Nutrition Research Institute, Department of Nutrition, University of North Carolina at Chapel Hill, Kannapolis, North Carolina, United States of America.
Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
PLoS Comput Biol. 2019 Apr 11;15(4):e1006937. doi: 10.1371/journal.pcbi.1006937. eCollection 2019 Apr.
Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcriptome sequencing (RNA-Seq) offer insights into mechanisms underlying FASD, but gene-level analysis provides limited information regarding complex transcriptional processes such as alternative splicing and non-coding RNAs. Moreover, traditional analytical approaches that use multiple hypothesis testing with a false discovery rate adjustment prioritize genes based on an adjusted p-value, which is not always biologically relevant. We address these limitations with a novel approach and implemented an unsupervised machine learning model, which we applied to an exon-level analysis to reduce data complexity to the most likely functionally relevant exons, without loss of novel information. This was performed on an RNA-Seq paired-end dataset derived from alcohol-exposed neural fold-stage chick crania, wherein alcohol causes facial deficits recapitulating those of FASD. A principal component analysis along with k-means clustering was utilized to extract exons that deviated from baseline expression. This identified 6857 differentially expressed exons representing 1251 geneIDs; 391 of these genes were identified in a prior gene-level analysis of this dataset. It also identified exons encoding 23 microRNAs (miRNAs) having significantly differential expression profiles in response to alcohol. We developed an RDAVID pipeline to identify KEGG pathways represented by these exons, and separately identified predicted KEGG pathways targeted by these miRNAs. Several of these (ribosome biogenesis, oxidative phosphorylation) were identified in our prior gene-level analysis. Other pathways are crucial to facial morphogenesis and represent both novel (focal adhesion, FoxO signaling, insulin signaling) and known (Wnt signaling) alcohol targets. Importantly, there was substantial overlap between the exomes themselves and the predicted miRNA targets, suggesting these miRNAs contribute to the gene-level expression changes. Our novel application of unsupervised machine learning in conjunction with statistical analyses facilitated the discovery of signaling pathways and miRNAs that inform mechanisms underlying FASD.
孕期饮酒会导致胎儿酒精谱系障碍（FASD），是神经发育障碍的主要原因。全转录组测序（RNA-Seq）可以深入了解 FASD 的发生机制，但基因水平的分析提供的关于复杂转录过程（如选择性剪接和非编码 RNA）的信息有限。此外，传统的分析方法使用经过错误发现率调整的多重假设检验，根据调整后的 p 值对基因进行优先级排序，这并不总是具有生物学意义。我们通过一种新的方法解决了这些限制，并实现了一种无监督机器学习模型，我们将其应用于外显子水平分析，将数据复杂性降低到最可能具有功能相关性的外显子，而不会丢失新的信息。这是在一个源自暴露于酒精的神经管阶段鸡颅骨的 RNA-Seq 配对末端数据集上完成的，其中酒精会导致面部缺陷，类似于 FASD 的缺陷。主成分分析和 K 均值聚类用于提取偏离基线表达的外显子。这鉴定了 6857 个差异表达的外显子，代表 1251 个基因 ID；其中 391 个基因在该数据集的先前基因水平分析中被鉴定出来。它还鉴定了编码 23 个 microRNAs（miRNAs）的外显子，这些 miRNAs 对酒精的表达有显著差异。我们开发了一个 RDAVID 管道来识别这些外显子所代表的 KEGG 途径，并分别鉴定了这些 miRNA 靶向的预测 KEGG 途径。其中一些（核糖体生物发生、氧化磷酸化）在我们之前的基因水平分析中被鉴定出来。其他途径对于面部形态发生至关重要，包括新的（焦点黏附、FoxO 信号转导、胰岛素信号转导）和已知的（Wnt 信号转导）酒精靶点。重要的是，外显子本身和预测的 miRNA 靶之间存在大量重叠，这表明这些 miRNA 有助于基因水平的表达变化。我们在无监督机器学习方面的新应用结合统计分析，促进了对 FASD 发生机制的信号通路和 miRNA 的发现。
