Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG, Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, PR China.
BMC Med Genomics. 2014 Mar 11;7:12. doi: 10.1186/1755-8794-7-12.
Variable responses to the Hepatitis B Virus (HBV) vaccine have recently been reported as strongly dependent on genetic causes. Yet, the details on such mechanisms of action are still unknown. In parallel, altered DNA methylation states have been uncovered as important contributors to a variety of health conditions. However, methodologies for the analysis of such high-throughput data (epigenomic), especially from the computational point of view, still lack of a gold standard, mostly due to the intrinsic statistical distribution of methylomic data i.e. binomial rather than (pseudo-) normal, which characterizes the better known transcriptomic data.We present in this article our contribution to the challenge of epigenomic data analysis with application to the variable response to the Hepatitis B virus (HBV) vaccine and its most lethal degeneration: hepatocellular carcinoma (HCC).
Twenty-five infants were recruited and classified as good and non-/low- responders according to serological test results. Whole genome DNA methylation states were profiled by Illumina HumanMethylation 450 K beadchips. Data were processed through quality and dispersion filtering and with differential methylation analysis based on a combination of average methylation differences and non-parametric statistical tests. Results were finally associated to already published transcriptomics and post-transcriptomics to gain further insight.
We highlight 2 relevant variations in poor-responders to HBV vaccination: the hypomethylation of RNF39 (Ring Finger Protein 39) and the complex biochemical alteration on SULF2 via hypermethylation, down-regulation and post-transcriptional control.
Our approach appears to cope with the new challenges implied by methylomic data distribution to warrant a robust ranking of candidates. In particular, being RNF39 within the Major Histocompatibility Complex (MHC) class I region, its altered methylation state fits with an altered immune reaction compatible with poor responsiveness to vaccination. Additionally, despite SULF2 having been indicated as a potential target for HCC therapy, we can recommend that non-responders to HBV vaccine who develop HCC are quickly directed to other therapies, as SULF2 appears to be already under multiple molecular controls in such patients. Future research in this direction is warranted.
最近有报道称，乙型肝炎病毒 (HBV) 疫苗的反应存在个体差异，这种差异强烈依赖于遗传原因。然而，关于这些作用机制的细节尚不清楚。与此同时，人们发现 DNA 甲基化状态的改变是多种健康状况的重要影响因素。然而，用于分析此类高通量数据（表观基因组学）的方法，特别是从计算角度来看，仍然缺乏黄金标准，这主要是由于甲基化组数据的内在统计分布是二项式的，而不是（伪）正态的，这与更为人熟知的转录组数据的特征不同。本文介绍了我们在分析表观基因组学数据方面的贡献，应用于乙型肝炎病毒 (HBV) 疫苗的可变反应及其最致命的退化：肝细胞癌 (HCC)。
招募了 25 名婴儿，并根据血清学检测结果将他们分为良好反应者和非反应者/低反应者。通过 Illumina HumanMethylation 450 K beadchips 对全基因组 DNA 甲基化状态进行了分析。通过质量和分散过滤以及基于平均甲基化差异和非参数统计检验的差异甲基化分析对数据进行了处理。最后将结果与已发表的转录组学和后转录组学相关联，以获得进一步的见解。
我们强调了乙型肝炎疫苗接种反应不佳的 2 个相关变化：RNF39（环指蛋白 39）的低甲基化和 SULF2 通过过度甲基化、下调和转录后控制的复杂生化改变。
我们的方法似乎能够应对甲基化组数据分布带来的新挑战，以保证候选物的稳健排序。特别是 RNF39 位于主要组织相容性复合体 (MHC) Ⅰ类区域内，其甲基化状态的改变与免疫反应改变一致，这与疫苗接种反应不佳相吻合。此外，尽管 SULF2 已被指出是 HCC 治疗的潜在靶点，但我们可以建议，对乙型肝炎疫苗无反应但发展为 HCC 的患者应尽快转向其他治疗方法，因为在这些患者中，SULF2 似乎已经受到多种分子的控制。有必要在这方面进行进一步的研究。
