Common disease signatures from gene expression analysis in Huntington's disease human blood and brain.
Common disease signatures from gene expression analysis in Huntington's disease human blood and brain.
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DOI：
10.1186/s13023-016-0475-2
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发表时间：
2016-08-01
影响因子：
3.7
通讯作者：
Roos M
中科院分区：
文献类型：
--
作者：
Mina E;van Roon-Mom W;Hettne K;van Zwet E;Goeman J;Neri C;A C 't Hoen P;Mons B;Roos M
亨廷顿舞蹈症 (HD) 是一种毁灭性的脑部疾病，目前尚无有效的治疗或治愈方法。脑组织的稀缺使得研究大脑的变化变得困难，也无法进行纵向研究。然而，HD 的外周病理学表明，可以使用外周组织作为疾病进展和/或新疗法疗效的监测工具来研究该疾病。在这项研究中，我们研究了血液是否可以用于监测大脑疾病的严重程度和进展。由于之前仅使用基因表达的尝试被证明是不成功的，因此我们在功能背景下比较了血液和大脑亨廷顿病特征。将来自三个大脑区域的微阵列 HD 基因表达谱与下一代测序生成的 HD 血液转录组进行比较。结合加权基因共表达网络分析和基于文献的功能分析（概念概况分析）进行比较。独特的是，我们对血液和大脑数据集的比较不是基于（非常有限的）基因重叠，而是基于四个不同语义类别的基因注释之间的相似性：“生物过程”、“细胞成分”、“分子功能”和“疾病或综合症”。我们在 HD 血液中发现了反映广泛病理生理学谱的特征，包括免疫反应、鞘脂生物合成过程、脂质运输、细胞信号传导、蛋白质修饰、剪接体、RNA 剪接、囊泡运输、细胞信号传导和突触传递的改变。这个谱的一部分让人想起大脑的病理学。无论使用何种语义注释，尾状核和 BA4 中的 HD 特征都表现出与血液的最高相似性。 BA9 表现出中等相似性，而小脑的相似性最低。我们提出了血液和大脑之间共有的两个特征：免疫反应和脊髓小脑共济失调。我们的结果表明，HD 血液在功能水平上表现出与大脑相似的失调，但不一定在个体基因水平上。我们报告了两个常见的特征，可用于以非侵入性方式监测 HD 患者大脑的病理学。我们的结果是如何使用血液数据中的信号来表示大脑疾病的范例。我们的方法可用于研究病理学涉及异质组织的疾病的疾病特异性特征。本文的在线版本 (doi:10.1186/s13023-016-0475-2) 包含补充材料，可供授权用户使用。
Huntington’s disease (HD) is a devastating brain disorder with no effective treatment or cure available. The scarcity of brain tissue makes it hard to study changes in the brain and impossible to perform longitudinal studies. However, peripheral pathology in HD suggests that it is possible to study the disease using peripheral tissue as a monitoring tool for disease progression and/or efficacy of novel therapies. In this study, we investigated if blood can be used to monitor disease severity and progression in brain. Since previous attempts using only gene expression proved unsuccessful, we compared blood and brain Huntington’s disease signatures in a functional context. Microarray HD gene expression profiles from three brain regions were compared to the transcriptome of HD blood generated by next generation sequencing. The comparison was performed with a combination of weighted gene co-expression network analysis and literature based functional analysis (Concept Profile Analysis). Uniquely, our comparison of blood and brain datasets was not based on (the very limited) gene overlap but on the similarity between the gene annotations in four different semantic categories: “biological process”, “cellular component”, “molecular function” and “disease or syndrome”. We identified signatures in HD blood reflecting a broad pathophysiological spectrum, including alterations in the immune response, sphingolipid biosynthetic processes, lipid transport, cell signaling, protein modification, spliceosome, RNA splicing, vesicle transport, cell signaling and synaptic transmission. Part of this spectrum was reminiscent of the brain pathology. The HD signatures in caudate nucleus and BA4 exhibited the highest similarity with blood, irrespective of the category of semantic annotations used. BA9 exhibited an intermediate similarity, while cerebellum had the least similarity. We present two signatures that were shared between blood and brain: immune response and spinocerebellar ataxias. Our results demonstrate that HD blood exhibits dysregulation that is similar to brain at a functional level, but not necessarily at the level of individual genes. We report two common signatures that can be used to monitor the pathology in brain of HD patients in a non-invasive manner. Our results are an exemplar of how signals in blood data can be used to represent brain disorders. Our methodology can be used to study disease specific signatures in diseases where heterogeneous tissues are involved in the pathology. The online version of this article (doi:10.1186/s13023-016-0475-2) contains supplementary material, which is available to authorized users.