缅甸陆龟线粒体全基因组的测序及分析

本文参照近缘物种的线粒体基因组序列,设计17对特异引物,采用LD-PCR、PCR及测序技术获得了我国广西产缅甸陆龟的线粒体全基因组序列,分析了其基因组特点和各基因的定位。结果表明:缅甸陆龟线粒体基因组全长为16813bp,碱基组成为35.30%A、26.47%T、12.09%G、26.14%C,包括13个蛋白质编码基因、2个rRNA基因、22个tRNA基因和1个非编码基因控制区(D-Loop区)。缅甸陆龟线粒体基因组各基因长度、位置与典型的脊椎动物相似,其编码蛋白质区域和rRNA基因与其它脊椎动物具有很高的同源性,显示龟类线粒体基因组在进化上十分保守。将缅甸陆龟的线粒体基因组序列提交到GenBank,获得的检索号为DQ656607。本文还结合GenBank中已发表的其它16种龟鳖类动物的线粒体基因组序列,探讨龟鳖类动物不同科间的系统进化关系。     

柳紫闪蛱蝶线粒体基因组全序列及与相关蛱蝶类的比较分析(英文)

该文对柳紫闪蛱蝶Apaturailia(鳞翅目:蛱蝶科)的线粒体基因组全序列进行了测定,同时结合其它已知蛱蝶类的相应序列进行了比较分析。结果显示:柳紫闪蛱蝶的线粒体基因组(GenBankaccessionno.:JF437925)是一个15242bp的环状DNA分子,包含13个蛋白质编码基因、2个rRNA基因和22个tRNA基因。13个蛋白编码基因中,除了COI基因的起始密码子是CGA外,其余12个蛋白编码基因都具有标准的ATN起始密码子;柳紫闪蛱蝶与其它已测的10种蛱蝶在基因定位和排列顺序方面几乎相同,只是在非编码序列上存在细微的差异,其核苷酸的构成及密码子使用频率都处于鳞翅目昆虫的范围之内。22个的tRNA基因中,除了tRNASer(AGN)缺少DHU臂,其余的tRNA基因都显示为典型的三叶草结构。基因组共存在9处基因间重叠区(总长度为33bp)以及12个基因间隔区(总长为155bp,最长间隔是49bp,最短的是1bp)。在ND6和Cytb间的间隔区中还发现有(TA)23似微卫星结构。与其他蛱蝶类相似,403bp的AT富集区包含有ATAGA,ATTTA二个保守模块(一个21bp的poly-T,一个10bp的poly-A),以及二个似微卫星的重复结构((TA)10和(TA)7)。     

乌龟线粒体全基因组序列和结构分析

龟鳖类同其它类群脊椎动物的系统进化关系一直存在争论。为进一步从分子水平上探讨这一问题,本文参照近源物种的线粒体基因组,设计了16对特异引物,采用PCR产物直接测序法测得了乌龟线粒体基因组全序列。结果表明:乌龟线粒体基因组序列全长16576bp,包括2个rRNA基因、22个tRNA基因、13个蛋白质编码基因和1个非编码控制区。乌龟线粒体基因组结构和基因排列顺序与其它龟鳖类相同,在“WANCY区”包含一个“stemloop”结构,ND3基因174位点存在一个额外插入的腺苷酸(A)。本文通过比较分析结构基因在主要脊椎动物类群中的排列顺序,探讨了龟鳖类与其它主要脊椎动物类群的系统进化关系     

赤麂线粒体全基因组的序列和结构

提取赤麂细胞株总DNA,参照我们实验室已测定的同属动物小麂线粒体全基因组序列设计引物,PCR扩增、测序、拼接,获得赤麂线粒体全基因组序列并进行生物信息学分析。赤麂线粒体全基因组序列全长16354bp。定位了22个tRNA基因、2个rRNA基因、13个蛋白编码基因和1个D-loop区。赤麂与小麂及其它哺乳动物线粒体的基因组结构相同,它们的序列同源性都较高。     

猫蛱蝶线粒体基因组全序列分析

采用PCR步移法对猫蛱蝶Timelaea maculata线粒体基因组全序列进行了测定和分析.分析结果表明:猫蛱蝶线粒体基因组全长15 178 bp,包括13个蛋白编码基因、22个tRNA基因、2个rRNA基因和一段长度为382 bp的A+T富含区,基因排列顺序与其它已知鳞翅目昆虫相同.猫蛱蝶线粒体基因组中存在很高的A+T含量(81.1％).13个蛋白编码基因中除CO Ⅰ以CGA作为起始密码外,其余蛋白质基因均以ATN作为起始密码子.COⅡ和ND4基因使用了不完全终止密码子T,其余基因均以典型的TAA、TAG为终止密码子.在所测得的22个tRNA基因中,除tRNAser(AGN)缺少DHU臂外,其余tRNA均能形成典型的三叶草结构.与其它多数鳞翅目昆虫一样,猫蛱蝶的A+T富含区中有一段由“ATAGAA”引导的保守的多聚T结构,长度为19 bp,并散在着一些长短不一的串联重复单元.     

麦穗鱼线粒体基因组序列测定及分析

利用麦穗鱼Pseudorasbora parva和相关鱼类的部分线粒体基因序列,设计出2对长批引物和30对短批引物,采用基于长PCR的2次PCR扩增法测定并注释麦穗鱼线粒体基因组全序列。结果表明,麦穗鱼线粒体基因组长16600bp,A+T含量为58.9%,37个基因位置及组成与其它硬骨鱼一致,均由13个蛋白编码基因、22个tRNA、2个rRNA基因和1个控制区(D-loop)组成。其中L链仅含8个tRNA(Pro、T yr、Ser、Ala、Asn、Cys、Glu、Gln)及ND6基因,其余基因皆由H链编码。基因排列紧密,间隔序列共计13处64bp,长度从1～32bp不等;基因重叠区7处23bp,重叠碱基数在1～7bp之间。13个蛋白编码基因中,除COI起始密码子为GTG外,其余均以ATG为起始密码子;有8个基因(ND1、ND2、COI、ATP6、ATP8、ND4L、ND5、ND6)3’端有完全的TAA或TAG终止密码子,其它5个基因终止密码子为不完整的TA(ND3和ND4)或T(COⅡ,COⅢ,Cyt b)。除tRNASer(AGY)外,其余21个tRNA基因的二级结构均为典型的三叶草结构。预测的lrRNA二级结构共有6个结构域,53个茎环结构,srRNA二级结构包含43个茎环结构。控制区(D-loop)存在3个结构区:终止序列区(TAS)、中央保守区(CSB-F、CSB-D)和保守序列区(CSB-1、CSB-2、CSB-3),其中TAS与DNA复制终止相关,出现茎环结构。     

SEQUENCE AND ANALYSIS OF COMPLETE MITOCHONDRIAL GENOME OF PSEUDORASBORA PARVA

利用麦穗鱼Pseudorasbora parva和相关鱼类的部分线粒体基因序列,设计出2对长批引物和30对短批引物,采用基于长PCR的2次PCR扩增法测定并注释麦穗鱼线粒体基因组全序列.结果表明,麦穗鱼线粒体基因组长16600 bp,A+T含量为58.9％,37个基因位置及组成与其它硬骨鱼一致,均由13个蛋白编码基因、22个tRNA、2个rRNA基因和1个控制区(D-loop)组成.其中L链仅含8个tRNA(Pro、Tyr、Ser、Ala、Asn、Cys、Glu、Gln)及ND6基因,其余基因皆由H链编码.基因排列紧密,间隔序列共计13处64 bp,长度从1～32 bp不等;基因重叠区7处23 bp,重叠碱基数在1～7bp之间.13个蛋白编码基因中,除COI起始密码子为GTG外,其余均以ATG为起始密码子;有8个基因(ND1、ND2、COI、ATP6、ATP8、ND4L、ND5、ND6)3端有完全的TAA或TAG终止密码子,其它5个基因终止密码子为不完整的TA (ND3和ND4)或T(COⅡ,COⅢ,Cyt b).除tRNAser(AGY)外,其余21个tRNA基因的二级结构均为典型的三叶草结构.预测的lrRNA二级结构共有6个结构域,53个茎环结构,srRNA二级结构包含43个茎环结构.控制区(D-loop)存在3个结构区:终止序列区(TAS)、中央保守区( CSB-F、CSB-D)和保守序列区(CSB-1、CSB-2、CSB-3),其中TAS与DNA复制终止相关,出现茎环结构.     

日本条螽线粒体基因组序列的测定与分析

日本条螽完整的线粒体基因组序列长16 281 bp,包括13个蛋白质编码基因、22个tRNA基因、2个r RNA基因和1个D-loop区,其基因次序和方向与祖先序列相同。该线粒体基因组排列紧凑,但在ND2和tRNA~(Trp)之间有一段长为650 bp的基因间隔区。为研究螽斯科的系统发育关系,本研究选取日本条螽及其它17个螽斯科物种线粒体基因组的蛋白质编码基因和r RNA基因序列构建贝叶斯系统发生树。     

大紫蛱蝶线粒体基因组全序列分析

通过PCR步移法对大紫蛱蝶Sasakia charonda coreana线粒体基因组全序列进行了测定和分析。分析结果表明:大紫蛱蝶线粒体基因组全长15233bp,包括13个蛋白编码基因、22个tRNA基因、2个rRNA基因以及长度为381bp的非编码区。A、T、C、G碱基含量分别为39.7%、40.2%、12.2%、7.9%。9个蛋白编码基因和14个tRNA基因在J链编码,其余4个蛋白编码基因和8个tRNA基因在N链编码,基因排列顺序与其它已知鳞翅目昆虫相同。13个蛋白编码基因中除COⅠ以CGA作为起始密码外,其余蛋白质基因均以ATN作为起始密码子,终止密码子多数为典型的TAA、TAG,只有COⅡ和ND4以单独的T作为终止密码子。在所测得的22个tRNA基因中,除tRNA Ser(AGN)缺少DHU臂外,其余tRNA均能形成典型的三叶草结构。与其它多数鳞翅目昆虫一样,大紫蛱蝶的非编码区序列中散在着一些长短不一的串联重复单元,在与其近缘物种非编码区的比较当中并未发现共同的保守序列区。     

ANALYSIS OF COMPLETE MITOCHONDRIAL GENOME OF SASAKIA CHARONDA COREANA (LEPIDOPTERA, NYMPHALIDA)

通过PCR步移法对大紫蛱蝶Sasakia charonda coreana线粒体基因组全序列进行了测定和分析.分析结果表明:大紫蛱蝶线粒体基因组全长15 233 bp,包括13个蛋白编码基因、22个tRNA基因、2个rRNA基因以及长度为381bp的非编码区.A、T、C、G碱基含量分别为39.7％、40.2％、12.2％、7.9％.9个蛋白编码基因和14个tRNA基因在J链编码,其余4个蛋白编码基因和8个tRNA基因在N链编码,基因排列顺序与其它已知鳞翅目昆虫相同.13个蛋白编码基因中除COⅠ以CGA作为起始密码外,其余蛋白质基因均以ATN作为起始密码子,终止密码子多数为典型的TAA、TAG,只有COⅡ和ND4以单独的T作为终止密码子.在所测得的22个tRNA基因中,除tRNASer (AGN)缺少DHU臂外,其余tRNA均能形成典型的三叶草结构.与其它多数鳞翅目昆虫一样,大紫蛱蝶的非编码区序列中散在着一些长短不一的串联重复单元,在与其近缘物种非编码区的比较当中并未发现共同的保守序列区.     

缅甸蟒脂肪酸分析

用气相色谱法测定了缅甸蟒油20种脂肪酸,其中不饱和脂肪酸含量达67.5%,多不饱和脂肪酸含量达10.3%.含量较高的脂肪酸有油酸、棕榈酸、亚油酸、棕榈油酸,特有脂肪酸DHA、α-亚麻酸,并且明显不同于其他蟒和蛇的脂肪酸含量.缅甸蟒油具有重要的药用和保健品开发利用价值.     

Humoral regulation of heart rate during digestion in pythons (Python molurus and Python regius)

Pythons exhibit a doubling of heart rate when metabolism increases several times during digestion. Pythons, therefore, represent a promising model organism to study autonomic cardiovascular regulation during the postprandial state, and previous studies show that the postprandial tachycardia is governed by a release of vagal tone as well as a pronounced stimulation from nonadrenergic, noncholinergic (NANC) factors. Here we show that infusion of plasma from digesting donor pythons elicit a marked tachycardia in fasting snakes, demonstrating that the NANC factor resides in the blood. Injections of the gastrin and cholecystokinin receptor antagonist proglumide had no effect on double-blocked heart rate or blood pressure. Histamine has been recognized as a NANC factor in the early postprandial period in pythons, but the mechanism of its release has not been identified. Mast cells represent the largest repository of histamine in vertebrates, and it has been speculated that mast cells release histamine during digestion. Treatment with the mast cell stabilizer cromolyn significantly reduced postprandial heart rate in pythons compared with an untreated group but did not affect double-blocked heart rate. While this study indicates that histamine induces postprandial tachycardia in pythons, its release during digestion is not stimulated by gastrin or cholecystokinin nor is its release from mast cells a stimulant of postprandial tachycardia.     

圈养蟒的人工饲养训练

对成都动物园2000年以来饲养过的14条蟒进行了训练.通过观察蟒的反应,采用试探、轻触、抚摩和用温热水给蟒洗浴或用温热湿巾给蟒檫拭身体等方式,让野性较大不易接近或对新饲养环境应激反应很大的蟒尽快适应人工饲养并变得更加温顺可接近.本文获得了圈养蟒接近和保定的一些经验.     

ScrumPy: metabolic modelling with Python

ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy.     

Object-oriented parsing of biological databases with Python

MOTIVATION: While database activities in the biological area are increasing rapidly, rather little is done in the area of parsing them in a simple and object-oriented way. RESULTS: We present here an elegant, simple yet powerful way of parsing biological flat-file databases. We have taken EMBL, SWISSPROT and GENBANK as examples. EMBL and SWISS-PROT do not differ much in the format structure. GENBANK has a very different format structure than EMBL and SWISS-PROT. Extracting the desired fields in an entry (for example a sub-sequence with an associated feature) for later analysis is a constant need in the biological sequence-analysis community: this is illustrated with tools to make new splice-site databases. The interface to the parser is abstract in the sense that the access to all the databases is independent from their different formats, since parsing instructions are hidden.     

Programming biological models in Python using PySB

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule‐based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule‐based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high‐level, action‐oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open‐source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.     

Modelling cellular processes with Python and Scipy

This paper shows how Python and Scipy can be used to simulate the time-dependent and steady-state behaviour of reaction networks, and introduces Pysces, a Python modelling toolkit.