Cancer-associated DNA Hypermethylation of Polycomb Targets Requires DNMT3A Dual Recognition of Histone H2AK119 Ubiquitination and the Nucleosome Acidic Patch.

During tumor development, promoter CpG islands (CGIs) that are normally silenced by Polycomb repressive complexes (PRCs) become DNA hypermethylated. The molecular mechanism by which de novo DNA methyltransferase(s) catalyze CpG methylation at PRC-regulated regions remains unclear. Here we report a cryo-EM structure of the DNMT3A long isoform (DNMT3A1) N-terminal region in complex with a nucleosome carrying PRC1-mediated histone H2A lysine 119 monoubiquitination (H2AK119Ub). We identify regions within the DNMT3A1 N-terminus that bind H2AK119Ub and the nucleosome acidic patch. This bidentate interaction is required for effective DNMT3A1 engagement with H2AK119Ub-modified chromatin in cells. Furthermore, aberrant redistribution of DNMT3A1 to Polycomb target genes inhibits their transcriptional activation during cell differentiation and recapitulates the cancer-associated DNA hypermethylation signature. This effect is rescued by disruption of the DNMT3A1-acidic patch interaction. Together, our analyses reveal a binding interface critical for countering promoter CGI DNA hypermethylation, a major molecular hallmark of cancer.

SMARCAL1 is a dual regulator of innate immune signaling and PD-L1 expression that promotes tumor immune evasion.

Genomic instability can trigger cancer-intrinsic innate immune responses that promote tumor rejection. However, cancer cells often evade these responses by overexpressing immune checkpoint regulators, such as PD-L1. Here, we identify the SNF2-family DNA translocase SMARCAL1 as a factor that favors tumor immune evasion by a dual mechanism involving both the suppression of innate immune signaling and the induction of PD-L1-mediated immune checkpoint responses. Mechanistically, SMARCAL1 limits endogenous DNA damage, thereby suppressing cGAS-STING-dependent signaling during cancer cell growth. Simultaneously, it cooperates with the AP-1 family member JUN to maintain chromatin accessibility at a PD-L1 transcriptional regulatory element, thereby promoting PD-L1 expression in cancer cells. SMARCAL1 loss hinders the ability of tumor cells to induce PD-L1 in response to genomic instability, enhances anti-tumor immune responses and sensitizes tumors to immune checkpoint blockade in a mouse melanoma model. Collectively, these studies uncover SMARCAL1 as a promising target for cancer immunotherapy.

Structural basis of histone H2A lysine 119 deubiquitination by Polycomb repressive deubiquitinase BAP1/ASXL1.

Histone H2A lysine 119 (H2AK119Ub) is monoubiquitinated by Polycomb repressive complex 1 and deubiquitinated by Polycomb repressive deubiquitinase complex (PR-DUB). PR-DUB cleaves H2AK119Ub to restrict focal H2AK119Ub at Polycomb target sites and to protect active genes from aberrant silencing. The PR-DUB subunits (BAP1 and ASXL1) are among the most frequently mutated epigenetic factors in human cancers. How PR-DUB establishes specificity for H2AK119Ub over other nucleosomal ubiquitination sites and how disease-associated mutations of the enzyme affect activity are unclear. Here, we determine a cryo-EM structure of human BAP1 and the ASXL1 DEUBAD in complex with a H2AK119Ub nucleosome. Our structural, biochemical, and cellular data reveal the molecular interactions of BAP1 and ASXL1 with histones and DNA that are critical for restructuring the nucleosome and thus establishing specificity for H2AK119Ub. These results further provide a molecular explanation for how >50 mutations in BAP1 and ASXL1 found in cancer can dysregulate H2AK119Ub deubiquitination, providing insight into understanding cancer etiology.

Structural basis of histone H2A lysine 119 deubiquitination by Polycomb Repressive Deubiquitinase BAP1/ASXL1.

The maintenance of gene expression patterns during metazoan development is achieved by the actions of Polycomb group (PcG) complexes. An essential modification marking silenced genes is monoubiquitination of histone H2A lysine 119 (H2AK119Ub) deposited by the E3 ubiquitin ligase activity of the non-canonical Polycomb Repressive Complex 1. The Polycomb Repressive Deubiquitinase (PR-DUB) complex cleaves monoubiquitin from histone H2A lysine 119 (H2AK119Ub) to restrict focal H2AK119Ub at Polycomb target sites and to protect active genes from aberrant silencing. BAP1 and ASXL1, subunits that form active PR-DUB, are among the most frequently mutated epigenetic factors in human cancers, underscoring their biological importance. How PR-DUB achieves specificity for H2AK119Ub to regulate Polycomb silencing is unknown, and the mechanisms of most of the mutations in BAP1 and ASXL1 found in cancer have not been established. Here we determine a cryo-EM structure of human BAP1 bound to the ASXL1 DEUBAD domain in complex with a H2AK119Ub nucleosome. Our structural, biochemical, and cellular data reveal the molecular interactions of BAP1 and ASXL1 with histones and DNA that are critical for remodeling the nucleosome and thus establishing specificity for H2AK119Ub. These results further provide a molecular explanation for how >50 mutations in BAP1 and ASXL1 found in cancer can dysregulate H2AK119Ub deubiquitination, providing new insight into understanding cancer etiology. One Sentence Summary: We reveal the molecular mechanism of nucleosomal H2AK119Ub deubiquitination by human BAP1/ASXL1.

Identification of condition-specific biomarker systems in uterine cancer.

Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

Exploration into biomarker potential of region-specific brain gene co-expression networks.

The human brain is a complex organ that consists of several regions each with a unique gene expression pattern. Our intent in this study was to construct a gene co-expression network (GCN) for the normal brain using RNA expression profiles from the Genotype-Tissue Expression (GTEx) project. The brain GCN contains gene correlation relationships that are broadly present in the brain or specific to thirteen brain regions, which we later combined into six overarching brain mini-GCNs based on the brain's structure. Using the expression profiles of brain region-specific GCN edges, we determined how well the brain region samples could be discriminated from each other, visually with t-SNE plots or quantitatively with the Gene Oracle deep learning classifier. Next, we tested these gene sets on their relevance to human tumors of brain and non-brain origin. Interestingly, we found that genes in the six brain mini-GCNs showed markedly higher mutation rates in tumors relative to matched sets of random genes. Further, we found that cortex genes subdivided Head and Neck Squamous Cell Carcinoma (HNSC) tumors and Pheochromocytoma and Paraganglioma (PCPG) tumors into distinct groups. The brain GCN and mini-GCNs are useful resources for the classification of brain regions and identification of biomarker genes for brain related phenotypes.