A diverse antibody repertoire is formed through the rearrangement of V, D, and J segments at the immunoglobulin heavy chain (Igh) loci. The C57BL/6 murine Igh locus has over 100 functional VH gene segments that can recombine to a rearranged DJH. While the non-random usage of VH genes is well documented, it is not clear what elements determine recombination frequency. To answer this question we conducted deep sequencing of 5′-RACE products of the Igh repertoire in pro-B cells, amplified in an unbiased manner. ChIP-seq results for several histone modifications and RNA polymerase II binding, RNA-seq for sense and antisense non-coding germline transcripts, and proximity to CTCF and Rad21 sites were compared to the usage of individual V genes. Computational analyses assessed the relative importance of these various accessibility elements. These elements divide the Igh locus into four epigenetically and transcriptionally distinct domains, and our computational analyses reveal different regulatory mechanisms for each region. Proximal V genes are relatively devoid of active histone marks and non-coding RNA in general, but having a CTCF site near their RSS is critical, suggesting that being positioned near the base of the chromatin loops is important for rearrangement. In contrast, distal V genes have higher levels of histone marks and non-coding RNA, which may compensate for their poorer RSSs and for being distant from CTCF sites. Thus, the Igh locus has evolved a complex system for the regulation of V(D)J rearrangement that is different for each of the four domains that comprise this locus.

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Ying Yang 1 (YY1) is a ubiquitously expressed transcription factor shown to be essential for pro-B-cell development. However, the role of YY1 in other B-cell populations has never been investigated. Recent bioinformatics analysis data have implicated YY1 in the germinal center (GC) B-cell transcriptional program. In accord with this prediction, we demonstrated that deletion of YY1 by Cγ1-Cre completely prevented differentiation of GC B cells and plasma cells. To determine if YY1 was also required for the differentiation of other B-cell populations, we deleted YY1 with CD19-Cre and found that all peripheral B-cell subsets, including B1 B cells, require YY1 for their differentiation. Transitional 1 (T1) B cells were the most dependent upon YY1, being sensitive to even a half-dosage of YY1 and also to short-term YY1 deletion by tamoxifen-induced Cre. We show that YY1 exerts its effects, in part, by promoting B-cell survival and proliferation. ChIP-sequencing shows that YY1 predominantly binds to promoters, and pathway analysis of the genes that bind YY1 show enrichment in ribosomal functions, mitochondrial functions such as bioenergetics, and functions related to transcription such as mRNA splicing. By RNA-sequencing analysis of differentially expressed genes, we demonstrated that YY1 normally activates genes involved in mitochondrial bioenergetics, whereas it normally down-regulates genes involved in transcription, mRNA splicing, NF-κB signaling pathways, the AP-1 transcription factor network, chromatin remodeling, cytokine signaling pathways, cell adhesion, and cell proliferation. Our results show the crucial role that YY1 plays in regulating broad general processes throughout all stages of B-cell differentiation.YY1 | B lymphocytes | differentiation | mitochondrial bioenergetics | transcription

The primary antigen receptor repertoire is sculpted by the process of V(D)J recombination, which must strike a balance between diversification and favoring gene segments with specialized functions. The precise determinants of how often gene segments are chosen to complete variable region coding exons remain elusive. We quantified Vβ use in the preselection Tcrb repertoire and report relative contributions of 13 distinct features that may shape their recombination efficiencies, including transcription, chromatin environment, spatial proximity to their DβJβ targets, and predicted quality of recombination signal sequences (RSSs). We show that, in contrast to functional Vβ gene segments, all pseudo-Vβ segments are sequestered in transcriptionally silent chromatin, which effectively suppresses wasteful recombination. Importantly, computational analyses provide a unifying model, revealing a minimum set of five parameters that are predictive of Vβ use, dominated by chromatin modifications associated with transcription, but largely independent of precise spatial proximity to DβJβ clusters. This learned model-building strategy may be useful in predicting the relative contributions of epigenetic, spatial, and RSS features in shaping preselection V repertoires at other antigen receptor loci. Ultimately, such models may also predict how designed or naturally occurring alterations of these loci perturb the preselection use of variable gene segments.lymphocytes | T-cell receptor | gene regulation G ene activity is regulated at multiple levels to coordinate expression during development. At a most basic level, the collection of cis-acting elements for a genetic locus recruits transcription factors that alter its chromatin environment to either induce or repress gene activity. Emerging studies indicate that the 3D conformation of a locus also plays an important role in the regulation of its composite genes (1). At most genes, many levels of control are integrated to achieve the requisite gene expression state. For example, transcriptional promoters interact with their cognate enhancers over considerable distances in the linear genome to generate "hubs" where the two cis elements are in spatial proximity (1, 2).All of these regulatory strategies are used to generate functional Ig (Ig) and T-cell receptor (Tcr) genes during lymphocyte development (3). Each antigen receptor (AgR) locus is composed of multiple variable (V), joining (J), and sometimes diversity (D) gene segments that are assembled by the process of V (D)J recombination, creating a potential variable region exon (4). Recombination is mediated by the RAG-1/2 enzymatic complex, which is expressed in all developing lymphocytes and recognizes semiconserved recombination signal sequences (RSSs) flanking all AgR gene segments (5). On selection of two compatible gene segments by RAG-1/2, recombination proceeds via a DNA break/repair mechanism, ultimately fusing the two selected segments (4, 5).The assembly of AgR genes is strictly regulated despite a common collection of ge...

The advent of precision medicine for genetic diseases has been hampered by the large number of variants that cause familial and somatic disease, a complexity that is further confounded by the impact of genetic modifiers. To begin to understand differences in onset, progression and therapeutic response that exist among disease-causing variants, we present the proteomic variant approach (ProVarA), a proteomic method that integrates mass spectrometry with genomic tools to dissect the etiology of disease. To illustrate its value, we examined the impact of variation in cystic fibrosis (CF), where 2025 disease-associated mutations in the CF transmembrane conductance regulator (CFTR) gene have been annotated and where individual genotypes exhibit phenotypic heterogeneity and response to therapeutic intervention. A comparative analysis of variant-specific proteomics allows us to identify a number of protein interactions contributing to the basic defects associated with F508del- and G551D-CFTR, two of the most common disease-associated variants in the patient population. We demonstrate that a number of these causal interactions are significantly altered in response to treatment with Vx809 and Vx770, small-molecule therapeutics that respectively target the F508del and G551D variants. ProVarA represents the first comparative proteomic analysis among multiple disease-causing mutations, thereby providing a methodological approach that provides a significant advancement to existing proteomic efforts in understanding the impact of variation in CF disease. We posit that the implementation of ProVarA for any familial or somatic mutation will provide a substantial increase in the knowledge base needed to implement a precision medicine-based approach for clinical management of disease.

CCCTC-binding factor (CTCF) is largely responsible for the 3D architecture of the genome, in concert with the action of cohesin, through the creation of long-range chromatin loops. Cohesin is hypothesized to be the main driver of these long-range chromatin interactions by the process of loop extrusion. Here, we performed ChIP-seq for CTCF and cohesin in two stages each of T and B cell differentiation and examined the binding pattern in all six antigen receptor (AgR) loci in these lymphocyte progenitors and in mature T and B cells, ES cells, and fibroblasts. The four large AgR loci have many bound CTCF sites, most of which are only occupied in lymphocytes, while only the CTCF sites at the end of each locus near the enhancers or J genes tend to be bound in non-lymphoid cells also. However, despite the generalized lymphocyte restriction of CTCF binding in AgR loci, the Igκ locus is the only locus that also shows significant lineage-specificity (T vs. B cells) and developmental stage-specificity (pre-B vs. pro-B) in CTCF binding. We show that cohesin binding shows greater lineage- and stage-specificity than CTCF at most AgR loci, providing more specificity to the loops. We also show that the culture of pro-B cells in IL7, a common practice to expand the number of cells before ChIP-seq, results in a CTCF-binding pattern resembling pre-B cells, as well as other epigenetic and transcriptional characteristics of pre-B cells. Analysis of the orientation of the CTCF sites show that all sites within the large V portions of the Igh and TCRβ loci have the same orientation. This suggests either a lack of requirement for convergent CTCF sites creating loops, or indicates an absence of any loops between CTCF sites within the V region portion of those loci but only loops to the convergent sites at the D-J-enhancer end of each locus. The V region portions of the Igκ and TCRα/δ loci, by contrast, have CTCF sites in both orientations, providing many options for creating CTCF-mediated convergent loops throughout the loci. CTCF/cohesin loops, along with transcription factors, drives contraction of AgR loci to facilitate the creation of a diverse repertoire of antibodies and T cell receptors.

The β-cell protein synthetic machinery is dedicated to the production of mature insulin, which requires the proper folding and trafficking of its precursor, proinsulin. The complete network of proteins that mediate proinsulin folding and advancement through the secretory pathway, however, remains poorly defined. Here we used affinity purification and mass spectrometry to identify, for the first time, the proinsulin biosynthetic interaction network in human islets. Stringent analysis established a central node of proinsulin interactions with endoplasmic reticulum (ER) folding factors, including chaperones and oxidoreductases, that is remarkably conserved in both sexes and across three ethnicities. The ER-localized peroxiredoxin PRDX4 was identified as a prominent proinsulin-interacting protein. In β-cells, gene silencing of PRDX4 rendered proinsulin susceptible to misfolding, particularly in response to oxidative stress, while exogenous PRDX4 improved proinsulin folding. Moreover, proinsulin misfolding induced by oxidative stress or high glucose was accompanied by sulfonylation of PRDX4, a modification known to inactivate peroxiredoxins. Notably, islets from patients with type 2 diabetes (T2D) exhibited significantly higher levels of sulfonylated PRDX4 than islets from healthy individuals. In conclusion, we have generated the first reference map of the human proinsulin interactome to identify critical factors controlling insulin biosynthesis, β-cell function, and T2D.

Source code for Omics Pipe is freely available on the web (https://bitbucket.org/sulab/omics_pipe). Omics Pipe is distributed as a standalone Python package for installation (https://pypi.python.org/pypi/omics_pipe) and as an Amazon Machine Image in Amazon Web Services Elastic Compute Cloud that contains all necessary third-party software dependencies and databases (https://pythonhosted.org/omics_pipe/AWS_installation.html).