Comparative gene expression profiling by oligonucleotide fingerprinting.

The use of hybridisation of synthetic oligonucleotides to cDNAs under high stringency to characterise gene sequences has been demonstrated by a number of groups. We have used two cDNA libraries of 9 and 12 day mouse embryos (24 133 and 34 783 clones respectively) in a pilot study to characterise expressed genes by hybridisation with 110 hybridisation probes. We have identified 33 369 clusters of cDNA clones, that ranged in representation from 1 to 487 copies (0.7%). 737 were assigned to known rodent genes, and a further 13 845 showed significant homologies. A total of 404 clusters were identified as significantly differentially represented (P < 0.01) between the two cDNA libraries. This study demonstrates the utility of the fingerprinting approach for the generation of comparative gene expression profiles through the analysis of cDNAs derived from different biological materials.     

Gene expression profiling of human diseases by serial analysis of gene expression

Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual genes. However, it is generally recognized that expression of a number of genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of gene expression (SAGE) and DNA microarrays have enabled researchers to determine the expression pattern of thousands of genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify gene expression without prior sequence information or information about genes that are thought to be expressed. SAGE has been successfully applied to the gene expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.     

Global functional profiling of gene expression

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.     

Modeling cholesterol metabolism by gene expression profiling in the hippocampus

An important part of the challenge of building models of biochemical reactions is determining reaction rate constants that transform substrates into products. We present a method to derive enzymatic kinetic values from mRNA expression levels for modeling biological networks without requiring further tuning. The core metabolic reactions of cholesterol in the brain, particularly in the hippocampus, were simulated. To build the model the baseline mRNA expression levels of genes involved in cholesterol metabolism were obtained from the Allen Mouse Brain Atlas. The model is capable of replicating the trends of relative cholesterol levels in Alzheimer's and Huntington's diseases; and reliably simulated SLOS, desmosterolosis, and Dhcr14/Lbr knockout studies. A sensitivity analysis correctly uncovers the Hmgcr, Idi2 and Fdft1 sites that regulate cholesterol homeostasis. Overall, our model and methodology can be used to pinpoint key reactions, which, upon manipulation, may predict altered cholesterol levels and reveal insights into potential drug therapy targets under diseased conditions.     

脾源性酪氨酸激酶基因启动子甲基化与髓母细胞瘤细胞侵袭转移的关系

目的：探讨甲基化转移酶抑制剂5-氮杂-2-脱氧胞苷（5-aza-CdR）抑制脾源性酪氨酸激酶（Syk）基因启动子的甲基化后对髓母细胞瘤Daoy细胞侵袭转移能力的影响。方法：用甲基化转移酶抑制剂5-aza-CdR处理体外培养的髓母细胞瘤Daoy细胞,通过甲基化特异性PCR（MSP）、Real time-PCR、Western blot及Transwell实验方法分别检测不同浓度5-aza-CdR处理后髓母细胞瘤Daoy细胞中脾源性酪氨酸激酶（Syk）基因启动子区甲基化、mRNA表达、蛋白表达及细胞穿膜数的变化。结果：髓母细胞瘤Daoy细胞中Syk基因启动子存在过甲基化,与对照组比较,经不同浓度5-aza-CdR处理后,其Syk基因启动子区甲基化受到不同程度抑制,Syk mRNA的表达量最高上调（3.40±0.24）倍（P<0.01）;Syk蛋白的表达量最高上调（3.23±0.19）倍（P<0.01）;细胞侵袭及转移能力降低（P<0.05）,差异有统计学意义。结论：髓母细胞瘤Daoy细胞中Syk基因启动子甲基化导致其表达下调,可能是髓母细胞瘤发生转移的机制之一;而甲基化转移酶抑制剂5-aza-CdR可抑制其启动子区的甲基化,使Syk的表达水平上调,抑制肿瘤细胞侵袭及转移能力。     

VizStruct: exploratory visualization for gene expression profiling

MOTIVATION: DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. RESULTS: The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. AVAILABILITY: Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.     

Microarray-based, high-throughput gene expression profiling of microRNAs

MicroRNAs (miRNAs) are small regulatory RNAs that serve fundamental biological roles across eukaryotic species. We describe a new method for high-throughput miRNA detection. The technique is termed the RNA-primed, array-based Klenow enzyme (RAKE) assay, because it involves on-slide application of the Klenow fragment of DNA polymerase I to extend unmodified miRNAs hybridized to immobilized DNA probes. We used RAKE to study human cell lines and brain tumors. We show that the RAKE assay is sensitive and specific for miRNAs and is ideally suited for rapid expression profiling of all known miRNAs. RAKE offers unique advantages for specificity over northern blots or other microarray-based expression profiling platforms. Furthermore, we demonstrate that miRNAs can be isolated and profiled from formalin-fixed paraffin-embedded tissue, which opens up new opportunities for analyses of small RNAs from archival human tissue. The RAKE assay is theoretically versatile and may be used for other applications, such as viral gene profiling.     

Cellulose synthase gene expression profiling of Physcomitrella patens

The cellulose synthase (CESA) gene family of seed plants comprises six clades that encode isoforms with conserved expression patterns and distinct functions in cellulose synthesis complex (CSC) formation and primary and secondary cell wall synthesis. In mosses, which have rosette CSCs like those of seed plants but lack lignified secondary cell walls, the CESA gene family diversified independently and includes no members of the six functionally distinct seed plant clades. There are seven CESA isoforms encoded in the genome of the moss Physcomitrella patens. However, only PpCESA5 has been characterised functionally, and little information is available on the expression of other PpCESA family members. We have profiled PpCESA expression through quantitative RT‐PCR, analysis of promoter‐reporter lines, and cluster analysis of public microarray data in an effort to identify expression and co‐expression patterns that could help reveal the functions of PpCESA isoforms in protein complex formation and development of specific tissues. In contrast to the tissue‐specific expression observed for seed plant CESAs, each of the PpCESAs was broadly expressed throughout most developing tissues. Although a few statistically significant differences in expression of PpCESAs were noted when some tissues and hormone treatments were compared, no strong co‐expression patterns were observed. Along with CESA phylogenies and lack of single PpCESA mutant phenotypes reported elsewhere, broad overlapping expression of the PpCESAs indicates a high degree of inter‐changeability and is consistent with a different pattern of functional specialisation in the evolution of the seed plant and moss CESA families.     

Impact of RNA degradation on gene expression profiling

Background

Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.

Method

To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.

Result

We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.

Conclusions

We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.