Error-suppressed whole genome sequencing for genotoxicant-induced structural variant detection

项目摘要 结构变异(SVS)是人类基因改变的一类重要类型，它会在 单个突变事件中的基因组含量。拷贝数变体(CNV)会更改 几千到几百万个DNA碱基对，可能包括多个基因，而拷贝数是中性的 倒位和易位导致基因融合和表观遗传控制机制的失调。SVS和 CNV是癌症的关键驱动因素，癌症是构成生殖系遗传病的主要机制，并且 越来越被认为是一种正在进行的影响组织功能的体细胞嵌合体形式。 SVS与单核苷酸变异体(SNV)受到相同类别的风险因素的影响。这些因素， 导致终生突变负担的个体间差异，从而导致疾病风险，包括遗传性 基于DNA修复能力和环境暴露于致突变遗传毒物的易感性。 导致染色体断裂或断裂的基因毒物被称为碎裂剂。重要的是，化验 通常用于鉴定裂解菌或双链断裂(DSB)诱导的基因毒物是非特异性的和 未能揭示特定的药物是否会导致细胞中新的、稳定的SV连接，如果是，性质和 这些结点的分布是。有大量的需求能够有效地通过 明确、积极的断点连接读数，这是毒物学家感兴趣的分子终点。这样的一种 作为研究镶嵌细胞群体的一种方法，化验将在基因组科学中产生很大的影响 病毒突变，包括在危险识别和评估研究中，以及在 癌症进化和异质性、组织体细胞嵌合体和DSB修复机制。 SNV进一步证明了误差最小化的高通量测序对于 突变检测和遗传毒物表征。在双链测序的领导下，这些方法揭示了 通过最大限度地提高信噪比，使基准和诱导SNV频率具有极高的灵敏度。然而， 以前关于误差最小化排序的工作在很大程度上局限于SNV。我们的数据证实了 方法不解决导致错误SV检测的不同错误机制。我们的理由是 为解决产生SV伪影的过程而设计的误差最小化排序方法将是 双链测序对SVS的影响与对SNV的影响一样。这个R21项目将设计、开发和 验证这种方法，并开始将其应用于重要的遗传毒理学范例，目的如下： (1)开发用于错误抑制的、非靶向SV连接序列的健壮的svWGS协议；以及(2) 使用控制良好、具有翻译潜力的高价值案例示例验证svWGS。我们的长期合作 目的是将svWGS应用于新出现的候选人类裂解原的详细表征。

Project Summary Structural variants (SVs) are an important class of human genetic alteration that creates large changes in genomic content in single mutational events. Copy number variants (CNVs) change the representation of thousands to millions of DNA base pairs, potentially including multiple genes, whereas copy number neutral inversions and translocations lead to gene fusions and dysregulation of epigenetic control mechanisms. SVs and CNVs are critical drivers of cancer, a primary mechanism of constitutional germline genetic disease, and are increasingly appreciated as an ongoing form of somatic mosaicism affecting the function of tissues. SVs are subject to the same classes of risk factors as single-nucleotide variants (SNVs). These factors, which lead to inter-individual variation in lifetime mutation burden and therefore disease risk, include inherited predispositions based on DNA repair capacity and environmental exposures to mutagenic genotoxicants. Genotoxicants that induce disruption or breakage of chromosomes are known as clastogens. Importantly, assays commonly used to identify clastogens or double-strand break (DSB)-inducing genotoxicants are non-specific and fail to reveal whether specific agents lead to new, stable SV junctions in cells and, if so, what the nature and distribution of those junctions is. There is a large need for assays that can efficiently reveal SV mutagens via definitive, positive readouts of breakpoint junctions, the molecular endpoint of interest to toxicologists. Such an assay would be highly impactful in the genome sciences as a way of surveying populations of cells for mosaic SV mutations, including in studies of hazard identification and evaluation, as well as in basic science studies of cancer evolution and heterogeneity, tissue somatic mosaicism, and DSB repair mechanisms. SNVs have further demonstrated the great value of error-minimized, high-throughput sequencing for mutation detection and genotoxicant characterization. Led by Duplex Sequencing, such approaches reveal baseline and induced SNV frequencies with exquisite sensitivity by maximizing signal-to-noise ratios. However, prior work on error-minimized sequencing has been largely limited to SNVs. Our data confirm that the approaches do not address the distinct error mechanisms that lead to false SV detections. Our rationale is that an error-minimized sequencing method designed to address the processes that give rise to SV artifacts will be as impactful for SVs as Duplex Sequencing has been for SNVs. This R21 project will devise, develop, and validate such a method and begin to apply it to important genetic toxicology paradigms, with the following aims: (1) Develop a robust svWGS protocol for error-suppressed, non-targeted SV junction sequencing; and (2) Validate svWGS using well-controlled, high-value case examples with translational potential. Our long-term objective is to apply svWGS in detailed characterizations of emerging candidate human clastogens.