A Mutation Affecting the Regulation of a Seca-Lacz Fusion Defines a New Sec Gene

It was shown previously that the secA gene of Escherichia coli is derepressed in cells that have a defect in protein export. Here it is demonstrated that the beta-galactosidase produced by a secA-lacZ gene fusion strain is regulated in the same way. Studies on the fusion strain reveal that the promoter or a site involved in regulation of the secA gene is located considerably upstream from the structural gene. The properties of the fusion strain provide a new selection for mutants that are defective in protein export. Selection for increased lac expression of a secA-lacZ fusion strain yields mutations in three of the known sec genes, secA, secD and prlA/secY. In addition, mutations in several genes not previously known to affect secA expression were obtained. A mutation in one of these genes causes a pleiotropic defect in protein export and a cold-sensitive growth defect; this gene, which maps at approximately 90 min on the bacterial chromosome, has been named secE.     

结合基因功能分类体系Gene Ontology筛选聚类特征基因

使用两套基因表达谱数据,按各基因的表达值方差,选择表达变异基因对样本聚类,发现一般使用方差较大的前10％的基因作为特征基因,就可以较好地对疾病样本聚类。对不同的疾病,包含聚类信息的特征基因有不同的分布特点。在此基础上,结合基因功能分类体系(Gene Ontology,GO),进一步筛选聚类的特征基因。通过检验在Gene Ontology中的每个功能类中的表达变异基因是否非随机地聚集,寻找疾病相关功能类,再根据相关功能类中的表达变异基因进行聚类分析。实验结果显示：结合基因功能体系进一步筛选表达变异基因作为聚类特征基因,可以保持或提高聚类准确性,并使得聚类结果具有明确的生物学意义。另外,发现了一些可能和淋巴瘤和白血病相关的基因。     

Sequencing Strategies for Fusion Gene Detection

Fusion genes formed by chromosomal rearrangements are common drivers of cancer. Recent innovations in the field of next-generation sequencing (NGS) have seen a dynamic shift from traditional fusion detection approaches, such as visual characterization by fluorescence, to more precise multiplexed methods. There are many different NGS-based approaches to fusion gene detection and deciding on the most appropriate method can be difficult. Beyond the experimental approach, consideration needs to be given to factors such as the ease of implementation, processing time, associated costs, and the level of expertise required for data analysis. Here, the different NGS-based methods for fusion gene detection, the basic principles underlying the techniques, and the benefits and limitations of each approach are reviewed. This article concludes with a discussion of how NGS will impact fusion gene detection in a clinical context and from where the next innovations are evolving.     

利用基因芯片技术区分禽流感病毒主要亚型

[目的]研制可同时区分AIV的H5、H7、H9血凝素亚型及N1、N2神经氨酸酶亚型的基因诊断芯片.[方法]分别克隆了禽流感病毒的M基因,H5、H7、H9亚型HA基因,N1、N2亚型NA基因以及看家基因GAPDH的重组质粒.以重组质粒为模板,用PCR方法扩增制备探针,纯化后点于氨基修饰的片基上,制备基因芯片.在PCR过程中对待检样品进行标记,然后与芯片杂交,洗涤,扫描并进行结果分析.[结果]结果显示检测探针可特异性的与相应的标记样品进行杂交,呈现较强的杂交信号,且无交叉杂交.同时用RT-PCR、鸡胚接种和基因芯片方法对H1-H15亚型AIV参考毒株、30份人工感染样品、21份现地疑似样品进行检测,结果发现,对人工感染样品芯片检测方法与鸡胚接种和RT-PCR的符合率分别为100%和96%,现地样品符合率为100%.[结论]研究表明该方法可用于同步鉴别部分主要流行的禽流感亚型,是一种有效的新方法.     

Correction of technical bias in clinical microarray data improves concordance with known biological information

The performance of gene expression microarrays has been well characterized using controlled reference samples, but the performance on clinical samples remains less clear. We identified sources of technical bias affecting many genes in concert, thus causing spurious correlations in clinical data sets and false associations between genes and clinical variables. We developed a method to correct for technical bias in clinical microarray data, which increased concordance with known biological relationships in multiple data sets.     

基于基因表达谱的肿瘤分型和特征基因选取

在分析基因表达谱数据特性的基础上,提出了一个将之用于肿瘤分子分型和选型和选取相应亚型特征基因的策略。该策略包括三个步骤：首先采用一个无监督的基因过滤算法以降低用于分型计算的数据的噪声,其次提出了一个概率模型对样本中的分类结构进行建模,最后基于聚类的结果采用相对熵的方法获得对分类贡献大的基因作为特征基因,应用该策略对两个公开发表的数据集进行了再挖掘,结果表明不但获得了其他方法可以得到的信息,而且还提供了更精细、更具有显著生物学意义的信息,具有明显的优越性。     

Detecting ALK,ROS1 and RET Fusion Genes in Cell Block Samples

Whether Cell block (CB) samples are applicable to detect anaplastic lymphoma kinase (ALK), c-ros oncogene 1 (ROS1) and ret proto-oncogene (RET) fusion genes in lung adenocarcinoma is still unknown. In this study, 108 cytological samples that contained lung adenocarcinoma cells were collected, and made into CB. The CB samples all contained at least 30% lung adenocarcinoma cells. In these patients, 48 harbored EGFR mutation. Among the 50 EGFR wild type patients who detected fusion genes, 14 carried EML4-ALK fusion (28%), 2 had TPM3-ROS1 fusion (4%), and 3 harbored KIF5B-RET fusion (6%). No double fusions were found in one sample. Patients with fusion genes were younger than those without fusion genes (p = 0.032), but no significant difference was found in sex and smoking status (p > 0.05). In the thirty-five patients who received first-line chemotherapy, patients with fusion gene positive had disease control rate (DCR) (72.7% VS 50%, p > 0.05) and objective response rate (ORR) (9.1% VS 4.2%, p > 0.05) compared with those having fusion gene negative. The median progression free survival (mPFS) were 4.0 and 2.7 months in patients harbored fusion mutations and wild type, respectively (p > 0.05). We conclude that CB samples could be used to detect ALK, ROS1 and RET fusions in NSCLC. The frequency distribution of three fusion genes is higher in lung adenocarcinoma with wild-type EGFR, compared with unselected NSCLC patient population. Patients with fusion genes positive are younger than those with fusion gene negative, but they had no significantly different PFS in first-line chemotherapy.     

基于耦合双向聚类技术的DLBCL异质性分析

基因芯片技术为疾病异质性研究提供了有力的工具。当前基于传统聚类分析的方法一般利用芯片上大量基因作为特征来发现疾病的亚型, 因此它们没有考虑到特征中包含的大量无关基因会掩盖有意义的疾病样本的分割。为了避免这个缺点, 提出了基于耦合双向聚类的异质性分析方法(Heterogeneous Analysis Based on Coupled Two-Way Clustering, HCTWC)来搜索有意义的基因簇以便发现样本的内在分割。该方法被应用于弥漫性大B细胞淋巴瘤(diffuse large B-cell lymphoma DLBCL)芯片数据集, 通过识别的基因簇作为特征对DLBCL样本聚类发现生存期分别为55%和25%的两类DLBCL亚型(P<0.05), 因此, HCTWC方法在解决疾病异质性是有效的。     

On sparse Fisher discriminant method for microarray data analysis

One of the applications of the discriminant analysis on microarray data is to classify patient and normal samples based on gene expression values. The analysis is especially important in medical trials and diagnosis of cancer subtypes. The main contribution of this paper is to propose a simple Fisher-type discriminant method on gene selection in microarray data. In the new algorithm, we calculate a weight for each gene and use the weight values as an indicator to identify the subsets of relevant genes that categorize patient and normal samples. A l(2) - l(1) norm minimization method is implemented to the discriminant process to automatically compute the weights of all genes in the samples. The experiments on two microarray data sets have shown that the new algorithm can generate classification results as good as other classification methods, and effectively determine relevant genes for classification purpose. In this study, we demonstrate the gene selection's ability and the computational effectiveness of the proposed algorithm. Experimental results are given to illustrate the usefulness of the proposed model.     

Gene expression profiling of cranial sensory ganglia that transmit food intake stimuli

Peripheral cranial sensory nerves projecting into the oral cavity receive food intake stimuli and transmit sensory signals to the central nervous system. They are derived from four cranial sensory ganglia, trigeminal, geniculate, petrosal, and nodose ganglia, each of which contains multiple kinds of sensory neurons with different cell morphologies and neuronal properties. We investigated the complex properties of these neurons from the viewpoint of gene expression using DNA microarrays. The 498 genes were selected from a total of 8,740 genes as showing tissue-dependent expression on the microarray by hierarchical cluster analysis, in which several genes known to be differentially expressed in cranial sensory ganglia are included. This suggests that DNA microarray cluster analysis revealed a number of characteristic genes for sensory neurons in these ganglia. Among the selected 498 genes, 44 genes are associated with neurotransmission, such as neuropeptides, their receptors, and vesicle transport, and 26 are ion channels regulating membrane potentials. The identification of a number of genes related directly to neural properties indicates that these sensory ganglia contain heterogeneous types of neurons with different neural properties.     

Match/X,A Gene Expression Pattern Recognition Algorithm Used to Identify Genes Which May be Related to CDC2 Function and Cell Cycle Regulation

Large-scale microarray gene expression studies can provide insight into complex genetic networks and biological pathways. A comprehensive gene expression database was constructed using Affymetrix GeneChip microarrays and RNA isolated from more than 6,400 distinct normal and diseased human tissues. These individual patient samples were grouped into over 700 sample sets based on common tissue and disease morphologies, and each set contained averaged expression data for over 45,000 gene probe sets representing more than 33,000 known human genes. Sample sets were compared to each other in more than 750 normal vs. disease pairwise comparisons. Relative up or down-regulation patterns of genes across these pairwise comparisons provided unique expression fingerprints that could be compared and matched to a gene of interest using the Match/X algorithm. This algorithm uses the kappa statistic to compute correlations between genes and calculate a distance score between a gene of interest and all other genes in the database. Using cdc2 as a query gene, we identified several hundred genes that had similar expression patterns and highly correlated distance scores. Most of these genes were known components of the cell cycle involved in G2/M progression, spindle function or chromosome arrangement. Some of the identified genes had unknown biological functions but may be related to cdc2 mediated mechanism based on their closely correlated distance scores. This algorithm may provide novel insights into unknown gene function based on correlation to expression profiles of known genes and can identify elements of cellular pathways and gene interactions in a high throughput fashion.     

Gene selection algorithms for microarray data based on least squares support vector machine

Background  

In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary to conduct the discriminant analysis with all the genes. Hence, gene selection is usually performed to select important genes.     

Comparative expression analysis of four breast cancer subtypes versus matched normal tissue from the same patients

Gene expression studies have been widely used in an effort to identify signatures that can predict clinical progression of cancer. In this study we focused instead on identifying gene expression differences between breast tumors and adjacent normal tissue, and between different subtypes of tumor classified by clinical marker status. We have collected a set of 20 breast cancer tissues, matched with the adjacent pathologically normal tissue from the same patient. The cancer samples representing each subtype of breast cancer identified by estrogen receptor ER(+/-) and Her2(+/-) status and divided into four subgroups (ER+/Her2+, ER+/Her2-, ER-/Her2+, and ER-/Her2-) were hybridized on Affymetrix HG-133 Plus 2.0 microarrays. By comparing cancer samples with their matched normal controls we have identified 3537 overall differentially expressed genes using data analysis methods from Bioconductor. When we looked at the genes in common of the four subgroups, we found 151 regulated genes, some of them encoding known targets for breast cancer treatment. Unique genes in the four subgroups instead suggested gene regulation dependent on the ER/Her2 markers selection. In conclusion, the results indicate that microarray studies using robust analysis of matched tumor and normal samples from the same patients can be used to identify genes differentially expressed in breast cancer tumor subtypes even when small numbers of samples are considered and can further elucidate molecular features of breast cancer.     

Using RNA sample titrations to assess microarray platform performance and normalization techniques

We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.     

Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy.