青皮核桃采后病害生防菌贝莱斯芽胞杆菌XRD006全基因组分析及防治效果研究

【目的】探究生防菌贝莱斯芽胞杆菌(Bacillus velezensis) XRD006对青皮核桃采后病害的生防能力及其贮藏保鲜效果,解析菌株的基因特性和次级代谢产物,了解菌株的抑菌机制。【方法】通过抑菌试验确定XRD006对青皮核桃采后病原菌的抑制能力。利用活体抑菌及贮藏试验探究生防菌对青皮核桃采后病原菌的抑制能力及对青皮核桃贮藏品质的影响。以全基因组测序了解菌株XRD006的基因组特征及潜在抑菌相关基因;利用antiSMASH软件预测XRD006的次级代谢产物;结合比较基因组学分析XRD006和贝莱斯芽胞杆菌标准株FZB42、SQR9之间的共线性关系和次级代谢产物基因簇差异。利用高效液相色谱(high performance liquid chromatography,HPLC)和质谱鉴定XRD006次级代谢产物并通过牛津杯法测定其抑菌能力。【结果】抑菌试验表明菌株XRD006对青皮核桃采后病原菌隐秘刺盘孢(Colletotrichum aenigma)、暹罗炭疽菌(Colletotrichum siamense)、葡萄座腔菌(Botryosphaeria dothidea)和藤仓...     

snpR: User friendly population genomics for SNP data sets with categorical metadata

The analysis of genomic data can be an intimidating process, particularly for researchers who are not experienced programmers. Commonly used analyses are spread across many programs, each requiring their own specific input formats, and so data must often be repeatedly reorganized and transformed into new formats. Analyses often require splitting data according to metadata variables such as population or family, which can be challenging to manage in large data sets. Here, we introduce snpR, a user-friendly data analysis package in R for processing SNP genomic data. snpR is designed to automate data subsetting and analyses across categorical metadata while also streamlining repeated analyses by integrating approaches contained in many different packages in a single ecosystem. snpR facilitates iterative and efficient analyses centred on a single R object for an entire analysis pipeline.     

小菜蛾热休克蛋白基因的鉴定及其表达模式分析

热休克蛋白(heat shock protein, HSP)在昆虫应对外界胁迫刺激时起着重要作用。为了系统研究小菜蛾Plutella xylostella HSP基因家族, 根据家蚕的HSP蛋白序列, 采用本地Blast程序对小菜蛾全基因组数据库进行同源序列检索, 从小菜蛾基因组数据库中鉴定了25个HSP基因, 包括2个HSP90、 8个HSP70和15个sHSP（small heat shock protein, sHSP）基因。小菜蛾、 家蚕Bombyx mori、 黑腹果蝇Drosophila melanogaster和赤拟谷盗Tribolium castaneum的HSP系统进化分析显示, 昆虫的小分子量热休克蛋白sHSP具有很强的种属特异性, HSP70家族的保守性比sHSP强。小菜蛾HSP基因表达模式分析显示, 与敏感品系对比, 抗性品系（抗毒死蜱和抗氟虫氰品系）中HSP基因具有不同的表达模式。小菜蛾1, 2和3龄幼虫HSP基因表达模式较为接近, 而与4龄幼虫中的表达模式相差较大; 4龄幼虫和蛹中的表达模式相近; 雌成虫和雄成虫中的表达模式显著不同, 与果蝇精子形成有关的两个热休克蛋白HSP23和HSP27基因［分别为CCG003980.1 (Px23.5)和CCG005412.2 (Px27.5)］, 在小菜蛾雄成虫中的表达量显著高于雌成虫。研究结果表明小菜蛾HSP基因不仅在杀虫剂抗性、 发育分化, 甚至在生殖上均可能起着重要的作用。本研究为深入研究小菜蛾HSP与生长发育、 抗逆行为的相互关系奠定了基础。     

Quantitative determination of gene strand bias in prokaryotic genomes

Comparative genometrics of microorganisms is a relatively new area, in which genome properties are translated into numerical indexes. Such indexes can be used for a comprehensive and comparative analysis of microbial genomes, contributing to the understanding of their evolution. This work presents a new method for quantitative determination of gene strand bias in prokaryotic chromosomes, in which data transformation of gene position skew leads to a numerical index that can be applied to quantitative comparisons of genome organization. It was applied in the comparative analysis of 49 completely sequenced Firmicutes genomes, allowing the distinction of groups defined according to their patterns of gene strand preference. The resulting groups revealed that, regarding gene strand bias, reduced genomes are, in general, the more disordered among Firmicutes, while genomes of extremophile organisms comprehend those with the highest degree of genome organization in this phylum.     

Distinguishing enzyme structures from non-enzymes without alignments

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.     

Fruit Flies in Biomedical Research

Many scientists complain that the current funding situation is dire. Indeed, there has been an overall decline in support in funding for research from the National Institutes of Health and the National Science Foundation. Within the Drosophila field, some of us question how long this funding crunch will last as it demotivates principal investigators and perhaps more importantly affects the long-term career choice of many young scientists. Yet numerous very interesting biological processes and avenues remain to be investigated in Drosophila, and probing questions can be answered fast and efficiently in flies to reveal new biological phenomena. Moreover, Drosophila is an excellent model organism for studies that have translational impact for genetic disease and for other medical implications such as vector-borne illnesses. We would like to promote a better collaboration between Drosophila geneticists/biologists and human geneticists/bioinformaticians/clinicians, as it would benefit both fields and significantly impact the research on human diseases.     

多肽配体技术在植物功能基因组学中的应用前景

人工智能配体或适配体(Aptamer)技术是近年来兴起的一项特异性极强的基因干扰技术。通过人工合成特异的智能配体结合靶基因的蛋白产物, 达到特异干扰靶基因的生物学功能, 这是人工智能配体技术的基本设想。文章综述了多肽配体(Peptide aptamer)技术在基因功能验证中的主要进展, 着重阐明它在植物基因功能验证和作物抗病毒育种中的应用前景, 并提出克服该技术主要风险对策。     

Gene Discovery and Functional Analyses in the Model Plant Arabidopsis

The present mini-review describes newer methods and strategies, including transposon and T-DNA insertions, TILLING, Deleteagene, and RNA interference, to functionally analyze genes of interest in the model plant Arabidopsis. The relative advantages and disadvantages of the systems are also discussed.     

转座子相关技术在微生物功能基因组研究中的应用

转座子是DNA插入因子的一种,是指能在基因组间或组内跳跃的DNA片段。转座子作为插入突变剂或分子标签已被广泛地应用于基因的分离和克隆,且因其独特的性质已成为发现新基因和基因功能分析的有效工具。这使得转座子无论是在单基因水平还是全基因组水平,都成为细菌、酵母和其他微生物研究的有力工具。简单而有效的体外转座反应可以对一些以往难以进行分析的顽固微生物进行转座诱变分析。而建立在转座子基础上的信号标签诱变技术和遗传足迹法的应用则发现了一些新的病原微生物毒力因子,从而可以更好地对这些病原微生物的致病机理进行阐述。这些再次说明转座子是微生物功能基因组研究中的有力工具。本文综述了转座子及其衍生载体介导的一些技术,并讨论其在微生物功能基因组研究中的应用。