PNAS：不孕不育致病基因是哪些？
不孕不育症困扰着大约15%的夫妇，其中很多夫妇不孕不育是因为机体存在缺陷的基因所致，但至今隐藏在不孕不育下的遗传原因研究者并不清楚。近日来自康奈尔大学的科学家通过研究开发了一种新型的实验性策略在人类机体中鉴别出了引发不孕不育的基因突变，这些突变被称为单核苷酸多态性（SNPs），其是人类机体中常见的遗传突变类型，每一个SNP都代表着单一DNA或核苷酸元件的差异。
研究者John Schimenti表示，如果我们阐明了SNP是有害的，随后当病人进行基因组测序时就应该帮其确定哪些SNPs是应该存在的；如果我们知道哪些突变是好的，哪些是坏的，医生们就会进行一定的遗传诊断。当前鉴别引发疾病的SNPs的标准方法包括将健康个体和患病个体的基因组进行对比来缩小染色体位点，随后利用计算机算法来预测哪些SNPs是有害的，但由于不孕不育是一个涉及许多基因的非常复杂的过程，因此并没有方法可以确定引发不孕不育的基因。
文章中，研究者开发了一种新型策略，他们浏览了引发小鼠不育的所有基因，随后利用计算机技术通过搜寻人类遗传突变数据库中的数据鉴别出了人类机体具有相同功能的SNPs；随后研究者探究了引发人类不孕不育的4个基因的SNPs，利用名为CRISPR/Cas的基因编辑技术，研究者就对小鼠机体的同源基因进行编辑以便小鼠可以模拟人类机体的不育SNPs；通过在小鼠机体中分析研究人源化的SNPs，研究者就检测了是否人类的这些突变会引发小鼠不育，在4种SNPs中研究者发现有一种SNP可以引发小鼠不育。
研究者预测，未来将会开发出新型的个体化疗法来对个体进行基因组测序，从而帮助医生和病人确定自身的遗传健康；而鉴别出相应的不孕不育基因或许也会帮助临床医生开发新型治疗不孕不育的疗法，来给广大不孕不育夫妇带来福利。（生物谷Bioon.com）
doi:10.1073/pnas.1506974112
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The genetics of human infertility by functional interrogation of SNPs in mice
Priti Singh and John C. Schimenti1
Infertility is a prevalent health issue, affecting ∼15% of couples of childbearing age. Nearly one-half of idiopathic infertility cases are thought to have a genetic basis, but the underlying causes are largely unknown. Traditional methods for studying inheritance, such as genome-wide association studies and linkage analyses, have been confounded by the genetic and phenotypic complexity of reproductive processes. Here we describe an association- and linkage-free approach to identify segregating infertility alleles, in which CRISPR/Cas9 genome editing is used to model putatively deleterious nonsynonymous SNPs (nsSNPs) in the mouse orthologs of fertility genes. Mice bearing “humanized” alleles of four essential meiosis genes, each predicted to be deleterious by most of the commonly used algorithms for analyzing functional SNP consequences, were examined for fertility and reproductive defects. Only a Cdk2 allele mimicking SNP rs3087335, which alters an inhibitory WEE1 protein kinase phosphorylation site, caused infertility and revealed a novel function in regulating spermatogonial stem cell maintenance. Our data indicate that segregating infertility alleles exist in human populations. Furthermore, whereas computational prediction of SNP effects is useful for identifying candidate causal mutations for diverse diseases, this study underscores the need for in vivo functional evaluation of physiological consequences. This approach can revolutionize personalized reproductive genetics by establishing a permanent reference of benign vs. infertile alleles.
