Understanding molecular mechanisms and predicting phenotypic effects of pathogenic tubulin mutations.
Understanding molecular mechanisms and predicting phenotypic effects of pathogenic tubulin mutations.
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DOI：
10.1371/journal.pcbi.1010611
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发表时间：
2022-10
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
细胞严重依赖微管进行几个过程，包括细胞分裂和分子运输。构成微管的不同微管蛋白-α和-β蛋白的突变与各种疾病相关，并且通常是显性的、散发的和先天性的。虽然最早报道的微管蛋白突变影响神经发育，但突变也与其他疾病如出血性疾病和不孕症有关。我们对所有同种型的微管蛋白突变进行了系统的调查，以提高我们对它们如何引起疾病的理解，并提高我们预测其表型效应的能力。蛋白质结构分析和计算变异效应预测因子在区分致病性和良性突变方面的效用非常有限。对于那些与非神经发育障碍相关的基因来说，情况更糟。我们选择了在实验表征中预测最差的微管蛋白-α和-β疾病突变。这些突变体共定位于HeLa细胞中的有丝分裂纺锤体，这表明它们可能通过改变微管特性来发挥显性负效应。我们的研究结果表明，微管蛋白突变代表了目前计算方法的盲点，比大多数人类疾病基因的突变预测更差。我们认为这可能是由于它们与显性负性和功能获得机制密切相关。丝状结构，称为微管，是细胞发挥功能，将物质分布在细胞和生物体周围并帮助细胞生长所必需的。微管的结构单元是一种叫做微管蛋白的蛋白质，它可以快速聚合和解聚。微管蛋白基因的突变可能对许多不同类型的细胞产生灾难性的后果，导致诸如出血缺陷、女性不孕症和损害大脑发育的疾病等疾病。然而，这些突变如何导致疾病以及它们是否可以预测仍然是未知的。我们使用计算和实验技术来解决这些问题。首先，我们比较了引起疾病的微管蛋白突变和健康人中发现的微管蛋白突变如何影响微管蛋白的结构。然后，我们测试了可用的计算预测器区分这两种类型的微管蛋白突变的能力。我们发现这些程序很难预测导致疾病的微管蛋白突变，限制了它们的实用性。接下来，我们研究了计算方法无法预测的致病突变。我们发现这些突变并不能阻止微管蛋白形成微管，这表明这些突变改变了微管蛋白的功能，而没有使其失活。我们的工作将微管蛋白作为当前计算预测的一个弱点，可能是因为它们没有考虑突变导致疾病的不同方式。
Cells rely heavily on microtubules for several processes, including cell division and molecular trafficking. Mutations in the different tubulin-α and -β proteins that comprise microtubules have been associated with various diseases and are often dominant, sporadic and congenital. While the earliest reported tubulin mutations affect neurodevelopment, mutations are also associated with other disorders such as bleeding disorders and infertility. We performed a systematic survey of tubulin mutations across all isotypes in order to improve our understanding of how they cause disease, and increase our ability to predict their phenotypic effects. Both protein structural analyses and computational variant effect predictors were very limited in their utility for differentiating between pathogenic and benign mutations. This was even worse for those genes associated with non-neurodevelopmental disorders. We selected tubulin-α and -β disease mutations that were most poorly predicted for experimental characterisation. These mutants co-localise to the mitotic spindle in HeLa cells, suggesting they may exert dominant-negative effects by altering microtubule properties. Our results show that tubulin mutations represent a blind spot for current computational approaches, being much more poorly predicted than mutations in most human disease genes. We suggest that this is likely due to their strong association with dominant-negative and gain-of-function mechanisms. Filament-like structures, called microtubules, are essential for cells to function, distribute material around the cell and organisms, and help cells grow. The building blocks of microtubules are proteins called tubulins, which can rapidly polymerise and depolymerise. Mutations in tubulin genes can have catastrophic consequences on many different types of cells, leading to diseases such as bleeding defects, female infertility, and disorders impairing brain development. However, how these mutations cause disease and whether they can be predicted is still unknown. We used computational and experimental techniques to address these issues. First, we compared how disease-causing tubulin mutations and ones found in healthy people impact the structure of tubulin. Then, we tested the ability of available computational predictors to distinguish between these two types of tubulin mutations. We found these programs poorly predict tubulin mutations that cause diseases, limiting their usefulness. Next, we studied disease-causing mutations that were not predicted by computational methods. We found that these did not prevent tubulin from forming microtubules, indicating these mutations change the function of tubulin without inactivating them. Our work presents tubulins as a weakness of current computational predictors, potentially because they fail to consider different ways in which mutations cause disease.
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影响因子：
3.5
作者：
Curiel, Julian;Bey, Guillermo Rodriguez;Vanderver, Adeline
通讯作者：
Vanderver, Adeline
影响因子：
3.5
作者：
Cederquist, Gustav Y.;Luchniak, Anna;Engle, Elizabeth C.
通讯作者：
Engle, Elizabeth C.
影响因子：
2.1
作者：
Dyer, Nigel
通讯作者：
Dyer, Nigel
DOI：
10.1038/s41580-018-0009-y
发表时间：
2018-07
期刊：
Nature reviews. Molecular cell biology
影响因子：
--
作者：
Brouhard GJ;Rice LM
通讯作者：
Rice LM
影响因子：
12.3
作者：
Bowling KM;Thompson ML;Amaral MD;Finnila CR;Hiatt SM;Engel KL;Cochran JN;Brothers KB;East KM;Gray DE;Kelley WV;Lamb NE;Lose EJ;Rich CA;Simmons S;Whittle JS;Weaver BT;Nesmith AS;Myers RM;Barsh GS;Bebin EM;Cooper GM
通讯作者：
Cooper GM