Comparison of microarray and SAGE techniques in gene expression analysis of human glioblastoma

To enhance glioblastoma (GB) marker discovery, we compared gene expression in GB with human normal brain (NB) by accessing the SAGE Genie web site and compared the results with published data. Nine GB and five NB SAGE libraries were analyzed using the Digital Gene Expression Displayer (DGED); the results of DGED were tested by Northern blot analysis and RT-PCR of arbitrarily selected genes. Review of available data from the articles on gene expression profiling by microarray-based hybridization showed as few as 35 overlapped genes with increased expression in GB. Some of them were identified in four articles, but most genes were identified in three or even in two investigations. Some differences were also found between SAGE results of GB analysis. The Digital Gene Expression Displayer approach revealed 676 genes differentially expressed in GB vs. NB with cutoff ratio: twofold change and P ≤ 05. Differential expression of selected genes obtained by DGED was confirmed by Northern analysis and RT-PCR. Altogether, only 105 of 955 genes presented in published investigations were among the genes obtained by DGED. Comparison of the results obtained by microarrays and SAGE is very complicated because the authors present only the most prominent differentially expressed genes. However, even available data give quite poor overlapping of genes revealed by microarrays. Some differences between results obtained by SAGE in different investigations can be explained by high dependence on the statistical methods used. As for now, the best solution to search for molecular tumor markers is to compare all available results and to select only those genes where significant expression in tumors combined with very low expression in normal tissues was reproduced in several articles. One hundred five differentially expressed genes, common to both methods, can be included in the list of candidates for the molecular typing of GBs. Some genes, encoded cell surface or extracellular proteins may be useful for targeting gliomas with antibody-based therapy. The text was submitted by the authors in English.     

Heart specific genes revealed by EST sampling

Background  

Cardio-vascular diseases are the first cause of death worldwide, particularly in the developed countries; the identification of genes specifically expressed in the cardiac muscle is thus of major biomedical interest. In this study, we performed a comprehensive analysis of the expression profiles to identify genes over-expressed in the human adult heart using the public Expressed Sequence Tags (ESTs) database. The initial set of genes expressed in the heart was constructed by clustering and assembling ESTs from human adult heart cDNA libraries. Expression profiles were then generated for each of these genes by counting their cognate ESTs in all libraries. Differential expression was assessed by applying to these profiles a previously published statistical procedure.     

Comparison of microarray and sage techniques in gene expression analysis of human glioblastoma

To enhance glioblastoma (GB) marker discovery we compared gene expression in GB with human normal brain (NB) by accessing SAGE Genie web site and compared obtained results with published data. Nine GB and five NB SAGE-libraries were analyzed using the Digital Gene Expression Displayer (DGED), the results of DGED were tested by Northern blot analysis and RT-PCR of arbitrary selected genes. Review of available data from the articles on gene expression profiling by microarray-based hybridization showed as few as 35 overlapped genes with increased expression in GB. Some of them were identified in four articles, but most genes in three or even in two investigations. There was found also some differences between SAGE results of GB analysis. Digital Gene Expression Displayer approach revealed 676 genes differentially expressed in GB vs. NB with cut-off ratio: twofold change and P < or = 0.05. Differential expression of selectedgenes obtained by DGED was confirmed by Northern analysis and RT-PCR. Altogether, only 105 of 955 genes presented in published investigations were among the genes obtained by DGED. Comparison of the results obtained by microarrays and SAGE is very complicated because authors present only the most prominent differentially expressed genes. However, even available data give quite poor overlapping of genes revealed by microarrays. Some differences between results obtained by SAGE in different investigations can be explained by high dependence on the statistical methods used. As for now, the best solution to search for molecular tumor markers is to compare all available results and to select only those genes, which significant expression in tumor combined with very low expression in normal tissues was reproduced in several articles. 105 differentially expressed genes, common to both methods, can be included in the list of candidates for the molecular typing of GBs. Some genes, encoded cell surface or extra-cellular proteins may be useful for targeting gliomas with antibody-based therapy.     

Digital analysis of cDNA abundance; expression profiling by means of restriction fragment fingerprinting

Background  

Gene expression profiling among different tissues is of paramount interest in various areas of biomedical research. We have developed a novel method (DADA, Digital Analysis of cDNA Abundance), that calculates the relative abundance of genes in cDNA libraries.     

A comparative analysis of the information content in long and short SAGE libraries

Background  

Serial Analysis of Gene Expression (SAGE) is a powerful tool to determine gene expression profiles. Two types of SAGE libraries, ShortSAGE and LongSAGE, are classified based on the length of the SAGE tag (10 vs. 17 basepairs). LongSAGE libraries are thought to be more useful than ShortSAGE libraries, but their information content has not been widely compared. To dissect the differences between these two types of libraries, we utilized four libraries (two LongSAGE and two ShortSAGE libraries) generated from the hippocampus of Alzheimer and control samples. In addition, we generated two additional short SAGE libraries, the truncated long SAGE libraries (tSAGE), from LongSAGE libraries by deleting seven 5' basepairs from each LongSAGE tag.     

Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments

Background  

Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.     

Comparison of ESTs from juvenile and adult phases of the giant unicellular green alga <Emphasis Type="Italic">Acetabularia acetabulum</Emphasis>

Background  

Acetabularia acetabulum is a giant unicellular green alga whose size and complex life cycle make it an attractive model for understanding morphogenesis and subcellular compartmentalization. The life cycle of this marine unicell is composed of several developmental phases. Juvenile and adult phases are temporally sequential but physiologically and morphologically distinct. To identify genes specific to juvenile and adult phases, we created two subtracted cDNA libraries, one adult-specific and one juvenile-specific, and analyzed 941 randomly chosen ESTs from them.     

Heart-specific genes revealed by expressed sequence tag (EST) sampling

Background  

Cardiovascular diseases are the primary cause of death worldwide; the identification of genes specifically expressed in the heart is thus of major biomedical interest. We carried out a comprehensive analysis of gene-expression profiles using expressed sequence tags (ESTs) to identify genes overexpressed in the human adult heart. The initial set of genes expressed in the heart was constructed by clustering and assembling ESTs from heart cDNA libraries. Expression profiles were then generated for each gene by counting their cognate ESTs in all libraries. Differential expression was assessed by applying a previously published statistical procedure to these profiles.     

SMART amplification combined with cDNA size fractionation in order to obtain large full-length clones

Background  

cDNA libraries are widely used to identify genes and splice variants, and as a physical resource for full-length clones. Conventionally-generated cDNA libraries contain a high percentage of 5'-truncated clones. Current library construction methods that enrich for full-length mRNA are laborious, and involve several enzymatic steps performed on mRNA, which renders them sensitive to RNA degradation. The SMART technique for full-length enrichment is robust but results in limited cDNA insert size of the library.     

Optimal cDNA microarray design using expressed sequence tags for organisms with limited genomic information

Background  

Expression microarrays are increasingly used to characterize environmental responses and host-parasite interactions for many different organisms. Probe selection for cDNA microarrays using expressed sequence tags (ESTs) is challenging due to high sequence redundancy and potential cross-hybridization between paralogous genes. In organisms with limited genomic information, like marine organisms, this challenge is even greater due to annotation uncertainty. No general tool is available for cDNA microarray probe selection for these organisms. Therefore, the goal of the design procedure described here is to select a subset of ESTs that will minimize sequence redundancy and characterize potential cross-hybridization while providing functionally representative probes.     

Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates

Background  

Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed.     

Comparative sequence analysis of the complete set of 40S ribosomal proteins in the Senegalese sole (Solea senegalensis Kaup) and Atlantic halibut (Hippoglossus hippoglossus L.) (Teleostei: Pleuronectiformes): phylogeny and tissue- and development-specific expression

Background  

Ribosomal proteins (RPs) are key components of ribosomes, the cellular organelle responsible for protein biosynthesis in cells. Their levels can vary as a function of organism growth and development; however, some RPs have been associated with other cellular processes or extraribosomal functions. Their high representation in cDNA libraries has resulted in the increase of RP sequences available from different organisms and their proposal as appropriate molecular markers for phylogenetic analysis.     

PAGE: Parametric Analysis of Gene Set Enrichment

Background  

Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.     

Bovine Mammary Gene Expression Profiling during the Onset of Lactation

Background

Lactogenesis includes two stages. Stage I begins a few weeks before parturition. Stage II is initiated around the time of parturition and extends for several days afterwards.

Methodology/Principal Findings

To better understand the molecular events underlying these changes, genome-wide gene expression profiling was conducted using digital gene expression (DGE) on bovine mammary tissue at three time points (on approximately day 35 before parturition (−35 d), day 7 before parturition (−7 d) and day 3 after parturition (+3 d)). Approximately 6.2 million (M), 5.8 million (M) and 6.1 million (M) 21-nt cDNA tags were sequenced in the three cDNA libraries (−35 d, −7 d and +3 d), respectively. After aligning to the reference sequences, the three cDNA libraries included 8,662, 8,363 and 8,359 genes, respectively. With a fold change cutoff criteria of ≥2 or ≤−2 and a false discovery rate (FDR) of ≤0.001, a total of 812 genes were significantly differentially expressed at −7 d compared with −35 d (stage I). Gene ontology analysis showed that those significantly differentially expressed genes were mainly associated with cell cycle, lipid metabolism, immune response and biological adhesion. A total of 1,189 genes were significantly differentially expressed at +3 d compared with −7 d (stage II), and these genes were mainly associated with the immune response and cell cycle. Moreover, there were 1,672 genes significantly differentially expressed at +3 d compared with −35 d. Gene ontology analysis showed that the main differentially expressed genes were those associated with metabolic processes.

Conclusions

The results suggest that the mammary gland begins to lactate not only by a gain of function but also by a broad suppression of function to effectively push most of the cell''s resources towards lactation.