【科普】“线粒体夏娃”:人类之母生活于二十万年前 - 丁香园论坛

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'Mitochondrial Eve': Mother of All Humans Lived 200,000 Years Ago

“线粒体夏娃”:人类之母生活于二十万年前

译者:Docofsoul

ScienceDaily (Aug. 17, 2010) — The most robust statistical examination to date of our species' genetic links to "mitochondrial Eve" -- the maternal ancestor of all living humans -- confirms that she lived about 200,000 years ago. The Rice University study was based on a side-by-side comparison of 10 human genetic models that each aim to determine when Eve lived using a very different set of assumptions about the way humans migrated, expanded and spread across Earth.

《每日科学》2010年8月17日报道 —— 迄今为止涉及我们这个种类与“线粒体夏娃”(所有活着的人的母系祖先)的基因关联的最强有力的统计学检验结果证实:“夏娃”生活于20万年以前。美国莱斯大学的这项研究结果是在10种人类遗传模型的并行比较基础上得出的;每种模型所运用的是一组迥然不同的假设,假设涉及方方面面: 人类迁徙、种群扩张与分布在地球上的各种方式;所有这些都旨在确定夏娃的具体生活时间。


Artist's cross section of a mitochondrion. (Credit: iStockphoto/David Marchal)
线粒体横截面艺术示意图(图片来源:iStockphoto/David Marchal)

The research is available online in the journal Theoretical Population Biology.
"Our findings underscore the importance of taking into account the random nature of population processes like growth and extinction," said study co-author Marek Kimmel, professor of statistics at Rice. "Classical, deterministic models, including several that have previously been applied to the dating of mitochondrial Eve, do not fully account for these random processes."

本研究在线发表于《理论种群生物学杂志》(the journal Theoretical Population Biology)。共同作者、莱斯大学统计学教授Marek Kimmel 说:“我们的发现强调了虑及种群过程(如种群增长与种群灭绝)的随机性质的重要性。经典的、确定性模型, 包括此前已被应用于测定线粒体夏娃年龄在内的几种模型,并不能完全解释这些随机过程。”

The quest to date mitochondrial Eve (mtEve) is an example of the way scientists probe the genetic past to learn more about mutation, selection and other genetic processes that play key roles in disease.

寻求测定线粒体夏娃(mtEve)年龄是科学家们探索遗传历史状态以学习更多有关变异、选择与其它遗传过程的方式的一种范例,因为这些遗传过程在疾病的发生发展与转归过程中扮演着关键角色。

"This is why we are interested in patterns of genetic variability in general," Kimmel said. "They are very important for medicine."

Kimmel说:“这就是为什么我们对遗传在总体上呈现易变倾向的特征模型感兴趣的原因。对于医学来说,这些模式非常重要。”

For example, the way scientists attempt to date mtEve relies on modern genetic techniques. Genetic profiles of random blood donors are compared, and based upon the likenesses and differences between particular genes, scientists can assign a number that describes the degree to which any two donors are related to one another.

比如说,科学家尝试确定线粒体夏娃年龄的方法取决于现代遗传技术。随机的血液捐赠者的遗传特征在此被比较分析,并且基于特定基因之间的相似性与差异性,科学家们就能够指定一个数字来形容任意两个捐赠者在遗传学上彼此相关的水平。

Using mitochondrial genomes to gauge relatedness is a way for geneticists to simplify the task of finding common ancestors that lived long ago. That is because the entire human genome contains more than 20,000 genes, and comparing the differences among so many genes for distant relatives is problematic, even with today's largest and fastest supercomputers.

用线粒体的基因组来测量相关性是基因学家用以发现很久以前生存过的共同祖先的这一艰巨任务的简化方式。 之所以这么做,是因为整个人类基因组包含了超过二万以上基因,而比较如此之多的基因彼此存在的差异以便找出其遥远的亲属是非常困难的,甚至用今天的最大型与最快的超级计算机也难以胜任。

But mitochondria -- the tiny organelles that serve as energy factories inside all human cells -- have their own genome. Besides containing 37 genes that rarely change, they contain a "hypervariable" region, which changes fast enough to provide a molecular clock calibrated to times comparable to the age of modern humanity. Because each person's mitochondrial genome is inherited from his or her mother, all mitochondrial lineages are maternal.
To infer mtEve's age, scientists must convert the measures of relatedness between random blood donors into a measure of time.

但是存在于细胞内的微小的细胞器并担任所有人类细胞的能量工厂的线粒体却有自己的基因组。除了包含37个罕有变化的基因,线粒体还包括一个“超变量”区域。这个区域变化非常快,足以提供一种特殊的分子时钟,即其刻度可以与现代人类的年龄直接比较。因为每个人的线粒体基因组都遗传自各自的母亲,所有线粒体的血统均属于母系。要推导出线粒体夏娃的相关年龄,科学家必须将随机血液捐赠者之间的相似性量度转化为时间量度。

"You have to translate the differences between gene sequences into how they evolved in time," said co-author Krzysztof Cyran, vice head of the Institute of Informatics at Silesian University of Technology in Gliwice, Poland. "And how they evolved in time depends upon the model of evolution that you use. So, for instance, what is the rate of genetic mutation, and is that rate of change uniform in time? And what about the process of random loss of genetic variants, which we call genetic drift?"

共同作者、位于波兰格利维策的西里西亚工业大学信息学研究所的 Krzysztof Cyran说: “你必须将基因序列之间的差异翻译成随时间进化的具体过程。而其随时间进化的具体过程则依赖于研究者所运用的进化模型。因此,比如说,遗传变异率的具体内容?以及该遗传变异率在时间上是否统一? 我们所称为遗传漂变的遗传变异的随机损失过程如何?(等等问题都应该予以考虑。)”

Within each model, the answers to these questions take the form of coefficients -- numeric constants that are plugged into the equation that returns the answer for when mtEve lived.

在每个模型中,对这些问题的答案以系数的形式呈现。系数的形式即加入方程的数字常量,最后得出线粒体夏娃生存的时间这一答案。

Each model has its own assumptions, and each assumption has mathematical implications. To further complicate matters, some of the assumptions are not valid for human populations. For example, some models assume that population size never changes. That is not true for humans, whose population has grown exponentially for at least several thousand generations. Other models assume perfect mixing of genes, meaning that any two humans anywhere in the world have an equal chance of producing offspring.

每种模型都有自己的假设,而每种假设有其数学含义。而让事情更复杂的是,有些假设对于人类种群无效。比如说,有些模型假设种群规模永远不变。这对人类来说并非真实,人类种群一直以指数增长,起码有几千代如此。其它模型假定基因存在理想混合,这意味着世界任何一个地方的两个人都有生产后代的均等机会。

Cyran said human genetic models have become more complex over the past couple of decades as theorists have tried to correct for invalid assumptions. But some of the corrections -- like adding branching processes that attempt to capture the dynamics of population growth in early human migrations -- are extremely complex. Which raises the question of whether less complex models might do equally well in capturing what's occurring.

Cyran说人类遗传模型在过去数十年来已经变得更加复杂,因为理论学家已经尝试纠正无效假设。但是其中的一些纠正,比如说增加旨在捕获早期人类迁徙时种群增长动态的分支过程,特别复杂。这样也就提出了一个问题:即复杂程度小得多的模型是否在捕获相关内涵方面也可能同样奏效?

"We wanted to see how sensitive the estimates were to the assumptions of the models," Kimmel said. "We found that all of the models that accounted for random population size -- such as different branching processes -- gave similar estimates. This is reassuring, because it shows that refining the assumptions of the model, beyond a certain point, may not be that important in the big picture."

Kimmel说:“我们想看看这些估计对于模型的假设的敏感度。我们发现所有解释随机种群规模的模型 —— 比如不同的分支过程 —— 给出了相似的估计。这是可靠的,因为它显示了:超越某一特定点去优化该模型的各种假设对于总体结论来说并不重要。”

The research was supported by grants from the Polish Ministry of Science and Higher Education and the Cancer Prevention and Research Institute of Texas. It has resulted from a standing collaboration between Rice University and Silesian University of Technology.

该研究是莱斯大学与西里西亚工业大学长期合作的成果, 得到了波兰科学与高等教育部与德克萨斯州癌症预防与研究研究院的资金支持。

(Docofsoul译于2010-8-18)