Chromatin landscapes of retroviral and transposon integration profiles.
Chromatin landscapes of retroviral and transposon integration profiles.
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DOI：
10.1371/journal.pgen.1004250
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发表时间：
2014-04
期刊：
影响因子：
4.5
通讯作者：
de Ridder J
中科院分区：
文献类型：
--
作者：
de Jong J;Akhtar W;Badhai J;Rust AG;Rad R;Hilkens J;Berns A;van Lohuizen M;Wessels LF;de Ridder J
逆转录病毒和转座子将其遗传物质插入宿主 DNA 的能力使其成为分子生物学、癌症研究和基因治疗中广泛使用的工具。然而，这些系统存在偏差，可能会严重影响研究结果。为了解决这个问题，我们生成了非常大的数据集，其中包括睡美人（SB）和piggyBac（PB）转座子以及小鼠乳腺肿瘤病毒（MMTV）在小鼠基因组中未选择的整合。我们分析了（表观）基因组特征，以生成局部和全基因组尺度的偏差图。 MMTV 显示整个基因组中整合的分布非常均匀。观察到两种转座子有更明显的偏好，其中 PB 显示出与鼠白血病病毒的偏好特征显着相似。此外，我们提出了一个模型，其中目标站点选择针对多个尺度。在大规模上，目标位点选择在不同系统中是相似的，并由面向域的特征定义，即近端基因的表达、与 CpG 岛和基因特征的接近度、染色质压缩和复制计时。系统之间的显着差异主要是在较小的尺度上观察到的，并且是由各种特征决定的。为了研究这些偏差对选择压力下占据的整合位点的影响，我们转向插入诱变（IM）筛选。在 IM 筛查中，通过查找频繁靶向的基因组区域或通用整合位点 (CIS) 来识别假定的癌症基因。在最近完成的三项 IM 筛查中，我们发现了 7%–33% 的假定假阳性 CIS，这可能不是致癌选择过程的结果。此外，结果表明，与 SB 相比，PB 更适合标记癌基因。逆转录病毒和转座子广泛应用于癌症研究和基因治疗。然而，这些系统表现出的集成偏差可能会严重影响结果。为了解决这个问题，我们生成了非常大的数据集，其中包括睡美人、piggyBac 转座子以及小鼠乳腺肿瘤病毒 (MMTV) 的未选定集成。我们分析了（表观）基因组特征，以生成局部和全基因组尺度的偏差图。 MMTV 在整个基因组中显示出非常均匀的整合分布，并且在piggyBac 和鼠白血病病毒之间观察到惊人的相似性。此外，我们发现目标站点选择是针对多个尺度的。在更大的尺度上，它在各个系统之间是相似的，并且由一组面向域的特征指导，包括染色质压缩、复制计时和 CpG 岛。系统之间的显着差异是通过各种表观遗传特征在较小的尺度上定义的。作为我们研究结果的实际应用，我们确定最近的三个插入诱变筛选（通常用于癌症基因发现）包含 7%–33% 的假定假阳性整合热点。
The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of to unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%–33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes. Retroviruses and transposons are widely used in cancer research and gene therapy. However, these systems show integration biases that may strongly affect results. To address this issue, we generated very large datasets consisting of to unselected integrations for the Sleeping Beauty and piggyBac transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (epi)genomic features to generate bias maps at local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome, and a striking similarity was observed between piggyBac and the Murine Leukemia Virus. Moreover, we find that target site selection is directed at multiple scales. At larger scales, it is similar across systems, and directed by a set of domain-oriented features, including chromatin compaction, replication timing, and CpG islands. Notable differences between systems are defined at smaller scales by a diverse range of epigenetic features. As a practical application of our findings, we determined that three recent insertional mutagenesis screens - commonly used for cancer gene discovery - contained 7%–33% putative false positive integration hotspots.
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