient PC formation or cleavage than those neighboring gene segments of more modest usage. This observation is consistent with recent findings that RSS strength, as assessed by the RSS information content (RIC) algorithm (11,(38)(39)(40), is only one of multiple parameters needed to be able to predict gene segment usage frequency (30,41). Furthermore, we found from analyzing single bp variations of the V4-57-1 RSS that the efficiencies of PC formation and cleavage are sensitive to single bp changes depending upon the conservation level at the respective position.…”

Regulated large-scale nucleosome density patterns and precise nucleosome positioning correlate with V(D)J recombination

“…As shown in Fig. S11C, nucleosome occupancy between −100 and 0 also correlated well with active vs. inactive gene segments defined by Bolland et al (59) and, in combination with RIC score, predicted active vs. inactive states with greater than 90% accuracy. Thus, nucleosome position is tightly correlated with these other known factors influencing VDJ recombination and provides a simple, but informative, method for predicting chromatin signatures, as well as active vs. inactive states.…”

“…Transcription, initiated from the promoter of the locus, is unlikely to play a role for several reasons. Recently the impacts of over 18 features were assessed for their ability to predict whether VH segments are active (59). Three distinct chromatin states were identified, two that are associated with active recombination ("A" and "E") and one with generally inactive gene segments ("Bg").…”

The rise and fall of machine learning methods in biomedical research

“…These quantitative approaches have been shown to be effective and are becoming increasingly popular in addressing challenges such as those outlined above. Highlights of their successful applications in functional genomics include, but are not limited to, learning and characterizing chromatin states by employing unsupervised approaches such as chromHMM ( Ernst & Kellis, 2012), predicting sequence specificities of DNA- and RNA-binding proteins using convolutional neural networks such as DeepBind ( Alipanahi et al , 2015), and employing a combination of supervised and unsupervised approach to determine the genetic and epigenetic contributors of antibody repertoire diversity ( Bolland et al , 2016). Nowadays it is almost impossible to publish a study on single cell assays without using dimensionality reduction methods such as Principal Component Analysis or t-SNE.…”