Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease.
Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease.
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DOI：
10.1371/journal.pgen.1009307
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发表时间：
2021-03
期刊：
影响因子：
4.5
通讯作者：
Haldar K
中科院分区：
文献类型：
--
作者：
Farris J;Alam MS;Rajashekara AM;Haldar K
单个基因中的数百个突变导致罕见疾病，但为什么突变会引起严重或减弱的状态仍然知之甚少。甘氨酸脱羧酶（GLDC）缺陷导致非酮症高甘氨酸血症（NKH），这是一种与血浆甘氨酸升高相关的神经系统疾病。我们统一了一个人类多参数NKH突变量表，该量表使用用于小鼠和人类基因组水平分析的新的计算机工具将严重的神经系统疾病与减毒的神经系统疾病分开，从用顶级减毒和高致病性突变工程化的小鼠中收集体内证据，并将数据整合到产前和产后疾病结局的模型中，与一百多个主要和次要神经原性突变相关。我们的研究结果表明，高度严重的神经源性突变预测致命的，产前疾病，可以补救的代谢补充母鼠，没有改善持续的血浆甘氨酸。这项工作还提供了一种系统方法来识别数百种遗传疾病中突变的功能后果。我们的研究为大规模了解突变功能和预测神经源性突变的严重程度是神经代谢NKH小鼠模型中产前疾病的直接衡量标准提供了一个新的框架。这一框架可以扩展到数百种单基因罕见疾病的分析，这些疾病的潜在基因是已知的，但对绝大多数突变以及它们为什么和如何导致疾病的理解尚未实现。建立人类遗传疾病的模型，包括计算模型和动物模型，是理解疾病、设计治疗方法和测试治疗方法的重要组成部分。在这里，我们开发了新的计算机工具来建立罕见的神经系统疾病非酮症高甘氨酸血症（NKH）的模型，这是由甘氨酸脱羧酶（GLDC）的突变引起的，GLDC是一种降解甘氨酸的蛋白质。我们首先将突变评分工具应用于人类和小鼠基因组中的GLDC，然后使用这些数据开发一个计算模型，用于预测哪些突变将在小鼠中得到很好的建模，以及它们的疾病有多严重。然后，我们通过遗传工程来验证这个计算模型，预测一个突变会导致轻度疾病，另一个预测会导致严重疾病。我们的预测是正确的，我们用它们开发了一个与100多个主要和次要神经原性突变相关的模型，该模型表明，突变越严重，导致出生前开始的疾病的可能性就越大，除非通过改变饮食来挽救，否则很可能是致命的。这项研究还证明了计算机分析的力量，可以指导遗传疾病模型的开发，并将其纳入可扩展的模型中，这些模型可以用于了解数百种导致疾病的突变。
Hundreds of mutations in a single gene result in rare diseases, but why mutations induce severe or attenuated states remains poorly understood. Defect in glycine decarboxylase (GLDC) causes Non-ketotic Hyperglycinemia (NKH), a neurological disease associated with elevation of plasma glycine. We unified a human multiparametric NKH mutation scale that separates severe from attenuated neurological disease with new in silico tools for murine and human genome level-analyses, gathered in vivo evidence from mice engineered with top-ranking attenuated and a highly pathogenic mutation, and integrated the data in a model of pre- and post-natal disease outcomes, relevant for over a hundred major and minor neurogenic mutations. Our findings suggest that highly severe neurogenic mutations predict fatal, prenatal disease that can be remedied by metabolic supplementation of dams, without amelioration of persistent plasma glycine. The work also provides a systems approach to identify functional consequences of mutations across hundreds of genetic diseases. Our studies provide a new framework for a large scale understanding of mutation functions and the prediction that severity of a neurogenic mutation is a direct measure of pre-natal disease in neurometabolic NKH mouse models. This framework can be extended to analyses of hundreds of monogenetic rare disorders where the underlying genes are known but understanding of the vast majority of mutations and why and how they cause disease, has yet to be realized. Building models of human genetic disease, both computational and animal, is an essential part of understanding the disease, designing treatments, and testing therapies. Here, we have developed new in silico tools to build models for the rare neurological disorder non-ketotic hyperglycinemia (NKH), which is caused by mutations in glycine decarboxylase (GLDC), a protein that degrades glycine. We first applied a mutation scoring tool to GLDC in both the human and mouse genomes, and then used this data to develop a computational model for predicting which mutations would be well-modeled in mice, and how severe their disease would be. We then validated this computational model by genetically-engineering a mutation predicted to cause mild disease and another predicted to cause severe disease. Our predictions were correct and we used them to develop a model relevant for over a hundred major and minor neurogenic mutations that suggests that the more severe the mutation, the greater chance it will cause disease that starts before birth and is likely to be fatal unless rescued by modifying diet. This study also demonstrates the power of in silico analyses for guiding the development of genetic disease models and incorporating them into scalable models that can be applied to understand hundreds of mutations that cause disease.
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