植物病原菌无毒基因分离及特性的研究进展

植物病原菌无毒基因分离及特性的研究进展鲁国东,黄大年（福建农学院植保系,福州３５０００２）（中国水稻研究所,杭州３１０００６）Ｆｌｏｒ（１９７１）在对亚麻及其锈菌小种特异性作用的研究中,提出“基因对基因”假说。后来的大量遗传学分析证明,许多病原菌与寄主之间确实存在着这种关系（Ｄｅｗｉｔ,１９９２）。抗性反应通常的表现是寄主发生过敏性反应,过敏性反应的发生被推测为病菌无毒基因产生的激发子与作物抗性基因产生的受体之间相互识别的结果（Keen,1990a）。     

葡萄座腔菌科研究进展——鉴定,系统发育学和分子生态学

葡萄座腔菌科（Botryosphaeriaceae）真菌是农业和林业上重要的病原菌、内生真菌或潜在的致病菌,主要引起树木溃疡病。这类真菌种类繁多,寄主范围广,广泛分布于全球,在生态系统中占有重要的地位。本文综述了近年来国内外在葡萄座腔菌科的分子生态学研究方面取得的新进展。简要介绍了葡萄座腔菌科真菌物种鉴定及其研究方法方面的发展,并列出了2006年以来发现的6个新属和38个新种;概述了该科各个种、属之间的系统发育关系以及科内区分的18个群。在真菌种群遗传结构及其与寄主关系方面,已有研究表明葡萄座腔菌科真菌大体可分为寄主专化型和广谱寄生型两种类型,并已经揭示了无性型为Diplodia,Lasiodiplodia和Dothiorella等部分种群的遗传结构及它们与寄主之间的联系。在种内遗传多样性和基因流动研究方面,展示了利用ISSR、SSR等分子标记方法取得的一些重要结果,有些种群（如Lasiodiplodia theobromae）没有寄主专化性,它们在不同寄主间表现出很强的基因流动,但在不同区域内的基因交流却很有限。文章最后讨论了该科分子生态学研究有待进一步解决的问题。     

病原菌与自然植物种群Ⅲ.病原菌与寄主植物的共进化及媒体传布的菌病的种群模型

在自然植物种群中,病原菌与寄主植物不仅在个体发育的水平上相互作用,而且在系统发育的水平上相互作用。这后一种相互作用的结果就是病原菌与寄主植物的共进化。本文论述了病原菌与寄主植物共进化的主要方面病原菌的致病力和寄主植物种群的遗传结构。鉴于传媒方式在进化上具有重要意义,本文还简单介绍了媒体传布的菌病的种群模型     

病原菌与自然植物种群

在自然植物种群中,病原菌与寄主植物不仅在个体发育的水平上相互作用,而且在系统发育的水平上相互作用。这后一种相互作用的不是病原菌与寄主植物的共进化。本文论述了病原菌与寄主植物共进化的主要方面原菌的致病力和寄主植物种群的遗传结构。鉴于传媒方式在进化上具有重要意义,本文还简单介绍了媒体传布的菌病的种群模型。     

二化螟寄主种群及种群间生殖隔离形成机制研究

水稻和茭白是二化螟的两种主要寄主植物。经过长期的适应进化,二化螟已分化出水稻种群和茭白种群。国内外的大量研究表明,二化螟水稻种群和茭白种群在形态特征、交配行为、寄主选择性、寄主适应性、田间种群动态、越冬生物学、生理生化及遗传多样性等方面均存在一定差异。二化螟水稻种群和茭白种群的发生期差异、交配节律差异和交配后配子不亲合等可能造成了寄主种群的部分生殖隔离。目前认为,二化螟两寄主种群间仍存在基因交流,处于物种形成的早期阶段,但尚需从寄主种群鉴定、表型差异区分和种群间生殖隔离机制等方面开展深入研究来进一步明确,为理解昆虫同域物种形成机制提供参考。     

濒危动物主要组织相容性复合体基因的研究现状及前景

主要组织相容性复合体（MHC）基因是脊椎动物体内与免疫应答调节密切相关的一个基因家族,由紧密连锁的高度多态性基因座位组成。MHC基因具有高变异性,在机体免疫系统中发挥着非常重要的作用,而且与物种的抗病性和易感性,以及种群繁殖力和生存力密切相关。对MHC基因进行研究,在种群遗传学,特别是濒危动物的保护遗传学中具有独特的优势和应用前景。     

一个新的体外快速鉴定十字花科黑腐病菌Ⅲ型效应物的报告质粒的构建

植物病原菌侵染寄主的过程就是病原菌和寄主植物相互作用的过程.在这个相互作用过程中,Ⅲ型分泌系统和Ⅲ效应物与病原菌致病密切相关.大部分革兰氏阴性植物病原菌通过Ⅲ型分泌系统定向的把效应物蛋白传递到宿主细胞,效应物蛋白进入植物体引起致病或过敏反应.本研究将百日咳杆菌的腺苷酸环化酶基因连接至含启动子的pLAFRJ载体上,从而构建出一个新的体外快速鉴定Ⅲ型效应物的报告质粒pJJA,并用已鉴定为Ⅲ型效应物基因的十字花科黑腐病菌XC1553的启动子和信号区验证该报告质粒,证明这个系统是可以工作的.该报告质粒为进一步精确筛选鉴定十字花科黑腐病菌Ⅲ型效应物提供了材料.     

中国东北地区亚洲玉米螟遗传多样性及寄主专化性分析

【目的】为了明确中国东北地区亚洲玉米螟Ostrinia furnacalis不同地理种群、不同田间寄主种群间的遗传分化程度和基因流水平,了解不同种群间是否产生寄主专化性和遗传结构的差异。【方法】采用双向测序法测定了采自中国东北3种主要寄主植物(玉米Zea mays、高粱Sorghum bicolor和谷子Setaria italica)上的23个地理种群400头亚洲玉米螟个体的线粒体细胞色素氧化酶亚基I (COI)基因的1 034 bp序列;利用DnaSP 5. 0和Arlequin3. 5. 1. 2等软件对亚洲玉米螟不同寄主种群以及不同地理种群间的遗传多样性、基因流水平、系统进化发育和分子变异进行分析。【结果】共获得400条长度为1 034 bp的亚洲玉米螟线粒体COI基因序列,包含60个单倍型。亚洲玉米螟总群体的单倍型多样度(Hd)为0. 793±0. 01,不同地理种群间的单倍型多样度范围在0~0. 916±0. 041之间。总基因交流水平较高(Nm=2. 67),总的群体Fst为0. 1579,不同地理种群的Fst范围在-0. 046~0. 627之间。总种群的Tajima’s D为-1. 73602,检验结果不显著,说明中国东北地区亚洲玉米螟未曾在近期经历种群扩张,群体大小稳定。AMOVA分子变异分析结果表明,不同地理种群间的遗传分化较大(Fst=0. 16236,P 0. 0001),83. 76%遗传变异主要来自种群内部,而种群间的遗传变异仅为16. 24%,不同寄主种群间的遗传分化(Fct=0. 01568,P 0. 05)很小,遗传变异仅占1. 57%,说明亚洲玉米螟的遗传分化主要来自种群内部,而非其地理种群间或寄主种群间。各寄主种群的单倍型在系统发育树上和中介网络图上散布在不同种群中,缺乏明显的地理分布和寄主分布格局。【结论】亚洲玉米螟较强的迁飞能力(或随气流进行远距离传播),使各种群间的基因交流未受到地理距离的影响,遗传距离与地理距离之间没有显著相关性。虽其寄主范围较广,但取食不同寄主植物的亚洲玉米螟种群间基因交流频繁,以玉米、高粱和谷子为寄主的种群尚未产生寄主专化性和种群遗传结构的差异。     

磷酸酶在病原菌侵染寄主中的作用

磷酸酶不仅在生物体正常细胞进程中具有重要作用,而且在病原菌与寄主相互作用中也起着至关重要的作用。目前,国内外在革兰氏阴性病原菌通过其Ⅲ型分泌系统(Type III secretion system,TTSS)分泌磷酸酶到寄主细胞以调控寄主免疫和扩大病原性方面研究较多,而在病原真菌逃避寄主免疫方面则报道很少。本课题组研究发现昆虫病原真菌绿僵菌分泌的一种胞外酪氨酸蛋白磷酸酶在体外能特异地使蝗虫体液免疫信号转导物质去磷酸化,暗示可能影响蝗虫的免疫防御。以下着重从磷酸酶的分类及其在病原菌侵染寄主中的作用研究等方面进行综述,以期为进一步研究磷酸酶的作用提供参考。     

《昆虫细胞内共生物:形态,生理,遗传,演化》

<正> 本书系由昆虫各重要类群的细胞内共生物的专家写成。其重点是:它们形态学的电镜研究、寄主与共生物的相互关系、寄主的免疫系统、共生物的遗传学及基因表达、它们的系统发育和分类等。目前已知,在蟑螂、白蚁、蚂蚁、蝉、蚧虫、蚜虫、蝇类、甲虫及蜱类中均有细胞内共生物。     

eSGA: E. coli synthetic genetic array analysis

Physical and functional interactions define the molecular organization of the cell. Genetic interactions, or epistasis, tend to occur between gene products involved in parallel pathways or interlinked biological processes. High-throughput experimental systems to examine genetic interactions on a genome-wide scale have been devised for Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans and Drosophila melanogaster, but have not been reported previously for prokaryotes. Here we describe the development of a quantitative screening procedure for monitoring bacterial genetic interactions based on conjugation of Escherichia coli deletion or hypomorphic strains to create double mutants on a genome-wide scale. The patterns of synthetic sickness and synthetic lethality (aggravating genetic interactions) we observed for certain double mutant combinations provided information about functional relationships and redundancy between pathways and enabled us to group bacterial gene products into functional modules.     

How to reconstruct a large genetic network from n gene perturbations in fewer than n(2) easy steps.

MOTIVATION: The reconstruction of genetic networks is the holy grail of functional genomics. Its core task is to identify the causal structure of a gene network, that is, to distinguish direct from indirect regulatory interactions among gene products. In other words, to reconstruct a genetic network is to identify, for each network gene, which other genes and their activity the gene influences directly. Crucial to this task are perturbations of gene activity. Genomic technology permits large-scale experiments perturbing the activity of many genes and assessing the effect of each perturbation on all other genes in a genome. However, such experiments cannot distinguish between direct and indirect effects of a genetic perturbation. RESULTS: I present an algorithm to reconstruct direct regulatory interactions in gene networks from the results of gene perturbation experiments. The algorithm is based on a graph representation of genetic networks and applies to networks of arbitrary size and complexity. Algorithmic complexity in both storage and time is low, less than O(n(2)). In practice, the algorithm can reconstruct networks of several thousand genes in mere CPU seconds on a desktop workstation. AVAILABILITY: A perl implementation of the algorithm is given in the Appendix. CONTACT: wagnera@unm.edu     

Predicting genetic interactions with random walks on biological networks

Background  

Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.     

Genome-wide prediction of functional gene-gene interactions inferred from patterns of genetic differentiation in mice and men

The human genome encodes a limited number of genes yet contributes to individual differences in a vast array of heritable traits. A possible explanation for the capacity our genome to generate this virtually unlimited range of phenotypic variation in complex traits is to assume functional interactions between genes. Therefore we searched two mammalian genomes to identify potential epistatic interactions by looking for co-adapted genes marked by excess two-locus genetic differentiation between populations/lineages using publicly available SNP genotype data. The practical motivation for this effort is to reduce the number of pair-wise tests that need to be performed in genome-wide association studies aimed at detecting GxG interactions, by focusing on pairs predicted to be more likely to jointly affect variation in complex traits. Hence, this approach generates a list of candidate interactions that can be empirically tested. In both the mouse and human data we observed two-locus genetic differentiation in excess of what can be expected from chance alone based on simulations. In an attempt to validate our hypothesis that pairs of genes showing excess genetic divergence represent potential functional interactions, we selected a small set of gene combinations postulated to be interacting based on our analyses and looked for a combined effect of the selected genes on variation in complex traits in both mice and man. In both cases the individual effect of the genes were not significant, instead we observed marginally significant interaction effects. These results show that genome wide searches for gene-gene interactions based on population genetic data are feasible and can generate interesting candidate gene pairs to be further tested for their contribution to phenotypic variation in complex traits.     

Effects of methionine synthase and methylenetetrahydrofolate reductase gene polymorphisms on markers of one-carbon metabolism

Genetic and nutritional factors play a role in determining the functionality of the one-carbon (1C) metabolism cycle, a network of biochemical reactions critical to intracellular processes. Genes encoding enzymes for methylenetetrahydrofolate reductase (MTHFR) and methionine synthase (MTR) may determine biomarkers of the cycle including homocysteine (HCY), S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH). MTHFR C677T is an established genetic determinant of HCY but less is known of its effect on SAM and SAH. Conversely, the relationship between MTR A2756G and HCY remains inconclusive, and its effect on SAM and SAH has only been previously investigated in a female-specific population. Folate and vitamin B₁₂ are essential substrate and cofactor of 1C metabolism; thus, consideration of gene–nutrient interactions may clarify the role of genetic determinants of HCY, SAM and SAH. This cross-sectional study included 570 healthy volunteers from Kingston, Ontario, Ottawa, Ontario and Halifax, Nova Scotia, Canada. Least squares regression was used to examine the effects of MTR and MTHFR polymorphisms on plasma HCY, SAM and SAH concentrations; gene–gene and gene–nutrient interactions were considered with the inclusion of cross-products in the model. Main effects of MTR and MTHFR polymorphisms on HCY concentrations were observed; however, no gene–gene or gene–nutrient interactions were found. No association was observed for SAM. For SAH, interactions between MTR and MTHFR polymorphisms, and MTHFR polymorphism and serum folate were found. The findings of this research provide evidence that HCY and SAH, biomarkers of 1C metabolism, are influenced by genetic and nutritional factors and their interactions.     

Age-specific changes in epistatic effects on mortality rate in Drosophila melanogaster

Models for the evolution of senescence assume that genes with age-specific effects act independently of one another. Although recent empirical data show that longevity is influenced in part by interactions between genes, there are currently few data on whether epistasis influences age-specific components of mortality. To gauge if and how interactions affect age-specific traits, we incorporated the Drosophila visible marker mutations ebony, forked, and purple into seven wild-caught strains of D. melanogaster to examine gene x genetic background interactions. We found significant natural genetic variation for longevity and baseline mortality rates. Gene x genetic background interactions were prevalent not only for longevity but also for baseline mortality rates and age-specific mortality rates. We conclude that gene x genetic background epistasis is prevalent for aging-related traits and could play a significant role in the evolution of aging. These results suggest that future genetic models for the evolution of aging should incorporate the effects of epistasis.     

Gene-Environment Dependence Creates Spurious Gene-Environment Interaction

Gene-environment interactions have the potential to shed light on biological processes leading to disease and to improve the accuracy of epidemiological risk models. However, relatively few such interactions have yet been confirmed. In part this is because genetic markers such as tag SNPs are usually studied, rather than the causal variants themselves. Previous work has shown that this leads to substantial loss of power and increased sample size when gene and environment are independent. However, dependence between gene and environment can arise in several ways including mediation, pleiotropy, and confounding, and several examples of gene-environment interaction under gene-environment dependence have recently been published. Here we show that under gene-environment dependence, a statistical interaction can be present between a marker and environment even if there is no interaction between the causal variant and the environment. We give simple conditions under which there is no marker-environment interaction and note that they do not hold in general when there is gene-environment dependence. Furthermore, the gene-environment dependence applies to the causal variant and cannot be assessed from marker data. Gene-gene interactions are susceptible to the same problem if two causal variants are in linkage disequilibrium. In addition to existing concerns about mechanistic interpretations, we suggest further caution in reporting interactions for genetic markers.     

The role of pleiotropy vs signaller-receiver gene epistasis in life history trade-offs: dissecting the genomic architecture of organismal design in social systems

Traditional life history theory ignores trade-offs due to social interactions, yet social systems expand the set of possible trade-offs affecting a species evolution--by introducing asymmetric interactions between the sexes, age classes and invasion of alternative strategies. We outline principles for understanding gene epistasis due to signaller-receiver dynamics, gene interactions between individuals, and impacts on life history trade-offs. Signaller-receiver epistases create trade-offs among multiple correlated traits that affect fitness, and generate multiple fitness optima conditional on frequency of alternative strategies. In such cases, fitness epistasis generated by selection can maintain linkage disequilibrium, even among physically unlinked loci. In reviewing genetic methods for studying life history trade-offs, we conclude that current artificial selection or gene manipulation experiments focus on pleiotropy. Multi-trait selection experiments, multi-gene engineering methods or multiple endocrine manipulations can test for epistasis and circumvent these limitations. In nature, gene mapping in field pedigrees is required to study social gene epistases and associated trade-offs. Moreover, analyses of correlational selection and frequency-dependent selection are necessary to study epistatic social system trade-offs, which can be achieved with group-structured versions of Price's (1970) equation.     

Assembly systems in molecular biology: A graph theory of genetic complementation between two related species

In this report I discuss the interpretation of the data of genetic complementation in vivo of assembly systems between two related species, such as the complementation of structural proteins between related bacteriophages. It is suggested that such experiments reveal interactions between gene products that are overlooked in many other experiments. A mathematical model based on the graph theory is presented, assuming that the assembly system consists of gene products and interactions between them. The model shows that information from the complementation experiment is limited to those interactions which are mismatching if the interacting gene products are produced by different species. Moreover, it shows that the number and positions of mismatching interactions cannot always be determined uniquely by the data of complementation. However, there is a mathematical method by which one can calculate all the possible solutions for the number and positions of mismatching interactions from experimental data. Actual calculation is performed for a simple example. Thus, the model clarifies validity and limitation of complementation experimiments between related species.