整合性接合元件SXT-R391分子生物学特性及其转移系统的研究进展

整合性接合元件(Integrative and conjugative elements,ICEs)主要介导原核生物间遗传信息的横向基因交换,在细菌毒性、耐药性、抗重金属等特性传播上发挥关键作用。ICEs的水平转移极大地加速了抗性基因在同种及不同种属之间的传播,造成细菌的耐药以至多重耐药问题日益严重,耐药机制日趋复杂;同时ICEs的接合转移过程受细菌Ⅳ型分泌系统(Type Ⅳ secretion system,T4SS)影响。本文着重从ICEs的基因结构、接合转移过程以及T4SS组成元件的结构进行概述,并对T4SS各组件间相互作用的研究进展进行了初步探讨。     

假单胞菌YL11对扩展青霉的抑制作用及其机理初探

【背景】苹果青霉病是由扩展青霉引起的一种重要的果实采后病害,影响果实品质导致苹果腐烂从而造成经济损失。【目的】研究假单胞菌YL11对扩展青霉的抑制作用和苹果采后青霉病的防治效果,并对抑菌机理进行初步探讨。【方法】以扩展青霉为供试菌株,研究不同浓度的假单胞菌YL11无菌发酵液对扩展青霉菌落直径、孢子萌发率、菌丝体干重、苹果损伤接种病斑直径扩展的影响,利用对电导率、核酸及蛋白释放量、AKP含量、SDH活性、ATP酶活性和ATP含量的影响对抑菌机理进行探究。【结果】假单胞菌YL11无菌发酵液能有效抑制扩展青霉生长,抑菌圈直径为22.33±0.27 mm,抑菌效价为71.67 mm/mL;能有效抑制孢子萌发,100%无菌发酵液对孢子萌发抑制率达到80.2%;对扩展青霉的生物量也有一定抑制作用,体积分数为100%时,菌丝体干重为4.7mg/mL,抑制率达到39.74%;无菌发酵液处理能有效抑制苹果青霉病病斑的扩展,3d时对病斑扩展的抑制率最大,达到47.1%;无菌发酵液处理均能引起电导率升高、胞内核酸和蛋白释放量增大、胞外AKP含量升高、SDH活性降低、ATP酶活性和ATP含量均降低,且随着发酵液浓度的增加效果越明显。【结论】假单胞菌YL11能显著抑制扩展青霉的生长,破坏细胞膜结构、降低能量代谢酶活性,从而扰乱扩展青霉的正常生长,对苹果青霉病有较好的生防效果,具有潜在的开发价值。     

贵州玉米籽粒镰刀菌污染情况调查

【背景】镰刀菌引起的作物病害在贵州省内时有发生,由于该地区低温高湿的气候特征并不适宜镰刀菌生长,之前未系统开展镰刀菌侵染和分布情况调查。【目的】调查贵州省玉米籽粒中镰刀菌病害的污染情况。【方法】收集贵州省58个县/县级市的玉米籽粒样品78份,利用原核表达的可用于检测镰刀菌的Fv SG7-AP融合蛋白对样品进行快速免疫学检测,并对部分样品进行生物学培养鉴定。【结果】共有67份样品检测结果为阳性,污染率高达85.90%,生物学培养后显微镜观察到镰刀菌菌丝和孢子。检测出镰刀菌污染的玉米样品分布在贵州省51个县/县级市,其中19个地区检测到轻度污染样品,20个地区检测到中度污染样品,12个地区检测到重度污染样品。【结论】贵州省绝大部分地区存在不同程度的镰刀菌污染,有必要在作物耕种、收获及贮藏期间采取有效的防控措施,以保障食品安全与人畜健康。     

红腹锦鸡血细胞的光镜和扫描电镜观察

为了探讨红腹锦鸡(Chrysolophus pictus)血细胞的形态特征,为生理学研究提供生物学基础资料,利用光镜和扫描电镜观察了红腹锦鸡血细胞的形态特征。结果表明,红腹锦鸡红细胞呈椭圆形或扁圆形,表面光滑,具核;白细胞为球形,体大,淋巴细胞表面有绒毛状突起,嗜中性粒细胞核一般分2~5叶,嗜酸性粒细胞核一般分2叶,嗜碱性粒细胞核分2~3叶,单核细胞表面粗糙不平,核大,呈肾形或圆形;凝血细胞呈球形或不规则形。     

红腹锦鸡和丽纹龙蜥视网膜的组织学观察

为了进一步探讨动物视网膜结构与机能的关系,利用光镜和扫描电镜比较观察了红腹锦鸡(Chrysolophus pictus)、丽纹龙蜥(Jspalura splendida)视网膜的结构。结果表明,红腹锦鸡、丽纹龙蜥的视网膜均由四层细胞构成,在光镜下均可分为十层结构。红腹锦鸡视网膜平均厚225·2μm,视细胞与节细胞数比约为2:1;丽纹龙蜥视网膜平均厚156.2μm,视细胞与节细胞数比为1:1。红腹锦鸡、丽纹龙蜥视网膜视细胞的平均密度分别为(124828±24404)个/mm2和(33165±7034)个/mm2。显示了红腹锦鸡和丽纹龙蜥均具有昼行性动物视网膜的结构特征。     

A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology

Based on the recent development in the gene ontology and functional domain databases, a new hybridization approach is developed for predicting protein subcellular location by combining the gene product, functional domain, and quasi-sequence-order effects. As a showcase, the same prokaryotic and eukaryotic datasets, which were studied by many previous investigators, are used for demonstration. The overall success rate by the jackknife test for the prokaryotic set is 94.7% and that for the eukaryotic set 92.9%. These are so far the highest success rates achieved for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology. The very high success rates also reflect the fact that the subcellular localization of a protein is closely correlated with: (1). the biological objective to which the gene or gene product contributes, (2). the biochemical activity of a gene product, and (3). the place in the cell where a gene product is active.     

Using functional domain composition and support vector machines for prediction of protein subcellular location

Proteins are generally classified into the following 12 subcellular locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic reticulum, 5) extracellular, 6) Golgi apparatus, 7) lysosome, 8) mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and 12) vacuole. Because the function of a protein is closely correlated with its subcellular location, with the rapid increase in new protein sequences entering into databanks, it is vitally important for both basic research and pharmaceutical industry to establish a high throughput tool for predicting protein subcellular location. In this paper, a new concept, the so-called "functional domain composition" is introduced. Based on the novel concept, the representation for a protein can be defined as a vector in a high-dimensional space, where each of the clustered functional domains derived from the protein universe serves as a vector base. With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location. High success rates are obtained by the self-consistency test, jackknife test, and independent dataset test, respectively. The current approach not only can play an important complementary role to the powerful covariant discriminant algorithm based on the pseudo amino acid composition representation (Chou, K. C. (2001) Proteins Struct. Funct. Genet. 43, 246-255; Correction (2001) Proteins Struct. Funct. Genet. 44, 60), but also may greatly stimulate the development of this area.     

Prediction of beta-turns with learning machines

The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn.     

Support vector machine for predicting alpha-turn types

Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, beta-turns and gamma-turns have been extensively studied, but alpha-turns were little investigated. Recently, a systematic search for alpha-turns classified alpha-turns into nine different types according to their backbone trajectory features. In this paper, Support Vector Machines (SVMs), a new machine learning method, is proposed for predicting the alpha-turn types in proteins. The high rates of correct prediction imply that that the formation of different alpha-turn types is evidently correlated with the sequence of a pentapeptide, and hence can be approximately predicted based on the sequence information of the pentapeptide alone, although the incorporation of its interaction with the other part of a protein, the so-called "long distance interaction", will further improve the prediction quality.     

小麦条锈病罹病植株对水分胁迫的响应

水分胁迫时,小麦条锈病病叶的叶片扩散阻力(LDR)明显增大,蒸腾速率(Tp)、相对含水量(RWC)、水势(ψw)、渗透势(ψπ)和压力势(ψp)明显降低。亲和与不亲和性反应寄主病叶对水分胁迫的反应明显不同。亲和性反应寄主叶片在水分胁迫时表现出较低的反应型,产孢期推迟;Tp、LDR、RWC、ψw、ψp和ψπ在产孢前轻微下降或升高;在开始产孢后病叶Tp急剧升高,LDR、Tp、RWC、ψw、ψπ和ψp大幅降低,在健叶尚可有效控制水分蒸腾散失时病叶已丧失了这种能力。不亲和性反应寄主病叶的蒸腾作用在显症期亦有轻微变化,但之后病叶Tp、LDR、RWC和ψw渐趋健叶水平,并保持了较健叶更低的ψπ和更高的ψw,具备了更强的水分调控能力。病株健叶与病叶具有相似的气孔行为。