Enhancer Sequence Variants and Transcription-Factor Deregulation Synergize to Construct Pathogenic Regulatory Circuits in B-Cell Lymphoma

Abstract

Summary Most B cell lymphomas arise in the germinal center (GC), where humoral immune responses evolve from potentially oncogenic cycles of mutation, proliferation, and clonal selection. Although lymphoma gene expression diverges significantly from GC-B cells, underlying mechanisms that alter the activities of corresponding regulatory elements (REs) remain elusive. Here we define the complete pathogenic circuitry of human follicular lymphoma (FL), which activates or decommissions REs from normal GC-B cells and commandeers enhancers from other lineages. Moreover, independent sets of transcription factors, whose expression was deregulated in FL, targeted commandeered versus decommissioned REs. Our approach revealed two distinct subtypes of low-grade FL, whose pathogenic circuitries resembled GC-B or activated B cells. FL-altered enhancers also were enriched for sequence variants, including somatic mutations, which disrupt transcription factor binding and expression of circuit-linked genes. Thus, the pathogenic regulatory circuitry of FL reveals distinct genetic and epigenetic etiologies for GC-B transformation.

Suppression of Enhancer Overactivation by a RACK7-Histone Demethylase Complex

“…Interestingly, we did not observe overt changes of transcript levels driven by the top 2,000 RACK7 bound promoters in the RACK7 null cells (Figure S2J), suggesting RACK7 functions mainly at enhancers. Using an established, enhancer activity reporter assay (Koues et al, 2015; Outchkourov et al, 2013), we were able to confirm that 10 out of 10 candidate active enhancers bound by RACK7 and KDM5C indeed possess enhancer activities and most, if not all, of them are regulated by RACK7 and KDM5C (Figure S3). Together with the finding discussed in Figure 3, we conclude that RACK7 or KDM5C loss both lead to hyper-activation of these enhancers and higher transcriptional activities.…”

Epigenetic Dysregulation in Follicular Lymphoma

“…This approach led to the identification of genes with altered expression as a consequence of disrupted TF binding including several with known key roles in lymphomagenesis. Enhancer profiling by Koues et al in FL demonstrated that FL tumors not only suppress specific enhancers so as to shed nonessential components of the GC transcriptional circuitry but additionally hijacked critical enhancers from other lineages to acquire specific advantageous tumorigenic functions [66]. Taken together, these recent studies highlight the contribution of the noncoding genome future science group Epigenetic dysregulation in follicular lymphoma Special Report and earmark the importance of this exciting new chapter in FL pathogenesis.…”

ChIP-seq in Studying Epigenetic Mechanisms of Disease and Promoting Precision Medicine: Progresses and Future Directions

“…For example, many disease-associated SNPs identified by genome-wide association studies are in the enhancer regions [44,51]. They can abolish known or create novelbinding motifs of transcription factors, thus altering their binding to enhancers [49,65]. Using ChIP-seq of H3K27ac and GATA3 transcription factor, Oldridge et al found that the risk SNP rs2168101 associated with neuroblastoma overlapped the GATA3-binding motif within the enhancer of LMO1 oncogene.…”

“…Consistency of narrow peaks called from replicates can be assessed using the irreproducible discovery rate, by which high-quality peaks can be extracted at a user-defined irreproducible discovery rate cutoff (like 1%) [2]. The functionality of peaks can be validated through chromatin interaction analysis by paired-end tag sequencing or chromosome conformation capture (3C)-based chromatin interaction mapping methods [25,86], reporter assays [16,49,86] or using genomeediting tools like CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 [86]. Three major aspects of data analysis are critical for future success of ChIP-seq applications in the clinic.…”

“…The ideal approach would be to generate ChIP-seq data from both diseased and normal tissues of the same individual at the same time [88], which will bypass biases and help identify disease specific epigenetic signature. Quite often, normal epigenome reference is generated using tissues from normal individuals [13,44,[49][50]54]. In both scenarios, the question remains to be answered is how many patients and normal controls are needed in order to confidently identify the disease epigenetic signature.…”