牡丹复合体的分子进化和系统学研究---细胞核核糖体基因变异的分析

牡丹复合体(Paeonia suffruticosa Andr. Complex)属芍药属牡丹组，为中国特有的落叶亚灌木，野生类型均为濒危种，仅局限分布于以秦岭为中心的较小区域。由于分布区有限，个体数量少，栽培历史长，育种广泛，种间极易杂交，网状进化的广泛发生，使得该复合体分类混乱。本文选取牡丹复合体6个野生种以及2个近缘类群，采用分别基于核酸印迹杂交和聚合酶链式反应的限制性酶切片段长度多态性分析，对细胞核核糖体基因片段ITS/18s的变异进行了分析，并且结合采用微卫星DNA指纹分析技术。选取18种内切酶对特异片段进行酶切消化。共得149个酶切位点，其中67个为变异位点，占45.0%。其中编码区(2．Okb，含18s，5.8s，26s)有突变位点29个，占该区段长度的1.4%;非编码区(490bp，含ITS-1，ITS-2)有突变位点38个，占该区段长度的7.8%。由此，可明显比较二者进化的保守程度和进化速率。两段间隔区的变异程度也存在差异。ITS-1为6.0%．ITS-2为9.9%。这说明构建系统树时二者的选用应得到综合考虑或加权。在复合体内不存在长度变异，即无缺失或插入发生，暗示了该复合体各种之间亲缘关系的紧密。根据Neighbor-joining法并计算遗传距离构建系统关系图，结果如下：(1)卵叶牡丹（神农架红花类群）与紫斑牡丹分化较早，考虑其与复合体内其它各种之间的遗传距离，支持将其定为新种的观点;(2)神农架白花类群与与卵叶牡丹亲缘关系非常相近，这一结果支持了来自其它分子和表型分析的结果;(3)延安牡丹与紫斑牡丹亲缘关系极近，但与矮牡丹关系较远，是否为上述两个种的杂交种，目前为止尚无充分的证据，作为存疑种处理;(4)川牡丹和矮牡丹进化关系密切，这一结果与ITS序列分析结果完全一致，加之其地理分布式样的不连续性说明了它们的古老和残存性质，这可进而推广至本复合体乃至整个牡丹组。我们认为现存的分布格局可能是地理与气候演化的产物，估计牡丹组的野牡丹复合体从本复合体分化出去的时间约为310 - 750万年前。由于基因间协调进化的不均一作用和该类群杂种的早期起源限制了核糖体基因在追溯其网状进化历程上的作用，这符合基因转换的梯度理论。最后讨论了nrDNA得到的基因树与其它的基因树和种系树之间的关系。

木根麦冬rRNA基因进化的研究

木根麦冬（Ophiopogon xylorrhizus Wang et Dai）属于铃兰科（Convallariaceae）或广义百合科（Liliaceae s.l.）沿阶草族（Ophiopogoneae）沿阶草属（Ophiopogon Ker-Gawl.），属于典型的濒危植物。前人已从细胞学、种群生态学、生殖生物学和遗传结构与多样性等方面对木根麦冬进行了研究，但在分子进化和分子细胞遗传学水平上的研究近为空白。本文运用染色体的荧光原位杂交(FISH)、PCR扩增和克隆、DNA测序、系统发育重建等方法，对18S rDNA作了染色体原位定位，研究了木根麦冬的Ss rRNA基因结构特点，并重建了该基因的系统发育树，探讨了5S rRNA多基因家族的分子进化模式和木根麦冬的濒危机制。主要结果如下： 1．对木根麦冬三个居群七个个体、及其最近姐妹种林生麦冬（Ophiopogon svlvicola Wang et Tang）一个个体的5S rRNA基因进行了PCR扩增和TA克隆，在两个种中共得到1085个具有插入片段的阳性克隆。 2．对木根麦冬三个居群六个个体的294个SS rRNA基因克隆，及林生麦冬一个个体的45个克隆，总计339个克隆进行了DNA序列测定，这是目前已完成的最大的单个物种的5S rRNA数据。结果表明：两个种的序列高度多样化，在339个拷贝中仅仅有13对(3.8%)是相同的，序列长度变化在307bp-548bp之间，长度变异主要发生在间隔区，单个碱基的插入和缺失(indel)频率很高，5bp以上片段的插入，缺失有11个，插入的序列通常是其两侧序列的重复和倒位。术根麦冬序列的分化指数（sequence differentiation index，SDI）是0.078，林生麦冬是0.032，两个物种间是0.149，木根麦冬的序列之间的分化明显大于林生麦冬。 3．以PAUP程序对339个5S rRNA基因拷贝的DNA序列（包括编码区和间隔区）作了系统发育分析，结果如下：在得到一个唯一的最俭约树中，所有木根麦冬的拷贝被聚成一支，而林生麦冬的则被聚到另一支，统计支持率(bootstrap)达到lOO%，表明这两个物种所有的的5S rRNA基因拷贝分别来自各自的一个祖先拷贝（建立者拷贝），而其共同祖先的其它拷贝则在物种形成中或之后丢失：在多基因家族中如此长期而单一的拷贝偏选( sorting)过程尚未有前人报道;由此基因系统发育树可以看出，在这两个物种形成之后，“建立者拷贝”经历了多次扩增过程而形成了一个直系的（orthologous）多基因家族。 4．在木根麦冬分支中，很少有亚分支是全部由一个居群或一个个体的拷贝组成的，不同居群、不同个体的拷贝混合在同一个亚分支中;对基因系统发育树、序列多样性和序列分化指数分析表明，5S rRNA基因家族内一致化（homogeruzation）过程很弱，不同拷贝是独立进化的，这在串联．重复的多拷贝基因家族中是不寻常的;由上述分析我们推测，在术根麦冬的进化历史上，居群间的基因交流远远比今天频繁，可能是某些外在因素在近期发生变化，导致自交和自交衰退，并进而导致濒危。 5．利用荧光原位杂交技术，成功地将18S rRNA基因定位在木根麦冬减数分裂期的染色体上，两对强信号和一对弱信号分别位于三对二价体染色体上。

Prediction of mesophotic coral distributions in the Au‘au Channel, Hawaii

The primary objective of this study was to predict the distribution of mesophotic hard corals in the Au‘au Channel in the Main Hawaiian Islands (MHI). Mesophotic hard corals are light-dependent corals adapted to the low light conditions at approximately 30 to 150 m in depth. Several physical factors potentially influence their spatial distribution, including aragonite saturation, alkalinity, pH, currents, water temperature, hard substrate availability and the availability of light at depth. Mesophotic corals and mesophotic coral ecosystems (MCEs) have increasingly been the subject of scientific study because they are being threatened by a growing number of anthropogenic stressors. They are the focus of this spatial modeling effort because the Hawaiian Islands Humpback Whale National Marine Sanctuary (HIHWNMS) is exploring the expansion of its scope—beyond the protection of the North Pacific Humpback Whale (Megaptera novaeangliae)—to include the conservation and management of these ecosystem components. The present study helps to address this need by examining the distribution of mesophotic corals in the Au‘au Channel region. This area is located between the islands of Maui, Lanai, Molokai and Kahoolawe, and includes parts of the Kealaikahiki, Alalākeiki and Kalohi Channels. It is unique, not only in terms of its geology, but also in terms of its physical oceanography and local weather patterns. Several physical conditions make it an ideal place for mesophotic hard corals, including consistently good water quality and clarity because it is flushed by tidal currents semi-diurnally; it has low amounts of rainfall and sediment run-off from the nearby land; and it is largely protected from seasonally strong wind and wave energy. Combined, these oceanographic and weather conditions create patches of comparatively warm, calm, clear waters that remain relatively stable through time. Freely available Maximum Entropy modeling software (MaxEnt 3.3.3e) was used to create four separate maps of predicted habitat suitability for: (1) all mesophotic hard corals combined, (2) Leptoseris, (3) Montipora and (4) Porites genera. MaxEnt works by analyzing the distribution of environmental variables where species are present, so it can find other areas that meet all of the same environmental constraints. Several steps (Figure 0.1) were required to produce and validate four ensemble predictive models (i.e., models with 10 replicates each). Approximately 2,000 georeferenced records containing information about mesophotic coral occurrence and 34 environmental predictors describing the seafloor’s depth, vertical structure, available light, surface temperature, currents and distance from shoreline at three spatial scales were used to train MaxEnt. Fifty percent of the 1,989 records were randomly chosen and set aside to assess each model replicate’s performance using Receiver Operating Characteristic (ROC), Area Under the Curve (AUC) values. An additional 1,646 records were also randomly chosen and set aside to independently assess the predictive accuracy of the four ensemble models. Suitability thresholds for these models (denoting where corals were predicted to be present/absent) were chosen by finding where the maximum number of correctly predicted presence and absence records intersected on each ROC curve. Permutation importance and jackknife analysis were used to quantify the contribution of each environmental variable to the four ensemble models.