Regulatory and epigenetic landscapes in biological discovery, diagnostics and disease mechanisms

生物发现、诊断和疾病机制中的调控和表观遗传学景观

• 批准号: 10267094
• 负责人: Laura L Elnitski
• 金额: $ 189.56万
• 依托单位: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10267094
• 关键词: Aberrant DNA Methylation; Address; Affect; African; Algorithms; Anatomy; Attention deficit hyperactivity disorder; Biological; Biological Assay; Biological Markers; Blood; Brain; CA-125 Antigen; Cancer Patient; Cancer cell line; Carcinoma; Categories; Cerebellum; ChIP-seq; Characteristics; Classification; Code; Colorectal Adenocarcinoma; Complex; Computer Models; Corpus striatum structure; CpG Island Methylator Phenotype; DNA Methylation; DNA Sequence Alteration; Data; Data Set; Development; Diagnostic; Diagnostic tests; Disease; Elements; Endometrioid Tumor; Enhancers; Environment; Epigenetic Process; Epithelial; Epithelium; Esophageal Adenocarcinoma; Esophageal Neoplasms; Ethnic group; Etiology; European; Evolution; Exons; Gastric Adenocarcinoma; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Gene Mutation; Genes; Genetic Enhancer Element; Genetic Polymorphism; Genetic Transcription; Genome; Genomics; Genotype; Geographic Locations; Germ-Line Mutation; Goals; Gold; Grouping; Human; Human Genome; Hypermethylation; Individual; Industrialization; Kalahari; Location; Machine Learning; Malignant Neoplasms; Malignant neoplasm of ovary; Manuscripts; Mediating; Messenger RNA; Methods; Methylation; Modeling; Modification; Monozygotic twins; Morphologic artifacts; Mutate; Mutation; Nigeria; Nonsense Codon; Nucleic Acid Regulatory Sequences; Nucleotides; Open Reading Frames; Ovarian Endometrioid Tumor; Pathway interactions; Pattern; Phenotype; Plasma; Population; Preparation; Principal Component Analysis; Process; Production; Protein Isoforms; Proteins; Publishing; RNA Splicing; Regulation; Regulator Genes; Regulatory Element; Reporting; Repression; Research; Sampling; Screening for Ovarian Cancer; Serous; Siblings; Site; Societies; Solid; Somatic Mutation; Temperature; Testing; Thalamic structure; The Cancer Genome Atlas; Therapeutic; Training; Transcriptional Activation; Transcriptional Silencer Elements; Treatment Efficacy; Twin Multiple Birth; Untranslated RNA; Uterus; Variant; Work; Yang; base; bisulfite; blood-based biomarker; cancer genome; cancer therapy; cancer type; causal variant; computational pipelines; diagnostic biomarker; driver mutation; epigenetic profiling; epigenetic regulation; epigenome; experimental study; genome-wide; human disease; improved; insertion/deletion mutation; insight; learning classifier; melting; method development; methylation biomarker; methylation pattern; methylome; novel; predictive test; pressure; prevent; promoter; response; simulation; synergism; tool; trend; tumor; tumor DNA; tumorigenesis

Development of methods and biomarkers for blood-based detection of ovarian cancer The Elnitski lab previously reported the first methylation biomarker, ZNF154, as having pan/multi-cancer relevance (Sanchez-Vega et al. 2013) and showed that it could discriminate tumor from normal samples in simulations of dilute blood-based analysis of circulating tumor DNA (Margolin et al. 2014). Over the past several years, I have developed diagnostic markers and assessment methods that use DNA methylation at the ZNF154 locus to detect a myriad of epithelial cancers from patient plasma samples (Miller et al., manuscript submitted). Our semi-quantitative PCR assay uses melting curve temperatures to assess DNA methylation in amplified products (Miller et al. 2020, in press). The test is highly sensitive and specific for circulating tumor DNA and detects tumor DNA in plasma samples from ovarian cancer patients. As a biomarker, ZNF154 detects both serous and endometrioid subtypes of late stage ovarian cancer making it better than the current gold-standard biomarker, protein antigen CA-125. Moreover, using these complementary biomarkers in tandem for ovarian cancer screening may be a promising, more sensitive approach than can be achieved with either marker alone. Profiling epigenetic features in up to 20 solid human epithelial cancer types To potentially improve cancer treatment efficacy, I previously sought to identify attributes that subdivide tumors into more homogeneous subsets using their epigenetic landscape profiles, in contrast to classification approaches that utilize mutational signatures or gene expression patterns. Building a computational approach allowed my group to demonstrate that the CpG island methylator phenotype (CIMP) is present in 14 distinct cancer types from TCGA (the Cancer Genome Atlas) and 23 cancer cell lines (Sanchez-Vega et al. 2015). My lab also published the first report of a CIMP in ovarian endometrioid tumors, which showed consistency with altered DNA methylation seen in uterine endometrioid tumors (Kolbe et al. 2012). We have shown that CIMP tumors from different cancers have shared biological pathways and may have a common underlying etiology (Miller at el. 2016). For example, my lab identified similarities and differences among CIMP tumors from esophageal, gastric, and colorectal adenocarcinomas (Sanchez-Vega et al. 2017), which further addresses how aberrant methylation may occur in multiple tumor types. In further exploration of aberrant DNA methylation patterns, my work demonstrated that associations between cancer methylomes in 18 cancer types and somatic driver mutations illustrate the relationships between the epigenome and the genome in cancers (Chen et al. 2017). Using principal component analyses of methylation-mutation associations applied at the CpG (i.e., nucleotide) level, my labs work showed that genome-wide patterns of aberrant hypomethylation or hypermethylation were associated with a small number of somatic mutations in specific cancer types, suggesting global regulation. In contrast, site-specific methylation changes were associated with an extensive number of mutated driver genes, suggesting local regulation. This year we expanded on our previous findings by showing that DNA methylation patterns in tumor genomes are associated with distinct isoform expression patterns predictive of sites of promoter repression, intragenic exon inclusion, and 3-terminal exon inclusion (Chen and Elnitski 2019). Our discovery of these relationships allows for the interpretation of which mRNA isoform is expressed in a tumor based on the analysis of the epigenome. Profiling epigenetic features in human populations The gold standard used to detect DNA methylation, bisulfite conversion, can result in data ambiguity or interpretation errors caused by polymorphisms at CpG methylation sites. We developed the algorithm MethylToSNP, which detects characteristic patterns of nucleotides that are likely to be confounded by polymorphisms (Goncearenco et al. 2020a), in the absence of corroborating genotype data. Using this tool, we distinguished altered DNA methylation indicative of SNP sites from samples of YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent), and KhoeSan (Southern African) populations. Furthermore, in these populations we identified uncharacterized polymorphisms and determined locations in the genome that are affected by altered methylation or sequence polymorphisms, including CTCF sites and enhancers. Similarly, I have also worked to develop new analysis tools to assess methylation enrichment of ChIP-seq data and integrate it with other types of genome-wide data (Lichtenberg et al. 2017). To address the diversity of DNA methylation in individuals living in distinct environments, my lab compared the epigenomes of KhoeSan individuals from the Kalahari Desert to individuals living in similar geographical locations, but from within industrialized societies of Bantu speaking individuals (Goncearenco et al. 2020b). Between these populations, our analysis identified more than 10,000 differentially methylated sites. The top 5% of differentially methylated sites distinguished the KhoeSan samples from various ethnic groups around the world, even after the data was cleaned for any confounding artifacts. We examined the sites of altered methylation across the KhoeSan genomes (in enhancers, gene bodies, and CpG shores and shelves) and found significant underrepresentation in the expected number of changes, suggesting the presence of selective pressure maintaining the integrity of the epigenetic landscape. Our discoveries about the Kalahari Desert KhoeSan epigenome contribute to the greater understanding of human genome diversity. Insights to mechanisms of epigenetic regulation in the human genome Altered regulation of enhancers can occur through DNA methylation. Our recent study (Chen et al. 2018) on 14 pairs of monozygotic twins discordant for attention deficit hyperactivity disorder found neuroanatomic and epigenetic differences between the siblings. Despite a lack of causal gene mutations, the ADHD-affected twins had a significantly smaller volume in the striatum and thalamus and a trend toward a larger cerebellum. Our analyses found no causal germline mutations, indels, or deletions that would explain the disparate results. However, the affected twins showed significant differences in DNA methylation patterns associated with some enhancer regions of genes expressed in the altered brain anatomical structures. These findings are consistent with the idea that subtle changes, such as the loss or alteration of enhancer elements in the genome, may be associated with discrete neuroanatomical anomalies. In addition to enhancer elements, noncoding regions of the genome also carry repressive functions. Our research examined characteristics of repressive elements and trained a multivariate model of silencer elements. We validated our model results with expression-based experiments. With this combined approach we showed a statistically significant loss of gene expression was attributed to our candidate silencers (Huang et al. 2019). Finally, splicing is an important post-transcriptional regulatory process in gene expression. Using mutation data, my lab predicted mutations that affect splicing as part of the CAGI (Critical Assessment of Genome Interpretation) competition. We used a machine learning classifier and accurately identified mutations that cause aberrant mRNA splicing (Gotea et al. 2019). We also addressed mRNA splicing fidelity by building a computational pipeline to identify factors that suppress latent splicing in intronic regions. This mechanism is fundamental to all genes in the human genome, by preventing the incorporation of premature stop codons into open reading frames (manuscript in preparation).

卵巢癌血液检测方法和生物标志物的开发 Elnitski 实验室之前报道了第一个甲基化生物标志物 ZNF154，它具有泛/多种癌症相关性（Sanchez-Vega 等人，2013 年），并表明它可以在循环肿瘤 DNA 的稀释血液分析模拟中区分肿瘤和正常样本（Margolin 等人，2014 年）。在过去的几年里，我开发了诊断标记和评估方法，利用 ZNF154 位点的 DNA 甲基化来检测患者血浆样本中的多种上皮癌（Miller 等人，已提交手稿）。我们的半定量 PCR 检测使用熔解曲线温度来评估扩增产物中的 DNA 甲基化（Miller 等人，2020 年，出版中）。该测试对循环肿瘤 DNA 具有高度敏感性和特异性，可检测卵巢癌患者血浆样本中的肿瘤 DNA。作为一种生物标志物，ZNF154 可检测晚期卵巢癌的浆液性和子宫内膜样亚型，使其优于当前的金标准生物标志物蛋白抗原 CA-125。此外，串联使用这些互补的生物标志物进行卵巢癌筛查可能是一种比单独使用任一标志物更有效、更有前景的方法。 分析多达 20 种人类实体上皮癌类型的表观遗传特征 为了潜在地提高癌症治疗效果，我之前试图利用肿瘤的表观遗传图谱来识别将肿瘤细分为更同质子集的属性，这与利用突变特征或基因表达模式的分类方法不同。通过构建计算方法，我的团队证明了 CpG 岛甲基化表型 (CIMP) 存在于 TCGA（癌症基因组图谱）的 14 种不同癌症类型和 23 种癌细胞系中（Sanchez-Vega 等，2015）。我的实验室还发表了第一份关于卵巢子宫内膜样肿瘤 CIMP 的报告，该报告与子宫内膜样肿瘤中观察到的 DNA 甲基化改变一致（Kolbe 等人，2012）。 我们已经证明，来自不同癌症的 CIMP 肿瘤具有共享的生物学途径，并且可能具有共同的潜在病因（Miller at el. 2016）。例如，我的实验室确定了食管癌、胃癌和结直肠腺癌的 CIMP 肿瘤之间的相似性和差异（Sanchez-Vega et al. 2017），这进一步解决了多种肿瘤类型中可能发生异常甲基化的问题。 在对异常 DNA 甲基化模式的进一步探索中，我的工作证明 18 种癌症类型中的癌症甲基化组与体细胞驱动突变之间的关联说明了癌症中表观基因组和基因组之间的关系 (Chen et al. 2017)。通过在 CpG（即核苷酸）水平上应用甲基化突变关联的主成分分析，我的实验室工作表明，异常低甲基化或高甲基化的全基因组模式与特定癌症类型中的少量体细胞突变相关，这表明全球调控。相反，位点特异性甲基化变化与大量突变驱动基因相关，表明局部调节。今年，我们扩展了之前的发现，表明肿瘤基因组中的 DNA 甲基化模式与预测启动子抑制、基因内外显子包含和 3 末端外显子包含位点的不同亚型表达模式相关（Chen 和 Elnitski 2019）。我们对这些关系的发现使得我们可以根据表观基因组的分析来解释肿瘤中表达的 mRNA 同工型。 分析人群的表观遗传特征 用于检测 DNA 甲基化、亚硫酸氢盐转化的金标准可能会导致数据模糊或由 CpG 甲基化位点多态性引起的解释错误。我们开发了 MmethylToSNP 算法，该算法可在缺乏确凿基因型数据的情况下检测可能被多态性混淆的核苷酸特征模式（Goncearenco 等人，2020a）。使用该工具，我们从 YRI（尼日利亚伊巴丹的约鲁巴人）、CEPH（欧洲血统）和 KhoeSan（南部非洲）人群样本中区分出指示 SNP 位点的 DNA 甲基化改变。此外，在这些群体中，我们鉴定了未表征的多态性，并确定了基因组中受甲基化改变或多态性影响的位置，包括 CTCF 位点和增强子。同样，我还致力于开发新的分析工具来评估 ChIP-seq 数据的甲基化富集，并将其与其他类型的全基因组数据整合（Lichtenberg et al. 2017）。 为了解决生活在不同环境中的个体 DNA 甲基化的多样性问题，我的实验室将来自喀拉哈里沙漠的 KhoeSan 个体的表观基因组与生活在相似地理位置但来自工业化社会的班图语个体的个体进行了比较（Goncearenco 等人，2020b）。在这些人群中，我们的分析确定了超过 10,000 个差异甲基化位点。前 5% 的差异甲基化位点能够区分来自世界各地不同种族的 KhoeSan 样本，即使在数据被清除以排除任何混杂伪影之后也是如此。我们检查了 KhoeSan 基因组中甲基化改变的位点（增强子、基因体以及 CpG 海岸和架子），发现预期变化数量明显不足，表明存在选择压力，维持了表观遗传景观的完整性。我们关于卡拉哈里沙漠 KhoeSan 表观基因组的发现有助于更好地了解人类基因组多样性。 对人类基因组表观遗传调控机制的见解 DNA 甲基化可以改变增强子的调节。我们最近对 14 对患有注意力缺陷多动障碍的同卵双胞胎进行了研究（Chen 等人，2018），发现兄弟姐妹之间存在神经解剖学和表观遗传差异。尽管缺乏因果基因突变，但患有多动症的双胞胎的纹状体和丘脑体积明显较小，并且小脑有变大的趋势。我们的分析发现没有因果性种系突变、插入缺失或缺失可以解释不同的结果。然而，受影响的双胞胎在与大脑解剖结构改变中表达的某些基因增强子区域相关的 DNA 甲基化模式上表现出显着差异。这些发现与微妙的变化（例如基因组中增强子元件的丢失或改变）可能与离散的神经解剖异常有关的观点是一致的。 除了增强子元件之外，基因组的非编码区域也具有抑制功能。我们的研究检查了压制元素的特征，并训练了消音元素的多元模型。我们通过基于表达的实验验证了我们的模型结果。通过这种组合方法，我们发现统计上显着的基因表达损失归因于我们的候选沉默子（Huang et al. 2019）。 最后，剪接是基因表达中重要的转录后调控过程。作为 CAGI（基因组解释批判性评估）竞赛的一部分，我的实验室使用突变数据预测了影响剪接的突变。我们使用机器学习分类器并准确识别了导致异常 mRNA 剪接的突变（Gotea et al. 2019）。我们还通过构建计算管道来识别抑制内含子区域潜在剪接的因素，从而解决了 mRNA 剪接保真度问题。这种机制是人类基因组中所有基因的基础，它可以防止过早的终止密码子掺入开放阅读框（手稿正在准备中）。