Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome.
Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome.
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自组织特征地图识别在唐氏综合症小鼠模型中学习至关重要的蛋白质。
DOI：
10.1371/journal.pone.0129126
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Cios KJ
中科院分区：
文献类型：
--
作者：
Higuera C;Gardiner KJ;Cios KJ
唐氏综合征（DS）是一种与智力残疾相关的染色体异常（人类21号染色体三体），在全世界每1000名活产婴儿中就有一名患有这种疾病。在DS中，由正常染色体的额外拷贝编码的基因的过表达被认为足以扰乱正常途径和对刺激的正常反应，导致学习和记忆缺陷。在这项工作中，我们设计了一种基于无监督聚类方法，自组织映射（SOM）的策略，以确定暴露于上下文恐惧条件反射（CFC）的小鼠中蛋白质水平的生物学重要差异。我们分析了77个蛋白质的表达水平，从正常基因型对照小鼠和从他们的三体同窝出生的（Ts65Dn）都与和没有治疗的药物美金刚。对照组小鼠学习成功，而三体小鼠学习失败，除非它们首先接受药物治疗，以挽救它们的学习能力。SOM方法确定了预测对正常学习、失败学习和挽救学习做出最关键贡献的蛋白质的减少的子集，并提供了数据的视觉表示，允许用户提取可能是对不同种类的学习和对美金刚胺的反应的新生物反应的基础的模式。结果表明，SOM的应用程序的新的实验数据集的复杂的蛋白质谱可用于确定共同的关键蛋白质的反应，这反过来可能有助于确定潜在的更有效的药物靶点。
Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.
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