食源性致病菌检测方法研究进展——Ⅱ.分子生物学检测方法

为方便对食品生产和流通的整个过程进行卫生监督和管理,就以核酸为分析对象的各种分子生物学食品微生物快速检测方法进行了综述。     

Cost-efficiency considerations in the choice of a microarray platform for time course experimental designs

Customarily, the choice between one- or two-colour microarray platforms is based on their respective practical and technical merits, contingent on objectives and constraints of the study at stake. Statistical efficiency, if accounted for, plays a secondary role. A cost-efficiency comparison of the one- and two-colour designs for a 2×4 time course experiment was conducted. It is shown that differences in costs between the platforms’ designs, once adjusted for statistical efficiency, are not always negligible. The extent of these differences is largely influenced by subjects and array prices as well as by biological and error variances in their relative magnitude. Circumstances are described, in which cost-efficiency considerations will have an added value in motivating the selection of a microarray platform at the design stage.     

Microarray-based Detection of Antibiotic Resistance Genes in Salmonella

In the presented study, 143 Salmonella isolates belonging to 26 different serovars were screened for the presence of antibiotic resistance genes by microarray analysis. The microarray contained a total of 223 oligonucleotides representing genes encoding for resistance to the following antibiotic classes: aminoglycoside, β-lactam, chloramphenicol, MLS, sulfonamide, tetracycline, trimethoprim, and vancomycin. To a large extent, the microarray data were consistent to the general findings concerning antibiotic resistance in Salmonella. Most of the analyzed isolates, harbored three or more resistance genes with the highest numbers found in isolates belonging to the Salmonella serovars Typhimurium, Paratyphi B var. Java, Bredeney, Saint Paul and Heidelberg and the only Give isolate investigated.     

基于过采样技术和随机森林的不平衡微阵列数据分类方法研究

近年来,应用DNA微阵列技术对疾病,尤其是癌症进行诊断,已逐渐成为生物信息学领域的研究热点之一。对比其它的数据载体,微阵列数据通常具有一些独有的特点。针对微阵列数据样本分布不平衡这一特点,提出了一种基于概率分布的过采样技术,通过该技术可以为少数类建立一些合理的伪样本,从而使各类的样本数达到均衡,然后使用随机森林分类器对其进行分类。该方法的有效性和可行性已经在两个标准的微阵列数据集上得到了验证。实验结果显示,与传统的方法相比,该方法可以获得更好的分类性能。     

Local likelihood regression in generalized linear single-index models with applications to microarray data

Searching for an effective dimension reduction space is an important problem in regression, especially for high-dimensional data such as microarray data. A major characteristic of microarray data consists in the small number of observations n and a very large number of genes p. This “large p, small n” paradigm makes the discriminant analysis for classification difficult. In order to offset this dimensionality problem a solution consists in reducing the dimension. Supervised classification is understood as a regression problem with a small number of observations and a large number of covariates. A new approach for dimension reduction is proposed. This is based on a semi-parametric approach which uses local likelihood estimates for single-index generalized linear models. The asymptotic properties of this procedure are considered and its asymptotic performances are illustrated by simulations. Applications of this method when applied to binary and multiclass classification of the three real data sets Colon, Leukemia and SRBCT are presented.     

Diversity of Salmonella enterica serovar Derby isolated from pig, pork and humans in Germany

Salmonella enterica serovar Derby (S. Derby) is one of the most prevalent serovars in pigs in Europe and in the U.S. and ranks among the 10 most frequently isolated serovars in humans. Therefore, a set of 82 epidemiologically unrelated S. Derby strains isolated between 2006 and 2008 from pigs, pork and humans in Germany was selected and investigated in respect to the transmission of clonal groups of the serovar along the food chain. Various phenotypic and genotypic methods were applied and the pathogenicity and resistance gene repertoire was determined. Phenotypically 72% of the strains were susceptible to all 17 antimicrobials tested while the others were monoresistant to tetracycline or multi-resistant with different resistance profiles. Four major clonal groups were identified based on PFGE, sequence data of the virulence genes sopA, sopB and sopD, VNTR-locus STTR5 and MLST revealing also the new sequence type ST774. Thirty different PFGE profiles were detected resulting in four clusters representing the four groups. The pathogenicity gene repertoire of 32 representative S. Derby strains analyzed by microarray showed six types with differences in the Salmonella pathogenicity islands, pathogenicity genes on smaller islets or prophages and fimbriae coding genes. The pathogenicity gene repertoire of the predominant types PAT DE1 and DE2 were most similar to the ones of S. Paratyphi B (dT+, O5−) and to a minor degree to S. Infantis and S. Virchow PATs. Overall this study showed that in Germany currently one major S. Derby clone is frequently isolated from pigs and humans. Contaminated pork was identified as one vehicle and consequently is a risk for human health. To prevent this serovar from entering the food chain, control measurements should be applied at the farm level.     

Antibiotic susceptibility of members of the Lactobacillus acidophilus group using broth microdilution and molecular identification of their resistance determinants

The range of antibiotic susceptibility to 13 antibiotics in 101 strains of the Lactobacillus acidophilus group was examined using the lactic acid bacteria susceptibility test medium (LSM) and broth microdilution. Additionally, microarray analysis and PCR were applied to identify resistance genes responsible for the displayed resistant phenotypes in a selection of strains. In general, narrow as well as broad unimodal and bimodal MIC distributions were observed for the Lactobacillus acidophilus group and the tested antimicrobial agents. Atypically resistant strains could be determined by visual inspection of the obtained MIC ranges for ampicillin, chloramphenicol, clindamycin, erythromycin, quinupristin/dalfopristin, streptomycin and tetracycline. For most of these atypically resistant strains underlying resistance determinants were found. To our knowledge erm(A) was detected in lactobacilli for the first time within this study. Data derived from this study can be used as a basis for reviewing present microbiological breakpoints for categorization of susceptible and resistant strains within the Lactobacillus acidophilus group to assess the safety of microorganisms intended for use in food and feed applications.     

Direct integration of microarrays for selecting informative genes and phenotype classification

The ability to provide thousands of gene expression values simultaneously makes microarray data very useful for phenotype classification. A major constraint in phenotype classification is that the number of genes greatly exceeds the number of samples. We overcame this constraint in two ways; we increased the number of samples by integrating independently generated microarrays that had been designed with the same biological objectives, and reduced the number of genes involved in the classification by selecting a small set of informative genes. We were able to maximally use the abundant microarray data that is being stockpiled by thousands of different research groups while improving classification accuracy. Our goal is to implement a feature (gene) selection method that can be applicable to integrated microarrays as well as to build a highly accurate classifier that permits straightforward biological interpretation. In this paper, we propose a two-stage approach. Firstly, we performed a direct integration of individual microarrays by transforming an expression value into a rank value within a sample and identified informative genes by calculating the number of swaps to reach a perfectly split sequence. Secondly, we built a classifier which is a parameter-free ensemble method using only the pre-selected informative genes. By using our classifier that was derived from large, integrated microarray sample datasets, we achieved high accuracy, sensitivity, and specificity in the classification of an independent test dataset.