Serial analysis of gene expression: from gene discovery to target identification

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Identification of Five Genes as a Potential Biomarker for Predicting Progress and Prognosis in Adrenocortical Carcinoma

Background: Adrenocortical carcinoma (ACC) is a limited endocrine fatality with a minor diagnosis and rare remedial options. The progressive and predictive meaning of message RNA (mRNA) expression oddity in ACC has been studied extensively in recent years. However, differences in measurement platforms and lab protocols as well as small sample sizes can render gene expression levels incomparable.

Methods: An extensive study of GEO datasets was conducted to define potential mRNA biomarkers for ACC. The study compared the mRNA expression profiles of ACC tissues and neighboring noncancerous adrenal tissues in the pair. The study covered a sum of 165 tumors and 36 benign control samples. Hub genes were identified through a protein-protein interaction (PPI) network and Robust Rank Aggregation method. Then the Cancer Genome Atlas (TCGA) and Oncomine database were used to perform the validation of hub genes. 4 ACC tissues and 4 normal tissues were collected and then Polymerase Chain Reaction (PCR), Western-blot and immunofluorescence were conducted to validate the expression of five hub genes.

Results: We identified five statistically significant genes (TOP2A, NDC80, CEP55, CDKN3, CDK1) corrected with clinical features. The expression of five hub genes in TCGA and Oncomine database were significantly overexpressed in ACC compared with normal ones. Among all the TCGA ACC cases, the strong expression of TOP2A (logrank p=1.4e-04, HR=4.7), NDC80 (logrank p=8.8e-05, HR=4.9), CEP55 (logrank p=5.2e-07, HR=8.6), CDKN3 (log rank p=2.3e-06, HR=7.6) and CDK1 (logrank p=7e-08, HR=11) were correlated with low comprehensive survival, disease free survival (logrank p < 0.001), pathology stage and pathology T stage (FDR < 0.001). PCR results showed that the transcriptional levels of these five genes were significantly higher in ACC tissues than in normal tissues. The western blotting results also showed that the translational level of TOP2A was significantly higher in tumor tissues than in normal tissues. The results of immunofluorescence showed that TOP2A was abundantly observed in the adrenal cortical cell membrane and nucleus and its expression in ACC tissues was significantly higher than that in normal tissues.

Conclusions: The distinguished five genes may be utilized to form a board of progressive and predictive biomarkers for ACC for clinical purpose.

Biotechnology and genetic engineering in the new drug development. Part I. DNA technology and recombinant proteins

Pharmaceutical biotechnology has a long tradition and is rooted in the last century, first exemplified by penicillin and streptomycin as low molecular weight biosynthetic compounds. Today, pharmaceutical biotechnology still has its fundamentals in fermentation and bioprocessing, but the paradigmatic change affected by biotechnology and pharmaceutical sciences has led to an updated definition. The biotechnology revolution redrew the research, development, production and even marketing processes of drugs. Powerful new instruments and biotechnology related scientific disciplines (genomics, proteomics) make it possible to examine and exploit the behavior of proteins and molecules. Recombinant DNA (rDNA) technologies (genetic, protein, and metabolic engineering) allow the production of a wide range of peptides, proteins, and biochemicals from naturally nonproducing cells. This technology, now approximately 25 years old, is becoming one of the most important technologies developed in the 20(th) century. Pharmaceutical products and industrial enzymes were the first biotech products on the world market made by means of rDNA. Despite important advances regarding rDNA applications in mammalian cells, yeasts still represent attractive hosts for the production of heterologous proteins. In this review we describe these processes.

In-depth global analysis of transcript abundance levels in porcine alveolar macrophages following infection with porcine reproductive and respiratory syndrome virus

Porcine reproductive and respiratory syndrome virus (PRRSV) is a major pathogen of swine worldwide and causes considerable economic loss. Identifying specific cell signaling or activation pathways that associate with variation in PRRSV replication and macrophage function may lead to identification of novel gene targets for the control of PRRSV infection. Serial Analysis of Gene Expression (SAGE) was used to create and survey the transcriptome of in vitro mock-infected and PRRSV strain VR-2332-infected porcine alveolar macrophages (PAM) at 0, 6, 12, 16, and 24 hours after infection. The transcriptome data indicated changes in transcript abundance occurring in PRRSV-infected PAMs over time after infection with more than 590 unique tags with significantly altered transcript abundance levels identified (P < .01). Strikingly, innate immune genes (whose transcript abundances are typically altered in response to other pathogens or insults including IL-8, CCL4, and IL-1β) showed no or very little change at any time point following infection.

Exploring the foundation of genomics: a northern blot reference set for the comparative analysis of transcript profiling technologies

In this paper we aim to create a reference data collection of Northern blot results and demonstrate how such a collection can enable a quantitative comparison of modern expression profiling techniques, a central component of functional genomics studies. Historically, Northern blots were the de facto standard for determining RNA transcript levels. However, driven by the demand for analysis of large sets of genes in parallel, high-throughput methods, such as microarrays, dominate modern profiling efforts. To facilitate assessment of these methods, in comparison to Northern blots, we created a database of published Northern results obtained with a standardized commercial multiple tissue blot (dbMTN). In order to demonstrate the utility of the dbMTN collection for technology comparison, we also generated expression profiles for genes across a set of human tissues, using multiple profiling techniques. No method produced profiles that were strongly correlated with the Northern blot data. The highest correlations to the Northern blot data were determined with microarrays for the subset of genes observed to be specifically expressed in a single tissue in the Northern analyses. The database and expression profiling data are available via the project website (http://www.cisreg.ca). We believe that emphasis on multitechnique validation of expression profiles is justified, as the correlation results between platforms are not encouraging on the whole. Supplementary material for this article can be found at: http://www.interscience.wiley.com/jpages/1531-6912/suppmat

PASS-predicted design, synthesis and biological evaluation of cyclic nitrones as nootropics

Out of 400 virtually designed imidazoline N-oxides, five cyclic nitrones were selected on the basis of PASS prediction as potent nootropics and were evaluated for their biological activities in albino mice. The selected N-alkyl and aryl-substituted nitrones were found to be excellent nootropics. A series of lead compounds acting as cognition enhancers have been provided, which can be further exploited in search of such New Chemical Entities (NCEs).

Using LongSAGE to Detect Biomarkers of Cervical Cancer Potentially Amenable to Optical Contrast Agent Labelling

Sixteen longSAGE libraries from four different clinical stages of cervical intraepithelial neoplasia have enabled us to identify novel cell-surface biomarkers indicative of CIN stage. By comparing gene expression profiles of cervical tissue at early and advanced stages of CIN, several genes are identified to be novel genetic markers. We present fifty-six cell-surface gene products differentially expressed during progression of CIN. These cell surface proteins are being examined to establish their capacity for optical contrast agent binding. Contrast agent visualization will allow real-time assessment of the physiological state of the disease process bringing vast benefit to cancer care. The data discussed in this publication have been submitted to NCBIs Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/ ) and are accessible through GEO Series accession number GSE6252.

Global gene expression profiling of Bovine immature B cells using serial analysis of gene expression

The Peyer's patches of the small intestine are important sites of antigen processing. The follicles of the Peyer's patches receive the antigen transported by the M cells at the mucosal surface and then play a major role in development of both protective humoral and mucosal immune responses. Serial analysis of gene expression (SAGE) was employed to derive the global gene expression profile of B lymphocytes isolated from the IPPF. Analysis of the SAGE data revealed the identity of genes and the level to which they are expressed by IPPF B lymphocytes. This analysis indicated that they were metabolically active and that the transcripts encoding proteins necessary for a response to antigen presentation were expressed. These transcripts included the B cell receptor components CD76a and b (Ig-alpha and Ig-beta) and accessory c-Src family kinases Lyn and Blk. Furthermore, many of the positive and negative regulators, and accessory proteins that are necessary for B cell antigen receptor signaling were identified. Also present were transcripts encoding interleukins and their cognate receptors. Overall, this SAGE analysis yielded a global picture of gene expression in IPPF B lymphocytes and provides a starting point for the comparison of gene expression in further functional studies.

SAGE2Splice: unmapped SAGE tags reveal novel splice junctions

Serial analysis of gene expression (SAGE) not only is a method for profiling the global expression of genes, but also offers the opportunity for the discovery of novel transcripts. SAGE tags are mapped to known transcripts to determine the gene of origin. Tags that map neither to a known transcript nor to the genome were hypothesized to span a splice junction, for which the exon combination or exon(s) are unknown. To test this hypothesis, we have developed an algorithm, SAGE2Splice, to efficiently map SAGE tags to potential splice junctions in a genome. The algorithm consists of three search levels. A scoring scheme was designed based on position weight matrices to assess the quality of candidates. Using optimized parameters for SAGE2Splice analysis and two sets of SAGE data, candidate junctions were discovered for 5%-6% of unmapped tags. Candidates were classified into three categories, reflecting the previous annotations of the putative splice junctions. Analysis of predicted tags extracted from EST sequences demonstrated that candidate junctions having the splice junction located closer to the center of the tags are more reliable. Nine of these 12 candidates were validated by RT-PCR and sequencing, and among these, four revealed previously uncharacterized exons. Thus, SAGE2Splice provides a new functionality for the identification of novel transcripts and exons. SAGE2Splice is available online at http://www.cisreg.ca.

Modelling the evolution of genetic regulatory networks

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.

Cancer target discovery using SAGE

Cancer is a genetic disease. Genetic events including mutations, chromosomal gains, losses and rearrangements, along with epigenetic alterations, lead to significant transcriptional changes in cancer cells. Changes in the expression of many genes associated with the onset and progression of cancer likely contribute to the cancerous phenotype. SAGE (Serial Analysis of Gene Expression) is an expression profiling method that allows for global, unbiased and quantitative characterisation of transcriptomes. The expression of thousands of genes can be analysed simultaneously without prior knowledge of their sequence, thus leading to the discovery of novel transcripts. In addition to characterising normal and malignant gene expression patterns, SAGE can be used to identify downstream targets of tumour suppressors and oncogenes and further annotate genomes. Comprehensive analyses of expression profiles using SAGE will yield many new diagnostic and prognostic markers as well as therapeutic targets in cancer.

Evaluation of the chicken transcriptome by SAGE of B cells and the DT40 cell line

Background

The understanding of whole genome sequences in higher eukaryotes depends to a large degree on the reliable definition of transcription units including exon/intron structures, translated open reading frames (ORFs) and flanking untranslated regions. The best currently available chicken transcript catalog is the Ensembl build based on the mappings of a relatively small number of full length cDNAs and ESTs to the genome as well as genome sequence derived in silico gene predictions.

Results

We use Long Serial Analysis of Gene Expression (LongSAGE) in bursal lymphocytes and the DT40 cell line to verify the quality and completeness of the annotated transcripts. 53.6% of the more than 38,000 unique SAGE tags (unitags) match to full length bursal cDNAs, the Ensembl transcript build or the genome sequence. The majority of all matching unitags show single matches to the genome, but no matches to the genome derived Ensembl transcript build. Nevertheless, most of these tags map close to the 3' boundaries of annotated Ensembl transcripts.

Conclusions

These results suggests that rather few genes are missing in the current Ensembl chicken transcript build, but that the 3' ends of many transcripts may not have been accurately predicted. The tags with no match in the transcript sequences can now be used to improve gene predictions, pinpoint the genomic location of entirely missed transcripts and optimize the accuracy of gene finder software.

The impact of SNPs on the interpretation of SAGE and MPSS experimental data

Serial Analysis of Gene Expression (SAGE) and Massively Parallel Signature Sequencing (MPSS) are powerful techniques for gene expression analysis. A crucial step in analyzing SAGE and MPSS data is the assignment of experimentally obtained tags to a known transcript. However, tag to transcript assignment is not a straightforward process since alternative tags for a given transcript can also be experimentally obtained. Here, we have evaluated the impact of Single Nucleotide Polymorphisms (SNPs) on the generation of alternative SAGE and MPSS tags. This was achieved through the construction of a reference database of SNP-associated alternative tags, which has been integrated with SAGE Genie. A total of 2020 SNP-associated alternative tags were catalogued in our reference database and at least one SNP-associated alternative tag was observed for approximately 8.6% of all known human genes. A significant fraction (61.9%) of these alternative tags matched a list of experimentally obtained tags, validating their existence. In addition, the origin of four out of five SNP-associated alternative MPSS tags was experimentally confirmed through the use of the GLGI-MPSS protocol (Generation of Long cDNA fragments for Gene Identification). The availability of our SNP-associated alternative tag database will certainly improve the interpretation of SAGE and MPSS experiments.

Serial analysis of gene expression (SAGE): unraveling the bioinformatics tools

Serial analysis of gene expression (SAGE) is a powerful technique that can be used for global analysis of gene expression. Its chief advantage over other methods is that it does not require prior knowledge of the genes of interest and provides qualitative and quantitative data of potentially every transcribed sequence in a particular cell or tissue type. This is a technique of expression profiling, which permits simultaneous, comparative and quantitative analysis of gene-specific, 9- to 13-basepair sequences. These short sequences, called SAGE tags, are linked together for efficient sequencing. The sequencing data are then analyzed to identify each gene expressed in the cell and the levels at which each gene is expressed. The main benefit of SAGE includes the digital output and the identification of novel genes. In this review, we present an outline of the method, various bioinformatics methods for data analysis and general applications of this important technology.

A novel homeobox gene overexpressed in thyroid carcinoma

Serial analysis of gene expression (SAGE) was applied to compare expression profiles of normal thyroid tissue and papillary thyroid carcinoma (PTC). A SAGE tag corresponding to the partial cDNA for the small protein 31 (SMAP31) is upregulated approximately 13-fold in papillary thyroid cancer (PTC) and was selected for further research. BLAST-searching the human genome database reveals that the SMAP31 gene is located on chromosome 4q11-12 and contains 6 exons. Alternative splicing results in seven transcripts encoding 2 possible open reading frames (ORF) of 73 and 95 amino acids. Database searching in GenBank's dbEST shows that SMAP31 transcripts are expressed mainly in brain, heart, gingiva, and lung tissue. Thyroid tissue contains three transcripts caused by alternatively splicing in the 5' untranslated region (UTR), which all encode an identical ORF of 73 amino acids. Homology search shows that this protein contains a homeobox domain. Thyroid and/or thyroid carcinoma-specific expression of SMAP31 is studied using Northern blot and reverse transcriptase-polymerase chain reaction (RT-PCR) on a multiple tissue panel. RT-PCR experiments on a cDNA panel containing samples from different normal and tumor tissues shows expression of SMAP31 mRNA in brain, placenta, lung, heart, thyroid and thyroid carcinoma. SMAP31 expression is elevated in 4 of 6 PTC tumor samples compared to 4 normal thyroid controls.

Gene expression changes in MDBK cells infected with genotype 2 bovine viral diarrhoea virus

Bovine viral diarrhoea viruses (BVDVs) are ubiquitous viral pathogens of cattle. These viruses exist as one of two biotypes, cytopathic and noncytopathic, based on the ability to induce cytopathic effect in cell culture. The noncytopathic biotypes are able to establish inapparent, persistent infections in both cell culture and in bovine foetuses of less than 150 days gestation. Interactions with the host cell and the mechanism by which viral tolerance is established are unknown. To examine the changes in gene expression that occur following infection of host cells with BVDV, serial analysis of gene expression (SAGE), a global gene expression technology was used. SAGE allows quantitation of virtually every transcript in a cell type without prior sequence information. Transcript expression levels and identities are determined by sequencing libraries composed of concatamers of 14 base DNA fragments (tags) derived from the 3'-end of each cellular mRNA transcript. Comparison of data obtained from uninfected and BVDV genotype 2-infected cell libraries revealed changes in gene expression associated with distinct biochemical pathways or functions. Isotypes of both alpha- and beta-tubulins were down-regulated, indicating possible dysfunction in cell division and other functions where microtubules play a major role. Expression of genes encoding proteins involved in energy metabolism were expressed at essentially equivalent levels in both infected and uninfected cells. Genes encoding proteins involved in protein translation and post-translational modifications, functions necessary for viral replication, were generally up-regulated. These data indicate that following infection with BVDV, changes in gene expression occur that are beneficial for virus replication while having only minor changes in energy metabolism.

Genes differentially expressed in thyroid carcinoma identified by comparison of SAGE expression profiles

To identify transcripts that distinguish malignant from benign thyroid disease serial analysis of gene expression (SAGE) profiles of papillary thyroid carcinoma and of normal thyroid are compared. Of the 21,000 tags analyzed, 204 tags are differentially expressed with statistical significance in the tumor. Thyroid tumor specificity of these transcripts is determined in silico using the tissue preferential expression (TPE) algorithm. TPE values demonstrate that 42 tags of the 204 are thyroid tumor specific. BC013035, a cDNA encoding a novel protein, is up-regulated from 0 to 24 tags in the thyroid tumor SAGE library. In a tissue panel of 30 thyroid tumors and 12 controls, it has an expression pattern similar to thyroid peroxidase, indicating possible involvement of BC013035 in thyroid differentiation. A tag coding for extracellular matrix protein 1 (ECM1) is absent in the normal thyroid SAGE library and present 55 times in the tumor. ECM1, a protein recently associated with angiogenesis and expressed in metastatic breast carcinoma, is up-regulated in 50% of all thyroid carcinoma and absent in normal controls and follicular adenoma. In conclusion, SAGE analysis and subsequent determination of TPE values facilitates the rapid distinction of genes specifically expressed in cancer tissues.

Gene expression changes in BVDV2-infected MDBK cells

Bovine viral diarrhoea virus (BVDV) is a ubiquitous viral pathogen of cattle. The virus exists as one of two biotypes, cytopathic and non-cytopathic, based on the ability to induce cytopathic effect in cell culture. The non-cytopathic biotypes are able to establish non-apparent, persistent infections in both cell culture and in bovine foetuses of fewer than 150 days gestation. The mechanism by which viral tolerance is established is unknown. To examine the changes in gene expression that occur following infection of host cells with BVDV, serial analysis of gene expression (SAGE), a global gene expression technology was used. SAGE, a sequence-based technology, allows quantification of virtually every transcript in a cell type without prior sequence information. Transcript expression levels and identities are determined by DNA sequencing of libraries composed of 14 base DNA fragments (tags) derived from the 3' end of each cellular mRNA transcript. Comparison of data obtained from non-infected and BVDV2-infected cell libraries revealed a number of changes in gene expression. Many of these transcriptional changes could be placed into distinct biochemical pathways or functions. Both alpha and beta tubulins were downregulated, indicating possible dysfunction in cell division and other functions where microtubules play a major role. Expression of several genes encoding proteins involved in energy metabolism were downregulated, indicating possible decreased ATP synthesis. Genes encoding proteins involved in protein translation and post-translational modifications were generally upregulated. These data indicate that following infection with BVDV, changes in gene expression occur that are beneficial for virus replication while placing the cell at a metabolic disadvantage.

Distinct transcriptional profiles of adrenocortical tumors uncovered by DNA microarray analysis

Comprehensive expression profiling of tumors using DNA microarrays has been used recently for molecular classification and biomarker discovery, as well as a tool to identify and investigate genes involved in tumorigenesis. Application of this approach to a cohort of benign and malignant adrenocortical tissues would be potentially informative in all of these aspects. In this study, we generated transcriptional profiles of 11 adrenocortical carcinomas (ACCs), 4 adrenocortical adenomas (ACAs), 3 normal adrenal cortices (NCs), and 1 macronodular hyperplasia (MNH) using Affymetrix HG_U95Av2 oligonucleotide arrays representing approximately 10,500 unique genes. The expression data set was used for unsupervised hierarchical cluster analysis as well as principal component analysis to visually represent the expression data. An analysis of variance on the three classes (NC, ACA plus MNH, and ACC) revealed 91 genes that displayed at least threefold differential expression between the ACC cohort and both the NC and ACA cohorts at a significance level of P < 0.01. Included in these 91 genes were those known to be up-regulated in adrenocortical tumors, such as insulin-like growth factor (IGF2), as well as novel differentially expressed genes such as osteopontin (SPP) and serine threonine kinase 15 (STK15). Increased expression of IGF2 was identified in 10 of 11 ACCs (90.9%) and was verified by quantitative reverse transcriptase-polymerase chain reaction. Select proliferation-related genes (TOP2A and Ki-67) were validated at the protein level using immunohistochemistry and adrenocortical tissue microarrays. Our results demonstrated significant and consistent gene expression changes in ACCs compared to benign adrenocortical lesions. Moreover, we identified several genes that represent potential diagnostic markers and may play a role in the pathogenesis of ACC.

DNA microarray technology: devices, systems, and applications

In this review, recent advances in DNA microarray technology and their applications are examined. The many varieties of DNA microarray or DNA chip devices and systems are described along with their methods for fabrication and their use. This includes both high-density microarrays for high-throughput screening applications and lower-density microarrays for various diagnostic applications. The methods for microarray fabrication that are reviewed include various inkjet and microjet deposition or spotting technologies and processes, in situ or on-chip photolithographic oligonucleotide synthesis processes, and electronic DNA probe addressing processes. The DNA microarray hybridization applications reviewed include the important areas of gene expression analysis and genotyping for point mutations, single nucleotide polymorphisms (SNPs), and short tandem repeats (STRs). In addition to the many molecular biological and genomic research uses, this review covers applications of microarray devices and systems for pharmacogenomic research and drug discovery, infectious and genetic disease and cancer diagnostics, and forensic and genetic identification purposes. Additionally, microarray technology being developed and applied to new areas of proteomic and cellular analysis are reviewed.

RNA-mediated interference as a tool for identifying drug targets

The nematode Caenorhabditis elegans is the first multicellular organism with a fully sequenced genome. As a model organism, C. elegans is playing a special role in functional genomic analyses because it is experimentally tractable on many levels. Moreover, the lessons learned from C. elegans are often applicable across phyla because many of the key biologic processes involved in development and disease have been well conserved. Many global approaches for analysing gene activity are being pursued in C. elegans. RNA-mediated interference (RNAi) is an efficient high-throughput method to disrupt gene function. The basic technique of RNAi involves introducing sequence-specific double-stranded RNA into C. elegans in order to generate a nonheritable, epigenetic knockout of gene function that phenocopies a null mutation in the targeted gene. This technique drastically reduces the time needed to jump from the identification of an interesting gene sequence to achieving an understanding of its function. Thus, RNAi facilitates the high-throughput functional analysis of gene targets identified during drug discovery. RNAi can also help to identify the biochemical mode of action of a drug or pesticide and to identify other genes encoding products that may respond or interact with specific compounds.

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.

Microarray, SAGE and their applications to cardiovascular diseases

The wealth of DNA data generated by the human genome project coupling with recently invented high-throughput gene expression profiling techniques has dramatically sped up the process for biomedical researchers on elucidating the role of genes in human diseases. One powerful method to reveal insight into gene functions is the systematic analysis of gene expression. Two popular high-throughput gene expression technologies, microarray and Serial Analysis of Gene Expression (SAGE) are capable of producing large amounts of gene expression data with the potential of providing novel insights into fundamental disease processes, especially complex syndromes such as cardiovascular disease, whose etiologies are due to multiple genetic factors and their interplay with the environment. Microarray and SAGE have already been used to examine gene expression patterns of cell-culture, animal and human tissues models of cardiovascular diseases. In this review, we will first give a brief introduction of microarray and SAGE technologies and point out their limitations. We will then discuss the major discoveries and the new biological insights that have emerged from their applications to cardiovascular diseases. Finally we will touch upon potential challenges and future developments in this area.

Tumors associated with oncogenic osteomalacia express genes important in bone and mineral metabolism

Oncogenic osteomalacia (OOM) is associated with primitive mesenchymal tumors that secrete phosphaturic factors resulting in low serum concentrations of phosphate and calcitriol, phosphaturia, and defective bone mineralization. To identify overexpressed genes in these tumors, we compared gene expression profiles of tumors resected from patients with OOM and histologically similar control tumors using serial analysis of gene expression (SAGE). Three hundred and sixty-four genes were expressed at least twofold greater in OOM tumors compared with control tumors. A subset of 67 highly expressed genes underwent validation with an extended set of OOM and control tumors using array analysis or reverse-transcription polymerase chain reaction (RT-PCR). Ten of these validated genes were consistently overexpressed in all OOM tumors relative to control tumors. Strikingly, genes with roles in bone matrix formation, mineral ion transport, and bone mineralization were highly expressed in the OOM tumors.

Trends in biomarker research for cancer detection

A key challenge in cancer control and prevention is detection of the disease as early as possible, enabling effective interventions and therapies to contribute to reduction in mortality and morbidity. Biomarkers are important as molecular signposts of the physiological state of a cell at a specific time. Active genes, their respective protein products, and other organic chemicals made by the cell create these signposts. As a normal cell progresses through the complex process of transformation to a cancerous state, biomarkers could prove vital for the identification of early cancer and people at risk of developing cancer. We discuss current research into the genetic and molecular signatures of cells, including microsatellite instability, hypermethylation and single-nucleotide polymorphisms. The use of genomic and proteomic high-throughput technology platforms to facilitate detection of early cancer by means of biomarkers, and issues on the analysis, validation, and predictive value of biomarkers based on these technologies are also discussed. We report on recent advances in identifying sources of biomarkers that can be accessed by noninvasive techniques, such as buccal-cell isolates, as well as traditional sources such as serum or plasma. We also focus on the work of the Early Detection Research Network at the National Cancer Institute, harnessing expertise from leading national and international institutions, to identify and validate biomarkers for the detection of precancerous and cancerous cells in assessing risk of cancer. The network also has a role in linking discovery to process development, resulting in early detection tests and clinical assessment.

Microarrays for studying the host transcriptional response to microbial infection and for the identification of host drug targets

It is essential to understand the molecular basis of a host's response to microbial infection so that disease and tissue damage can be prevented. Modulation of host RNA expression is a critical set of molecular changes that occur upon infection. Global analysis of gene expression should provide an understanding of host RNA transcriptional changes that occur upon host-pathogen interaction. This series of articles focuses on the use of microarrays for analyzing the transcriptional response of a host to microbial infection and for drug target identification.

Ham56-immunoreactive macrophages in untreated infiltrating gliomas

CONTEXT

Classic diagnostic neuropathologic teachings have cautioned against making the diagnosis of neoplasia in the presence of a macrophage population. The knowledge of macrophage distribution should prove useful when confronted with an infiltrating glioma containing macrophages.

OBJECTIVE

To identify macrophages in untreated, infiltrating gliomas using the monoclonal antibody HAM56, and to confirm their presence in an untreated glioblastoma multiforme (GBM) with the serial analysis of gene expression (SAGE) method.

METHODS

We evaluated the presence of macrophages in 16 cases of untreated, supratentorial infiltrating gliomas with the macrophage monoclonal antibody HAM56. We performed SAGE for one case of GBM and for normal brain tissue.

RESULTS

In World Health Organization (WHO) grade II well-differentiated astrocytoma and oligodendroglioma, HAM56 reactivity was noted only in endothelial cells, and unequivocal macrophages were not identified. In WHO grade III anaplastic astrocytoma and anaplastic oligodendroglioma, rare HAM56-positive macrophages were noted in solid areas of tumor. In WHO grade IV GBM, HAM56-positive macrophages were identified in areas of solid tumor (mean labeling index, 8.6%). In all cases of GBM, nonquantitated HAM56-positive macrophages were identified in foci of pseudopalisading cells abutting necrosis and in foci of microvascular proliferations. In none of the cases were granulomas or microglial nodules found, and there was no prior history of surgical intervention, radiation therapy, chemotherapy, or head trauma in these cases. By SAGE, the macrophage-related proteins osteopontin and macrophage-capping protein were overexpressed 12-fold and eightfold, respectively, in one untreated GBM compared with normal brain tissue. In this case, numerous HAM56-positive macrophages (labeling index, 24.5%) were present in the solid portion of tumor, and abundant nonquantified macrophages were identified in foci of pseudopalisading cells abutting necrosis and in foci of microvascular proliferations.

CONCLUSIONS

This study confirms the utility of the monoclonal antibody HAM56 in identifying macrophages within untreated infiltrating gliomas. The overexpression of macrophage-related proteins in one case of GBM as detected by SAGE signifies that macrophages may be present in untreated GBMs.

Expression profiling in knockout mice: lymphotoxin versus tumor necrosis factor in the maintenance of splenic microarchitecture

Expression profiling provides a powerful approach to define the underlying molecular mechanisms in disease. Several techniques referred collectively to as gene profiling may be also helpful in the analysis of the phenotype of mice with targeted mutations, especially if applied to distinct histological compartments, to specific cell types or to evaluate the effect of specific challenges, such as infection. Here we review several of the existing techniques applicable to genetic knockout studies, and share our experience from the study of mice with tumor necrosis factor (TNF) and lymphotoxin (LT) deficiencies, with specific emphasis on the distinction between TNF- and LT-mediated signalling pathways in vivo. Gene expression profiling analysis of TNF/LT-deficient mice supports the notion that TNF and LT, originally discovered as distinct biological activities, manifest both distinct and redundant functions in vivo.

miniSAGE: gene expression profiling using serial analysis of gene expression from 1 microg total RNA

The use of serial analysis of gene expression (SAGE) to determine gene expression profiles is increasing because the technique can provide absolute transcript numbers in a digital format and identify new genes. We developed a miniSAGE technique, which uses only 1 microg total RNA and reduces the amount of the starting material by 250- to 500-fold. Unlike the other modified SAGE methods, the miniSAGE technique does not require the additional PCR amplifications. The additional PCR amplifications potentially introduce bias and compromise the quantitative aspects of the SAGE method. Three key modifications in the miniSAGE technique are: (i) using the phase lock gel (PLG, Eppendorf) to increase the recovery and the purity of DNA material after each phenol extraction step; (ii) reducing the amount of linkers in the ligation, thereby minimizing their interference with SAGE ditag amplification and increasing the SAGE ditag yield; and (iii) employing the mRNA capture kit (Boehringer Mannheim) to allow the first five steps: mRNA isolation, cDNA synthesis, enzyme cleavage of cDNA, binding of the cleaved biotin-cDNA to the streptavidin-magnetic beads, ligating linkers to the bound cDNA, and the release of cDNA tags to occur within one tube to significantly reduce the loss of material between successive steps. Two fibroblast SAGE libraries have been successfully prepared. The preliminary analysis of 3838 tags from one library demonstrated a typical fibroblast gene expression pattern. This miniSAGE technique will permit a broader application of SAGE.