Bearer Elaine L, Mulligan Brianna S
1Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM87131, USA; 2Department of Computer Science, University of New Mexico, Albuquerque, NM87131, USA.
Curr Genomics. 2018 Dec;19(8):676-698. doi: 10.2174/1389202919666180307150508.
Adverse Childhood Experiences (ACEs), which include traumatic injury, are associated with poor health outcomes in later life, yet the biological mechanisms mediating this association are unknown. Neurocircuitry, immune system and hormone regulation differ from normal in adults reporting ACEs. These systems could be affected by epigenetic changes, including methylation of cytosine (5mC) in genomic DNA, activated by ACEs. Since 5mC levels influence gene expression and can be long-lasting, altered 5mC status at specific sites or throughout the genome is hypothesized to influence mental and physical outcomes after ACE(s). Human and animal studies support this, with animal models allowing experiments for attributing causality. Here we provide a lengthy introduction and background on 5mC and the impact of early life adversity.
Next we address the issue of a mixture of cell types in saliva, the most accessible biospecimen for 5mC analysis. Typical human bio-specimens for 5mC analysis include saliva or buccal swabs, whole blood or types of blood cells, tumors and post-mortem brain. In children saliva is the most accessible biospecimen, but contains a mixture of keratinocytes and white blood cells, as do buccal swabs. Even in saliva from the same individual at different time points, cell composition may differ widely. Similar issues affect analysis in blood, where nucleated cells represent a wide array of white blood cell types. Unless variations in ratios of these cells between each sample are included in the analysis, results can be unreliable.
Several different biochemical assays are available to test for site-specific methylation levels genome-wide, each producing different information, with high-density arrays being the easiest to use, and bisulfite whole genome sequencing the most comprehensive. We compare results from different assays and use high-throughput computational processing to deconvolve cell composition in saliva samples.
Here we present examples demonstrating the critical importance of determining the relative contribution of blood cells versus keratinocytes to the 5mC profile found in saliva. We further describe a strategy to perform a reference-based computational correction for cell composition, and therefore to identify differential methylation patterns due to experience, or for the diagnosis of phenotypes that correlate between traits, such as hormone levels, trauma status and various mental health outcomes.
Specific sites that respond to adversity with altered methylation levels in either blood cells, keratinocytes or both can be identified by this rigorous approach, which will then be useful as diagnostic biomarkers and therapeutic targets.
童年不良经历（ACEs），其中包括创伤性损伤，与晚年的不良健康结果相关，但介导这种关联的生物学机制尚不清楚。报告有ACEs的成年人的神经回路、免疫系统和激素调节与正常情况不同。这些系统可能会受到表观遗传变化的影响，包括基因组DNA中胞嘧啶（5mC）的甲基化，而ACEs会激活这种变化。由于5mC水平会影响基因表达，并且可能是持久的，因此推测特定位点或整个基因组中5mC状态的改变会影响ACEs后的心理和生理结果。人类和动物研究支持这一点，动物模型允许进行因果关系实验。在这里，我们提供了关于5mC以及早期生活逆境影响的详细介绍和背景。
接下来，我们讨论唾液中细胞类型混合的问题，唾液是用于5mC分析的最容易获取的生物样本。用于5mC分析的典型人类生物样本包括唾液或口腔拭子、全血或血细胞类型、肿瘤和死后大脑。在儿童中，唾液是最容易获取的生物样本，但它包含角质形成细胞和白细胞的混合物，口腔拭子也是如此。即使在同一个体不同时间点的唾液中，细胞组成也可能有很大差异。类似的问题也会影响血液分析，其中有核细胞代表了多种白细胞类型。除非在分析中考虑每个样本中这些细胞比例的变化，否则结果可能不可靠。
有几种不同的生化检测方法可用于全基因组范围内检测位点特异性甲基化水平，每种方法产生不同的信息，高密度阵列最易于使用，亚硫酸氢盐全基因组测序最全面。我们比较不同检测方法的结果，并使用高通量计算处理来解卷积唾液样本中的细胞组成。
在这里，我们给出了一些例子，证明了确定血细胞与角质形成细胞对唾液中5mC谱的相对贡献的至关重要性。我们进一步描述了一种策略，用于对细胞组成进行基于参考的计算校正，从而识别由于经历导致的差异甲基化模式，或用于诊断与激素水平、创伤状态和各种心理健康结果等特征相关的表型。
通过这种严谨的方法，可以识别血细胞、角质形成细胞或两者中因逆境而甲基化水平改变的特定位点，这些位点随后可作为诊断生物标志物和治疗靶点。
