A quality-controlled microarray method for gene expression profiling

Gene expression profiling on microarrays is widely used to measure the expression of large numbers of genes in a single experiment. Because of the high cost of this method, feasible numbers of replicates are limited, thus impairing the power of statistical analysis. As a step toward reducing technically induced variation, we developed a procedure of sample preparation and analysis that minimizes the number of sample manipulation steps, introduces quality control before array hybridization, and allows recovery of the prepared mRNA for independent validation of results. Sample preparation is based on mRNA separation using oligo(dT) magnetic beads, which are subsequently used for first-strand cDNA synthesis on the beads. cDNA covalently bound to the magnetic beads is used as template for second-strand cDNA synthesis, leaving the intact mRNA in solution for further analysis. The quality of the synthesized cDNA can be assessed by quantitative polymerase chain reaction using 3'- and 5'-specific primer pairs for housekeeping genes such as glyceraldehyde-3-phosphate dehydrogenase. Second-strand cDNA is chemically labeled with fluorescent dyes to avoid dye bias in enzymatic labeling reactions. After hybridization of two differently labeled samples to microarray slides, arrays are scanned and images analyzed automatically with high reproducibility. Quantile-normalized data from five biological replica display a coefficient of variation 45% for 90% of profiled genes, allowing detection of twofold changes with false positive and false negative rates of 10% each. We demonstrate successful application of the procedure for expression profiling in plant leaf tissue. However, the method could be easily adapted for samples from animal including human or from microbial origin.     

Custom microarray for glycobiologists: considerations for glycosyltransferase gene expression profiling

The development of microarray technology offers the unprecedented possibility of studying the expression of thousands of genes in one experiment. Its exploitation in the glycobiology field will eventually allow the parallel investigation of the expression of many glycosyltransferases, which will ultimately lead to an understanding of the regulation of glycoconjugate synthesis. While numerous gene arrays are available on the market, e.g. the Affymetrix GeneChip arrays, glycosyltransferases are not adequately represented, which makes comprehensive surveys of their gene expression difficult. This chapter describes the main issues related to the establishment of a custom glycogenes array.     

阿维链霉菌基因表达谱芯片的初步研究

用表达谱芯片分析阿维链霉菌在发酵过程中转录组表达情况,优化微生物转录组分析条件。提取RNA,进行荧光探针的合成,杂交,扫描对数据进行分析。RNA的提取要注意破壁方法的选择以及茵体量;合成cDNA时,RNA的加入量一般在10μg左右;当cDNA用逆转录的方法得到时,用专门的纯化试剂盒要比传统的异丙醇纯化效果好;杂交温度对于合成基因来说一般在42％,对于比较长的PCR产物来说,温度相比较要高点;对于杂交时间来说,杂交12h之上,效果要明显比6h的好,所以杂交时间一般选择12h之上。对芯片上的197个合成基因进行分析：大约有60个基因进行了表达,其中包括18个双组分或激酶基因;10个糖酵解或者三羧酸循环途径基因;3个阿维基因簇;7个调节基因;5个细胞分化或孢子形;4个运输或固定基因;两个伴侣分子;5个RNA聚合酶σ因子;3个转录基因;两个脂肪酸合成基因;1个RNA聚合酶β亚基。     

Mouse strain specific gene expression differences for illumina microarray expression profiling in embryos

ABSTRACT: BACKGROUND: In the field of mouse genetics the advent of technologies like microarray based expression profiling dramatically increased data availability and sensitivity, yet these advanced methods are often vulnerable to the unavoidable heterogeneity of in vivo material and might therefore reflect differentially expressed genes between mouse strains of no relevance to a targeted experiment. The aim of this study was not to elaborate on the usefulness of microarray analysis in general, but to expand our knowledge regarding this potential "background noise" for the widely used Illumina microarray platform surpassing existing data which focused primarily on the adult sensory and nervous system, by analyzing patterns of gene expression at different embryonic stages using wild type strains and modern transgenic models of often non-isogenic backgrounds. RESULTS: Wild type embryos of 11 mouse strains commonly used in transgenic and molecular genetic studies at three developmental time points were subjected to Illumina microarray expression profiling in a strain-by-strain comparison. Our data robustly reflects known gene expression patterns during mid-gestation development. Decreasing diversity of the input tissue and/or increasing strain diversity raised the sensitivity of the array towards the genetic background. Consistent strain sensitivity of some probes was attributed to genetic polymorphisms or probe design related artifacts. CONCLUSION: Our study provides an extensive reference list of gene expression profiling background noise of value to anyone in the field of developmental biology and transgenic research performing microarray expression profiling with the widely used Illumina microarray platform. Probes identified as strain specific background noise further allow for microarray expression profiling on its own to be a valuable tool for establishing genealogies of mouse inbred strains.     

Real-time polymerase chain reaction-based exponential sample amplification for microarray gene expression profiling

Conventional approaches to target labeling for gene expression analysis using microarray technology typically require relatively large amounts of RNA, a serious limitation when the available sample is limited. Here we describe an alternative exponential sample amplification method by using quantitative real-time polymerase chain reaction (QRT-PCR) to follow the amplification and eliminate the overamplified cDNA which could distort the quantitative ratio of the starting mRNA population. Probes generated from nonamplified, PCR-amplified, and real-time-PCR-amplified cDNA samples were generated from lipopolysaccharide-treated and nontreated mouse macrophages and hybridized to mouse cDNA microarrays. Signals obtained from the three protocols were compared. Reproducibility and reliability of the methods were determined. The Pearson correlation coefficients for replica experiments were r=0.927 and r=0.687 for QRT-PCR-amplification and PCR-overamplification protocols, respectively. Chi2 test showed that overamplification resulted in major biases in expression ratios, while these alterations could be eliminated by following the cycling status with QRT-PCR. Our exponential sample amplification protocol preserves the original expression ratios and allows unbiased gene expression analysis from minute amounts of starting material.     

Peripheral blood gene expression profiling for cardiovascular disease assessment

Whole blood gene expression profiling has the potential to be informative about dynamic changes in disease states and to provide information on underlying disease mechanisms. Having demonstrated proof of concept in animal models, a number of studies have now tried to tackle the complexity of cardiovascular disease in human hosts to develop better diagnostic and prognostic indicators. These studies show that genomic signatures are capable of classifying patients with cardiovascular diseases into finer categories based on the molecular architecture of a patient's disease and more accurately predict the likelihood of a cardiovascular event than current techniques. To highlight the spectrum of potential applications of whole blood gene expression profiling approach in cardiovascular science, we have chosen to review the findings in a number of complex cardiovascular diseases such as atherosclerosis, hypertension and myocardial infarction as well as thromboembolism, aortic aneurysm, and heart transplant.     

Combination of PCR subtraction and cDNA microarray for differential gene expression profiling.

PCR subtraction hybridization has been used effectively to enrich and single out differentially expressed genes. However identification of these genes by means of cloning and sequencing individual cDNAs is a tedious and lengthy process. In this report, an attempt has been made to combine the use of PCR select cDNA subtraction hybridization and cDNA microarrays to identify differentially expressed genes using a nonradioactive chemiluminescent detection method. mRNA from human prolactin (hPRL) or human prolactin antagonist (hPRL-G129R) treated and non-treated breast cancer cells was isolated, and cDNAs were synthesized and used for the PCR subtraction to enrich the differentially expressed genes in the treated cells. The PCR-amplified and subtracted cDNA pools were purified and labeled using the digoxigenin method. Labeled cDNAs were hybridized to a human apoptosis cDNA microarray membrane and identified by chemiluminescence. The results suggest that the strategy of combining all three methods will allow for a more efficient, nonradioactive way of identifying differentially expressed genes in target cells.     

Reliable gene signatures for microarray classification: assessment of stability and performance

MOTIVATION: Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions. METHODS: We address these two issues by repeatedly evaluating the classification performance of all models, i.e. pairwise combinations of various gene selection and classification methods, for random subsets of arrays (sampling). A model score is used to select the most appropriate model for the given dataset. Consensus gene signatures are constructed by extracting those genes frequently selected over many samplings. Sampling additionally permits measurement of the stability of the classification performance for each model, which serves as a measure of model reliability. RESULTS: We analyzed a large gene expression dataset with 78 measurements of four different cartilage sample classes. Classifiers trained on subsets of measurements frequently produce models with highly variable performance. Our approach provides reliable classification performance estimates via sampling. In addition to reliable classification performance, we determined stable consensus signatures (i.e. gene lists) for sample classes. Manual literature screening showed that these genes are highly relevant to our gene expression experiment with osteoarthritic cartilage. We compared our approach to others based on a publicly available dataset on breast cancer. AVAILABILITY: R package at http://www.bio.ifi.lmu.de/~davis/edaprakt     

An oligonucleotide microarray for mouse imprinted genes profiling

Genomic imprinting is an epigenetic phenomenon unique to mammals that causes some genes to be expressed according to their parental origin. It results in developmental asymmetry in the function of the parental genomes. We describe here a method for the profiling of imprinted genes based on the development of a mouse imprinting microchip containing oligonucleotides corresponding to 493 genes, including most of the known imprinted genes (IG = 63), genes involved in epigenetic processes (EPI = 15), in metabolism (= 147), in obesity (= 10) and in neurotransmission (= 256) and housekeeping reference genes (= 2). This custom oligonucleotide microarray has been constructed to make data analysis and handling more manageable than pangenomic microarrays. As a proof of concept we present the differential expression of these 493 genes in different tissues (liver, placenta, embryo) of C57BL6/J mice fed different diets. Appropriate experimental strategies and statistical tools were defined at each step of the data analysis process with regard to the different sources of constraints. Data were confirmed by expression analyses based on quantitative real-time PCR. These oligochips should make it possible to increase our understanding of the involvement of imprinted genes in the timing of expression programs, tissue by tissue, stage by stage, in response to nutrients, lifestyles and other as yet unknown critical environmental factors in a variety of physiopathological situations, and in animals of different strains, ages and sexes. The use of oligonucleotides makes it possible to expand this microchip to include the increasing number of imprinted genes discovered.     

Advances in spotted microarray resources for expression profiling.

The new millennium has ushered in a new era in human biology. The elucidation of the human genome sequence, together with those of model organisms, provides us with an unprecedented insight into the makeup of our genetic blueprint. The challenge now is to figure out how all the constituent pieces fit together to form the whole picture, and the consequences of what happens when the process goes awry. One experimental tool that has the potential to provide enormous insights into this complex process is expression profiling using microarrays. The past few years have seen a considerable growth in the availability and use of microarrays. Fuelled in part by the many genome projects currently underway, there has been a large increase in the number of organisms for which microarray reagents are available from both commercial and academic sources. In addition to the increasing number of genome-wide probe sets that are available, a significant amount of attention has been focussed on generating more targeted probe sets that focus in on specific pathways or biological processes. Finally, the microarray field is starting to see a shift away from the use of cDNAs or polymerase chain reaction (PCR) products as probes towards the use of 50-70mer oligonucleotide probes with all of the potential advantages that they offer. The aim of this review is to provide an overview of what is currently available in terms of spotted microarray reagents both with respect to pre-made arrays and to probe sets available for arraying.     

Gene expression profiling in mitochondrial disease: assessment of microarray accuracy by high-throughput Q-PCR

Mitochondrial diseases are a heterogeneous array of disorders with a complex etiology. Use of microarrays as a tool to investigate complex human disease is increasingly common, however, a principle drawback of microarrays is their limited dynamic range, due to the poor quantification of weak signals. Although it is generally understood that low-intensity microarray 'spots' may be unreliable, there exists little documentation of their accuracy. Quantitative PCR (Q-PCR) is frequently used to validate microarray data, yet few Q-PCR validation studies have focused on the accuracy of low-intensity microarray signals. Hence, we have used Q-PCR to systematically assess microarray accuracy as a function of signal strength in a mouse model of mitochondrial disease, the superoxide dismutase 2 (SOD2) nullizygous mouse. We have focused on a unique category of data--spots with only one weak signal in a two-dye comparative hybridization--and show that such 'high-low' signal intensities are common for differentially expressed genes. This category of differential expression may be more important in mitochondrial disease in which there are often mosaic expression patterns due to the idiosyncratic distribution of mutant mtDNA in heteroplasmic individuals. Using RNA from the SOD2 mouse, we found that when spotted cDNA microarray data are filtered for quality (low variance between many technical replicates) and spot intensity (above a negative control threshold in both channels), there is an excellent quantitative concordance with Q-PCR (R2 = 0.94). The accuracy of gene expression ratios from low-intensity spots (R2 = 0.27) and 'high-low' spots (R2 = 0.32) is considerably lower. Our results should serve as guidelines for microarray interpretation and the selection of genes for validation in mitochondrial disorders.     

Three microarray platforms: an analysis of their concordance in profiling gene expression

Background

Bacillus cereus, Bacillus anthracis and Bacillus thuringiensis belong to the genetically close-knit Bacillus cereus sensu lato group, a family of rod-shaped Gram-positive bacteria. pAW63 is the first conjugative plasmid from the B. cereus group to be completely sequenced.

Results

The 71,777 bp nucleotide sequence of pAW63 reveals a modular structure, including a 42 kb tra region encoding homologs of the Type IV secretion systems components VirB11, VirB4 and VirD4, as well as homologs of Gram-positive conjugation genes from Enterococcus, Lactococcus, Listeria, Streptococcus and Staphylococcus species. It also firmly establishes the existence of a common backbone between pAW63, pXO2 from Bacillus anthracis and pBT9727 from the pathogenic Bacillus thuringiensis serovar konkukian strain 97-27. The alignment of these three plasmids highlights the presence of well conserved segments, in contrast to distinct regions of high sequence plastiCity. The study of their specific differences has provided a three-point reference framework that can be exploited to formulate solid hypotheses concerning the functionalities and the molecular evolution of these three closely related plasmids. This has provided insight into the chronology of their divergence, and led to the discovery of two Type II introns on pAW63, matching copies of the mobile element IS231L in different loci of pXO2 and pBT9727, and the identification on pXO2 of a 37 kb pathogeniCity island (PAI) containing the anthrax capsule genes.

Conclusion

The complete sequence determination of pAW63 has led to a functional map of the plasmid yielding insights into its conjugative apparatus, which includes T4SS-like components, as well as its resemblance to other large plasmids of Gram-positive bacteria. Of particular interest is the extensive homology shared between pAW63 and pXO2, the second virulence plasmid of B. anthracis, as well as pBT9727 from the pathogenic strain B. thuringiensis serovar konkukian strain 97-27.     

Mining microarray expression data by literature profiling

Background  

The rapidly expanding fields of genomics and proteomics have prompted the development of computational methods for managing, analyzing and visualizing expression data derived from microarray screening. Nevertheless, the lack of efficient techniques for assessing the biological implications of gene-expression data remains an important obstacle in exploiting this information.