人类mtDNA的研究进展

阐明mtDNA的分子结构、遗传学特点;论述人类的mtDNA疾病及其防治方法,mtDNA与人的衰老及细胞凋亡的关系.     

区域人群mtDNA序列聚类分析

人类mtDNA序列是遵循母系遗传的重要生物信息学资源,利用遗传算法和k-modes模型结合的聚类算法,对西安和长沙两个区域人群mtDNA序列进行聚类分析,在分子层次上阐明了西安和长沙两地区人口结构特点.发现西安地区人口是发散性分布,而长沙地区人口具有主导性类群.     

Leber氏病的mtDNA突变

Leber氏病是一种典型的母系遗传病,表现为急性、亚急性视神经萎缩,Wallace于1988年首次证实了此病患者中存在mtDNA的特异性改变—Wallace突变。我们研究了8个独立来源的中国汉族人Leber氏病患者,其中在4个患者中找到了mtDNA的Wallace突变,支持了Wallac。关于Leber氏病发病机理的假说。     

鲤鱼mtDNA酶切图谱的构建

采用密度梯度离心法及ＲＮａｓｅ消化法制备并纯化了鲤（ＧｙｐｒｉｎｕｓｃａｒｐｉｏＬｉｎｎａｅｕｓ）肝脏线粒体ＤＮＡ（ｍｔＤＮＡ）,用１０种限制性内切酶对ｍｔＤＮＡ进行了分析,鲤鱼ｍｔＤＮＡ分子量约１０．１２×１０￣６,约１６．４９ｋｂ．ＳａｌⅠ、ＰｓｔⅠ、ＢａｍＨⅠ、ＸｂａⅠ、ＢｇｌⅠ、ＰｖｕⅡ、ＸｈｏⅠ、ＥｃｏＲⅠ、ＤｒａⅠ和ＨｉｎｄⅢ分别为１、１、３、３、３、４、１、４、４、和６个切点。根据单酶解及双酶解结果,构建了鲤ｍｔＤＮＡ１０种具酶３０个切点的限制性酶切图谱。     

动物mtDNA控制区及保守与异质

本文通过文献综述,对动物线粒体DNA控制区进行了阐述.从线粒体控制区（control region）基因组的研究出发,重点介绍了动物线粒体控制区基因组结构特点.主要结论：由于碱基替换、插入和缺失以及重复序列数目的变异致使D-loop成为mtDNA中变异最多的区域,但突变和结构重排并不是发生在整个D-loop区域,而是在高变区;大多研究集中在mtDNA D-loop保守区和异质方面：对D-loop序列分析,能较好地阐明动物的起源,在动物亲缘关系鉴定、系统进化和物种形成方式的研究等领域具有广阔的研究和应用前景.     

藏羚羊mtDNA D-Loop区遗传多样性研究

该研究采用非损伤性DNA基因分型技术,对可可西里地区10个藏羚羊（Pantholops hodgsonii）个体的mtDNA非编码区部分片段（444~446bp）进行了序列分析,结果显示A、T%含量（61.8%）明显高于G、C%含量（38.2%）,共发现10种单倍型,包括48个多态位点,其中转换位点44个、颠换位点1个、插入位点1个、缺失位点2个。单倍型间平均遗传距离为0.031,单倍型多态性（h）为1.000,核苷酸多态性（π）为2.96%。说明藏羚羊线粒体控制区存在着丰富变异,最后从藏羚羊的生态习性及地理分布两方面对这一结果进行了分析探讨。      

When man got his mtDNA deletions?

Somatic mtDNA mutations and deletions in particular are known to clonally expand within cells, eventually reaching detrimental intracellular concentrations. The possibility that clonal expansion is a slow process taking a lifetime had prompted an idea that founder mutations of mutant clones that cause mitochondrial dysfunction in the aged tissue might have originated early in life. If, conversely, expansion was fast, founder mutations should predominantly originate later in life. This distinction is important: indeed, from which mutations should we protect ourselves – those of early development/childhood or those happening at old age? Recently, high-resolution data describing the distribution of mtDNA deletions have been obtained using a novel, highly efficient method (Taylor et al., 2014). These data have been interpreted as supporting predominantly early origin of founder mutations. Re-analysis of the data implies that the data actually better fit mostly late origin of founders, although more research is clearly needed to resolve the controversy.mtDNA mutations, and in particular deletions, progressively increase with age and are suspected culprits of several age-related degenerative processes. Because there are hundreds or even thousands of mtDNA genomes per cell, increase in mutational load may include not only the increase in the number of cells containing mutant genomes, but also increase in the fraction of mutant mtDNA in each cell. Studies of mutational composition of individual cells showed that accumulation of mutations within a cell usually does not occur via accrual of random hits. Instead, mtDNA mutations ‘clonally expand’, that is, a single initial mutation multiplies within cell, replaces normal mtDNAs, eventually takes over the cell, and may impair its mitochondrial function. Expansion is possible because mtDNA molecules in a cell are persistently replicated, even in nondividing cells, where some of them are destroyed and replaced by replication of others. Half-life of murine mtDNA is on the order of several weeks (Korr et al., 1998). The result of clonal expansion is that different cells typically contain different types of mutations, while mutant genomes within a cell carry the same mutation. Mechanisms of expansion are still debated; possibilities range from neutral genetic drift to selection within the ‘population’ of intracellular mitochondria. In this commentary, we do not assume any particular mechanism and concentrate on the kinetics of expansion.Because expansion takes time, it is possible that founder mutations of expanded mutant clones that compromise mitochondria at old age might have occurred early in life. Indeed, if expansion was a slow process taking about a lifetime to conclude (Fig. ​(Fig.1A,1A, upper panel), then only those mutations that were generated early in life would have enough time to reach harmful intracellular concentration. In an utmost version of this scenario, there is little de novo mutagenesis and increase in mutations with age is mostly driven by clonal expansion of early founder mutations. The ‘slow’ scenario implies that, as far as mtDNA mutagenesis is concerned, we need to preserve mtDNA during early years or even during development and to be less worried about mutations that arise in older individuals.Open in a separate windowFigure 1The ‘slow’ and the ‘fast’ expansion scenarios and the predicted changes in the diversity and extent of expansion of mtDNA mutations with age. Diversity and extent of expansion can be directly measured and used to distinguish between the two scenarios. mtDNA molecules with different deletions are depicted by small circles of different bright colors. Wild-type mtDNA molecules and cells that never get mutations are not shown for simplicity. Of course in a real tissue, mutant cells are surrounded by a great majority of nonmutant cells.If, on the other hand, clonal expansion was rapid (Fig. ​(Fig.1B1B upper panel), then expanding mutations would swiftly fill up the cells in which founders had arisen and therefore stop expanding. Consequently, overall mutation load would soon ‘plateau off’ if mutants were not continuously occurring. So, in this scenario, the observed persistent increase in mutations with age must be driven by de novo mutagenesis, and most impairment in this scenario is caused by ‘late in life’ mutations, which therefore should be of primary concern. Despite importance of this question, there is still no consensus on which scenario is correct (Payne et al., 2011), (Khrapko, 2011), although the ‘early mutations’ hypothesis appeared more than a decade ago (Elson et al., 2001), (Khrapko et al., 2003).Figure ​Figure11 schematically depicts two characteristics of the mutational dynamics that distinguish between the two scenarios. First, the diversity, that is, number of different types of deletions (Supplemental Note 0), remains constant in the slow scenario (Fig. ​(Fig.1A),1A), but steadily increases in the fast scenario (Fig. ​(Fig.1B).1B). Second, the extent of expansion, that is, the average number of mutant mtDNA molecules per clonal expansion, should increase steadily throughout the lifespan in the slow, early mutations scenario. In contrast, in the fast scenario, extent of expansion should increase rapidly early in life, up to the point when the earliest mutations had enough time to expand to the limits of their host cells and increase much slower thereafter.Recent paper by Taylor et al. (Taylor et al., 2014) describes a new method called Digital Deletion Detection, based on enrichment of deletions by wild-type specific restriction digestion, massive single-molecule PCR in microdroplets, followed by next generation sequencing of the PCR products. This ‘3D’ approach for the first time provided a detailed frequency distribution for a large set of different deleted mtDNA molecules in human brain as a function of age. These data are sufficient to estimate diversity and level of expansion of mutations and therefore promise to help to distinguish between the fast and the slow scenarios. The authors found that a) the number of different types of deletions per sample (used as proxy of diversity) does not increase with age, while b) the ratio of total deletion frequency over the number of deletion types (used as proxy of expansion) does steadily increase with age. Consequently, the authors concluded that ‘diversity of unique deletions remains constant’ and that the ‘data supported the hypothesis that expansion of pre-existing mutations is the primary factor contributing to age-related accumulation of mtDNA deletions’, that is, the slow expansion scenario. We believe, however, that these data deserve more detailed analysis and more cautious interpretation.A striking feature of the data (Taylor et al., 2014) is that the types of deletions found in any two samples are almost completely different (Supplemental Note 1). The same pattern has been previously observed in muscle (Nicholas et al., 2009). To explain this, consider that deletions originate mostly from individual cells each containing clonal expansion of deletion of a certain type. Because there are very many potential types of deletions and much fewer clonal expansions per sample, only a small proportion of possible types of deletions are found in each sample, which explains why two samples typically have almost no deletions in common. Similarly, any two cells with clonal expansions from the same sample usually carry different types of deletion. With this in mind, we will reconsider interpretation of the data.First, consider diversity of deletions. Unfortunately, number of deletion types per sample normalized against the total number of deletions used by Taylor et al. as proxy of diversity is not an adequate measure. First, normalization against the total number of sampled deletion molecules is not justified because in a sample with clonal expansions, the number of types of deletions is not proportional to the number of sequenced molecules (Supplemental Note 2). Instead, the number of deletion types per sample is proportional to the size of the sample (i.e., the size of the tissue piece actually used for DNA isolation). Indeed, increasing sample size means including proportionally more cells with expansions. As discussed above, these additional cells contain different deletion types, so the number of deletion types will also increase roughly proportionally to the sample size. Sample size must be factored out of a rational measure of deletion diversity. The best proxy of sample size available in the original study (Taylor et al., 2014) is the number of mtDNA copies isolated from each sample. Thus, to factor out the sample size, we used the number of deletion types per 10¹⁰ mtDNA, (Fig. ​(Fig.2A).2A). This corrected measure shows rather strong (P < 0.0003) increase in diversity of mtDNA deletions with age (Supplemental Note 3), which fits the ‘fast’ expansion scenario (Fig. ​(Fig.1B1B).Open in a separate windowFigure 2The observed changes in diversity and extent of expansion of mtDNA mutations in brain with age in Taylor et al. data. (A) Diversity of mtDNA deletions (number of deletion types per 10¹⁰ mtDNA) shows strong increase with age (P < 0.0003), corroborating the ‘fast’ expansion scenario (Fig. ​(Fig.1B).1B). (B) The extent of expansion shows excessive variance and does not seem to support any of the two scenarios (neither ‘fast’ nor ‘slow’) to any significant extent. Interpretation of these data requires more detailed analysis.Next, we revisited the extent of expansion of clonal mutations. As a measure of expansion, we used the average of the actual numbers of deleted molecules per deletion type, same as in Fig 1A,B. Note that this measure is different from ‘expansion index’ (Taylor et al., 2014), defined as deletion frequency per deletion type. This is essentially the same measure we use, additionally divided by the number of all mtDNA molecules in the sample. Unfortunately, ‘expansion index’ so defined systematically increases with decreased sample size. This is because deletion frequency is not expected to systematically increase with sample size, while the number of deletion types is, as shown in the previous paragraph. Thus, in particular because old samples in this set tend to be smaller (Supplemental Fig. 4A), this measure is biased.Extent of expansion of mtDNA mutations is plotted versus age in Fig. ​Fig.2B.2B. Which theoretical expansion pattern, the ‘slow’ (Fig. ​(Fig.1A)1A) or the ‘fast’ (Fig. ​(Fig.1B),1B), better fits the actual data (Fig. ​(Fig.2B)?2B)? It looks like either fit is poor: the data are notoriously variable. We conclude that it is necessary to look beyond the coarse average measure of the expansion to interpret the data and explain the excessive variance (Supplemental Note 4).The characteristic biphasic shape of the predicted ‘fast’ plot (Fig. ​(Fig.1B)1B) results from the early large expanded mutations, which are absent in the ‘slow’ scenario (Fig. ​(Fig.1A).1A). We therefore used the data (Taylor et al., 2014) to estimate the size of expansions (Supplemental Note 5, Supplementary Table S1) and in particular, to look for large expansions in young tissue. Indeed, young samples do contain large clonal expansions, and there are four expansions more than 1000 copies in samples 30 years and younger (Table S1). This is consistent with our own observations of large expansions of deletions in single neurons of the young brain using a different approach – single-molecule amplification (Kraytsberg et al., 2006). In other words, although rapid expansion pattern in Fig. ​Fig.2B2B is obscured by large variance of the data, the hallmarks of fast expansion, that is, large early mutant expansions, are present in the tissue.An aspect of the data, however, is at odds either with the fast or the slow scenario. The distribution of expansion sizes at any age is rather gradual; that is, there is a large proportion of expansions of intermediate sizes, ranging from smallest detectable (typically about 10 molecules) up to those more than 1000 molecules. In contrast, according to the ‘slow’ expansion scenario, all expansions should be of approximately the same size, which should increase with age, turning ‘large’ at approximately the same time. The fast scenario, also in contradiction with observations, predicts that proportion of mutants contained in expansions of intermediate sizes markedly decreases with age (Supplemental Note 6).If neither of the scenarios fits the data, what kind of mutational dynamics could be responsible for the observed distribution (Taylor et al., 2014)? We believe that most plausible is a ‘mixed’ scenario, where expansions are fast in some cells and slow in other (‘fast’ and ‘slow expanders’, correspondingly), probably with the whole spectrum of expansion rates in between. Expansion rates may differ between cell types or between cells of the same type differing in individual activity, stress, levels of ROS, length of deletion (Fukui & Moraes, 2009), etc. An example of such a difference is given by myoblasts, which, unlike their descendant myofibers, support only very slow, if any, expansion of mtDNA deletions (Moraes et al., 1989).What does this mean with respect to the question in the title of this commentary – when do mtDNA deletions arise? In fact, if we accept the mixed scenario, then it follows that the share of late mutations is at least significant. Indeed, if late mutations played little role, then accumulation of mutations should have been markedly decelerating with age. This is because ‘fast expander’ cells are saturated with mutations early in life and increase in mutation load at older age is driven by progressively ‘slower expanders’, meaning slower increase in mutational load. In contrast with this prediction, accumulation of deletions observed in most tissues appears to aggressively accelerate with age and is traditionally approximated with an exponent. This is also true for the Taylor et al. (2014), which are better fit by accelerating curves than they are by linear function (Fig. S6). The fraction of deleted mtDNA increases over the lifespan by up to four orders of magnitude in highly affected brain areas such as substantia nigra and about three in less affected, such as cortex (Meissner et al., 2008). In principle, even such a dramatic increase in mutant fraction might be entirely driven by expansion of early founder mutations in slow scenario. Neurons contain thousands of mtDNA copies, so expansion alone could potentially sustain about four orders of magnitude increase in mutant fraction from single founder mutants mtDNA to fully mutant cells. However, accelerated accumulation of (expanded) mutations in mixed scenario can only be explained by generation of de novo mutations at older ages.The reality is probably more complicated than idealized scenarios considered above. For example, cells with expanded mutations may die preferentially. If true, this would make fast scenario/late origin even more plausible. Indeed, that would mean that the actual number of mutations that have reached full expansion at any age is higher than observed (extra mutations being those that had died), implying that mutations expand faster than it appears. Other refinements of the model are certainly possible. However, notable variability of the data makes testing hypotheses, in particular, complex ones, difficult. Excessive variability of data on mtDNA deletions has been observed before, for example Meissner et al., 2008, but have never been duly explored. Lack of replicate analyses hampers understanding of the source of variance and of the shape of the frequency distributions of mutations. The latter are indispensable for interpreting the data. Future studies seeking to explain dynamics of mutations with age must include multiple replicate measurements (Supplemental Note 7).In conclusion, re-analysis of the data (Taylor et al., 2014) challenges the authors’ inference that diversity of unique deletions remains constant with age and that expansion of pre-existing deletions is the primary factor contributing to age-related accumulation of mtDNA deletions. The data are more consistent with increasing diversity of deletions and significant impact of mutagenesis at older age. However, the issue is far from being solved, in part because of high variability of the data, and it awaits more detailed studies (Supplemental Note 7).     

贵州黄牛mtDNA D-loop 遗传多样性研究

对贵州4个地方黄牛品种共计82个个体的线粒体DNA D-loop区全序列910 bp进行分析,检测到31种单倍型,其核苷酸多态位点65个,约占所测核苷酸总长的7.14%,其中有62个转换,2个颠换,1个转换/颠换共存。贵州4个黄牛品种mtDNA D-loop区核苷酸多样度（π值）为2.16%～2.61%,单倍型多样度（H）为0.695～0.909,表明贵州黄牛mtDNA遗传多样性比较丰富。根据单倍型构建了贵州4个黄牛品种的NJ分子系统树。聚类表明,贵州黄牛有普通牛和瘤牛2大母系起源,其影响较为均一。并探讨了用核苷酸多样度π值的大小来衡量黄牛群体遗传分化程度的可行性。      

用mtDNA控制区序列鉴定长须鲸标本

测定了采自长江口和杭州湾交汇海域的一头死亡大型须鲸骨骼标本的线粒体DNA(mt DNA)控制区序列(Control region)976 bp(登录号MF781125)、细胞色素C氧化酶Ⅰ基因(COⅠ)序列642 bp(登录号MG010134)和Cyt b序列307 bp(登录号MG010133)。通过与Gen Bank已发表的同源序列blast结果表明,与美国加利福尼亚海域长须鲸(Balaenoptera physalus)的控制区序列相似度达99%,仅在756 bp处有一个碱基T和C转换的差异;基于HKG+G模型,使用UPGMA聚类分析法和最大似然法(ML)构建的系统发育树与blast结果一致,故将标本鉴定为长须鲸,推测该个体来源于北太平洋。获取的细胞色素C氧化酶Ⅰ和Cyt b序列存在多个终止密码子,无法获取同源性较高的序列,推断可能为线粒体假基因。     

20例藏族人mtDNA多态性的研究

本文通过对20例藏族产妇胎盘线粒体DNA (mtDNA）限制性类型的分析,发现藏族mtDNA是高度多态的,其每个核昔酸位点的平均替换率为0.035,比世界上迄今所报道的值都大。     

云南猕猴mtDNA控制区全序列测定

目的测定云南猕猴线粒体DNA控制区全序列,对其进行鉴定及进化分析。方法利用PCR技术扩增猕猴线粒体DNA控制区全序列,结合GenBank中下载的猕猴参考序列（AY612638）,采用多个生物学软件对序列碱基组成、同源性、转换／颠换比等遗传信息进行分析,并基于邻接法（NJ）和最小进化法（ME）构建系统进化树。结果云南猕猴线粒体DNA控制区全长为（1084-1089）bp,A、T、G和c四种碱基平均含量分别为29．9％、26．9％、12．3％和30．9％,A＋T含量（56．8％）高于G＋C含量（43．2％）。所分析序列间的同源性为91．5％-99．5％,平均核苷酸变异率为4．5％,变异类型包括转换、颠换、插入和缺失4种形式,转换／颠换比值平均为26．1。进化树显示云南猕猴存在两个平行进化的姐妹分支。结论本研究获得了云南猕猴mtDNA控制区全序列,为猕猴进化关系研究及mtDNA控制区功能研究奠定基础。     

mtDNA拷贝数的生物学意义及其调控

线粒体是细胞能量和自由基代谢中心,并在细胞凋亡、钙调控、细胞周期和信号转导中发挥重要作用,维持线粒体功能正常对于细胞正常行使职能意义重大。线粒体的功能与线粒体DNA（mitochondrial DNA,mtDNA）的数量和质量紧密相关,mtDNA的数量即mtDNA拷贝数又受到mtDNA质量的影响,因此mtDNA拷贝数可作为线粒体功能的重要表征。mtDNA拷贝数变异引起线粒体功能紊乱,进而导致疾病发生。本文综述了mtDNA拷贝数变异与神经退行性疾病、心血管疾病、肿瘤等疾病的发生发展和个体衰老之间的关系,以及mtDNA复制转录相关因子、氧化应激、细胞自噬等因素介导mtDNA拷贝数变异的调控机制。以期为进一步深入探究mtDNA拷贝数调控的分子机制,以及未来治疗神经退行性疾病、肿瘤及延缓衰老等提供一定的理论基础。     

褐马鸡圈养种群的mtDNA控制区多态性

褐马鸡(Crossoptilon mantchuricum)是我国特有的濒危鸟类,国家一级保护动物。为了保护褐马鸡的种质资源,从分子水平上评价褐马鸡的遗传多样性,对山西省庞泉沟自然保护区、太原市动物园的褐马鸡种群20个个体,线粒体DNA D-loop区全序列进行了克隆和测定,使用Clustal X、DnaSP4.0、Mega3.1等生物信息学软件,对全部20条序列开展了比对分析,确定了多态位点与单倍型数目,计算了核苷酸多样性和单倍型多样性;比较了两个种群的遗传变异,初步探讨了褐马鸡种群的遗传多样性和遗传结构。结果表明:20条褐马鸡线粒体DNA D-loop区全序列长度在1236 1237bp之间,其中A、T、G和C 4种核苷酸的平均含量分别为31.0%、26.8%、27.5%和14.8%,A+T含量(57.8%)高于G+C含量(42.3%)。排除1处核苷酸的插入或缺失后,共检测出26个突变位点,占分析序列总长度的2.1%,其中包括25处单一多态位点和1处简约信息位点。20个个体检测出13个单倍型,其中11个个体具有独特的单倍型,2个共享单倍型。褐马鸡两个种群的单倍型多样性(h)为0.911 0.933,核苷酸多样性(π)为0.002 0.003,单倍型间的遗传距离为0.003 0.002。根据单倍型构建了褐马鸡两个种群的NJ分子系统树。聚类表明,2个种群的个体并没有按相应的地理位置进行聚类。揭示褐马鸡具有较高的单倍型多样性和较低的核苷酸多样性,种群的遗传变异较低;两个种群单倍型间的遗传距离较近,遗传多样性参数接近,统计分析无显著差异,两个种群尚未表现出明显的遗传分化,且两个种群间有基因流存在。     

Does premature aging of the mtDNA mutator mouse prove that mtDNA mutations are involved in natural aging?

Recent studies have demonstrated that transgenic mice with an increased rate of somatic point mutations in mitochondrial DNA (mtDNA mutator mice) display a premature aging phenotype reminiscent of human aging. These results are widely interpreted as implying that mtDNA mutations may be a central mechanism in mammalian aging. However, the levels of mutations in the mutator mice typically are more than an order of magnitude higher than typical levels in aged humans. Furthermore, most of the aging-like features are not specific to the mtDNA mutator mice, but are shared with several other premature aging mouse models, where no mtDNA mutations are involved. We conclude that, although mtDNA mutator mouse is a very useful model for studies of phenotypes associated with mtDNA mutations, the aging-like phenotypes of the mouse do not imply that mtDNA mutations are necessarily involved in natural mammalian aging. On the other hand, the fact that point mutations in aged human tissues are much less abundant than those causing premature aging in mutator mice does not mean that mtDNA mutations are not involved in human aging. Thus, mtDNA mutations may indeed be relevant to human aging, but they probably differ by origin, type, distribution, and spectra of affected tissues from those observed in mutator mice.     

婆罗门牛mtDNA D-loop变异及其遗传背景

对10头原种婆罗门牛mtDNAD-loop全序列912 bp测序,婆罗门牛遗传多样性丰富,检测到的9种单倍型兼有瘤牛(B.indicus)与普通牛(B.taurus)的遗传背景,核苷酸变异率为6.25 %,单倍型多态度为0.978±0.054 ,核苷酸多态度为0.014 30±0.008 68。所有单倍型聚为明显的两大分支,婆罗门牛的大部分单倍型为普通牛单倍型类群,并占绝对优势(90 %) ,仅Brah-6与亚洲瘤牛聚在一起,属于亚洲瘤牛线粒体单倍型,表明婆罗门牛的确是集亚洲瘤牛、欧洲普通牛等优良特性于一身(易产犊、产肉性能好、耐热与体表寄生虫等)的瘤牛品种之一。育种学家引种瘤牛的目的是改善当地牛的生产力与适应性,现代普通牛表现出明显又普遍的瘤牛渐渗现象。对现代的瘤牛品种而言,除亚洲瘤牛品种外,普通牛对其他瘤牛品种育成的贡献同样高。支持瘤牛(B.indicus)为独立驯化、起源于印度次大陆的假说。     

华东地区地方鸡品种mtDNA控制区遗传多样性

为了探明我国华东地区地方鸡品种的遗传多样性和群体遗传结构,追溯其母系起源和进化过程,利用PCR技术扩增了11个地方鸡品种的线粒体DNA(mtDNA)控制区(D-loop)序列,并结合NCBI数据库中已发表的红色原鸡(Gallus gallus)D-loop区全序列,分析了它们的遗传多样性与亲缘关系,构建了11个品种与红色原鸡系统发生邻接树。结果表明:11个地方品种mtDNA D-loop区全长为1,231或1,232 bp,其中1,231 bp的序列有196条,1,232 bp的序列有123条,经过比对发现,两者在859 bp处存在单碱基缺失。11个地方品种319个个体共计检测到变异位点37个,总体单倍型多样度核苷酸多样度和平均核苷酸差异分别为0.901±0.009、0.00573±0.000001和6.833。按照鸡mtDNA单倍型分类通用标准,共包含35种单倍型,可以分为A、B、C和E共4个分支(单倍型群),分别包括11、10、9和5个单倍型。中介网络图中11个鸡品种也很明显地分成了4个支系,分别含有100、118、47和54条序列。系统发育树分为4个大枝,海南亚种(G.gallus jabouillei)自成一枝;C单倍型群与4个亚种的红色原鸡聚为一枝;E单倍型群与2个亚种红色原鸡聚为一枝;A和B单倍型群只与滇南亚种(G.gallus spadiceus)聚为一枝。11个品种中,除了狼山和丝羽乌骨鸡2个标准化品种外,都有很高的遗传多样性,可开发选择潜力很大。没有发现线粒体品种特异性DNA序列。华东地区地方品种至少有4个母系起源,部分品种可能受到了欧美高产品系的渗入。     

Origins and Evolution of the Etruscans’ mtDNA

The Etruscan culture is documented in Etruria, Central Italy, from the 8^th to the 1^st century BC. For more than 2,000 years there has been disagreement on the Etruscans’ biological origins, whether local or in Anatolia. Genetic affinities with both Tuscan and Anatolian populations have been reported, but so far all attempts have failed to fit the Etruscans’ and modern populations in the same genealogy. We extracted and typed the hypervariable region of mitochondrial DNA of 14 individuals buried in two Etruscan necropoleis, analyzing them along with other Etruscan and Medieval samples, and 4,910 contemporary individuals from the Mediterranean basin. Comparing ancient (30 Etruscans, 27 Medieval individuals) and modern DNA sequences (370 Tuscans), with the results of millions of computer simulations, we show that the Etruscans can be considered ancestral, with a high degree of confidence, to the current inhabitants of Casentino and Volterra, but not to the general contemporary population of the former Etruscan homeland. By further considering two Anatolian samples (35 and 123 individuals) we could estimate that the genetic links between Tuscany and Anatolia date back to at least 5,000 years ago, strongly suggesting that the Etruscan culture developed locally, and not as an immediate consequence of immigration from the Eastern Mediterranean shores.     

POPU金钱豹mtDNA ND5基因序列及其遗传变异

测定山西7个不同地域金钱豹9个体mtDNA ND5基因部分序列,结合GenBank下载的37条序列,进行遗传多样性分析,用豹属的狮Panthera leo和虎Panthera tigris作外群,构建其不同单倍型的NJ分子聚类关系。46条序列共产生18个单倍型,单倍型多样度和核苷酸多样度指数表明金钱豹的遗传多样性比较丰富,AMOVA分析显示其种群间出现显著的遗传分化。单倍型聚类分析表明,亚洲和非洲种群尽管有一定的分化,但其置信度很低,不足以形成各自独立的分支。