Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA.
Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA.
Int J Mol Sci. 2023 Mar 7;24(6):5122. doi: 10.3390/ijms24065122.
Mutations in MeCP2 result in a crippling neurological disease, but we lack a lucid picture of MeCP2's molecular role. Individual transcriptomic studies yield inconsistent differentially expressed genes. To overcome these issues, we demonstrate a methodology to analyze all modern public data. We obtained relevant raw public transcriptomic data from GEO and ENA, then homogeneously processed it (QC, alignment to reference, differential expression analysis). We present a web portal to interactively access the mouse data, and we discovered a commonly perturbed core set of genes that transcends the limitations of any individual study. We then found functionally distinct, consistently up- and downregulated subsets within these genes and some bias to their location. We present this common core of genes as well as focused cores for up, down, cell fraction models, and some tissues. We observed enrichment for this mouse core in other species MeCP2 models and observed overlap with ASD models. By integrating and examining transcriptomic data at scale, we have uncovered the true picture of this dysregulation. The vast scale of these data enables us to analyze signal-to-noise, evaluate a molecular signature in an unbiased manner, and demonstrate a framework for future disease focused informatics work.
MECP2 基因突变会导致严重的神经疾病，但我们对 MECP2 的分子作用缺乏清晰的认识。个别转录组研究产生了不一致的差异表达基因。为了克服这些问题，我们展示了一种分析所有现代公共数据的方法。我们从 GEO 和 ENA 获得了相关的原始公共转录组数据，然后对其进行了均匀处理（QC、与参考序列比对、差异表达分析）。我们提供了一个交互式访问小鼠数据的网络门户，并发现了一组普遍受到干扰的核心基因，这些基因超越了任何单个研究的局限性。然后，我们在这些基因中发现了功能不同、始终上调和下调的亚组，以及它们位置的一些偏向。我们将这些常见的核心基因以及上调、下调、细胞分数模型和一些组织的焦点核心呈现出来。我们观察到其他物种的 MECP2 模型中的这种小鼠核心富集，并观察到与 ASD 模型的重叠。通过整合和大规模检查转录组数据，我们揭示了这种失调的真实情况。这些数据的巨大规模使我们能够分析信号噪声，以无偏倚的方式评估分子特征，并展示未来针对疾病的信息学工作的框架。
