利用基因芯片对一高保守基因进行肿瘤相关分析

本对大规模人类cDNA测序过程中获得的一条高保守基因进行了初步功能研究,生物信息学研究发现该基因在人类、小鼠、果蝇、拟南芥和裂殖酶母中都有很高的保守性,其他分析预测该基因可能具有肿瘤相关性。RT－PCR分析表明,该基因在成人和胎儿组织中广泛谱表达。利用基因芯片分析该基因在7例肝癌、5例胰腺癌、2例喉癌和2例肺癌中表达情况,结果证实了该基因的肿瘤相关性,并且提示该基因在不同类型中可能处于不同的地位。     

Identifying Subspace Gene Clusters from Microarray Data Using Low-Rank Representation

Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.     

Detection of Gene Expression in an Individual Cell Type within a Cell Mixture Using Microarray Analysis

Background

A central issue in the design of microarray-based analysis of global gene expression is the choice between using cells of single type and a mixture of cells. This study quantified the proportion of lipopolysaccharide (LPS) induced differentially expressed monocyte genes that could be measured in peripheral blood mononuclear cells (PBMC), and determined the extent to which gene expression in the non-monocyte cell fraction diluted or obscured fold changes that could be detected in the cell mixture.

Methodology/Principal Findings

Human PBMC were stimulated with LPS, and monocytes were then isolated by positive (Mono+) or negative (Mono−) selection. The non-monocyte cell fraction (MonoD) remaining after positive selection of monocytes was used to determine the effect of non-monocyte cells on overall expression. RNA from LPS-stimulated PBMC, Mono+, Mono− and MonoD samples was co-hybridised with unstimulated RNA for each cell type on oligonucleotide microarrays. There was a positive correlation in gene expression between PBMC and both Mono+ (0.77) and Mono− (0.61–0.67) samples. Analysis of individual genes that were differentially expressed in Mono+ and Mono− samples showed that the ability to detect expression of some genes was similar when analysing PBMC, but for others, differential expression was either not detected or changed in the opposite direction. As a result of the dilutional or obscuring effect of gene expression in non-monocyte cells, overall about half of the statistically significant LPS-induced changes in gene expression in monocytes were not detected in PBMC. However, 97% of genes with a four fold or greater change in expression in monocytes after LPS stimulation, and almost all (96–100%) of the top 100 most differentially expressed monocyte genes were detected in PBMC.

Conclusions/Significance

The effect of non-responding cells in a mixture dilutes or obscures the detection of subtle changes in gene expression in an individual cell type. However, for studies in which only the most highly differentially expressed genes are of interest, separating and analysing individual cell types may be unnecessary.     

High Level Secretion of Calf Chymosin Using a Glucoamylase-prochymosin Fusion Gene in Aspergillus oryzae

A recombinant chymosin was secreted at high levels using fusion genes with A. oryzae glucoamylase gene (glaA) and a wheat bran solid-state culture system. Two portions of the A. oryzae glucoamylase, one with almost the entire glucoamylase (GA1–603) lacking 9 amino acids at the carboxyl terminal, and the other (GA1–511) lacking the starch binding-domain, were fused in frame with prochymosin cDNA. Western blot analysis indicated that the mature chymosin was released from the secreted fusion protein by autocatalytic processing. The transformant harboring the GA1-511-prochymosin construct showed about 5-fold chymosin production of the transformant in which the chymosin gene was directly expressed under the control of the glaA promoter in submerged culture. Moreover, wheat bran solid-state culture gave about 500-fold higher yield of the chymosin (approximately 150 mg/kg wheat bran) compared with the submerged culture.     

Converting a Microarray Signature into a Diagnostic Test: A Trial of Custom 74 Gene Array for Clarification and Prediction the Prognosis of Gastric Cancer

Background

Gastric cancer (GC) is associated with high mortality rates and an unfavorable prognosis at advanced stages. In addition, there are no effective methods for diagnosing gastric cancer at an early stage or for predicting the outcome for the purpose of selecting patient-specific treatment options. Therefore, it is important to investigate new methods for GC diagnosis.

Methodology/Principal Findings

To facilitate its use in a diagnostic setting, a group of 74 genes with diagnostic and prognostic information was translated into a customized microarray containing a reduced set of 1,042 probes suitable for high throughput processing. In this report, we demonstrate for the first time that the custom mini-array can be used as a reliable diagnostic tool in gastric cancer. With an AUC value of 0.565 (95% CI 0.305-0.825) indicating a perfect test, the sensitivity and specificity of diagnosis from the ROC curve were calculated to be 70% and 80%, respectively.

Conclusions/Significance

The data clearly demonstrate the reproducibility and robustness of the small custom-made microarray. The array is an excellent tool for classifying and predicting the outcome of disease in gastric cancer patients.     

A Measure of the Signal-to-Noise Ratio of Microarray Samples and Studies Using Gene Correlations

Background

The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies.

Results

As a proxy for quality, we propose a signal-to-noise ratio for microarray data, the “Signal-to-Noise Applied to Gene Expression Experiments”, or SNAGEE. SNAGEE is based on the consistency of gene-gene correlations. We applied SNAGEE to a compendium of 80 large datasets on 37 platforms, for a total of 24,380 samples, and assessed the signal-to-noise ratio of studies and samples. This allowed us to discover serious issues with three studies. We show that signal-to-noise ratios of both studies and samples are linked to the statistical significance of the biological results.

Conclusions

We showed that SNAGEE is an effective way to measure data quality for most types of gene expression studies, and that it often outperforms existing techniques. Furthermore, SNAGEE is platform-independent and does not require raw data files. The SNAGEE R package is available in BioConductor.     

Assessment of Protein Expression Levels After Transient Global Cerebral Ischemia Using an Antibody Microarray Analysis

An antibody microarray was used to analyze potential modifications in brain protein levels induced by ischemic reperfusion. Total brain extracts from rats subjected to 15 min of transient global ischemia followed by 3 days of reperfusion and sham control animals were compared within the same array. Separate arrays were run to analyze resistant (cortex) and vulnerable (CA1) regions to ischemia. Candidate components distinguishing the two cellular populations were selected under stringent criteria. IR significantly decreased the expression of Bcl-x, caspase 11, GADD153, Cdk4, E2F1, Retinoblastoma-P, SMAD4, AP-1/c-jun, ATF2, PCAF, MAP1b and cofilin within both regions. NGF and NMDA 2A receptors and IκB were specifically down-regulated in CA1, while Pyk2-P, b-NOS, and tyrosine hydroxylase were slightly up-regulated in the same region. Some of the array results were validated by western blot. Both the array and western blot results suggested a relevant IR induced activation of calpain specifically at CA1.     

RubisCO Gene Clusters Found in a Metagenome Microarray from Acid Mine Drainage

The enzyme responsible for carbon dioxide fixation in the Calvin cycle, ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO), is always detected as a phylogenetic marker to analyze the distribution and activity of autotrophic bacteria. However, such an approach provides no indication as to the significance of genomic content and organization. Horizontal transfers of RubisCO genes occurring in eubacteria and plastids may seriously affect the credibility of this approach. Here, we presented a new method to analyze the diversity and genomic content of RubisCO genes in acid mine drainage (AMD). A metagenome microarray containing 7,776 large-insertion fosmids was constructed to quickly screen genome fragments containing RubisCO form I large-subunit genes (cbbL). Forty-six cbbL-containing fosmids were detected, and six fosmids were fully sequenced. To evaluate the reliability of the metagenome microarray and understand the microbial community in AMD, the diversities of cbbL and the 16S rRNA gene were analyzed. Fosmid sequences revealed that the form I RubisCO gene cluster could be subdivided into form IA and IB RubisCO gene clusters in AMD, because of significant divergences in molecular phylogenetics and conservative genomic organization. Interestingly, the form I RubisCO gene cluster coexisted with the form II RubisCO gene cluster in one fosmid genomic fragment. Phylogenetic analyses revealed that horizontal transfers of RubisCO genes may occur widely in AMD, which makes the evolutionary history of RubisCO difficult to reconcile with organismal phylogeny.     

Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

Background

Legionella, the causative agent for Legionnaires’ disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data.

Methods

Microarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in the Netherlands in the period 2002–2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental.

Results

Four genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme.

Conclusions

The Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.     

Calling Biomarkers in Milk Using a Protein Microarray on Your Smartphone

Here we present the concept of a protein microarray-based fluorescence immunoassay for multiple biomarker detection in milk extracts by an ordinary smartphone. A multiplex immunoassay was designed on a microarray chip, having built-in positive and negative quality controls. After the immunoassay procedure, the 48 microspots were labelled with Quantum Dots (QD) depending on the protein biomarker levels in the sample. QD-fluorescence was subsequently detected by the smartphone camera under UV light excitation from LEDs embedded in a simple 3D-printed opto-mechanical smartphone attachment. The somewhat aberrant images obtained under such conditions, were corrected by newly developed Android-based software on the same smartphone, and protein biomarker profiles were calculated. The indirect detection of recombinant bovine somatotropin (rbST) in milk extracts based on altered biomarker profile of anti-rbST antibodies was selected as a real-life challenge. RbST-treated and untreated cows clearly showed reproducible treatment-dependent biomarker profiles in milk, in excellent agreement with results from a flow cytometer reference method. In a pilot experiment, anti-rbST antibody detection was multiplexed with the detection of another rbST-dependent biomarker, insulin-like growth factor 1 (IGF-1). Milk extract IGF-1 levels were found to be increased after rbST treatment and correlated with the results obtained from the reference method. These data clearly demonstrate the potential of the portable protein microarray concept towards simultaneous detection of multiple biomarkers. We envisage broad application of this ‘protein microarray on a smartphone’-concept for on-site testing, e.g., in food safety, environment and health monitoring.     

Gene Expression Profiling of Histiocytic Sarcomas in a Canine Model: The Predisposed Flatcoated Retriever Dog

Background

The determination of altered expression of genes in specific tumor types and their effect upon cellular processes may create insight in tumorigenesis and help to design better treatments. The Flatcoated retriever is a dog breed with an exceptionally high incidence of histiocytic sarcomas. The breed develops two distinct entities of histiocytic neoplasia, a soft tissue form and a visceral form. Gene expression studies of these tumors have value for comparable human diseases such as histiocytic/dendritic cell sarcoma for which knowledge is difficult to accrue due to their rare occurrence. In addition, such studies may help in the search for genetic aberrations underlying the genetic predisposition in this dog breed.

Methods

Microarray analysis and pathway analyses were performed on fresh-frozen tissues obtained from Flatcoated retrievers with localized, soft tissue histiocytic sarcomas (STHS) and disseminated, visceral histiocytic sarcomas (VHS) and on normal canine spleens from various breeds. Expression differences of nine genes were validated with quantitative real-time PCR (qPCR) analyses.

Results

QPCR analyses identified the significantly altered expression of nine genes; PPBP, SpiC, VCAM1, ENPEP, ITGAD (down-regulated), and GTSF1, Col3a1, CD90 and LUM (up-regulated) in the comparison of both the soft tissue and the visceral form with healthy spleen. DAVID pathway analyses revealed 24 pathways that were significantly involved in the development of HS in general, most of which were involved in the DNA repair and replication process.

Conclusions

This study identified altered expression of nine genes not yet implicated in histiocytic sarcoma manifestations in the dog nor in comparable human histiocytic/dendritic sarcomas. Exploration of the downside effect of canine inbreeding strategies for the study of similar sarcomas in humans might also lead to the identification of genes related to these rare malignancies in the human.     

结合单酶切位点的融合PCR技术对SCN1A基因的定点诱变

目的:利用结合单酶切位点的融合PCR技术对癫痫相关基因SCN1A进行定点突变。方法:首先设计两对引物PF1/PR1和PF2/PR2,PF1和PR2均位于突变位点最近的单酶切位点处,而突变位点设计在第一对反向引物(PR1)和第二对正向引物上(PF2)。通过重叠延伸法两次PCR扩增:第一次用PF1/PR1和PF2/PR2分开扩增,以扩增产物作模板,PF1/PR2作引物进行融合PCR,得到的扩增产物即含有所需要的突变位点,最后将扩增片段克隆入pMD18-T载体,经测序筛选阳性克隆。结果:DNA测序表明SCN1A基因所编码的第946位密码子由精氨酸(Arg)突变为组氨酸(His),再通过酶切和连接反应将重组质粒上的突变片段替换SCN1A表达质粒上的对应片段,成功构建了SCN1A突变载体。结论:与现在常用的长距离PCR定点诱导突变相比较,结合单酶切位点的融合PCR定点突变技术具备扩增距离短的优点,大大降低了自发突变的概率,适合于大肠杆菌中易自发突变的较大载体的定点诱变。     

Modulation of Gene Expression Made Easy

A new approach for modulating gene expression, based on randomization of promoter (spacer) sequences, was developed. The method was applied to chromosomal genes in Lactococcus lactis and shown to generate libraries of clones with broad ranges of expression levels of target genes. In one example, overexpression was achieved by introducing an additional gene copy into a phage attachment site on the chromosome. This resulted in a series of strains with phosphofructokinase activities from 1.4 to 11 times the wild-type activity level. In this example, the pfk gene was cloned upstream of a gusA gene encoding β-glucuronidase, resulting in an operon structure in which both genes are transcribed from a common promoter. We show that there is a linear correlation between the expressions of the two genes, which facilitates screening for mutants with suitable enzyme activities. In a second example, we show that the method can be applied to modulating the expression of native genes on the chromosome. We constructed a series of strains in which the expression of the las operon, containing the genes pfk, pyk, and ldh, was modulated by integrating a truncated copy of the pfk gene. Importantly, the modulation affected the activities of all three enzymes to the same extent, and enzyme activities ranging from 0.5 to 3.5 times the wild-type level were obtained.