Longtin Amy, Watowich Marina M, Sadoughi Baptiste, Petersen Rachel M, Brosnan Sarah F, Buetow Kenneth, Cai Qiuyin, Gurven Michael D, Higham James P, Highland Heather M, Huang Yi-Ting, Kaplan Hillard, Kraft Thomas S, Lim Yvonne A L, Long Jirong, Melin Amanda D, Montague Michael J, Roberson Jamie, Ng Kee Seong, Platt Michael L, Schneider-Crease India A, Stieglitz Jonathan, Trumble Benjamin C, Venkataraman Vivek V, Wallace Ian J, Wu Jie, Snyder-Mackler Noah, Jones Angela, Bick Alexander G, Lea Amanda J
Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.
Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, United States of America.
PLoS Genet. 2025 May 22;21(5):e1011667. doi: 10.1371/journal.pgen.1011667. eCollection 2025 May.
Characterizing DNA methylation patterns is important for addressing key questions in evolutionary biology, development, geroscience, and medical genomics. While costs are decreasing, whole-genome DNA methylation profiling remains prohibitively expensive for most population-scale studies, creating a need for cost-effective, reduced representation approaches (i.e., assays that rely on microarrays, enzyme digests, or sequence capture to target a subset of the genome). Most common whole genome and reduced representation techniques rely on bisulfite conversion, which can damage DNA resulting in DNA loss and sequencing biases. Enzymatic methyl sequencing (EM-seq) was recently proposed to overcome these issues, but thorough benchmarking of EM-seq combined with cost-effective, reduced representation strategies is currently lacking. To address this gap, we optimized the Targeted Methylation Sequencing protocol (TMS)-which profiles ~4 million CpG sites-for miniaturization, flexibility, and multispecies use. First, we tested modifications to increase throughput and reduce cost, including increasing multiplexing, decreasing DNA input, and using enzymatic rather than mechanical fragmentation to prepare DNA. Second, we compared our optimized TMS protocol to commonly used techniques, specifically the Infinium MethylationEPIC BeadChip (n = 55 paired samples) and whole genome bisulfite sequencing (n = 6 paired samples). In both cases, we found strong agreement between technologies (R2 = 0.97 and 0.99, respectively). Third, we tested the optimized TMS protocol in three non-human primate species (rhesus macaques, geladas, and capuchins). We captured a high percentage (mean = 77.1%) of targeted CpG sites and produced methylation level estimates that agreed with those generated from reduced representation bisulfite sequencing (R2 = 0.98). Finally, we confirmed that estimates of 1) epigenetic age and 2) tissue-specific DNA methylation patterns are strongly recapitulated using data generated from TMS versus other technologies. Altogether, our optimized TMS protocol will enable cost-effective, population-scale studies of genome-wide DNA methylation levels across human and non-human primate species.
表征DNA甲基化模式对于解决进化生物学、发育生物学、老年科学和医学基因组学中的关键问题至关重要。尽管成本在不断下降，但对于大多数群体规模的研究来说，全基因组DNA甲基化分析仍然过于昂贵，因此需要经济高效的、降低代表性的方法（即依赖微阵列、酶切或序列捕获来靶向基因组子集的检测方法）。最常见的全基因组和降低代表性技术都依赖亚硫酸氢盐转化，这可能会损坏DNA，导致DNA损失和测序偏差。最近有人提出酶促甲基测序（EM-seq）来克服这些问题，但目前缺乏对EM-seq与经济高效的降低代表性策略相结合的全面基准测试。为了填补这一空白，我们对靶向甲基化测序方案（TMS）进行了优化——该方案可分析约400万个CpG位点——以实现小型化、灵活性和多物种使用。首先，我们测试了提高通量和降低成本的改进方法，包括增加多重性、减少DNA输入量，以及使用酶切而非机械破碎来制备DNA。其次，我们将优化后的TMS方案与常用技术进行了比较，特别是Infinium MethylationEPIC BeadChip（n = 55对配对样本）和全基因组亚硫酸氢盐测序（n = 6对配对样本）。在这两种情况下，我们都发现技术之间具有很强的一致性（R2分别为0.97和0.99）。第三，我们在三种非人类灵长类动物（恒河猴、狮尾狒和卷尾猴）中测试了优化后的TMS方案。我们捕获了高比例（平均 = 77.1%）的靶向CpG位点，并生成了与降低代表性亚硫酸氢盐测序产生的甲基化水平估计值一致的结果（R2 = 0.98）。最后，我们证实，使用TMS与其他技术生成的数据，能够很好地重现1）表观遗传年龄和2）组织特异性DNA甲基化模式的估计值。总之，我们优化后的TMS方案将能够在人类和非人类灵长类物种中开展经济高效的全基因组DNA甲基化水平群体规模研究。
