Modeling epigenetic lesions that cause gliomas.

Epigenetic lesions that disrupt regulatory elements represent potential cancer drivers. However, we lack experimental models for validating their tumorigenic impact. Here, we model aberrations arising in isocitrate dehydrogenase-mutant gliomas, which exhibit DNA hypermethylation. We focus on a CTCF insulator near the PDGFRA oncogene that is recurrently disrupted by methylation in these tumors. We demonstrate that disruption of the syntenic insulator in mouse oligodendrocyte progenitor cells (OPCs) allows an OPC-specific enhancer to contact and induce Pdgfra, thereby increasing proliferation. We show that a second lesion, methylation-dependent silencing of the Cdkn2a tumor suppressor, cooperates with insulator loss in OPCs. Coordinate inactivation of the Pdgfra insulator and Cdkn2a drives gliomagenesis in vivo. Despite locus synteny, the insulator is CpG-rich only in humans, a feature that may confer human glioma risk but complicates mouse modeling. Our study demonstrates the capacity of recurrent epigenetic lesions to drive OPC proliferation in vitro and gliomagenesis in vivo.

Integrative dissection of gene regulatory elements at base resolution.

Although vast numbers of putative gene regulatory elements have been cataloged, the sequence motifs and individual bases that underlie their functions remain largely unknown. Here, we combine epigenetic perturbations, base editing, and deep learning to dissect regulatory sequences within the exemplar immune locus encoding CD69. We converge on a ∼170 base interval within a differentially accessible and acetylated enhancer critical for CD69 induction in stimulated Jurkat T cells. Individual C-to-T base edits within the interval markedly reduce element accessibility and acetylation, with corresponding reduction of CD69 expression. The most potent base edits may be explained by their effect on regulatory interactions between the transcriptional activators GATA3 and TAL1 and the repressor BHLHE40. Systematic analysis suggests that the interplay between GATA3 and BHLHE40 plays a general role in rapid T cell transcriptional responses. Our study provides a framework for parsing regulatory elements in their endogenous chromatin contexts and identifying operative artificial variants.

Detecting sample swaps in diverse NGS data types using linkage disequilibrium.

As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing ~1% of ENCODE datasets.

Altered chromosomal topology drives oncogenic programs in SDH-deficient GISTs.

Epigenetic aberrations are widespread in cancer, yet the underlying mechanisms and causality remain poorly understood1-3. A subset of gastrointestinal stromal tumours (GISTs) lack canonical kinase mutations but instead have succinate dehydrogenase (SDH) deficiency and global DNA hyper-methylation4,5. Here, we associate this hyper-methylation with changes in genome topology that activate oncogenic programs. To investigate epigenetic alterations systematically, we mapped DNA methylation, CTCF insulators, enhancers, and chromosome topology in KIT-mutant, PDGFRA-mutant and SDH-deficient GISTs. Although these respective subtypes shared similar enhancer landscapes, we identified hundreds of putative insulators where DNA methylation replaced CTCF binding in SDH-deficient GISTs. We focused on a disrupted insulator that normally partitions a core GIST super-enhancer from the FGF4 oncogene. Recurrent loss of this insulator alters locus topology in SDH-deficient GISTs, allowing aberrant physical interaction between enhancer and oncogene. CRISPR-mediated excision of the corresponding CTCF motifs in an SDH-intact GIST model disrupted the boundary between enhancer and oncogene, and strongly upregulated FGF4 expression. We also identified a second recurrent insulator loss event near the KIT oncogene, which is also highly expressed across SDH-deficient GISTs. Finally, we established a patient-derived xenograft (PDX) from an SDH-deficient GIST that faithfully maintains the epigenetics of the parental tumour, including hypermethylation and insulator defects. This PDX model is highly sensitive to FGF receptor (FGFR) inhibition, and more so to combined FGFR and KIT inhibition, validating the functional significance of the underlying epigenetic lesions. Our study reveals how epigenetic alterations can drive oncogenic programs in the absence of canonical kinase mutations, with implications for mechanistic targeting of aberrant pathways in cancers.

A statistical model for improved membrane protein expression using sequence-derived features.

The heterologous expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino acid sequence for a given IMP can dramatically affect expression levels, yet these observations have not resulted in generalizable approaches to improve expression levels. Here, we develop a data-driven statistical predictor named IMProve that, using only sequence information, increases the likelihood of selecting an IMP that expresses in Escherichia coli The IMProve model, trained on experimental data, combines a set of sequence-derived features resulting in an IMProve score, where higher values have a higher probability of success. The model is rigorously validated against a variety of independent data sets that contain a wide range of experimental outcomes from various IMP expression trials. The results demonstrate that use of the model can more than double the number of successfully expressed targets at any experimental scale. IMProve can immediately be used to identify favorable targets for characterization. Most notably, IMProve demonstrates for the first time that IMP expression levels can be predicted directly from sequence.