Super-enhancer driven SOX2 promotes tumor formation by chromatin re-organization in nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a malignant head and neck cancer with a high incidence in Southern China and Southeast Asia. Patients with remote metastasis and recurrent NPC have poor prognosis. Thus, a better understanding of NPC pathogenesis may identify novel therapies to address the unmet clinical needs. METHODS: H3K27ac ChIP-seq and HiChIP was applied to understand the enhancer landscapes and the chromosome interactions. Whole genome sequencing was conducted to analyze the relationship between genomic variations and epigenetic dysregulation. CRISPRi and JQ1 treatment were used to evaluate the transcriptional regulation of SOX2 SEs. Colony formation assay, survival analysis and in vivo subcutaneous patient-derived xenograft assays were applied to explore the function and clinical relevance of SOX2 in NPC. FINDINGS: We globally mapped the enhancer landscapes and generated NPC enhancer connectomes, linking NPC specific enhancers and SEs. We found five overlapped genes, including SOX2, among super-enhancer regulated genes, survival related genes and NPC essential genes. The mRNA expression of SOX2 was repressed when applying CRISPRi targeting different SOX2 SEs or JQ1 treatment. Next, we identified a genetic variation (Chr3:181422197, G > A) in SOX2 SE which is correlated with higher expression of SOX2 and poor survival. In addition, SOX2 was highly expressed in NPC and is correlated with short survival in patients with NPC. Knock-down of SOX2 suppressed tumor growth in vitro and in vivo. INTERPRETATION: Our study demonstrated the super-enhancer landscape with chromosome interactions and identified super-enhancer driven SOX2 promotes tumorigenesis, suggesting that SOX2 is a potential therapeutic target for patients with NPC. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

A DNA tumor virus globally reprograms host 3D genome architecture to achieve immortal growth.

Epstein-Barr virus (EBV) immortalization of resting B lymphocytes (RBLs) to lymphoblastoid cell lines (LCLs) models human DNA tumor virus oncogenesis. RBL and LCL chromatin interaction maps are compared to identify the spatial and temporal genome architectural changes during EBV B cell transformation. EBV induces global genome reorganization where contact domains frequently merge or subdivide during transformation. Repressed B compartments in RBLs frequently switch to active A compartments in LCLs. LCLs gain 40% new contact domain boundaries. Newly gained LCL boundaries have strong CTCF binding at their borders while in RBLs, the same sites have much less CTCF binding. Some LCL CTCF sites also have EBV nuclear antigen (EBNA) leader protein EBNALP binding. LCLs have more local interactions than RBLs at LCL dependency factors and super-enhancer targets. RNA Pol II HiChIP and FISH of RBL and LCL further validate the Hi-C results. EBNA3A inactivation globally alters LCL genome interactions. EBNA3A inactivation reduces CTCF and RAD21 DNA binding. EBNA3C inactivation rewires the looping at the CDKN2A/B and AICDA loci. Disruption of a CTCF site at AICDA locus increases AICDA expression. These data suggest that EBV controls lymphocyte growth by globally reorganizing host genome architecture to facilitate the expression of key oncogenes.

The Epstein-Barr Virus Enhancer Interaction Landscapes in Virus-Associated Cancer Cell Lines.

Epstein-Barr virus (EBV) persists in human cells as episomes. EBV episomes are chromatinized and their 3D conformation varies greatly in cells expressing different latency genes. We used HiChIP, an assay which combines genome-wide chromatin conformation capture followed by deep sequencing (Hi-C) and chromatin immunoprecipitation (ChIP), to interrogate the EBV episome 3D conformation in different cancer cell lines. In an EBV-transformed lymphoblastoid cell line (LCL) GM12878 expressing type III EBV latency genes, abundant genomic interactions were identified by H3K27ac HiChIP. A strong enhancer was located near the BILF2 gene and looped to multiple genes around BALFs loci. Perturbation of the BILF2 enhancer by CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) altered the expression of BILF2 enhancer-linked genes, including BARF0 and BALF2, suggesting that this enhancer regulates the expression of linked genes. H3K27ac ChIP followed by deep sequencing (ChIP-seq) identified several strong EBV enhancers in T/NK (natural killer) lymphoma cells that express type II EBV latency genes. Extensive intragenomic interactions were also found which linked enhancers to target genes. A strong enhancer at BILF2 also looped to the BALF loci. CRISPRi also validated the functional connection between BILF2 enhancer and BARF1 gene. In contrast, H3K27ac HiChIP found significantly fewer intragenomic interactions in type I EBV latency gene-expressing primary effusion lymphoma (PEL) cell lines. These data provided new insight into the regulation of EBV latency gene expression in different EBV-associated tumors. IMPORTANCE EBV is the first human DNA tumor virus identified, discovered over 50 years ago. EBV causes ~200,000 cases of various cancers each year. EBV-encoded oncogenes, noncoding RNAs, and microRNAs (miRNAs) can promote cell growth and survival and suppress senescence. Regulation of EBV gene expression is very complex. The viral C promoter regulates the expression of all EBV nuclear antigens (EBNAs), some of which are very far away from the C promoter. Another way by which the virus activates remote gene expression is through DNA looping. In this study, we describe the viral genome looping patterns in various EBV-associated cancer cell lines and identify important EBV enhancers in these cells. This study also identified novel opportunities to perturb and eventually control EBV gene expression in these cancer cells.

Comparison of scRNA-seq data analysis method combinations.

Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data analysis refers to the use of appropriate methods to analyze the dataset generated by RNA-sequencing performed on the single-cell transcriptome. It usually contains three steps: normalization to eliminate the technical noise, dimensionality reduction to facilitate visual understanding and data compression and clustering to divide the data into several similarity-based clusters. In addition, the gene expression data contain a large number of zero counts. These zero counts are considered relevant to random dropout events induced by multiple factors in the sequencing experiments, such as low RNA input, and the stochastic nature of the gene expression pattern at the single-cell level. The zero counts can be eliminated only through the analysis of the scRNA-seq data, and although many methods have been proposed to this end, there is still a lack of research on the combined effect of existing methods. In this paper, we summarize the two kinds of normalization, two kinds of dimension reduction and three kinds of clustering methods widely used in the current mainstream scRNA-seq data analysis. Furthermore, we propose to combine these methods into 12 technology combinations, each with a whole set of scRNA-seq data analysis processes. We evaluated the proposed combinations using Goolam, a publicly available scRNA-seq, by comparing the final clustering results and found the most suitable collection scheme of these classic methods. Our results showed that using appropriate technology combinations can improve the efficiency and accuracy of the scRNA-seq data analysis. The combinations not only satisfy the basic requirements of noise reduction, dimension reduction and cell clustering but also ensure preserving the heterogeneity of cells in downstream analysis. The dataset, Goolam, used in the study can be obtained from the ArrayExpress database under the accession number E-MTAB-3321.

Meta-Analysis of Efficacy From CTLA-4 and PD-1/PD-L1 Inhibitors in Cancer Patients.

Introduction: Immune checkpoint inhibitors (ICIs) have been approved to prolong overall survival (OS), compared to other treatments. However, the recent studies reported consistent and inconsistent results. Hence, we conducted this meta-analysis to evaluate the efficacy of ICIs. Materials and Methods: The articles were identified by searching PubMed, Embase, and Google Scholar published up to December 2021. A total of 12,126 participants (6,450 cases and 5,676 controls) were involved in the meta-analysis. Median OS and median progression-free survival (PFS) were selected to evaluate the efficacy of cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed death ligand 1 (PD-L1) inhibitors (ipilimumab, nivolumab or pembrolizumab, and atezolizumab, respectively). Utilizing the random-effect model, hazard ratios (HRs) with 95 confidence intervals (CIs) were calculated by R software. Results: We observed a significant association between cancer patients and ICIs in OS (HR = 0.79, CI = 0.74-0.84) and PFS (HR = 0.80, CI = 0.75-0.86). Conclusions: The meta-analysis suggested that ICIs were associated with obvious improvements in PFS and OS compared with non-ICI therapies.

Primary effusion lymphoma enhancer connectome links super-enhancers to dependency factors.

Primary effusion lymphoma (PEL) has a very poor prognosis. To evaluate the contributions of enhancers/promoters interactions to PEL cell growth and survival, here we produce H3K27ac HiChIP datasets in PEL cells. This allows us to generate the PEL enhancer connectome, which links enhancers and promoters in PEL genome-wide. We identify more than 8000 genomic interactions in each PEL cell line. By incorporating HiChIP data with H3K27ac ChIP-seq data, we identify interactions between enhancers/enhancers, enhancers/promoters, and promoters/promoters. HiChIP further links PEL super-enhancers to PEL dependency factors MYC, IRF4, MCL1, CCND2, MDM2, and CFLAR. CRISPR knock out of MEF2C and IRF4 significantly reduces MYC and IRF4 super-enhancer H3K27ac signal. Knock out also reduces MYC and IRF4 expression. CRISPRi perturbation of these super-enhancers by tethering transcription repressors to enhancers significantly reduces target gene expression and reduces PEL cell growth. These data provide insights into PEL molecular pathogenesis.

Meta-analysis of association between TCF7L2 polymorphism rs7903146 and type 2 diabetes mellitus.

BACKGROUND: Large scale association studies have found a significant association between type 2 diabetes mellitus (T2DM) and transcription factor 7-like 2 (TCF7L2) polymorphism rs7903146. However, the quality of data varies greatly, as the studies report inconsistent results in different populations. Hence, we perform this meta-analysis to give a more convincing result. METHODS: The articles, published from January 1st, 2000 to April 1st, 2017, were identified by searching in PubMed and Google Scholar. A total of 56628 participants (34232 cases and 22396 controls) were included in the meta-analysis. A total of 28 studies were divided into 4 subgroups: Caucasian (10 studies), East Asian (5 studies), South Asian (5 studies) and Others (8 studies). All the data analyses were analyzed by the R package meta. RESULTS: The significant association was observed by using the dominant model (OR = 1.41, CI = 1.36 - 1.47, p < 0.0001), recessive model (OR = 1.58, CI = 1.48 - 1.69, p < 0.0001), additive model(CT vs CC) (OR = 1.34, CI = 1.28-1.39, p < 0.0001), additive model(TT vs CC) (OR = 1.81, CI = 1.69-1.94, p < 0.0001)and allele model (OR = 1.35, CI = 1.31-1.39, p < 0.0001). CONCLUSION: The meta-analysis suggested that rs7903146 was significantly associated with T2DM in Caucasian, East Asian, South Asian and other ethnicities.